Enterprise tech is having a weird moment: innovation is real, but so is the noise. AI shows up in every pitch, “leading” is used in every sentence, and buyers who actually have budgets and deadlines are stuck asking the same question: what’s genuinely worth my time? I’m joined by Rose Ross, founder and chief Trailblazer at the Tech Trailblazers Awards, to talk about how credible startup awards can act as a practical filter for enterprise IT leaders and a meaningful growth lever for founders.

We dig into what makes an enterprise tech awards program valuable instead of performative: clear criteria, a respected panel of judges, and an emphasis on proof over polish. Rose shares how the Tech Trailblazers Awards grew into its 15th edition across categories like cybersecurity, networking, telecoms, containers, sustainability, AI in a B2B context, plus newer frontiers like robotics and quantum computing. We also talk about why customer impact matters more than “sexiness,” and how buyers can use awards as a shortlist without outsourcing their judgment.

If you’re a founder or operator, we get very specific on the practical side: who can enter (nine years or younger and not beyond Series C), the Firestarter subset (three years or younger with no Series A), how to think about the entry like an investor pitch deck, and what judges hate most. The simplest advice wins: answer the question, drop the vague buzzwords, and back every claim with evidence.

Subscribe for more founder and enterprise tech conversations, share this with a builder or buyer who needs a better filter, and leave a review with the best buzzword you never want to hear again.

Support the show

More at https://linktr.ee/EvanKirstel

Frontier AI is changing the rules of security, and enterprises feel it first. From the floor at VMware Explore, we sit down with Purnima Padmanabhan
Vice President and General Manager, Tanzu Division, to unpack what customers are asking for as AI agents move into real systems and real data. The headline: private AI is getting a serious bump, not as a trend, but as a control strategy for regulated industries and anyone worried about risk, governance, and cost. 

We walk through the “threat side” of AI, where advanced models can amplify attacks and push open source security to a breaking point. Purima explains why so many enterprise applications depend on open source, especially Spring and Java, and what happens when vulnerability reports spike. We also dig into TrueSource by Broadcom, the idea of a secure software supply chain for curated open source packages, libraries, and images that enterprises can trust and operationalize. 

Then we shift to the “opportunity side”: moving beyond chat and RAG into workflow automation with enterprise AI agents. That raises hard questions about sandboxing, limiting autonomy, and keeping agents secure. Just as important, we talk about why data projects slow everything down, and why governed access to the right data is what makes agents useful at scale. 

Finally, we get practical about developer experience and SDLC automation, including Purima’s example of running a swarm of 21+ agents across planning, Jira creation, coding, and testing, plus the caution that not all AI-generated code is equal. If you’re building on VMware Tanzu, thinking about private cloud AI, or trying to balance security, sovereignty, and TCO, this is for you. Subscribe, share with a builder on your team, and leave a review. What would make you trust an AI agent in production?

Support the show

More at https://linktr.ee/EvanKirstel

Agentic AI is showing up everywhere, and it is not waiting for your security program to catch up. From the VMware Explore floor, we talk with Umesh Mahajan, General Manager for Broadcom’s Application Networking and Security Division, about what changes when AI agents become real workloads that talk constantly, call tools through APIs, and evolve by the week. The headline is uncomfortable but useful: AI helps defenders ship faster, but Frontier AI also helps attackers find vulnerabilities and drive more zero-day behavior than many teams are ready to handle.

We dig into the security model that holds up when agents are dynamic and mistakes travel fast. Umesh explains why you have to solve a dual problem: protecting agents from attacks while also preventing a compromised or misdirected agent from attacking others. That leads to concrete priorities like lateral segmentation, distributed firewalling, IDS/IPS, and NDR-style behavioral detection, plus network-level controls to reduce the risk of sensitive data exfiltration. We also talk about discovery, because shadow AI is already a reality. If you cannot identify which agents, MCP tools, servers, and models are running, you cannot confidently separate authorized usage from risky sprawl.

Finally, we tackle the fear every operator has: security that slows the business down. We cover why performance and latency matter more for AI workloads, how enforcement close to the hypervisor can reduce overhead, and what it takes to deploy protection quickly so “bought but not deployed” does not become your weak spot. If you’re building enterprise AI on VMware infrastructure, this is a practical map for getting safer without stalling momentum.

Subscribe for more VMware Explore conversations, share this with the person owning AI rollout, and leave a review with your biggest question about securing agentic AI.

Support the show

More at https://linktr.ee/EvanKirstel

AI is making decisions that shape real lives, yet most people cannot see how those decisions get made. We sit down with Scott Zoldi, Chief Analytics Officer at FICO, to unpack what “trustworthy AI” actually requires when the stakes include fraud, credit risk, and customer outcomes in heavily regulated financial services. If you have ever wondered why black box models create so much fear and backlash, this conversation puts clear language around the real issues: data provenance, explainable AI, ethical testing, robustness, and the ability to audit a decision after the fact. 

We go beyond buzzwords and get specific about AI governance. Scott explains why responsible AI starts with a shared model development standard, so a large organization is not running a hundred different approaches that no one can consistently defend. We talk about why monitoring is often the weakest link in real world machine learning, when to retire models that drift, and why enterprises need to stay in control instead of outsourcing critical decisions to models they did not build. 

Then we dig into a practical enforcement mechanism: coupling AI governance with blockchain to create an immutable record of requirements, testing, verification, and release decisions. Think of it as an operating manual that travels with the model and can be inspected years later by regulators, customers, or internal teams. We also look ahead to what changing regulation could mean, including the push toward interpretable models, trust scoring for generative AI, and focused language models or small language models built for narrow tasks with auditable data. 

If you care about responsible AI, AI transparency, and building systems people can actually trust, hit play, then subscribe, share this with a friend who works in AI or compliance, and leave a review with the one governance rule you think every model should follow.

Support the show

More at https://linktr.ee/EvanKirstel

Drug discovery is one of the hardest engineering problems on Earth, except it has not always been treated like engineering. Costs can hover around $2 billion per successful drug, timelines can run 10+ years, and too many patients still wait without a cure. We sit down with Rory Kelleher, who leads global business development for life sciences at NVIDIA, to talk about what changes when accelerated computing meets foundation models, generative AI, and agentic AI that can actually do work.

We break down how scientific agents differ from chatbots, and why tools matter as much as models. Rory explains NVIDIA’s BioNEMO Agent Toolkit and the idea of turning core life sciences capabilities into “agent skills” so biologists and chemists can run complex workflows through natural language. We talk protein design and protein binder design, co-folding, bioinformatics, target identification, and ADMET prediction for toxicity and safety, plus why this wave can “democratize” computational drug discovery for scientists who were never trained as programmers.

You’ll also hear a real example from Bristol Myers Squibb, where foundation models trained on proprietary sequences and compound libraries helped improve a sickle cell molecule profile until it reached first-in-human testing. We dig into what an “AI factory” looks like inside pharma, why teams want to run open models and local LLMs on secure infrastructure, and why scientific judgment becomes more important, not less, as agents increase throughput.

If you care about biotech, pharma R&D, life sciences AI, and the future of medicine, this conversation is for you. Subscribe, share the episode with a friend in research, and leave a review with the one workflow you want agents to tackle next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Everyone is obsessed with bigger models and faster GPUs, but the part that decides whether AI actually works in the real world is the data behind it. We talk with Paul Speciale CMO at Scality, about what happens when enterprises try to store, protect, and serve AI data at petabyte and exabyte scale, and why the rise of huge context windows turns “just a chat” into a massive storage and latency problem.

We walk through the full enterprise AI pipeline, from data collection and cleansing to inference and the long archive tail most teams never plan for. Paul breaks down which AI phases truly need the fastest storage, why inference can demand microsecond access, and how tiered designs (hot, warm, cool) align to modern GPU stacks. We also dig into what’s changing in the market right now: flash price spikes, constrained supply, and the reality that power per rack often matters more than raw capacity.

From there, we get practical about operations and risk. We explore autonomous infrastructure as “tell the system what you want, not how to do it,” including human-in-the-loop recommendations that can move data to cheaper tiers and cut power bills. We also cover cyber resilient storage, immutability for ransomware defense, and the growing pressure around data sovereignty and even code transparency. If you’re building an AI infrastructure plan, this is the blueprint mindset that keeps GPUs busy and data trustworthy. Subscribe, share this with your infrastructure team, and leave a review with the biggest AI data challenge you’re facing.

Support the show

More at https://linktr.ee/EvanKirstel

AI is cranking out more code than ever, but that doesn’t mean teams are shipping the right outcomes. From PegaWorld, I sat down with Steph Lewis, Senior Director of Community Developer Programs at Pega Systems, to unpack what’s actually changing for developers, architects, and business stakeholders as AI, agents, and LLMs reshape enterprise delivery. 

We get specific about the Solution Designer role Steph helped build and why it’s showing up now. We talk about Pega Blueprint as a practical way to turn business ideas into a live application earlier, and how that changes the skills teams need across low-code development, workflow automation, and digital transformation. Steph explains why the goal never changes (delivering value), but the day-to-day work does, especially as “hands-on keyboard” tasks shrink and the need to challenge AI outputs grows. 

A big thread is the human edge: judgment, creativity, and the discipline to align what you build with what stakeholders truly need. We dig into the skills gap Steph sees most often, why workshops and real conversations beat passive training, and what sits behind eye-catching outcomes like 80% of projects going live in 90 days. We also zoom out to what Steph wants for the developer community over the next year: guiding architecture decisions, placing Pega in the right spots, and incorporating other agents only when it makes sense. 

If you care about modern software delivery, developer enablement, and the real-world impact of AI on building systems, this one’s for you. Subscribe, share this with a teammate, and leave a review with your take: what “human skill” will matter most as AI becomes default?

Support the show

More at https://linktr.ee/EvanKirstel

Your CRM knows a lot about your customers, so why does it still feel like a place you go to do homework? We talk with SugarAI CEO David Roberts about the hard truth many sellers live every week: CRM has mostly evolved into a management tool, not a system that makes reps better in the moment that matters, right before a call, a meeting, or a critical follow-up.

We dig into why the “360-degree view of the customer” often backfires. More dashboards and more reports can create more overwhelm, especially when a rep is managing hundreds of accounts, dozens of deals, and a product catalog with thousands of SKUs. David lays out a more practical promise for AI in CRM: move from data straight to action. Instead of forcing sellers through a rigid process, AI can interpret unstructured sales work like emails and meetings, spot gaps like missing executive sponsors, and recommend the next best step that improves retention, expansion, or new business.

We also explore contextual intelligence and “precision selling” and why generic AI advice is easy to ignore. The best guidance comes from combining customer data with the real selling context: sales methodology, pitch decks, competitive battle cards, and objection handling. For manufacturing and distribution teams, connecting front-office CRM with back-office ERP data like inventory and shipping can surface reorder opportunities without sending reps into endless spreadsheets.

If you care about sales productivity, revenue team performance, AI CRM, and faster sales onboarding, you’ll get a lot from this conversation. Subscribe, share this with a sales leader or RevOps partner, and leave a review with the biggest “next step” you wish your CRM would tell you.

Support the show

More at https://linktr.ee/EvanKirstel

Your AI demo is easy. Shipping AI inside a regulated insurance workflow is the hard part and that’s where the real advantage gets built. We sit down with Doug Marquis, CTO at ZyWave, to get specific about what’s changing in insurance technology and why “agentic AI” only matters when it reliably drives growth outcomes for agencies and brokers.

We dig into ZyWave’s front office focus from lead identification and marketing to quoting, service, and renewals and why that matters as the insurance workforce ages and capacity tightens. Doug breaks down the two inputs that separate winners from copycats: access to high-quality insurance data and the discipline to encode domain knowledge and real producer workflows into artifacts that AI agents can actually use. We also talk through how automation can cut the administrative load so producers spend more time being consultative with customers.

Then we get practical about enterprise AI: compliance and security up front, licensing and data access controls, and the “AI harness” you need around agents, including observability, testing, cost management, and explainability. Doug also explains ZyWave’s insurance MCP server and how plugging insurance-specific data into LLM tools turns generic answers into specialized, contextual guidance. If you’re a CIO, CTO, or product leader trying to move from pilots to production, this is a clear look at what to prioritize next.

If this helped, subscribe, share it with a teammate building enterprise AI, and leave a review with the biggest challenge you’re facing right now.

Support the show

More at https://linktr.ee/EvanKirstel

AI isn’t just changing cybersecurity tools, it’s changing the pace of the entire fight. When attackers can scale campaigns with automation and operate at machine speed, the old rhythm of annual pen tests and quarterly reviews stops making sense. We sit down with Hom Bahmanyar Global Enablement Officer at Ridge Security, to talk about what security teams are up against right now and how continuous security validation can turn an endless backlog of findings into a short list of exposures that truly matter.

We dig into the problem every enterprise security team feels: information overload. Vulnerability scanners generate massive reports, but severity scores don’t reliably predict what gets exploited in the wild. Our focus shifts to a more practical approach to vulnerability management and exposure management, prioritizing issues based on exploitability in your specific environment and whether the weakness is actually visible to a threat actor. That perspective helps reduce alert fatigue and aligns remediation work with real-world risk.

From there, we explore how agentic AI and autonomous security testing change the workflow. Hom explains why being model agnostic matters, especially for organizations that must run in air-gapped environments and rely on open source or open weight models. We also talk about why remediation is the bottleneck, how compensating controls like firewalls can buy time while patching catches up, and what early results can look like when teams start validating continuously, including uncovering previously missed SQL injection.

If you care about continuous security validation, AI-powered penetration testing, and keeping pace with modern cyber threats, subscribe, share this episode with a teammate, and leave a review. What part of vulnerability triage or remediation slows your team down the most?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

AI is rewriting the rules of healthcare software development, but building a secure scalable healthcare platform still comes down to fundamentals: clear requirements, strong ownership, and serious engineering discipline around HIPAA compliance and PHI protection. We sit down with Ghazenfer Mansoor, founder and CEO of TechnologyRivers, to talk through what’s working right now as digital health teams race from prototype to production.

We get practical about how AI fits into the real build process, including rapid POCs, code generation, and stronger testing through AI-assisted unit tests and code coverage. Then we dig into the part most teams learn the hard way: you can’t treat AI like a simple Google search when the data is sensitive and the scale is real. We talk about why “upload your company docs” breaks down, and how patterns like RAG architecture and vector databases can support safer internal search and analysis without losing control of private data.

We also cover the repeat mistakes that sink health tech MVPs, especially unclear requirements and fuzzy decision-making. From there, we talk UX and UI in healthcare, focusing on designing for patients, clinicians, and admins based on what they actually need to do. Finally, we look at low-code and no-code in healthcare, how to turn white-coded prototypes into production-ready HIPAA compliant software, and what interoperability and emerging ideas like MCP could mean for the next wave of EHR-connected apps.

Subscribe for more conversations on digital health, healthcare IT, and AI in clinical workflows, then share this with a builder on your team and leave a review. What’s the hardest part of shipping healthcare software in your world right now?

Support the show

More at https://linktr.ee/EvanKirstel

Phishing used to be a numbers game. With AI, it’s becoming a personalization game and that’s exactly why “good enough” login security is quietly turning into a liability. We sit down with Dawn Manley, PhD, SVP, Product Management of Yubico to talk about why phishing-resistant authentication is now a must-have for anyone protecting high-value accounts, especially as AI tools become central to daily work and decision-making. 

We get specific about OpenAI’s Advanced Account Security and why YubiKeys are part of the story. AI accounts can hold an incredible amount of sensitive context: drafts, strategies, internal notes, customer data, and the breadcrumbs of how you think. That makes ChatGPT account security and account takeover prevention far more than a technical checkbox. Dawn explains how hardware-backed security keys work in real life, why they’re considered phishing resistant, and how they avoid the weak points of SMS codes, intercepted OTPs, and push-notification tricks. 

Then we zoom out to the next wave: AI agents and agentic commerce. When software can act on your behalf, the question shifts from “who logged in?” to “who approved this action?” We talk about proving human intent for high-risk moments, using strong cryptography as a trust anchor, and what it looks like to deploy hardware-backed passkeys across an entire organization without relying on users to spot every scam. 

If you care about identity and access management, FIDO2 passkeys, enterprise security, or protecting AI workflows, you’ll walk away with a clearer model for what “strong authentication” really means now. Subscribe, share this with someone still relying on codes, and leave a review with your biggest question about phishing-resistant security.

Support the show

More at https://linktr.ee/EvanKirstel

The leadership playbook a lot of us grew up with is starting to fail in the moments that matter most: high pressure decisions, fast growth, founder stress, and team conflict that can’t be solved with a new script. We sit down with Peter Carnochan , an executive coach who is also a psychologist and trained psychoanalyst, to talk about a simple idea with big consequences: the next frontier of leadership development is within, not without.

We unpack why “good enough” emotional skills stop working when you’re building at the highest levels, and why surface changes can feel impressive until stress hits and the veneer cracks. Peter introduces what he calls “radiant change” and explains how real, durable growth starts at the center of the self and spreads outward into communication, decision-making, and culture. Along the way, we talk about why a therapeutic approach to executive coaching is less generic, why not everyone should try to lead like the loudest celebrity CEO, and why the future belongs to a plurality of leadership styles that fit the person, not the stereotype.

The conversation gets personal through the story behind the documentary “Andre Is An Idiot,” a comedy about dying made after a stage four cancer diagnosis. It becomes a lens for meaning, mortality, and purpose and a reminder that people want leaders who care about the whole organization and community, not only the bottom line at all costs. If you’re a founder, CEO, or operator looking for sustainable high performance, emotional resilience, and deeper leadership presence, this one will challenge you in the best way.

Subscribe, share this with a leader who’s under pressure, and leave a review if it helps you lead with more clarity. What inner pattern do you think most limits great leadership?

Support the show

More at https://linktr.ee/EvanKirstel

AI isn’t a side project for marketing anymore. It’s becoming the way work gets done, and that shift is happening faster than most teams can measure, govern, or even fully see. We sit down with Ed from Callan Consulting to unpack what he’s hearing directly from CMOs and heads of marketing about real-world AI adoption in tech marketing, from early-stage startups to multi-billion-dollar enterprises. 

We talk about the move from experimental “skunkworks” use to embedded AI across the marketing tech stack, including LLMs like ChatGPT and Claude, AI features inside core MarTech platforms, and a growing wave of AI-native tools designed for specific workflows. Ed shares why so many leaders report major impact while still struggling to quantify ROI, and how “born-in-AI” companies are rethinking org design and productivity from day one, sometimes even putting agents on the org chart. 

Then we get into the tradeoffs: token budgets, tool sprawl, and the rising risk of overreliance. If everyone ships AI-generated content at scale, everything starts to sound the same, mistakes slip through, and the internet fills with “AI slop” that models train on again. We lay out a practical path that protects brand voice: keep the hero content human-led, then use AI for atomization, localization, optimization, and distribution. Finally, we look ahead at generative engine optimization (GEO), the early dip in traditional SEO traffic, and why “machine engine optimization” could matter as buyers use agents to research vendors. 

If you want a grounded, executive-level view of generative AI in marketing, listen now, then subscribe, share with a teammate, and leave a review so more marketers can find it.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

AI is everywhere on stage, but production AI is won in the details. From HPE Discover, we sit down with Jason Schradel, Director of Enterprise Platforms at NVIDIA, to unpack what it really takes to build an “AI factory” that an enterprise can deploy, operate, and scale without turning every upgrade into a science project. If you’re trying to move from AI proofs of concept to real business outcomes, this conversation maps the stack in plain terms. 

We start with what’s being showcased on the floor and why it matters: next-generation platforms like Vera Rubin and the new Vera CPU, enterprise-ready GPUs including RTX Pro Blackwell Server Edition options, and the networking layer that keeps modern AI workloads moving, from Spectrum-X Ethernet switching to BlueField DPUs. We also talk about how HPE systems, storage, and the private cloud experience come together with NVIDIA accelerated computing and NVIDIA AI software to form a repeatable blueprint for enterprise AI infrastructure. 

From there, we zoom out to the go-to-market reality: global customers, real deployments across industries like healthcare, manufacturing, financial services, and telecom, plus the growing role of ISVs and partner ecosystems in making AI usable for specific workflows. Jason also shares what he’s watching on the roadmap, especially agentic AI and the importance of confidential computing to protect sensitive data and model weights as hybrid cloud AI becomes the norm. 

If you’re planning an enterprise AI strategy, you’ll leave with a clearer view of the components that matter most and the tradeoffs you can’t ignore. Subscribe for more conversations like this, share this episode with a teammate building your AI platform, and leave a review. What part of the AI factory stack feels hardest to get right right now?

Support the show

More at https://linktr.ee/EvanKirstel

A hospital breach can turn the network “dirty” in minutes. A fiber cut can take a grocery store offline at the worst possible time. And almost every CIO has said some version of the same thing: “I love the technology, help me find the money.” That’s the tension we dig into with John Tonthat CRO of Cellhub, one of T-Mobile’s longest-tenured agency partners operating right at the intersection of telco, wireless, and IT systems integration.

We start with the real-world enterprise problems Cell Hub helps solve across healthcare, retail, and beyond, including how large organizations manage provisioning, procurement, and billing across complex wireless estates. John shares why hospital CIOs are juggling three mandates at once: clinical communications that work inside old buildings, remote patient monitoring and care that can scale safely, and security strong enough to withstand relentless attacks. We get specific about where Wi-Fi struggles and how enterprise 5G can be designed as a resilient backup network to protect continuity of care when primary systems are compromised.

Then we shift to the connected grocery store, where uptime, in-building coverage, and refrigerated warehouse connectivity directly impact revenue and customer experience. John explains Super Broadband, combining fixed wireless with Starlink to hit service levels at a compelling price, plus why retail media networks demand “always up” secondary connectivity that doesn’t ride on the core network. Finally, we unpack SCOT, a cost reconciliation engine that uses automation to surface hidden spend across wireline, wireless, and IT, and Design X, a faster way to iterate network designs with a proper system of record. We close with what John sees as the next frontier: securing not just the device, but the communication itself with peer-to-peer encrypted approaches.

If you care about enterprise connectivity, 5G transformation, network resilience, and mobile security, subscribe, share this with a colleague, and leave a review so more builders can find the show.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

The fastest attackers don’t “hack” like they used to, they drift through systems, blend into normal behavior, and move at machine speed. That reality forces a hard question: if you cannot defend the perimeter anymore, what should a modern security architecture look like?

We sit down with James Karimi, CIO and CISO of GTT Communications, one of the world’s largest tier one internet operators, to break down the practical moves behind a containment-first strategy. We talk candidly about the human layer of risk, why awareness training still matters, and why GTT chose a draconian but effective approach: eliminating lateral movement so a compromise stays small. James also shares what it really takes to “unflatten” applications with firewall contexts, explicit network rules, and the painful discovery work most teams underestimate.

From there, we zoom into the GTT Envision platform and how software-based service chaining at the edge improves resiliency, agility, and managed security. Then we get into AI governance and operations: how GTT built AI factories with Dell and NVIDIA, why documenting data is the make-or-break step for enterprise AI, and how they designed secure AI operators that are isolated by default. We also explore behavior-based detection and response, CVE analysis with mitigation guidance, real-time topology for threat hunting, and where autonomous mitigation fits depending on a customer’s tolerance.

If you care about zero trust, microsegmentation, AI observability, network detection and response, SOC modernization, and measurable AI ROI, this conversation gives you a blueprint you can adapt. Subscribe, share this with a security leader, and leave a review with the one change you think every enterprise should make next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

A two-week simulation was all it took for “autonomous AI agents with rules” to reveal how fragile our current guardrails really are. We sit down with Satya Nitta from Emergence AI, an autonomous AI lab working at the intersection of neural networks and symbolic AI, to unpack the Emergence World Experiment: five virtual cities, ten agents per city, and different frontier language models powering each world, including a mixed-model society where agents influence each other.

What we saw is the kind of long horizon autonomy story most benchmarks can’t capture. One world collapses into fighting and resource failure in days. Another becomes eerily stable through near-total conformity. And the most important signal for enterprise AI shows up in the mixed world: agents that look “well behaved” alone can be pulled into unsafe behavior when they interact with other models. If your company is rolling out agentic systems across a messy stack of vendors, tools, and models, that is not an edge case, it is the default reality.

We also dig into a concrete safety direction: neuroformal AI, proof-carrying code, and formally enforced constraints using mathematical methods like dependent type theory. The argument is simple and provocative: before an AI agent takes actions that touch production code, sensitive data, or critical operations, it should be able to prove it is staying within constraints, not just promise it in natural language. If you care about AI safety, autonomous agents, multi-agent systems, and real-world deployment risk, this conversation will sharpen how you think about what comes next.

Subscribe for more deep dives, share this with a friend building with AI agents, and leave a review with your biggest question about long-horizon autonomy.

Support the show

More at https://linktr.ee/EvanKirstel

AI isn’t magic, and it definitely isn’t free. We sit down with Ken from Pega Systems to get brutally practical about the economics of enterprise AI: why token costs are a symptom, why infrastructure spend is so high, and how “murky ROI” happens when companies deploy AI for novelty instead of measurable business value.

From Ken’s perspective as a former CFO and current COO, the best mental model is surprisingly simple: treat AI like a utility. If electricity has taught us anything, it’s that the winners don’t just consume more, they manage consumption better. We talk about how to reduce waste, how to avoid paying for frontier-model overkill, and why boards and finance teams are starting to demand tokenomics tied to outcomes. We also dig into a provocative corner of the market: incentives that can turn the AI ecosystem into a circular hype machine unless leaders insist on real examples and hard metrics.

We then shift to what this means inside large organizations. Agentic AI can accelerate judgment-heavy work in finance, legal, HR, and marketing, while deterministic workflows still anchor reliability in core operations. Finally, Ken shares career advice for the next generation: as execution gets automated, the premium rises on strategy, product management, and validation skills, plus the curiosity to keep learning as roles evolve.

If you care about enterprise AI ROI, workflow automation, and the real operating model behind digital transformation, hit play. Subscribe, share this with a colleague, and leave a review with the metric you think will prove AI is paying off.

Support the show

More at https://linktr.ee/EvanKirstel

Hybrid communications is easy to praise and hard to pull off, especially when your reality includes on-prem systems, private cloud requirements, public cloud apps, and a growing buy-in committee that can hit dozens of stakeholders. We talk with Jonathan Buckle, VP of the Americas at Mitel, about what hybrid unified communications actually looks like when you refuse to force customers into a single model and instead design around how organizations really operate. 

We get concrete about the process: why discovery matters more than demos, how vertical expertise in healthcare, education, hospitality, retail, and the public sector speeds up decision-making, and why workflow integration is often the quickest route to real outcomes. Jonathan shares what he’s seeing in the market as vendors consolidate or exit categories and why that shift is pushing more organizations to rethink voice, UC, and the day-to-day systems their teams rely on. 

Frontline workers are a major focus, from nurses and operators to school staff and hotel teams. We dig into what changes when you sit next to the people doing the work, how simplicity beats feature creep, and why Mitel’s WX UC client is built to make training easier while surfacing workflow triggers directly in the user experience. 

If you’re modernizing business communications and you’re tired of “either cloud or on-prem” debates, this conversation will help you pressure-test your plan. Subscribe, share this with your IT team, and leave a review, then tell us: what would “no compromise” need to mean for your organization to believe it?

Support the show

More at https://linktr.ee/EvanKirstel

AI in telecom is finally graduating from slide decks to real operational impact, but the jump from pilot to production is where most teams get stuck. I sit down with Guy Lupo from the TM Forum, who leads the trustworthy AI and data mission, to talk about what it actually takes to become an AI native telco and why the industry’s next gains depend less on flashy demos and more on operational proof.

We break down where operators are seeing traction right now, like network fault management, faster mean time to resolve, fewer tickets, and churn reduction, and why those wins correlate directly with clean, structured signals. Then we dig into the uncomfortable middle ground: AI that augments people feels manageable, but AI embedded into tools and workflows raises hard questions about governance, monitoring, and accountability. Guy’s point lands hard: trust cannot be claimed, it must be demonstrated continuously, especially as autonomy increases.

From there, we connect the dots to risk-based regulation and sovereignty. Frameworks like the EU AI Act signal a shift away from checklist compliance toward auditable evidence over time, with telecom increasingly treated as high risk critical infrastructure. We also explore emerging concepts like agent passports, plus why the industry is asking for a shared “agent factory” reference architecture and practical, no regret patterns such as Model as a Service for consistent, governable model access. We close by looking ahead to physical AI and robotics and the surprising telecom advantage: the operational workforce that can install, maintain, and safely support devices at scale.

If you care about AI governance, autonomous networks, agentic AI, and the real-world path to production in telecom, subscribe, share this with a colleague, and leave a review with the one trust gap you want the industry to solve first.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

The easiest system to hack is the one that’s always there to be found. We sit down with Steve Visconti, CEO and co-founder of XIID, to talk about a different cybersecurity mindset: architectural invisibility, where the goal isn’t to build a bigger wall, it’s to make the target unreachable in the first place.

We dig into what “no inbound communication” really means, including removing public IP dependence, reducing DNS exposure, and enforcing process-to-process connectivity so only the exact executable you approve can talk to the exact service it needs. Steve explains how outbound-only tunnels can be established on both sides, and why strong encryption and post-quantum secure tunneling matter when you’re protecting high-value systems in an increasingly autonomous, machine-to-machine world.

We also get practical about where this fits in today’s security stack. Because it operates at the application layer, it can complement existing tools without a rip-and-replace overhaul, and it can roll out one app at a time while still scaling through orchestration. Along the way, we connect the dots to real risks in modern software delivery, like AI-generated code and CI/CD pipelines that accidentally leave behind discoverable test endpoints.

Finally, we zoom out to critical infrastructure, including EV charging networks and the growing connection between vehicles, cloud billing systems, and the electrical grid. If you care about reducing attack surface, protecting OT environments, and building zero trust security that survives automation at scale, this is for you. Subscribe, share this with a security-minded friend, and leave a review with your biggest question about making systems “unreachable by design.”

Support the show

More at https://linktr.ee/EvanKirstel

A 2005 malware sample sounds like ancient history, until it looks like cyber sabotage that may predate Stuxnet. We sit down with Jags from SentinelOne’s Sentinel Labs to unpack Fast 16, a rare framework that doesn’t just break computers, it quietly corrupts high precision calculations. If you’ve ever treated simulation results, engineering models, or AI outputs as “the answer,” this conversation will make you pause.

We walk through the unexpected discovery path: a curious reference tied to the Shadow Brokers leak, years of researchers staring at a strange sample that “felt important” but refused to give up its secrets, and the moment an internal project using AI for reverse engineering helped unlock what Fast 16 was built to do. Along the way, we connect the dots to the Stuxnet era, cyber threat intelligence “paleontology,” and why truly high end nation state toolkits look like platforms, not one off scripts.

Then we get uncomfortably current. Sabotaging calculations is an integrity attack, and integrity is the foundation of modern scientific computing, cloud workloads, and frontier AI model training. We talk about how subtle degradation can waste millions, derail decision making, and even turn teams against their own experts. We close with practical lessons for CISOs and enterprise leaders: invest in visibility, telemetry, and log retention before the crisis, and start treating output verification as a core security problem.

Subscribe for more deep dives on cyber sabotage, APT tradecraft, and AI security, and if this made you rethink what “trust” means in computing, share it and leave a review. What system in your world would be hardest to verify?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Your phone rings, you hesitate, and you let it go to voicemail because it might be a scam. Meanwhile your team is juggling Microsoft Teams, Webex, mobile calling, messaging apps and a growing stack of AI tools that promise better customer experience but often add complexity. We sit down with William Rubio to unpack what’s actually changing in cloud communications and what a managed service provider needs to deliver in 2026: not just licenses, but outcomes across UCaaS, CCaaS and AI.

We talk through Call Tower’s evolution and the recent strategic investment from Court Square Capital Partners, including how growth, global expansion and M&A fit into a fast-moving market. Then we get practical about mobile identity and eSIM for Teams and Webex: why “clicking the app” is friction, how caller ID consistency affects trust, and why compliance, recording and analytics become more important when work follows you from car to laptop to office to home.

On the CX side, we zoom in on conversational AI and agentic AI in the contact center: what major platforms are shipping, where specialized AI vendors can add real value, and why industry-specific AI for healthcare, finance and manufacturing is likely to define the next wave. We also cover WhatsApp integration with Microsoft Teams and what it signals about enterprise communications finally adopting consumer-like channels without giving up security.

If you care about cloud calling, AI contact centers, mobile-first collaboration and stopping spam calls from poisoning business communications, hit play. Subscribe, share this with a teammate, and leave a review with the one communications headache you most want fixed next.

Support the show

More at https://linktr.ee/EvanKirstel

Personalized phishing is no longer “spray and pray.” It’s targeted, multi-channel, and increasingly powered by exposed employee data that’s sitting in plain sight. We sit down with Paul Mander, Chief Commercial Officer at Optery for Business, to unpack what’s driving the next wave of AI-driven social engineering and why so many security teams are rethinking where the real attack surface begins.

Paul walks us through eye-opening survey findings from more than 400 cybersecurity leaders: social engineering attempts are rising sharply, most attacks are moderately or highly personalized, and a large share of those successful attempts lead to credential compromise. We also dig into why there’s no single channel to defend anymore. Email still matters, but attackers are mixing phone calls, SMS, social media, and impersonation to make their stories feel “verified” from multiple angles.

The biggest shift is where attackers get their homework done. Data brokers and people search sites compile dossiers that include phone numbers, home addresses, relatives, employment history, and even org chart details that help threat actors pick high-leverage targets. We talk about why IT, HR, and finance often take more heat than executives, and what practical teams can do today: strengthen MFA and training, then get proactive by finding and removing exposed PII through opt-out and deletion workflows at scale.

If you’re a CISO, IT leader, or security practitioner trying to reduce phishing risk, social engineering risk, and account takeover risk, this is the playbook for treating privacy exposure as a core cybersecurity control. Subscribe, share this with your team, and leave a review with the one data source you think attackers rely on most.

Support the show

More at https://linktr.ee/EvanKirstel

Your product can have a world-class cloud stack and a blazing-fast 5G link, then lose the whole experience in the last half meter. That’s the “last meter” problem, and it’s why we sat down with Dr. Frederic Nabki Co-founder and Chief Technology Officer Spark Microsystems, to talk about ultra-wideband wireless that targets wirelike responsiveness instead of “good enough” latency.

We dig into where Bluetooth and Wi‑Fi still shine and where they hit real limitations for deterministic wireless, ultra-low latency, and interference-heavy environments. Frederick explains why Spark’s approach uses impulse radio UWB, how sub-nanosecond-scale pulses change the game for multipath and coexistence, and how wide UWB spectrum enables frequency agility when the airwaves get crowded. If you’ve ever been in a trade show hall where microphones and earbuds fall apart, you’ll recognize why interference robustness is no longer optional for industrial IoT, medical devices, wearables, and robotics.

The examples get concrete: a gaming mouse that targets about 150 microseconds end-to-end latency, robots that need fast control loops to avoid collisions, and brain-computer interface systems where cables create infection risk and power budgets are unforgiving. We also cover Spark’s go-to-market details, including transceiver silicon, an SDK, reference designs, antenna guidance for FR4 PCBs, and why modules can simplify certification.

If you care about ultra-wideband, UWB data communication, ultra-low power wireless, and real-time connectivity, hit play, then subscribe, share the episode, and leave a review so more builders can find it.

Support the show

More at https://linktr.ee/EvanKirstel

Your carrier has more data than almost any company you interact with, yet most telcos still struggle to turn that advantage into growth. We sit down with Miguel Carames, the Chief Product Officer at Mobileum to sort out what’s real, what’s next, and what’s pure hype when it comes to AI in telecom, 5G monetization, and the future of operators as intelligence-driven businesses. Along the way, we get honest about why “we invested billions” doesn’t automatically translate to new revenue and why regulation and privacy expectations reshape every AI roadmap.

We also challenge the idea that AI only arrived with generative tools. Telecom has used machine learning for years in automation, anomaly detection, and capacity planning, but the story hasn’t been told well. Miguel shares concrete, production-minded examples: using LLM-style interfaces to make deeply technical testing platforms usable for roaming managers and analysts, moving toward automated root cause analysis, and deploying agent workflows in fraud and revenue assurance so cases arrive pre-analyzed with evidence and a human still making the final call.

From there we go into customer experience, where proactive network intelligence can prevent tickets before customers ever feel the pain, and into churn reduction, where the opportunity is huge but the privacy line is delicate. We wrap with fraud and security, the whack-a-mole reality of bad actors, and what it takes to escape pilot purgatory so telecom can move at AI speed. If you found this useful, subscribe, share it with a telecom leader, and leave a review. What’s the best AI use case you’ve seen a telco actually scale?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

If your SOC is buried under alert noise, another flashy AI demo won’t save you. We go deeper into what actually works: starting with data strategy and detection quality so automation has real signal to work with, not chaos to summarize. Our guest CEO and Founder Karthik Kannan from Anvilogic explains what “agentic SecOps” looks like in practice, from data onboarding and normalization to detection engineering, hunting, triage, investigation, and the integrations that move outcomes into your ticketing or case management systems.

We talk through why many AI security operations tools jump straight to alert triage and why that can turn into a band aid. The more durable path is end-to-end context: knowing exactly which data sources fed a detection, what logic fired, and how the alert was produced. That lineage supports higher accuracy, cleaner investigations, and consistent mapping to frameworks like MITRE ATT&CK. We also dig into “show your work” explainability, why black box answers stall adoption, and how a decision trace helps teams build trust step by step.

On the architecture side, we explore federated security operations across the tools enterprises already run, including Splunk, Microsoft Sentinel, Snowflake, and Databricks. Instead of forcing every byte into a monolithic SIEM, federated queries and data lake strategies let teams correlate where the data lives while controlling cost and complexity. We close with a grounded take on whether AI replaces security analysts and why the real win is reducing burnout and up-leveling people into higher judgment work.

If this helped you rethink SOC automation, subscribe, share the episode with your team, and leave a review with the biggest bottleneck you want AI to tackle next.

Support the show

More at https://linktr.ee/EvanKirstel

Cloud was supposed to simplify everything. Instead, a lot of CIOs are staring at bills that are far higher than anyone forecast, feeling locked into hyperscalers, and wondering where the business value went. I sit down with David Linthicum, former Deloitte chief cloud strategy officer turned tech influencer, to give an unvarnished reality check on cloud computing costs, cloud repatriation, and what “pragmatic architecture” looks like when budgets are real and timelines are slow.

We also get blunt about enterprise AI. David explains why so many AI-driven transformations stall out on two constraints: money and talent. We dig into why AI can cost 10 to 20 times more than traditional software, why “AI-first enterprise” messaging can be dangerous, and how leaders can pick high-impact use cases instead of trying to bolt generative AI onto everything. Along the way, we talk about how AI is reshaping SaaS economics as agents start using systems on behalf of humans, and what that means for vendors and buyers.

Then we tackle the loudest buzzword of the moment: agentic AI. Where does it shine as a productivity force multiplier, and where is it mostly hype when you try to deploy it at enterprise scale? We round out with underhyped edge computing opportunities and the growing backlash around data centers, power, and the grid. If you care about enterprise architecture, cloud strategy, generative AI, and what’s actually deployable right now, you’ll get a clear set of takeaways you can use this week. Subscribe, share this with a CIO or architect, and leave a review with the most overrated tech trend you want us to challenge next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Your backup system is sitting on a gold mine and most companies are treating it like a fire extinguisher behind glass. From the floor of Dell Technologies World, we talk with Simon Taylor CEO of HYCU about their next chapter: HYCU Air, an AI resiliency platform designed to turn SaaS backup data into something you can actually interrogate, learn from, and use to stay ahead of cyber risk. 

We dig into the big idea that the most valuable asset is not the LLM itself, but the unique datasets inside your systems of record. HYCU Air pairs a knowledge graph and context engine with an LLM so you can ask natural-language questions of your backup history, the same way you would investigate a security camera recording. That reframes data protection from “pay for recovery” to “use backup data every day” across modern cloud applications, collaboration tools, and enterprise SaaS sprawl. 

Then we get practical: cybersecurity posture management when AI agents and integrations are “running amok,” spotting policy drift, and using data classification to find sensitive data like PII that never should have been where it ended up. We also share what we’re hearing from customers, why demand is accelerating, and how this approach starts to look like the “brain of an organization” by connecting corporate memory across dozens of SaaS services. 

If you want to see where AI resiliency is heading, hit play, share this with a security or IT leader, and leave a review with the one question you wish your backups could answer.

Support the show

More at https://linktr.ee/EvanKirstel

The fastest way to break a modern network isn’t your Netflix download, it’s the quiet, constant upload from sensors, logs, and telemetry that nobody ever reads. We sit down with Julien Dersey from AtomBeam to unpack why data efficiency is suddenly a front-line issue for IoT networking, edge computing, and cloud operations, even in a world with 5G and new satellite options like Starlink. The uncomfortable reality is that bandwidth grows, then data expands to fill it, especially once cybersecurity teams demand near real-time visibility into who connected to what, from where, and when.

We get concrete about the uplink bottleneck that hits IoT deployments first, and why “just filter the data” is a risky workaround. Julian shares a field deployment with an oil and gas fracking operator transmitting over Starlink, where compaction reduced traffic dramatically and kept gigabytes per day flowing reliably for months, while also helping identify odd behavior coming from a sensor. From there, we explore how AtomBeam’s lossless “compaction tunnel” differs from traditional compression, how it can run with extremely low CPU and memory, and why keeping applications unchanged is a big deal for real teams.

We also dig into enterprise and operator integrations: testing with Ericsson over a 5G router and SD-WAN style network bonding, the latency and performance questions engineers always ask, and the security posture using TLS 1.3 with an added obfuscation effect. Finally, we widen the lens to point-of-sale receipt transmission at scale, disaster recovery replication speedups, and what’s coming as connected vehicles, smart meters, and smart grid AMI 2.0 generate even more machine data.

If you care about IoT bandwidth, edge efficiency, secure data transport, and the future of connected devices, subscribe, share this with a colleague, and leave a review. What’s the single noisiest data stream on your network right now?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

ANZ moves fast, and that speed exposes the difference between “cloud by default” and communications that actually hold up under pressure. We sit down with Mitel and Ethan Global to unpack what it takes to deliver unified communications, contact center, and customer experience platforms across Australia, New Zealand, and the South Pacific where geography is huge, budgets demand efficiency, and resilience is non-negotiable.

We talk about why Australia and New Zealand are early adopter markets, how hybrid work has shifted to a role-based model, and why complex verticals like government, healthcare, education, emergency services, mining, and transport can’t rely on one-size-fits-all deployments. You’ll hear what customers are asking for right now: redundancy, tight integration into core business systems, managed services, and clear answers on data sovereignty and regulatory expectations.

Then we get into the AI reality check. Instead of vague hype, we focus on what’s delivering immediate value in CX and contact centers, including agent assist, conversation summarization, quality monitoring, and AI that improves IT operations through faster issue resolution and smarter provisioning. We also explore cloud-first mandates, the surprising rise of cloud repatriation when organizations move too quickly, and why the partner ecosystem now drives innovation as much as the platform itself.

If you’re planning a UCaaS or CCaaS modernization, building an AI roadmap for customer experience, or supporting a hybrid workforce at scale, this conversation will help you pressure-test your strategy. Subscribe, share with a colleague, and leave a review with your biggest question about cloud, AI, or enterprise communications.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Your AI assistant is only as smart as the mess behind your dashboards. When business data lives across CRM, accounting, HR, and job systems, “connect ChatGPT to our data” quickly turns into rate limits, broken joins, confusing IDs, and answers nobody trusts.

We sit down with Craig Morrall, co-founder of Tugger, to unpack a practical architecture for enterprise AI that actually holds up in the real world: pulling data from many platforms into a warehouse, then layering on a semantic model that explains what the data means and how records connect across systems. That extra context is what turns a chatbot into something you can rely on for revenue questions, profitability analysis, and cross-platform reporting without spending months on custom pipelines.

Craig also shares what customers are doing once the foundation is in place, including building interactive dashboards in minutes and generating repeatable board packs that used to take finance teams hours. We dig into time to value, early ROI stories, and how Tugger approaches security and governance with ring-fenced data storage, ISO 27001 certification, and guidance on using business-grade LLM plans to reduce training risk.

If you’re evaluating enterprise AI, data warehousing, semantic layers, or secure analytics with Claude or ChatGPT, this conversation will help you separate real capability from hype. Subscribe for more practical AI stories, share this with a friend building on enterprise data, and leave a review with the biggest data problem you want AI to solve.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

AI agents are starting to do real work inside real companies and they often do it by acting as us. That’s exciting, and it’s also a security wake-up call. We sit down with Matthew Immler Regional CSO, Americas at Okta, to unpack why identity security has become the primary battleground and why attackers increasingly prefer impersonation over breaking through a “front door” with zero-days.

We get concrete about what “non-human identities” actually means in plain English, and how agentic AI changes the rules. When employees connect new tools and click consent, an AI agent can gain access not just to a calendar, but to email, files, and other sensitive systems through broad OAuth scopes. From the security team’s perspective, the activity can look like normal user behavior, which creates a visibility problem at the exact moment enterprises are being pushed to adopt AI faster than their controls can mature.

We also talk solutions: treating AI agents as first-class identities with owners, managers, and access reviews; spotting non-human behavior through signals like abnormal client secret flows and extreme refresh token patterns; and why blocking AI outright can drive “shadow AI” instead of safety. Matt shares how standards work like cross-app access can shift control from end-user consent to IT policy so teams can approve tools, lock scopes down, and keep tight governance.

If you care about AI security, identity and access management, OAuth risk, and practical guardrails for agentic AI, this conversation will help you think clearly and act faster. Subscribe, share this with your security or IT team, and leave a review with the one control you think every AI agent should have.

Support the show

More at https://linktr.ee/EvanKirstel

Healthcare keeps getting more expensive, yet most of us feel like we’re doing more work just to get the same care: more portals, more forms, more phone calls, and more confusing lab results. We sit down with Greg Brady the founder and CEO of Connect4Patients to dig into the root cause he’s spent decades solving in other industries: fragmented data. His claim is direct and a little startling. If we can’t assemble a complete, real-time medical record, we can’t reliably reduce errors, we can’t simplify administration, and we can’t move the system upstream toward prevention. 

We talk about what a patient-centric system actually looks like in practice: one unified “single version of the truth” for your health record that can be shared across providers, while still working with existing EMR/EHR systems. Greg explains how an AI-based network can fuse and cleanse records in a HIPAA-compliant way, then translate medical jargon into plain English so patients can understand what their numbers mean and what actions to take. That shift is bigger than convenience. It’s the foundation for catching trends early, like rising glucose before prediabetes, and for preventing dangerous mistakes, like prescriptions that conflict with other meds a patient is already taking. 

We also get into the uncomfortable incentives that keep healthcare stuck in a treatment loop: more tests, more procedures, more friction in prior authorization, and a system where insurers can delay care through manual workflows. Greg shares a view of what could change if large employers, cities, or states act as self-insured organizations and reward preventive behaviors directly, using data and personalized guidance to lower chronic disease rates over time. 

If you’ve ever wondered why healthcare feels “designed” to be hard, this conversation offers a concrete infrastructure-level answer and a practical path forward. Subscribe, share this with someone who’s tired of managing their care across multiple portals, and leave a review with the biggest healthcare friction you want fixed next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

AI is finally forcing a scoreboard moment in telecom: some organizations are seeing massive productivity gains, while others are stuck between fear of missing out and fear of getting it wrong. From the floor at IBM Think in Boston, we sit down with IBM’s global CTO for telecom, media, and entertainment Eoin Coughlan to get practical about what separates “AI pilots” from AI that actually lands in production and earns trust.

We start with the hard truth that hasn’t changed for decades: data is the bottleneck. Clean, timely, governed data determines whether AI helps you run a network or quietly amplifies bad decisions. From there we move into telecom operations where fragmented observability makes it hard to see what’s really happening. We talk about pulling signals into a unified view, using AI to correlate root causes, and keeping control as you introduce agentic AI. Autonomous networks come up as a real journey, not a magic switch: time series models for network telemetry, multiple agents that can read tickets and vendor manuals, and then automation that begins with humans in the loop and expands as trust grows.

Then we zoom out to the ecosystem: hyperscaler dependence, rising sovereignty requirements, and what it means to run compliant, air-gapped platforms that enterprises can rely on. One of the biggest opportunities may be hiding in plain sight: SMEs often trust their telecom provider more than software vendors or hyperscalers, opening the door for CSPs to deliver packaged AI assistants and managed platforms. We also hit legacy modernization and 5G monetization realities, and finish with what might surprise us next, including early quantum computing use cases. If you found this valuable, subscribe, share it with a telecom leader on your team, and leave a review with the AI or automation challenge you’re tackling right now.

Support the show

More at https://linktr.ee/EvanKirstel

AI didn’t just change how enterprises innovate, it changed how they get breached. One month you’re racing to deploy new copilots and agentic workflows; the next you’re asking a harder question: did we build any of this to match our risk posture?

We talk with Chris Bonavita, Vice President of Strategy and Technology Adoption at GTT, about what he’s hearing from enterprise security leaders right now and why the mood has shifted from excitement to panic. We dig into the real-world convergence of CIO and CISO responsibilities, and how a unified data view across network operations and security operations can replace the “swivel chair” handoff between teams. When netflow, logs, identity, device posture, and edge behavior get correlated in one place, you can finally decide faster whether you’re looking at a performance issue, a resiliency gap, an optimization opportunity, or a malicious actor.

Chris also shares a sneak peek at GTT’s direction with AI factories, GPU-enabled capabilities, and AI-driven correlation that can shrink vulnerability and CVE matching from weeks to near real time. The bottom line is simple and practical: security wins on time to recognition, time to categorization, and time to action. We close with grounded advice for leaders who feel overwhelmed by the pace of change: stay curious, keep learning, and keep the human conversation alive alongside the machines.

If you found this useful, subscribe, share the episode with a colleague, and leave a review with the one security metric you’re trying to improve most.

Support the show

More at https://linktr.ee/EvanKirstel

Your browser is no longer a passive window to the internet. It’s becoming a worker with autonomy and that single shift changes everything about cybersecurity, identity, and data protection.

We sit down with Anupam Upadhyaya SVP, Products, SASE and Network Security, Palo Alto Networks to unpack what “agentic browsing” really means: the jump from an AI copilot that helps you to an agent that works for you by clicking, filling forms, and moving across tabs with your permissions. When the browser becomes the office for SaaS, cloud apps, and AI tools, it also becomes the most important place to enforce modern security controls. We dig into how the definition of “user” expands to include agents and even sub-agents with delegated access, and why that creates both huge productivity gains and real operational risk.

We also map the threat landscape: AI models that can surface hidden vulnerabilities, chain simple issues into complex exploits, and compress attacker speed to near instant. Then we bring it down to earth for SMB cybersecurity, where most teams don’t have a CISO or a SOC. You’ll hear practical steps to reduce blast radius, when to keep a human in the loop, and why incognito or logged-out research can reduce accidental AI memory of sensitive info. We close with what to look for in a secure enterprise browser, including last mile data controls and protections that understand user-to-agent interactions, plus how Prisma Browser for Business aims to deliver enterprise-grade browser security with simpler deployment for small and mid-sized businesses.

Subscribe for more conversations on AI security and modern work, share this with a founder or IT lead, and leave a review if it helps. What’s the first rule you’d set for employees using AI agents in the browser?

Support the show

More at https://linktr.ee/EvanKirstel

6G is closer than most people think, and the biggest surprise is that the headline might not be “faster.” We sit down with Mats Karlsson from Ericsson to talk about the move toward physical AI, where networks help systems sense and act in the real world. When robots, vehicles, and digital twins depend on connectivity for safety and performance, “more bandwidth” stops being the product and guaranteed outcomes become the real promise.

We unpack what outcome based services actually mean in practice: collision avoidance, factory uptime, immersive experience quality, and other measurable KPIs that enterprises can justify paying for. That naturally leads to the toughest question for telecom operators and service providers: monetization. Matt explains why the business model has to evolve along with the network, translating intent into offerings, pricing, and even revenue sharing in real time, while still being able to prove the network can fulfill what it sells.

From there, we get practical about AI in telecom, OSS/BSS transformation, and where ROI shows up today. The message is blunt: don’t start with AI, start with trusted data. We talk about common OSS and BSS pain points like siloed datasets, uneven data quality, and limited end to end visibility, plus real examples of value like revenue assurance, billing anomaly detection, predictive operations, and faster root cause analysis. We also dig into agentic AI and why industry collaboration through TM Forum and open standards is key to making autonomous networks work at scale and unlock new revenue streams, not just cost savings.

If you care about 6G, autonomous networking, AI in telecom, and the future of outcome based connectivity, hit subscribe, share this with a colleague, and leave a review. What outcome would you pay for first: uptime, safety, or experience quality?

Support the show

More at https://linktr.ee/EvanKirstel

Marketing isn’t “soft” when you can tie it to the numbers that run the business. Evan sits down with Meghan Keough, a modern, business-first CMO with decades in enterprise tech, to unpack how marketing leaders can operate at the intersection of brand, revenue, and transformation without losing the plot. We get specific about the metrics that matter most in B2B go-to-market strategy: pipeline by source, cost per pipeline, win rates, sales velocity, and the margin impact behind the dashboard.

From there, we zoom out to the reality every team is facing: constant change with imperfect information. Meghan shares a practical approach to transformation that favors fast learning over perfect plans, plus the discipline of revisiting decisions, running experiments, and being ruthless about what’s actually working. If you’re trying to modernize demand generation or reposition a company upmarket, you’ll hear why quick wins build credibility and why foundations still matter even in a world moving at AI speed.

AI comes up as more than a shiny tool problem. We talk marketing operating models and end-to-end workflows, where AI can streamline steps and even enable more autonomous execution. That leads to a candid look at martech stack complexity and why many organizations are at a consolidation tipping point, along with a clear way to balance experimentation versus scaling: dedicate a small slice of quarterly capacity to pilots, then operationalize the winners across the team.

If you want fewer silos, better alignment with sales and product, and a marketing strategy that holds up under revenue scrutiny, this conversation delivers. Subscribe for more, share this with a growth-minded leader, and leave a review with the one marketing metric you think deserves more attention.

Support the show

More at https://linktr.ee/EvanKirstel

Pool season is back and so is the annual question: why does keeping water clean still feel so manual? We sit down with Patrick from Beatbot to talk about what changes when a robotic pool cleaner stops being a “dumb” tethered machine and becomes a cordless AI pool robot that can map your pool, plan an efficient route, and adapt when it hits real-world obstacles like ladders and tight corners.

We dig into what modern smart pool cleaning actually looks like: cleaning the floor, climbing walls up to the waterline, scrubbing that ring that never goes away, and skimming the surface for floating debris. Patrick also explains how app control and scheduling fit into everyday pool maintenance, plus why sensors matter more than buzzwords when you just want consistent results and fewer headaches. If you’ve been comparing options for a robotic pool vacuum, this conversation helps you separate must-have features from marketing.

Then we look forward. Beatbot’s newest direction includes a dock that can flush debris out of the robot’s filter basket into a larger base, aiming to eliminate one of the most annoying parts of pool ownership. We also talk about the longer-term future of smart home integration, weather-aware cleaning, solar-friendly charging timing, and how pool service pros can use robots to work more efficiently while they focus on water testing and chemicals. Subscribe, share this with a pool owner, and leave a review with the feature you most want in the next generation of pool robots.

Support the show

More at https://linktr.ee/EvanKirstel

Autonomy in telecom sounds like a pure technology race until you look at where operators actually get stuck. It turns out the models aren't the bottleneck. The humans around them are.

We're joined by @Ibrahim Eldeftar, who leads Cognitive Software and Services at Ericsson, to unpack the real path from partial automation to Level 4 autonomous networks, and why the hardest part is often the human system around the tools.

Ibrahim walks us through the two hurdle categories every CSP runs into. The first is the technology foundation: multi-vendor support, scalable AI platforms, data management, deployment at scale. The second is the organizational side: change management, upskilling, new ways of working, and breaking down silos that have been cemented in place for decades. The industry keeps underestimating that second category, even when the AI roadmap looks finished on paper. Ibrahim explains why, and what it actually takes to move an operator forward.

From there we get concrete. rApps and a service management and orchestration platform can replace the fragmented automation stack most operators are living with today, giving teams a common SDK, consistent interfaces, and an ecosystem model where operators build apps themselves or source them from partners. Ibrahim shares real proof points from live networks, including modernizing worst cell hunting with AI anomaly detection and root cause analysis, and taming massive MIMO complexity where the search space is simply too large for humans to tune in any reasonable timeframe.

Then we get into what changes when GenAI and agentic coordination enter the picture on public cloud with AWS. Natural language "talk to the network" interfaces. Orchestrating dozens of RApps at once. A shift toward RApps as a service and SaaS delivery, where operators pay for outcomes rather than software licenses.

Subscribe for more deep dives on telco AI and network automation, share this one with a colleague who's living the automation grind, and leave a review if it landed. And think about this while you listen. What would you automate first if you could truly trust the outcome?

Support the show

More at https://linktr.ee/EvanKirstel

Identity is where the fight is moving fastest, and RSAC 2026 proved it. Fresh off the show floor, we sit down with Jim Taylor, President, Chief Product and Strategy Officer at RSA Security, to break down what’s truly changing in identity security as AI reshapes both the threat landscape and the defenses enterprises rely on.

We dig into why “sovereign” and “deploy anywhere” identity deployments are suddenly mission critical. Cloud convenience can quietly trade away resiliency and control, and recent disruptions show how quickly authentication outages can become business outages. Jim explains what customers are asking for now: the same identity platform capabilities whether it runs as SaaS, in a private cloud, on-prem, or in highly constrained environments where failure is not an option.

Then we get practical about modern identity attacks beyond phishing. If passkeys and phishing resistant MFA harden the front door, attackers pivot to the session with token theft, adversary-in-the-middle scams, and help desk bypass that exploits people and process. We also explore agentic AI and the rise of non-human identities, including how to inventory agents, set entitlements, and apply identity governance so “mini workers” don’t inherit unlimited permissions.

We close with a grounded take on passwordless authentication as a step-by-step journey and what we hope the industry looks like by RSAC 2027 and 2028. If this helped you rethink IAM strategy, subscribe, share with your security team, and leave a review. What identity risk are you most worried about right now?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Agentic AI is no longer a side experiment running in a lab. It’s starting to look like the next operating layer inside the enterprise, and that raises a hard question: do you want a scattered collection of point tools, or a standardized agentic operating system that your teams can actually run?

I sit down with Anurag Gurtu Chief Executive Officer @ AIRRIVED to unpack what “agentic OS” means in practical terms. We get specific about the three pillars that make agentic AI useful at scale: adapting a language model to your enterprise data, adding deep reasoning so it can synthesize and rationalize like a real analyst, and then deploying autonomous agents to take controlled action. We also dig into real enterprise cybersecurity needs across security operations, identity management and governance, risk, compliance, vulnerability management, and the growing challenge of shadow AI.

We zoom out to the messy reality of adoption: too many pilots, too many vendors, and too many tools designed for developers instead of practitioners. Anurag explains why objective-driven automation beats brittle playbooks, why governance and auditability have to be built in, and how fast proof-of-concepts can turn “AI hype” into measurable ROI. We also touch on open source momentum and why Arrived is building in a more secure, governed direction with Etherclaw.

If you’re building an enterprise AI strategy, leading a security program, or trying to prove value beyond demos, this conversation will sharpen how you think about standardization, productivity, and control. Subscribe, share this with a colleague, and leave a review with the biggest hurdle you’re facing in adopting agentic AI.

Support the show

More at https://linktr.ee/EvanKirstel

AI is everywhere in IT operations right now, but most sysadmins don’t need more buzzwords. We need fewer tickets, cleaner patching, faster deployments, and a clear view of what’s happening across every device. That’s why we sat down with Jaren Nichols, President and COO of PDQ to talk about modern enterprise device management the way admins actually live it: too many endpoints, too many apps, constant updates, and zero tolerance for security gaps.

We get into the real pressure point behind “software sprawl” as every department adopts new tools, including AI-driven apps, and IT inherits the responsibility for uptime, support, and security. Jaron breaks down three practical places AI can help right now: faster how-to research, smarter reporting that surfaces outdated versions and risks, and higher-level support for creating policies and workflows. We also dig into the part that matters most when automation gets powerful: transparency. If you can’t see permissions, execution order, and outcomes, you’re building a black box that will fail at the worst time.

From there, we zoom out to the bigger trends shaping endpoint management and AIOps: the shift toward a single pane of glass, the consolidation of roles across Windows, Mac, networking, DevOps, and security, and why legacy tools won’t disappear as fast as people claim. We close with what the next generation of sysadmins looks like when things go right: more visibility, policy-driven objectives, and faster execution without sacrificing control.

Subscribe for more conversations like this, share the episode with the admin who owns patch Tuesday, and leave a review if it helped. What’s the biggest “this shouldn’t be that hard” moment in your IT environment right now?

Support the show

More at https://linktr.ee/EvanKirstel

The martech stack used to feel complicated. Then generative AI showed up and turned “complicated” into “constantly changing.” We sit down with Scott Brinker, the analyst behind ChiefMartec, to unpack what’s really happening as marketing teams move from a familiar martech stack to a broader AI ecosystem filled with new tools, copilots, and early stage agents layered on top of the systems we already depend on.

We get concrete about what still anchors modern marketing technology: a system of record for customer data (CRM in many B2B orgs and often a CDP in B2C), a platform for orchestration through marketing automation and messaging, and a web layer like a CMS or DXP. From there, the stack diversifies fast based on industry, maturity, and team bandwidth, which explains why some organizations can experiment aggressively while smaller teams are still holding marketing ops together with sheer willpower.

From a leadership angle, Scott makes a blunt point: you can’t automate what you can’t define. If you want AI automation that protects authentic brand voice, you need clear guardrails, documented standards, and real ownership, not vague “it’s in our culture” assumptions. We also look ahead to a shift that may surprise a lot of marketers, AI used by customers, including AI search behavior and the possibility of inbox agents that reshape email marketing and customer engagement.

If you care about AI strategy, marketing operations, martech governance, and what skills the next generation of marketing leaders will need, this conversation will sharpen how you think. Subscribe, share this with a marketing leader who’s drowning in tools, and leave a review with the biggest AI change you’re navigating right now.

Support the show

More at https://linktr.ee/EvanKirstel

Manual SOC work is collapsing under its own weight. After RSAC, we sit down with Dave Mcginnis, who leads IBM Consulting’s threat management practice, to get brutally practical about what “autonomous security operations” really means when you strip away the marketing. The headline is simple: humans can’t be the bottleneck in threat monitoring anymore, and “AI-assisted” alert triage won’t cut it when machines can generate more detections than teams can ever click through.

We talk through the hard parts that decide whether autonomous SOC automation helps or harms: investigation depth, evidence, and accountability. Dave explains why the new problem isn’t finding a needle in a haystack, it’s finding a needle in a stack of needles and why autonomous investigation has to examine every IP, domain, email, and hash, then document the reasoning for forensics. From there, we explore how response can move past traditional SOAR runbooks toward agents that can connect directly to identity systems, cloud controls, and application platforms.

The conversation also turns to people and risk. What happens to SOC roles when tier-one work fades, where domain expertise still matters, and why tuning, threat intelligence, and integration become the real jobs. Finally, we look at the uncomfortable truth: adversaries use generative AI too, lowering the barrier to sophisticated attacks. If you’re building a modern cybersecurity program, this is a roadmap for thinking end to end, not tool by tool.

Subscribe for more, share this with a security leader on your team, and leave a review with your biggest question about autonomous security operations.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Trade businesses keep our world running, but most of them operate on painfully thin margins. We talk with Fred Voccola CEO from Simpro Group about what happens when you bring practical AI to the job site and the back office, not as hype, but as an operating platform for the trades that helps contractors finish more work right the first time.

We get specific about where the ROI shows up fast: cutting “retreads” (repeat visits) by improving job prep, making sure the right technician arrives with the right materials, and optimizing routes and scheduling. Fred breaks down how AI agents can act like affordable digital staff, doing the kind of job-prep, collections follow-up, documentation, and optimization work that only huge companies can normally afford. The payoff is better job profitability, fewer wasted truck rolls, and a real chance to move from 5% to 7% profit margins toward something closer to 20%+.

We also dig into customer experience and why service expectations are rising. With ambient listening and automated documentation, the system can capture the “little” details that matter, then prep the next technician with the right context. Finally, we look at the next generation of skilled trades workers, including real-time training support via wearables, plus a preview of Simpro Lightning and its new AI brain and agents.

If you care about AI in construction, field service management, job site productivity, and the future of skilled trades, listen, share this with a contractor friend, and leave a review with your biggest question about AI on the job.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Pressing zero to reach a human should not be the default plan. We talk with Fred Fontes CEO from Acclaim about what’s finally making enterprise voice-first AI work in the real world, especially inside regulated industries like banking and financial services where compliance, auditability, and data security are not negotiable. For teams burned by old IVR trees and brittle chatbots, the conversation gets practical fast: what has changed in the underlying models, and what has to change in how we deploy and control them.

We dig into the idea of sovereignty and why many CIOs and CTOs feel trapped between the need to innovate and the risk of sending sensitive customer data through multiple third-party clouds. Fred explains how controllable voice AI agents, strong guardrails, and enterprise-grade orchestration can turn “cool demos” into dependable contact center automation. We also get into domain-specific benchmarking, because a universal speech-to-text score does not matter if you cannot accurately transcribe a noisy telephony call about banking topics.

Then we go beneath the hood on outcomes: banking collections use cases showing six to eight percentage points higher recovery rates, the ability to A/B test messaging quickly, and why interaction costs can drop dramatically when conversations are faster, more accurate, and handled in parallel. We also talk about the human side, shifting agents toward higher-value customer experience work, and the hardest obstacle left: integration with systems of record and enterprise workflows.

If you’re building or buying conversational AI, listen closely, share this with a teammate who owns CX or security, and subscribe, leave a review, and tell us what your biggest blocker is to deploying voice AI at scale.

Support the show

More at https://linktr.ee/EvanKirstel

AI is making a weird problem even worse: it’s getting harder to tell what’s authentic. That’s why we sat down with Russ from Business Intelligence Group to talk about the We Love Tech Awards ( https://welovetechawards.com/) and what real, transparent judging looks like when trust is on the line. We get specific about how awards can be more than marketing, especially when real people review nominations, score the work, and give feedback that founders and product teams can actually use.

We also zoom out to what we’re seeing across the tech landscape right now. From CES to Mobile World Congress, HIMSS, and Enterprise Connect, the energy isn’t just “more AI.” It’s the shift from trials and proofs of concept to real deployments in hospitals, warehouses, and frontline environments. We talk MedTech and digital health, customer experience and contact center technology, cloud apps, and why this moment feels like a true burst of innovation even with macro uncertainty hanging over everything.

Then we go where awards don’t go often enough: people. Russ shares a striking stat that fewer than 10% of business award nominations are for individuals, and we make the case that recognition should match the humanity behind the work. We also cover digital certificates, including blockchain-based credentials that can live on LinkedIn, and we lay out the practical timeline: the nomination deadline is March 27, followed by a judging window supported by thousands of judges worldwide.

If you’re building a product, leading an innovation team, or know someone who deserves real recognition, listen now, share this with your network, and leave a review. Who are you nominating this year?

Support the show

More at https://linktr.ee/EvanKirstel

AI hype is everywhere at Mobile World Congress, so we went looking for something rarer: real operational proof. From the Ericsson booth in Barcelona, we sit down with Ibrahim Eldeftar, who leads Cognitive Network Solutions product and portfolio for telco AI software, and Claudia Muñiz Garcia, Global Head of Sales for the same unit, to talk about what it actually takes to run autonomous networks.

We break down the autonomy journey in plain language: moving from manual ways of working to systems that can sense, analyze, decide, and execute, with intent-based networking as the layer that helps unlock Level 4 autonomy. Ibrahim shares where carriers really are today (around Level 2 on average) and why many are publicly targeting Level 4 by 2028 to 2030. The driver is network complexity plus rising expectations for “networks for AI” that can support new AI workloads and millions of connected devices without brittle operations.

Claudia explains why Ericsson is being recognized for 5G RAN automation platforms, from standards compliance and security to commercial deployments and scale, and how an open rApp ecosystem accelerates innovation. Then we get into the part operators care about most: outcomes. You’ll hear concrete results from trials and deployments, including major OPEX efficiency gains, fewer issues through anomaly detection and root-cause approaches, spectral efficiency improvements, and a standout uplink story where AI optimization drives meaningful uplink quality and throughput gains.

If you care about network automation, 5G RAN, telco AI, rApps, and the practical road to Level 4 autonomous networks, hit play. Subscribe, share this with a network leader on your team, and leave a review with the one autonomy question you want answered next.

Support the show

More at https://linktr.ee/EvanKirstel

CRM is about to feel less like a database and more like a conversation. We sit down with Brevo to unpack how a fast-growing customer engagement platform thinks about the next era of CRM, where large language models change the “entry point” for customer data and where the old battle over slick UI starts to disappear.

We walk through Brevo’s evolution from an agency to an email automation tool (many will remember the Sendinblue days) and then into a broader customer engagement and CRM platform built for B2C brands. The big idea is the engagement layer: the system that turns customer data into action across channels, agents, and third-party tools. As LLMs become cheaper and more interchangeable, the winners won’t be the tools with the flashiest interface, they’ll be the platforms that orchestrate workflows, permissions, integrations, and real outcomes.

Then we get practical about ROI. Customer engagement software should be measurable because it exists to move KPIs tied to revenue: subscriber growth, net new customers, repeat purchase, and bigger baskets. We also break down how to evaluate AI inside CRM without hype by looking at efficiency, automation, and output per marketer so you can grow without constantly adding headcount. To round it out, we talk martech consolidation, what M&A looks like as valuations reset, and a smart loyalty and advocacy approach that turns happy customers into demand generators.

Subscribe for more conversations on AI in CRM, customer engagement, marketing automation, and martech strategy and if this sparked a new idea, share it and leave a review. What part of your customer lifecycle would you automate first?

Support the show

More at https://linktr.ee/EvanKirstel

The fastest way to understand agentic AI is to stop thinking about chatbots and start thinking about outcomes. From Enterprise Connect, we sit down with RingCentral’s John Finch to talk about agentic voice AI that can handle real customer work end to end: answering the call, connecting to back-end systems, completing a transaction, confirming the result, and closing the loop without bouncing the customer between departments.

We also get specific about what makes this hard and why it matters. Voice is still the most demanding channel in the contact center, and RingCentral’s view is that a strong communications layer unlocks everything else: omnichannel customer engagement, smoother handoffs, and higher containment where it actually helps the customer. From there, the CX revolution becomes orchestration. We talk about scheduling AI agents alongside human agents, tracking performance across both, and using signals like CSAT and NPS to continuously improve. The goal is not “AI replaces people,” but “AI removes the repetitive parts so humans can do higher-value work.”

Healthcare raises the bar even further, so we dig into how agentic AI can validate patients, schedule appointments like imaging, and operate with strict guardrails that prevent unsafe medical advice. With templates and deep integrations, teams can deploy faster in complex environments while keeping compliance and safety in view. If you’re evaluating agentic AI, voice AI platforms, or contact center automation, you’ll walk away with a clearer picture of what’s real today and what’s coming next.

Subscribe for more conversations like this, share the episode with a CX leader on your team, and leave a review with the biggest question you have about agentic AI.

Support the show

More at https://linktr.ee/EvanKirstel

Your phone rings. The screen shows an unknown number or “spam likely.” Do you pick up, or do you protect yourself and ignore it? That split-second decision has become the default for millions of people, and it’s crushing legitimate communication from banks, healthcare providers, insurers, and service teams that genuinely need to reach customers.

From the Enterprise Connect show floor, we talk with Nick Moss from TransUnion’s communications product team about how the voice channel lost trust and how to earn it back. We get into verified caller identity, branded caller ID, and the real-world impact of call authentication built on STIR/SHAKEN principles. The conversation breaks down why spoofing is so easy, what it takes to vet enterprises, and how carriers can help deliver clear identity at scale so a legitimate call looks legitimate when it matters most.

We also explore practical outcomes across industries: fewer missed pharmacy and provider calls, faster resolution for financial services alerts, and stronger defenses against fraudsters who target contact centers as well as consumers. If you care about robocalls, phone spoofing, call labeling, and the future of trusted telephony, this one connects the dots with concrete examples and a clear direction forward.

If you find this helpful, subscribe, share it with someone fighting “spam likely” fatigue, and leave a review with your biggest question about trusted calling and caller ID verification.

Support the show

More at https://linktr.ee/EvanKirstel

Agentic AI sounds like magic until you have to ship it inside a real enterprise contact center where every mistake becomes a compliance problem and every bad interaction hits customer trust. From Enterprise Connect, we sit down with Dialpad to unpack how they’re building agentic customer service on top of an end-to-end UCaaS and CCaaS platform, and why having AI deeply integrated into the communications stack matters more than flashy demos.

We dig into the hardest first step: figuring out what to automate. Dialpad explains “skill mining,” a way to analyze your past customer conversations and surface a practical hit list of workflows that are both common and realistically automatable. Instead of guessing which user flows will deliver ROI, you start with the data you already have, then keep refining as new conversations reveal new opportunities.

Then we get into the part that makes or breaks adoption in regulated industries like healthcare and financial services: safety. Dialpad walks through Guardian, a live conversation analyzer designed to protect agentic voice and chat with layers for security and compliance, scope and intent alignment, and frustration detection, plus a built-in path to hand off to human agents when the customer needs it. If you’re evaluating AI for contact centers, customer experience automation, or enterprise-ready agentic AI, this is a practical look at what “trust” really requires. Subscribe, share this with your ops team, and leave a review with the workflow you most want AI to handle next.

Support the show

More at https://linktr.ee/EvanKirstel

A check-in tablet passes from one patient to the next, and a single leftover photo of an ID or an auto-filled keyboard suggestion becomes a PHI nightmare. From the Samsung booth at HIMSS, we sit down with Ray to unpack how Samsung SDS approaches healthcare IT with a simple goal: make devices and workflows safer, easier, and more useful for the people on the front line.

We get specific about what Samsung SDS does beyond distribution, including helping health systems get the right Samsung phones and tablets into hospitals and clinics, and even porting key applications from iOS to Android when teams need the same functionality across platforms. We also talk about Samsung DeX and the bigger operational idea behind it: reducing dependence on costly nursing carts and PCs by enabling a one-to-one mobile device model that follows the nurse, supports real mobility, and keeps care more connected to the patient.

Then we dig into privacy, compliance, and practical risk. Ray shares how shared tablets used for patient check-in can accidentally carry over sensitive information, and how an approach like Data Sweeper can reset devices between patients without removing the core app staff depends on. We close with a look at the connected patient room and the potential of Google XR for patient distraction and comfort during difficult moments like burn wound care or long dialysis sessions, plus why Samsung’s broader investment in healthcare shapes this “work backwards from the patient” mindset.

If you found this useful, subscribe for more healthcare technology conversations, share the episode with a colleague, and leave a review with the biggest workflow problem you want solved next.

Support the show

More at https://linktr.ee/EvanKirstel

RSAC Season Is Here. Your Certificates Expired Last Month.

RSAC is coming, and I'll be there — so expect more conversations like this one dropping in the weeks ahead.

But here's the thing: the most important security story this season isn't happening on the conference floor. It's already inside your network, running on devices most organizations barely think about.

In this episode, I sit down with OmniTrust co-founders David Sequino and Bill Lattin to unpack what "trust" actually means when CPUs are embedded in everything: cars, medical devices, industrial controllers, payment terminals, routers, and the AI agents that are increasingly making decisions in the physical world, not just on a screen.

We build the conversation from the ground up — starting at the silicon root of trust, then working through secure boot, operating systems, applications, and the network infrastructure that ties it all together. And we get specific about where organizations actually fail. Expired certificates on a switch that drop a VPN tunnel. Static credentials that never rotate. "Fix it later" thinking that simply does not survive contact with embedded and IoT environments. These aren't edge cases; they're common, and they're preventable.

We also draw a line between certificate lifecycle management and identity lifecycle management — and make the case that passwords, secrets, cryptographic keys, and certificates cannot be managed in silos. They're one lifecycle. Treat them as separate problems and you'll have separate failures.
The back half of the conversation puts AI under pressure. It can help defenders move faster, analyze more, and surface what matters. But it also hands attackers new tools: prompt injection, automated attack chains, and "vibe coding" that generates plausible-looking cryptographic implementations that don't actually hold up. Nuance matters in cryptography. Vibes do not.

We close with what CISOs should be measuring right now: PKI posture, SBOM paired with a cryptographic bill of materials, and a credible roadmap toward post-quantum cryptography. Regulation like the EU Cyber Resiliency Act and DORA is raising the bar, and "we're working on it" isn't a compliance posture.
Subscribe for more RSAC-ready conversations, share this with your security team, and leave a review if it was useful. And tell me: what's the weakest link in your chain of trust today?

Support the show

More at https://linktr.ee/EvanKirstel

Talent gaps don’t feel strategic when they hit, they feel personal: missed deadlines, burned-out managers, and the slow bleed of “we can’t grow because we can’t hire.” From the floor at Enterprise Connect, we sit down with Brett and Sebastian from Gr8 Global to talk about a practical answer: building global capability center (GCC) teams that give companies real access to experienced remote talent.

We dig into Gr8 Global’s origin story as a GCC provider created during COVID for eight Top 100 accounting firms, and what it took to stand up around 100 professionals in roughly six months. Then we zoom out into the commercial market: how global staffing can support not only tax and accounting roles (from staff accountant up through manager and director level talent), but also executive assistants, HR support, graphic design, and other remote-friendly positions. The thread running through it all is scalability without the endless recruiting cycle.

AI is the turning point in the conversation. We share why the best operators will combine an AI workforce with humans, letting AI agents handle repeatable tasks while people move into higher-value work like review, advisory, and client relationships. We also look at Latin America’s nearshore momentum, where strong talent and time-zone alignment help teams build more with fewer people, especially as companies choose to create tools instead of buying everything off the shelf.

If you’re wrestling with hard-to-fill roles, rising labor costs, or a mid-level experience shortage, you’ll leave with a clearer hiring strategy and a more realistic view of how AI and global talent fit together. Subscribe for more conversations like this, share the episode with a leader who owns hiring, and leave a review with the role you’re struggling to fill most.

Support the show

More at https://linktr.ee/EvanKirstel

Meetings create the most valuable raw material in a company: the ideas people say out loud. The problem is that those ideas usually evaporate into scattered notes, half-remembered decisions, and action items no one can trace. From the floor of Enterprise Connect, we sit down with Zoom Chief Product Officer Jeff Smith to unpack how Zoom is trying to close that gap by turning conversation into completed work.

Jeff explains why transcription is only the starting point and how “My Notes” is designed to capture interactions across platforms, building a durable repository that feels like perfect recall. We get into the less-hyped but crucial AI work that makes outputs reliable, like noise reduction, voice understanding, and meeting-room identity so tasks go to people instead of “Conference Room One.” Then we zoom out to the big product bet: AI canvases. With Zoom Docs as a text surface and new AI Slides and AI Sheets, AI Companion can translate what you discussed into a project plan, a deck, or a spreadsheet without the blank-page grind.

We also explore how customer obsession shapes the AI roadmap, how a unified data layer connects customer experience, revenue, and support insights, and why healthcare could be a breakout use case for AI workflow automation, better clinician preparation, and more focused patient conversations. If you care about AI productivity, meeting intelligence, knowledge management, and practical enterprise AI, this is a clear look at where collaboration software is going next.

Subscribe for more conversations like this, share this with a teammate who lives in meetings, and leave a review with your take: what should AI automate first in your daily workflow?

Support the show

More at https://linktr.ee/EvanKirstel

Missed calls cost more than you think, especially when leads come in after hours or demand spikes without warning. From the Enterprise Connect show floor, we sit down with https://www.girikon.com and their product and innovation leaders to unpack how AI-first customer engagement is changing the way contact centers and sales teams respond in real time.

We talk through Girikon’s path from a decade of CRM onboarding and consulting into Girikon AI, including Girik Connect, a unified customer engagement platform that brings telephony, two-way messaging, and chat under one roof. The big promise is practical: stay CRM-agnostic so teams can keep Salesforce, HubSpot, Dynamics, or ServiceNow as the system of record while still connecting every conversation across channels. We explore what it looks like to deploy AI voice agents, AI message agents, and AI chat agents as the first touchpoint, so inbound callers get help fast and sales gets qualified opportunities instead of a messy backlog.

Then we get specific about enterprise realities: legacy integrations, multiple platforms, and the need to map conversation history back into the CRM with actionable insights. Think call takeaways, meeting notes, next steps, lead scoring signals, and case trends that reveal the true root cause behind hundreds of support tickets. We also dig into rollout timelines, native integrations, and open APIs, plus the hard parts of making LLMs work on real calls with noise, accents, multilingual speakers, and the risk of hallucinations.

If you care about AI customer experience, contact center automation, CRM integration, and faster speed-to-lead, this one is for you. Subscribe, share with your team, and leave a review with the AI use case you want to automate next.

Support the show

More at https://linktr.ee/EvanKirstel

Your contact center already tells you what’s happening. The harder question is why and what you should do next. From the Enterprise Connect floor, we sit down with Ray Bohoac CEO of Spearfish.ai to unpack a practical approach to contextual intelligence that connects customer conversations to the back-office data that actually explains outcomes.

We get specific about how Spearfish moves teams beyond classic contact center KPIs like service level, QA, and CSAT, using a “signals” architecture that learns from calls and correlates them with systems like ERP, sales, and operational data. Ray shares a standout customer story: an apparel brand hears repeated complaints about a shoe wearing out, then traces those calls to the exact product and lot number, tracks the issue into the supply chain, and gets proactive with customers before the problem snowballs. That’s root cause analysis built for speed, not a report you read a month later.

We also talk about reality: multi-vendor environments, legacy tech, and a market crowded with agentic AI companies. The big takeaway is the feedback loop. When AI and humans do more work automatically, you still need a way to measure what changed, what worked, and what needs adjusting in real time across support, operations, training, and marketing. Ray also highlights expansion into non-traditional “contact center” settings like 211 organizations, proving the same foundation can drive mission-critical outcomes.

Subscribe for more conversations on contact center innovation, customer experience analytics, and applied AI, then share this episode and leave a review. What back-office data would you connect to customer calls first?

Support the show

More at https://linktr.ee/EvanKirstel

A single customer call can reveal why people churn, what drives complaints, and where sales get stuck, but only if you can actually see the patterns. On the Enterprise Connect show floor, we sit down with John from MiaRec to unpack how AI-powered conversation analytics turns everyday contact center recordings into clear actions you can take the same day.

We get specific about the biggest wins: automated QA that replaces slow manual scoring, customer experience intelligence that infers CSAT and NPS without relying on biased surveys, and churn-risk detection that can alert a retention team the moment a caller is truly unhappy. John explains how this “visibility first” approach helps teams protect revenue by catching problems early, while also improving coaching and consistency across agents.

Then we move into revenue intelligence for sales conversations: spotting buying intent, understanding how reps handle objections, and finding missed opportunities that quietly drain conversions. We also talk deployment realities in complex environments, including cloud integrations with popular CCaaS and UCaaS platforms like RingCentral and Twilio, plus what onboarding and configuration can look like.

The most exciting part is what’s new: Ask AI, a chat-style way to query large datasets and uncover trends across negative calls in seconds, and Zapier-powered workflows that can summarize a call, draft a personalized follow-up email, and automate next steps to improve CX and revenue outcomes. If you care about contact center AI, speech analytics, and practical automation, this one is built for you. Subscribe, share this with a teammate, and leave a review with your biggest takeaway.

Support the show

More at https://linktr.ee/EvanKirstel

Cloud native doesn’t mean what it used to. What started as a self-service way to buy compute fast has turned into a full application platform transformation where Kubernetes, desired state, and automation decide how software scales, heals, and lands on compute, storage, and networking.

From Mobile World Congress in Barcelona, we sit down with Dilpreet from Broadcom’s VMware Cloud Foundation (VCF) organization to talk about what’s actually changing on the ground. We get into why Kubernetes has moved from a niche tool to a mainstream standard, and why that success brings a new set of problems: operational complexity, painful upgrades, lifecycle management, and the skills required to keep clusters stable over time. If your cloud native journey feels slower than the slide decks promised, this is the part you’ve been running into.

We also unpack VCF’s product strategy for simplifying the cloud consumption experience by bringing key capabilities together, including automation and multi-tenant cloud operations. Dilpreet explains how combining ARIA Automation with vCloud Director can create a more unified interface for enterprises and cloud service providers, then tying that experience more deeply to the underlying Kubernetes and infrastructure fabric. The aim is a Kubernetes-like workflow where you declare what you want and the platform does the heavy lifting.

Finally, we talk about a big shift we’re hearing from customers: they want that cloud experience on-prem. We connect that to telco modernization as networks move from VNFs to CNFs and the push toward modern telco cloud platforms announced at MWC. If you care about cloud native, Kubernetes operations, VMware Cloud Foundation, platform engineering, and what AI-era apps will demand next, this one is for you.

Subscribe for more conversations like this, share the episode with a teammate, and leave a review so more builders can find it. What’s been the hardest part of your Kubernetes or cloud native journey?

Support the show

More at https://linktr.ee/EvanKirstel

If your contact center only finds problems after customers complain, you are operating blind. We sit down with Klearcom at Enterprise Connect to unpack what “CX end-to-end assurance” really looks like when you have voice, IVR, chat, SMS, and now AI-driven journeys all stitched together across multiple regions and vendors.

We get specific about the pain points teams keep running into: manual testing that is slow and subjective, migrations from legacy platforms to CCaaS that introduce silent failures, and the growing need to validate experiences before changes hit production. Klearcom explains a non-intrusive approach to contact center testing and service assurance so you can spot routing issues, latency, and broken paths early, then measure performance like a repeatable confidence metric instead of a gut feeling.

AI is the turning point. Everyone talks about what chatbots and conversational AI can improve, but far fewer talk about how to test them. We dig into chatbot testing that checks accuracy, brand guardrails, structured and unstructured conversations, and the moment that matters most: when a customer asks for an agent, how quickly do they actually get there, and does it meet your SLA? You will also hear how SaaS on AWS supports global teams while handling data residency needs, and why industries with high downtime costs test on a tight cadence.

If you care about customer experience, contact center reliability, and testing AI before it tests your reputation, this one is for you. Subscribe, share with a teammate who owns CX or CCaaS migration, and leave a review, then tell us: what would you automate and test first in your customer journey?

Support the show

More at https://linktr.ee/EvanKirstel

AI is everywhere right now, but the hard part is turning dozens of pilots into durable systems that actually run a business. We sit down with Tom, SVP of Product Management at GTT, to unpack what changes when enterprises move from “testing AI” to deploying agentic AI at scale across thousands of sites, users, and applications.

We start with how GTT thinks about networking and security as a service, and why the promise is not just bandwidth or a product SKU but a simpler experience that helps customers connect, secure, and simplify. Tom explains the Envision platform and how it spans the edge, the core IP backbone, and public cloud so teams can deliver consistent connectivity, SD-WAN, and security outcomes while preparing for new AI workloads that increasingly want compute closer to the premises.

Then we get practical about what agentic AI requires: data readiness, trustworthy context, and APIs that let agents act safely without constant human validation. We talk frameworks versus one-time deployments, why vendor lock-in is riskier in a fast-changing AI cycle, and how an “AI factory” mindset brings manufacturing discipline to data pipelines, orchestration, validation, deployment, and continuous improvement.

We also share real internal examples, including a cash application agent that helps match remittances to invoices across messy real-world variations, plus how GPU infrastructure supports operational intelligence and proactive network issue detection. If you care about enterprise AI, SASE and SSE, edge computing, and building a scalable agentic architecture, this conversation is built for you. Subscribe, share with a teammate, and leave a review, what part of your AI foundation needs the most work right now?

Support the show

More at https://linktr.ee/EvanKirstel

Most AI teams are learning the hard way that dumping more text into a prompt does not guarantee better answers. We sit down with Philip Rathle 
Chief Technology Officer from Neo4j to talk about the missing ingredient: relationships. When your data is inherently connected, a graph database can turn scattered facts into usable context, so LLMs and agentic AI systems can respond with more precision and less noise.

We walk through why graph technology is showing up as a “quiet power layer” behind enterprise AI, from knowledge graphs and digital twins to metadata, lineage, and even relationships between vector chunks for graph RAG. Philip explains the practical difference between raw data and knowledge, why multi-hop reasoning matters in domains like financial services and supply chain, and how an AI system can delegate deterministic parts of a problem to a graph while the model focuses on language and judgment.

We also get specific about engineering tradeoffs: why relational databases struggle with constant schema changes, what index-free adjacency means for performance, and how graph queries can run 100x to 2000x faster with less hardware for deeply connected questions. Then we look ahead at where the category is going, including why “graph as a bolt-on feature” often misses the real benefits, plus a roadmap update on Infinigraph for scaling graphs into the 100+ terabyte range. Finally, we cover how AI is making graph adoption easier by inferring graph models from relational sources and helping teams write Cypher queries quickly.

If you’re building enterprise AI, graph RAG, or agentic workflows and you care about accuracy, context, and causality, this conversation will sharpen your architecture instincts. Subscribe, share this with a builder on your team, and leave a review. What’s the hardest connected-data problem you want AI to solve?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if one install could fix coverage and unlock an entire layer of building intelligence? We sit down with Michiel Lotter of Nextivity at Mobile World Congress to explore how modern enterprise DAS has evolved from “make the bars appear” to a multi-use platform that delivers public cellular, plug‑in private 5G, and sensor-driven insights across the same footprint.

We break down the Do More with DAS philosophy and why it changes the ROI math for facilities that once viewed in-building coverage as a reluctant expense. Mikhail explains how Nextivity’s system lets teams map antennas to solve dead zones, then mirror that design for private networking by simply connecting a 5G signal source. From there, smart server antennas act as nodes for panic buttons, leak detectors, asset tracking, and environmental sensors, streaming data to an on‑prem edge compute node for fast, secure action.

You’ll hear concrete examples: big box retailers lighting up service in under a week with overnight work; hotel-casino operators meeting staff safety mandates while stopping costly water damage; aged care facilities giving residents dependable connectivity and instant alerts; and hospitals turning their coverage grid into an acoustic awareness network. With embedded microphones and lightweight AI in each antenna, teams can measure noise levels across wards, improving patient experience without deploying a separate sensor mesh.

Along the way, we touch on global carrier approvals, a channel-first approach with system integrators, and the operational win of controlling timelines instead of waiting on lengthy builds. If you care about enterprise connectivity, private 5G, and practical building intelligence, this conversation shows how a single DAS can become the backbone for communications, safety, and analytics.

Subscribe for more deep dives into real-world connectivity, share this with a colleague wrestling with in-building coverage, and leave a review to tell us which multi-use DAS application you’d deploy first.

Support the show

More at https://linktr.ee/EvanKirstel

What if the fastest way to fix American healthcare is the simplest: put people back at the center. Sitting down with Gil Bashe—chair of Health and Purpose at Finn Partners, editor of Medical Life, and author of Healing the Sick Care System: Why People Matter—we unpack the paradox of a nation that spends more, trains the best, and still delivers poorer outcomes than its peers. Gil brings hard numbers, lived stories, and a clear blueprint for moving from fragmented “sick care” to coordinated care grounded in relationships and results.

We explore the rising frustration across every corner of the system: patients feeling sidelined by delays and denials, clinicians burning out under prior auth and paperwork, payers caught between risk and regulation, and innovators struggling to communicate value. Gil argues for a mission-first mindset where business supports care, not the other way around. He shares how continuity of care, team-based primary care, transparent data-sharing, and measurement that values function and time-to-therapy can rebuild trust while bending the cost curve.

With a global lens—from integrated systems like Clalit to policy lessons shaped by his work across countries—Gil highlights what the U.S. can adapt without importing entire models. We also get tactical on artificial intelligence: where AI can reduce cognitive load, triage more effectively, and streamline documentation, and where governance, bias checks, and explainability must anchor deployment. Along the way, Gil previews the growing bookshelf of patient-centered reform and reads a striking paragraph from his book that reframes health as mission plus discipline.

If you’re hungry for practical hope, this conversation delivers clear steps and a human compass: pay for relationships, simplify the rules, open the data, and design technology that frees clinicians to care. Subscribe, share with a colleague who needs a lift, and leave a review with one change you’d make tomorrow—what would put people first where you work?

Support the show

More at https://linktr.ee/EvanKirstel

The energy on the MWC floor said it all: AI for telecom has moved past “someday” and into “show me how fast.” We sit down with Broadcom’s Anupama Mahabhashyam to unpack what sovereignty really means for carriers, why local control and continuous compliance matter, and how operators can turn regulated infrastructure and rich datasets into a durable AI advantage.

We trace the shift from AI hype to execution, highlighting the operational realities telcos face as they build private and sovereign AI clouds. That includes the unglamorous but essential work of model governance—curating approved catalogs, tracking versions and provenance, and enforcing access policies—so teams stop firefighting model sprawl. We also cover the emerging traffic pattern of AI workloads, why early signals may be invisible on traditional links, and how the rise of voice and video inference changes bandwidth planning and east‑west flows across the network.

From there, we dig into the architecture: intelligent orchestration that matches models to GPU capacity, keeps workloads close to data, and prevents oversubscription. Anupama explains how AI as a Service abstractions can remove plumbing while preserving control, letting teams focus on high‑value use cases like anomaly detection, automated triage, and customer care copilots. We emphasize data readiness as the make‑or‑break factor—organizing datasets, enforcing metadata standards, and eliminating silos so generative systems don’t amplify fragmentation. Finally, we connect the dots to outcomes: improved reliability, faster MTTR, better customer experiences, and new revenue streams such as GPU‑as‑a‑Service and compliant enterprise copilots.

If you’re a carrier leader, network architect, or product owner mapping a path to sovereign AI, this conversation offers a clear blueprint: build on governed data, enforce model discipline, and invest in an intelligent infrastructure layer that scales with demand. Enjoy the episode, then subscribe, share with a colleague, and leave a quick review to help more builders find the show.

Support the show

More at https://linktr.ee/EvanKirstel

AI feels fast until memory and storage slow everything down. We sit with Michael Wu to unpack a blunt truth: inference is where value happens, and storage now sits on the critical path. Instead of treating SSDs as cold capacity, Phison’s adaptive middleware turns them into a live cache that expands usable memory and keeps models, embeddings, and long context windows close to compute. The payoff is practical and immediate—lean AIPCs and mini workstations run bigger workloads with steadier latency, and teams can scale inference without waiting for DRAM supply to catch up.

We trace the story from CES announcements to real-world deployment. Michael breaks down how OEM integrations and consumer upgrade kits bring adaptive caching to both new and existing machines, why developer and education communities are the first winners, and how this bottom-up momentum seeds better software and on-device AI experiences. For enterprise leaders, we map the route from local experiments to global rollouts: consistent performance across distributed teams, lower cloud egress, and a storage layer tuned for retrieval-augmented generation, fine-tuning, and high-concurrency serving.

Zooming out, we explore where we are in the AI cycle—early, hungry, and building—and how edge devices and “physical AI” will broaden demand for fast, cache-aware storage. Michael also shares Phison’s fabless strategy, the new Pascari enterprise lineup, and the push toward Gen 6 performance that aligns with next-gen model serving. If you care about real-world AI velocity, this conversation shows how to turn a bottleneck into an advantage by rethinking the memory hierarchy from the ground up.

If this helped you think differently about scaling AI, follow the show, share it with a teammate, and leave a quick review so more builders can find it. What’s the first AI workflow you’d speed up with adaptive caching?

Support the show

More at https://linktr.ee/EvanKirstel

AI is loud at MWC, but we wanted signal, not noise. We sat down with Angus CEO from BeyondNow to unpack how telcos can turn hype into real revenue by building an “AI factory” for SMBs, digitizing the front office, and orchestrating partner ecosystems that deliver complete, easy-to-buy solutions. The conversation gets practical fast: what it takes to package connectivity with AI-driven services, why data readiness still gates everything, and how to replace price wars with a disciplined upsell motion that deepens relationships and boosts stickiness.

We explore the shift from back-office obsession to front-office acceleration—where CPQ discipline, guided selling, and AI agents create a faster, cleaner path from quote to cash. Angus breaks down why B2B is different from B2C, how standard blueprints cut complexity, and where prompts can and can’t launch new services yet. Instead of betting on magic, the focus is on proven building blocks: structured data, simplified migrations, partner co-selling, and outcome-based offers that customers immediately understand.

If you work in telco, MSP, or enterprise networking, you’ll hear a playbook for growth: unify internal stovepipes into coherent bundles, add partners to enrich the solution, automate fulfillment end to end, and use AI to amplify—not replace—sound operations. We also touch on Beyond Now’s 45% growth trajectory, portfolio expansion, and plans to bring AI factories to life across adjacent markets. Ready to connect AI buzz to booked revenue? Follow the show, share this episode with a colleague who needs a better upsell strategy, and leave a review with your biggest AI roadblock—we’ll tackle it next.

Support the show

More at https://linktr.ee/EvanKirstel

Ever wonder why your 4K stream freezes even though your plan boasts blazing speeds? We sat down at MWC with Metin from AirTies to unpack the real reason people switch broadband providers—and it’s not the price tag. Drawing on fresh survey data from the US, UK, and Japan, we dig into churn rates, intent to switch, and the number one culprit behind dissatisfaction: inconsistent user experience. From video stutter to choppy video calls, the pain points are surprisingly common, and they point straight at Wi‑Fi as the everyday bottleneck.

We break down how AirTies tackles the problem with a lightweight software agent embedded in home and small business routers, feeding real‑time performance data to a cloud platform that troubleshoots and optimizes automatically. Think channel optimization, band steering, client balancing, and policy tweaks that prioritize what people actually feel—smooth video and stable calls—over theoretical peak speeds. When software can’t fix it, the system flags precise next steps for providers, whether that’s an extender, a router swap, or a line investigation, cutting support costs and stopping churn before it happens.

The conversation also explores the messy reality inside big ISPs: multi‑vendor fleets, technical debt, and the challenge of deploying at scale. We get into why AI‑driven personalization is the next leap—networks that learn each home’s patterns and adapt on the fly without the user touching a setting. Metin shares a recent US launch of personalized Wi‑Fi with a major provider and the momentum behind smarter networks that measure success by experience, not just Mbps. If you care about reliable streaming, rock‑solid video calls, and a home network that quietly gets out of the way, this one’s for you.

Enjoyed the conversation? Follow the show, share it with a friend who battles buffering, and leave a quick review to help others find us.

Support the show

More at https://linktr.ee/EvanKirstel

What happens when connectivity grows from a niche tool into the backbone of modern industry? We sit with Erik Brenneis CEO of Vodafone IoT, to explore how a team that started in the M2M era now supports 230 million connections across 180 countries and 760 networks. From connected cars that update themselves over the air to smart meters reshaping utilities, we trace the systems, standards, and strategy that turned scattered pilots into dependable, planet-scale services.

Eric breaks down where the demand is strongest—automotive, energy, industrial equipment, payments—and why connected health is surging with pacemakers, sleep apnea devices, and dialysis machines that need authenticated, encrypted, and reliable links. We go inside the operating model: embedded technical teams near customer R&D centers, direct access to experts, and local solutions for complex markets like Turkey, Brazil, and the UAE to meet data residency and regulatory needs without redesigning products per country.

Security takes center stage as we contrast consumer SIM behavior with a closed IoT system that authenticates all traffic and blocks unauthorized access. Then we zoom out to the unexpected: conservation stories from tracking seals, rhinos, and whales, and environmental protection through early forest fire detection. Finally, we look ahead to a major shift—evolving from mobile-only to a hybrid mobile plus satellite network through partnerships with Iridium and Skylo, delivering ubiquitous coverage without new hardware. That leap doesn’t just connect more places; it feeds industrial AI with the steady, trustworthy data it needs to drive real outcomes.

If you enjoy conversations that blend real-world deployments with what’s next in connectivity, subscribe, leave a review, and share this episode with a friend who loves tech that actually ships. What would you connect first?

Support the show

More at https://linktr.ee/EvanKirstel

What if a telecom could think with one brain? We sit down at MWC with Gil Rosen CMO from Amdocs to unpack how the company is shifting from building point solutions to delivering AOS, an agentic operating system that orchestrates many AI agents as a single, contextual layer across the business. Instead of bolt-on automations, AOS connects decisions end to end—governed, explainable, and secure—so outcomes are consistent and measurable.

We walk through why domain expertise matters: telecom-specific ontologies, prebuilt process libraries, and open integration with non-Amdocs BSS, financial, and logistics systems. Gil explains how trust becomes non-negotiable when AI touches money and identity, outlining controls for repeatability, policy adherence, auditability, and scale. Then we go practical. With Market One data across tens of millions of subscriptions, operators can see which OTT bundles trend, how device cycles forecast upgrades, and where local behavior shapes winning offers. The result is smarter pricing, higher ARPU, and faster testing without rip and replace.

The conversation takes a human turn with “waiting is gone.” Voice-first agents shift service from taps to talk, powered by two new layers: personality engineering to encode brand tone, and a customer digital twin that aggregates thousands of signals to tailor language, depth, and empathy. A techie hears throughput and latency; a casual user gets plain guidance and reassurance. Early results are striking, with personalized agents showing up to 3x NPS gains versus generic interactions. Beyond speed, customers feel understood—and that feeling moves retention, referrals, and revenue.

Change is urgent. Three years ago the story was 5G; now it’s outcomes customers can feel. AOS offers a collaborative path forward, coordinating multi-vendor agents and existing stacks while upgrading the experience layer. If you’re ready to reimagine service, unlock new bundles, and replace hold music with helpful conversation, tune in—and then share your take. Subscribe, leave a review, and tell us what part of the customer journey AI should transform next.

Support the show

More at https://linktr.ee/EvanKirstel

The hallways at MWC were buzzing, but our conversation cut straight to what matters for operators right now: shipping new services faster without getting boxed in. We sat down with Broadcom to unpack VMware Telco Cloud Platform 9 and how it blends cloud-native speed with the reliability telco networks demand. This release pulls previously separate components into a single, horizontal stack, pairing one automation engine for both virtual machines and containers with an embedded Kubernetes runtime. The payoff is simpler operations, shorter launch cycles, and real freedom to choose best-of-breed network functions.

We also dive into AI where talk meets traction. Broadcom is rolling out integrated management for NVIDIA GPUs directly in the platform, making it practical to offer GPU-as-a-service and manage accelerator resources with the same tooling operators already trust. That creates a clean path for enterprise AI workloads and sets the stage for the next leap: virtualization of GPUs. By bringing their virtualization heritage to accelerators, Broadcom is targeting higher utilization, better isolation, and elastic scaling for inference and training across the network and at the edge.

Vendor lock-in has long slowed telco transformation, so we explore how a true horizontal platform changes the equation. CSPs can run virtualized and containerized network functions from multiple suppliers on the same infrastructure, swap vendors as needs evolve, and rely on a rigorous ecosystem program for pre-qualification and certification. That means less integration risk, fewer surprises in deployment, and faster routes to revenue. We round out the conversation with Europe’s rising demand for sovereign cloud—country-level compliance, data privacy, and security—and how recent national certifications prove the model is working in practice.

If you care about open ecosystems, sovereign-ready architectures, and an AI-enabled telco cloud that actually reduces time to market, this conversation is for you. Follow the show, share it with a colleague who’s building next-gen networks, and leave a quick review to tell us what you want covered next.

Support the show

More at https://linktr.ee/EvanKirstel

What if your network could sense a problem, explain it, and fix it before anyone files a ticket? We sit down with Cisco’s to unpack the leap from AIOps and GenAI to true agentic operations—systems that detect, diagnose, and remediate across complex, multi-domain environments where the internet is your backbone and SaaS is your front door.

We trace the journey from ThousandEyes’ “Google Maps for the internet” to today’s AI-driven NetOps. You’ll hear how teams move from a find-and-fix mindset to evidence and escalate, using shared telemetry to pinpoint where issues live—whether that’s an ISP interface, a cloud region, or a misconfigured DHCP pool. Joe shares a live example showcased at Cisco Live where a days-long incident class, like DHCP pool exhaustion, is now identified in real time and resolved with guided workflows, shrinking resolution from days to minutes.

Then we look ahead. As AI agents proliferate, networks must support machine-to-machine traffic that never sleeps and often surges in bursts. We break down the essentials: identity for agents, least-privilege access, and microsegmentation that aligns with intent. We also explore how an AI canvas brings networking, security, and observability into a multiplayer workspace, replacing giant war rooms with a precise, shared picture and deterministic actions. Trust is the final mile to autonomy, and we detail how deep network models—trained on decades of TAC knowledge—can make automated actions explainable, auditable, and safe for mission-critical environments.

By the end, you’ll have a clear playbook: use cross-domain evidence to localize issues, standardize incident response, automate the repeatable, and measure success by tickets avoided rather than just MTTR. If you’re preparing for AI-to-AI traffic, aligning security with agent behavior, and aiming to prevent problems before users ever notice, this conversation is your roadmap. If this resonates, follow, share with your team, and leave a quick review to help more builders find it.

Support the show

More at https://linktr.ee/EvanKirstel

Ready to move from AI pilots to P&L? We sit down with HCLTech’s Anil Ganjoo to unpack how telecom, media, and technology are converging into an AI-native future where outcomes rule, stacks unify, and partners co-create at speed. The conversation starts with HCLTech’s engineering roots and tracks a bold evolution into an IP-led, platform-driven strategy that spans networks, cloud, edge, data, and silicon—giving operators and media leaders the tools to turn infrastructure into monetizable products.

We dig into the realities of 5G monetization, where enterprise use cases like private 5G, network slicing, fixed wireless access, and edge AI are generating measurable ROI, while consumer ARPU remains a longer play. On the media front, Gen AI is transforming vast content libraries into searchable, reusable, and hyper-personalized experiences that raise engagement and cut churn. Anil explains why AI must be embedded across the entire stack—think AI as the brain, with cloud, networks, edge, and data as the nervous system—to unlock dynamic pricing, predictive maintenance, churn prevention, and planet-scale personalization.

The shift from telco to techco takes center stage as we explore network APIs, platform ecosystems, and product-aligned operating models. We get practical on commercial innovation too: outcome-based engagements, gain-share structures, and transparent KPIs tied to revenue growth, cost-to-serve reduction, billing accuracy, and NPS. Partnerships power the journey, from co-built AI factories with Nvidia to agentic solutions with Microsoft and Google Cloud, all industrialized into OSS/BSS and modern network architectures to scale beyond proofs of concept.

We close with sharp predictions for what will accelerate next: agentic AI in core operations, autonomous and AI-native networks, edge AI plus enterprise 5G crossing the chasm, custom silicon for real-time inference, and services-as-software powered by AI agents. Headed to MWC Barcelona? Come see the demos and strategy sessions at Hall 2. 

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Building great health tech isn’t about stacking features. It’s about clarity, secure architecture, and the courage to automate the real bottlenecks. We sit down with Technology Rivers founder and CEO to unpack how AI is changing the way we design, build, and scale HIPAA-compliant digital health products without cutting corners on privacy or performance.

We talk through the full development lifecycle and where AI actually pulls its weight: rapid proofs of concept, code generation that respects standards, unit testing and coverage, and fast UX prototyping that gets teams aligned. Then we draw a hard line around risk. If you’re uploading sensitive docs to generic endpoints or treating PHI like search fuel, you’re setting yourself up for trouble. We break down practical strategies like retrieval-augmented generation, clean vector design, strict access control, audit logging, and the human-in-the-loop practices that keep systems safe as they scale.

From there, we tackle the real reasons projects fail: blurry requirements and no single owner. You’ll hear a playbook for defining outcomes, narrowing scope to a lovable version one, and building for specific users—clinicians, patients, and admins—with interfaces that are simple, informed, and fast. We also explore how to graduate low-code MVPs into production systems without tossing your work: evolve schemas, enforce coding standards, add encryption and RBAC, and ship with CI and observability. On interoperability, we go beyond EHR APIs and highlight model context protocol—the next step in connecting AI agents to your data and workflows in a controlled, auditable way.

If you’re planning for VIVE or HIMSS, or you’re mapping your next quarter, this conversation gives you a sharp lens: build automation where it matters, protect data by design, and use AI to amplify well-defined processes. Subscribe, share with your team, and leave a review with the one build challenge you want us to unpack next.

Support the show

More at https://linktr.ee/EvanKirstel

Telco's growth model is broken. Here's the blueprint to fix it.

5G returns are under pressure. Hardware costs keep climbing. And operators are drowning in complexity they can't monetize.

We sat down with VMware by Broadcom leaders to cut through the noise and what emerged is a sharp, actionable roadmap for what comes next.

Three big shifts every telco leader needs to understand:

**1. The mobile core is your highest-leverage asset right now.**
Not the edge. Not the RAN. The core — and the operators who productize it with disciplined lifecycle management will outpace those still treating software as an afterthought on hardware.

**2. AI monetization is real — but only if you architect for it.**
The team walks through VMware Cloud Foundation as the engine for GPU-as-a-service and AI-as-a-service: model stores, runtimes, vector databases, compliance baked in, data isolation from day one. Not someday. Now.

**3. Sovereign cloud isn't a checkbox anymore — it's a revenue line.**
Especially in Europe. Jurisdiction and residency are becoming competitive differentiators, not legal formalities. The operators moving fast here will own the enterprise stack.

We also go deep on:
→ Co-innovation with Nokia, Ericsson, Mavenir, and Oracle — and why full-stack ecosystem orchestration is replacing the old "software on hardware" mindset
→ How consolidated dashboards, certification, observability, and license governance cut change risk and accelerate upgrades — and why this matters *more* as Kubernetes complexity stacks up
→ Intelligent operations: embedded AI that reads signals across storage, network, compute, and Kubernetes — and recommends next actions while keeping humans in the loop
→ Agentic AI traffic: bursty, hard to cache, and arriving fast. Telcos sit at the crossroads of inter-DC connectivity, edge placement, and quality guarantees — and that's a strategic position worth owning

The bold takeaway: stop selling raw connectivity. Start selling trusted AI capacity and outcomes.

If you're lowering TCO, launching AI services, or charting a pragmatic path toward autonomous networks — this conversation will help you act with confidence, not just follow the hype.

🎧 Listen now. Subscribe for more candid strategy talks.
📤 Share with your team if you're rethinking your telco roadmap.
💬 Drop a comment: **What's the one business outcome you're chasing in 2025?**

Support the show

More at https://linktr.ee/EvanKirstel

How do you protect teenagers online without turning every app into an ID checkpoint?

That's the question governments, platforms, and parents are all wrestling with right now — and most of the answers so far have been blunt, binary, and broken.

We sat down with our guest from TELUS Digital to go beyond the headlines and into the actual design challenge: what age verification gets right, what it gets dangerously wrong, and how to build systems that protect young people without making privacy feel like a casualty.

Here's what we unpacked:

The smartest approach isn't one-size-fits-all. It's layered and proportionate:

→ **Low-risk spaces** (forums, general content) can rely on self-declaration and behavioral signals
→ **Medium-risk spaces** use facial age estimation — a quick confidence range, image deleted immediately, no data stored
→ **High-risk spaces** (adult content, dating, gambling) justify stronger verification with human-in-the-loop review

The architecture matters as much as the intent. Poor design is how safety becomes surveillance.

Transparency is the trust engine.Us ers need to know *why* their data is requested, *how* it's processed, and *what* they can do when the system gets it wrong. Appeals aren't a nice-to-have — they're the difference between a system people accept and one they route around.

We also got into the real trade-offs nobody talks about enough: accuracy, privacy, inclusion, and the very real risk that blanket bans — like those emerging in Australia, Spain, and across the EU — backfire without safer defaults, stronger parental tools, and genuine digital literacy investment.

Our guest walks through how TELUS Digital supports clients across the full stack: content moderation, fraud prevention, bias testing, account security, age estimation models, and verification systems built to correct mistakes at scale.

And we close on where this is all heading — zero-knowledge proofs, privacy-preserving credentials, and portable age attestations that raise protections while *reducing* data exposure. The technology is ahead of the policy. The question is whether platforms will lead or wait to be forced.

If you're building products that touch teenagers, this conversation is for you.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Your IT Asset Inventory Is Lying to You

Asset sprawl isn't coming. It's already inside your organization, quietly growing through every new SaaS subscription, remote device, and shadow tool your team adopted without a ticket.

Most IT leaders know this. Few want to say it out loud.

In a recent conversation with Peter from NinjaOne, we got into the real reason IT asset management is back on everyone's radar -- and it's not because the category got flashier. It's because the pressure finally got undeniable. Hybrid work exploded device counts. SaaS spending spiraled. Compliance auditors stopped accepting "we think we have about 400 endpoints" as an answer. Spreadsheets, once a perfectly acceptable stopgap, became a liability.

That pressure is what pushed NinjaOne beyond endpoint management into a full ITAM strategy. And the conversation that followed is one worth sharing with any IT director who still runs discovery through manual walk-throughs and outdated files.

From Guesswork to Ground Truth

The practical core of unified ITAM starts with continuous discovery -- pulling data from Active Directory, Intune, SNMP, network scans -- and pairing it with lifecycle context: who owns this device, what's its status, when does the warranty expire? Software license reconciliation becomes a living process instead of a once-a-year scramble. Asset records stay current. Clean data syncs automatically into CMDBs like ServiceNow and into the ERP systems finance and procurement actually use.

Peter told a story most IT leaders will recognize immediately: the pre-audit ritual of hunting down the most recent spreadsheet, calling facilities, walking floors, and still not being confident in the numbers. Automation doesn't just save time. It removes the guesswork entirely.

The Business Case Is Hiding in Your Asset Data

Here's what changes when hardware and software inventory finally live in one place: you get leverage. Teams can spot noisy OEM vendors, stretch refresh cycles, kill redundant purchases, and surface shadow devices that are quietly sitting outside your security perimeter. Real-time visibility tightens both security posture and compliance footing. Ops teams get cleaner root-cause analysis because they can actually map relationships between network gear, servers, and peripherals.

Peter outlined a ROI model worth borrowing for your next exec conversation: measurable cost control, quantified risk reduction, and audit readiness backed by actual numbers rather than estimates and optimism.

Where the Category Goes Next

The shift underway isn't just better asset tracking. It's toward a unified IT operations control plane -- broader coverage, deeper integrations, and tools explicitly designed to collapse sprawl rather than add another layer to it. Fewer tabs. Fewer systems of record. One place where asset data, security context, and lifecycle history actually connect.

For teams still chasing asset data across tickets, procurement emails, and disconnected portals, this conversation is a practical starting point -- not a vision deck. It's about centralization, automation, and building the kind of business case that gets budget approved.

If this hits close to home, follow the show, share it with your team, and drop a quick review. It helps more people find the conversation.

Support the show

More at https://linktr.ee/EvanKirstel

What if your best trainer is the person who’s retiring next month—and an AI that turns their brain dump into accurate, engaging lessons overnight? We unpack how a scrappy chat-with-PDF experiment grew into an enterprise-grade course creation engine that slashes time, cost, and confusion while boosting trust and adoption.

Ro shares the early product choices that mattered: attacking hallucinations head-on, grounding content in source material, and designing guided workflows that mirror real instructional design standards. We dive into the moment enterprise demand hit—when a major manufacturer needed to capture critical know-how from a retiring workforce—and how AI made it possible to transform raw voice notes and messy documents into structured training with assessments, examples, and clear outcomes. The conversation widens beyond linear courses to the realities of learning at work: microlearning, deep search across knowledge bases, and just-in-time guidance that supports real performance.

Then comes the power move—joining LearnUpon. By pairing Courso’s AI-driven content generation with LearnUpon’s LMS strengths in skills mapping, role-based delivery, and gap analysis, creation finally meets distribution. Think on-the-fly lessons triggered by skill needs, rapid updates that keep content fresh, and spaced repetition that helps learners remember what matters. Ro’s philosophy grounds it all: AI should be invisible, human-guided, and relentlessly practical. It’s not a thinker; it’s a multiplier for experts, a backstage crew that helps knowledge scale beyond silos and survive turnover.

If you care about L&D strategy, instructional design, LMS integration, knowledge capture, and personalization that actually helps people learn faster, this story-rich deep dive brings clarity and a real blueprint for action. Subscribe, share with your learning team, and leave a review with one course you’d automate first—we’d love to feature your ideas next.

Support the show

More at https://linktr.ee/EvanKirstel

Think gamers are just kids with consoles? We pull back the curtain with Zynga to show how mobile gaming has become mass-market entertainment—and a must-buy channel for brands. From FarmVille roots to a powerhouse inside Take-Two Interactive, Zynga sits where free-to-play design, rewarded ads, and deep engagement meet real business outcomes.

We start with the core shift: only a small fraction of players pay, so advertising powers the experience for everyone else. That’s where rewarded ads shine. When players choose to watch a spot to earn an extra life or perk, brands become part of the game’s momentum instead of an interruption. It’s native, opt-in, and it builds goodwill that lasts longer than a passing impression. We also bust stereotypes about who plays. Mobile skews female, with millennial and Gen X women—often the chief decision-makers at home and at work—driving a massive share of purchasing. If you care about the female economy, you care about mobile gaming.

Gabriella walks through how Zynga maps brand KPIs to the right titles across 150 games, from word and match-three to sports and invest-and-express. We talk creative formats that invite two-way interaction, why gaming is the perfect companion to TV on the couch, and how mid-funnel metrics—preference, favorability, and consideration—quietly win the day. Categories seeing standout results include retail, CPG, entertainment, insurance, pharma, and QSR, all benefiting from time spent that ranks among the highest in mobile.

Looking forward, we hit the measurement problem and the fix on the horizon. Traditional media mix models undercount gaming, but AI-driven MMM promises a clearer read across retail media, CTV, social, creator, audio, and mobile apps. Add new devices, foldables, and AR into the mix and the canvas for brand experiences only gets bigger. If you’re planning budgets, building creative, or just curious how games became the heartbeat of digital attention, this conversation is a field guide to what works now and what’s next.

If this resonated, follow the show, share it with a teammate, and leave a quick review—what part of the gaming-media shift surprised you most?

Support the show

More at https://linktr.ee/EvanKirstel

We sit down with Swiss Tech campaign manager to crack open the machine. This isn't luck—it's a surgical combination of world-class universities, patient capital, and a public-private alliance that treats every spinout like a potential Rolex.

From AI and robotics to medtech, biotech, and precision manufacturing, Switzerland weaponizes its obsession with quality. The ecosystem doesn't just fund research; it drags it kicking and screaming into the market, then flies it around the world to prove it works.

**Enter the Swiss Tech Pavilion: startup diplomacy on steroids.**

CES. VivaTech. MWC Barcelona. SusHi Tech Tokyo. Web Summit Vancouver. These aren't just booths—they're engineered collision zones where founders lock down investor meetings, corporate partnerships, and media hits in seventy-two hours. Patricia walks us through the formula: Swissnex handles the intel, Switzerland Global Enterprise opens corporate doors, and Innosuisse bankrolls the ambition. The right platform. The right rooms. The right moment.

Then there's the Global Deep Tech Battle—a gladiator arena for founders. International teams compete in front of investors and journalists who double as judges. It's not about participation trophies. It's about forcing Swiss startups to measure up against global killers and come out sharper.

**The pipeline starts in the lab.**

ETH Zurich and EPFL aren't just top-ranked universities—they're idea factories with built-in incubators and accelerators that turn research into revenue. Clinical trials. Industrial pilots. Product-market validation. But Patricia doesn't sugarcoat the gaps: early-stage support is world-class, but late-stage capital for hardware-heavy or regulated plays? That's where things get tight. Stability and quality of life keep teams grinding, but cross-border expansion, corporate venture checks, and Series B+ rounds remain the final boss.

If you're obsessed with how AI, robotics, and life sciences jump from whiteboards to the real world, this is your blueprint. From the hidden champions supplying Apple and Tesla to the festivals that turn unknown founders into marquee names—this is how Switzerland plays the long game and wins.

Support the show

More at https://linktr.ee/EvanKirstel

Redundancy only feels like a boring insurance policy… until it starts delivering real, everyday performance wins.

In this conversation with Julian Jacquez, President and Chief Operating Officer (COO) of BCN Telecom, we dig into how dual connections, SD-WAN overlays, and intelligent automation transform networks from “hope nothing breaks” into always-on, high-performing platforms that keep cloud apps flying and employees focused on actual work (not buffering wheels).

We walk through the journey from single links and MPLS to today’s world of diverse access, policy-driven routing, real-time failover, and direct-to-cloud optimization. Julian breaks down why many enterprises start with managed connectivity, then layer in security through SASE and zero trust to create a unified model for identity, segmentation, and inspection.

For distributed environments like restaurants, healthcare, and retail, the ability to roll out consistent changes across hundreds or even thousands of locations is a massive unlock. No more touching every box. Firewall updates, policy shifts, and standards enforcement happen centrally and instantly.

And then there’s execution, where most network projects either shine or stall. We get practical about project-managed deployments, coordinating carriers and cutovers, and deciding between OpEx and CapEx based on refresh cycles, cash flow, and internal expertise. We also cover co-managed versus fully managed operations, what to automate first, and how AI-driven visibility turns raw telemetry into proactive tickets and recommended fixes.

We even explore access diversity in underserved markets, from fiber and broadband to 4G/5G and LEO, making sure sites stay online wherever work happens. With storms, power outages, and security incidents putting constant pressure on infrastructure, resilience isn’t a nice-to-have anymore. It’s a core business strategy.

If uptime, user experience, and smarter network spend matter to you, this episode will definitely make you rethink the modern WAN for the AI era.

Subscribe, share it with a colleague, and drop a review with the resilience tactic you’re prioritizing next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Your quoting process shouldn’t feel like a scavenger hunt. We sat down with GTT’s channel leaders to unpack how a partner-obsessed strategy and a new digital platform, GTT EnvisionDX, shrink telco quoting from weeks to minutes while boosting transparency, accuracy, and partner ROI. The conversation gets practical fast: what it means to orchestrate sales, marketing, enablement, and product around outcomes; why legacy programs disintermediated partners; and how a modern model respects that MSPs, SIs, and advisors hold the strongest customer relationships.

We dive into the product innovations that make the difference. GTT EnvisionDX is built around location-first design, live vendor pricing, on-net visibility, and side-by-side term comparisons. Large language models translate plain English into telco details, so partners don’t need to speak jargon to configure DIA, broadband, wireless, SD-WAN, SASE, or pro services. For multi-site deals, AI parses messy spreadsheets, validates addresses, and applies reusable templates, letting teams scale from two sites to hundreds with confidence. Digital twinning tests network designs before rollout, reducing risk and rework. Every step surfaces transparent choices, recommended equipment, and full costs—including install and shipping—so decisions are clear and fast.

 Beyond quoting, the team focuses on lifecycle management and marketplace integrations, aiming to make network and security management fade into the background so customers can focus on revenue, cost control, and experience. If you’ve been waiting for telecom to feel more like cloud—self-serve workflows, real-time data, API-first integrations, and a clean UX—this conversation is proof it’s here.

If you found this valuable, follow the show, share it with a teammate, and leave a quick review to help more channel leaders discover it.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Your next hire shouldn’t spend their first week waiting on a laptop while projects sit in limbo. In this episode, we pull back the curtain on the $5B device lifecycle management market and explore how modern IT teams are shipping fully provisioned devices worldwide, recovering assets cleanly, and protecting data without becoming a global logistics operation.

Francis Osifo, third-time founder and CEO of Rayda, breaks down how his team delivers day one ready devices in as little as five business days across 170 countries. By integrating directly with HR systems, standardizing device catalogs, and automating provisioning and returns, Rayda removes the friction that slows global hiring. We dig into the real cost of devices stuck in customs, the offboarding mess created by buying hardware from consumer retailers, and why Apple Business Manager and MDM platforms are the backbone of enterprise control.

The conversation gets practical fast. We cover how to close the HR IT gap with automated triggers and clear SLAs, how to maintain real time visibility into assets across borders, and how to avoid the compliance risks of forgotten devices floating in the wild. Francis also shares why IT must evolve from a perceived cost center into a strategic growth enabler and how seamless onboarding directly impacts culture, productivity, and retention.

Looking ahead, we explore the rise of agentic AI in IT operations. From handling routine tickets and password resets to orchestrating software access and even device procurement through intelligent automation, this next wave of tooling is set to free small teams to focus on security, architecture, and innovation. For lean IT orgs supporting hundreds of employees per admin, this is how scale actually becomes manageable.

If you’re hiring globally, struggling with device sprawl, or looking to show real ROI from IT operations, this conversation is a must listen. Subscribe, share it with a teammate who’s tired of onboarding fire drills, and drop a review with your toughest device challenge so we can tackle it next.

Support the show

More at https://linktr.ee/EvanKirstel

Patch Tuesday doesn’t just deliver updates. It delivers pressure. Suddenly, you’re staring at a mountain of fixes, racing to decide what ships now, what waits, and how to keep a single reboot from blowing up your entire week. In our latest conversation with NinjaOne, we dig into what practical AI actually looks like when you’re responsible for millions of endpoints and have zero tolerance for downtime.

Forget the buzzwords. We walk through how real telemetry, public sentiment signals, and human-in-the-loop workflows remove latency from patching, shrink rollbacks, and turn operational chaos into confidence. We also tackle a long-standing pain point: messy asset data. When your CMDB is noisy, everything downstream starts to wobble. Licensing, vulnerability management, compliance, and budgeting all take a hit. You’ll hear how AI can reconcile unstructured inputs and normalize device records to finally deliver a clean system of record without an army of spreadsheet warriors.

From there, we draw a clear line between smart automation and risky overreach. Full autonomy sounds great on paper, but legal and technical realities demand a pragmatic approach. We discuss a future where AI drafts the policies and packages while experts validate the results, allowing repeatable, low-risk issues to close automatically.

There is also encouraging momentum in the public sector. Streamlined FedRAMP pathways and growing reciprocity across defense impact levels are speeding up adoption and helping agencies move closer to private-sector velocity. Combined with budget pressure, this is accelerating the move toward a Digital Operations Center with centralized visibility, fewer tools, and outcomes you can actually measure.

We wrap with a look ahead to what resilience means in 2026. Think performance you can prove through deep instrumentation and a surge in mobile device management as tablets and phones become the new frontline. If you’re ready to replace ritual scream tests with data-driven confidence and finally align operations and security in real time, this conversation lays out a path you can start using today.

Check out the full episode, share it with the teammate who owns your patching or MDM, and let us know in the comments: what is the one workflow you want to see automated next?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

When the CES show floor goes dark and the crowds thin out, the real conversations begin.

We sat down with Dassault Systèmes’ Fred Vacher and Abhishek Bali to explore how early-stage startups turn flashy demos into certified, manufacturable products. Using virtual twins, hands-on mentorship, and a global partner ecosystem, they walk founders through the hard part of innovation, moving from prototype to production. If you’ve ever wondered why some ideas stall while others scale, this conversation breaks down the journey step by step.

We dig into the mission of the 3DEXPERIENCE Lab and how it works hand in hand with SOLIDWORKS for Startups to support founders across design, simulation, compliance, and industrialization. Many teams join for CAD and quickly discover a full-stack platform that includes multiphysics simulation, quality systems, clinical workflows, data management, and real-time collaboration. The mentorship layer adds a critical advantage: expert design reviews, manufacturing guardrails, and introductions to enterprise partners across more than a dozen industries. That’s how a clever concept becomes a product that survives audits, pilots, and real-world production.

The broader tech landscape is evolving fast. Impact is no longer an afterthought, it’s the goal. Deep tech is on the rise, especially in medical devices, industrial systems, and sustainable hardware. We dive into the emergence of “physical AI,” where embodied systems like exoskeletons and humanoid robots gain intelligence at the edge. At CES, the team highlighted startups working on pill-based endoscopy, mobility exoskeletons, biosensors, and desktop dialysis. Meanwhile, Dassault’s main booth showcased life sciences innovation at scale, from virtual hospitals and organ twins to patient-specific implants designed in CATIA and manufactured for real-world deployment. Sustainability was front and center too, with energy harvesting and ultra-thin batteries that could eliminate disposable power sources for sensors and smart tags.

Founders will walk away with a practical playbook: design for manufacturing from day one, simulate early to cut costs and risks, plan certification upfront, and build long-term partnerships that support growth. We also preview what’s coming at 3DEXPERIENCE World, including community showcases, maker spaces, and generative AI tools reshaping 3D design and engineering.

Ready to move beyond hype and into real-world impact? Listen in, subscribe for more deep dives, and share what you’re building next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if pain relief, faster recovery, and better sleep were a matter of getting the right light to the right place with the right dose? We sit down with Tom Kerber, engineer-turned-inventor and founder of SunPowerLED, alongside Dr. Hayman Buwan, physician and medical advisor, are bringing together deep engineering rigor and clinical insight to rethink how light-based technologies are applied in real-world health and performance settings.

We unpack photobiomodulation—the clinical side of red and near infrared light—and why some devices deliver real change while others barely scratch the surface.

Tom shares the unlikely path from blue-light dental curing to red-light cancer adjuncts and a personal back injury that proved how fast high-power LEDs can dial down pain. He explains the physics that most people miss: tissue is hungry, so you need total optical power and a large emitting area to drive photons deep into joints and brain tissue. That’s why cooling and engineering matter—air-cooled arrays that sustain output without bulky heat sinks let you get meaningful doses in minutes, not half-hour marathons.

Hayman brings the clinic: shoulder mobility restored in a six-minute session, knee and tendon pain easing quickly, dental patients healing from oral mucositis, and wounds closing faster with pre- and post-op light. On the brain side, we dig into concussion recovery, migraine interruption at aura, improved sleep quality across users, and early work in aphasia, autism, long COVID, and cognitive decline. We talk safety, where pulsing may require oversight, and why underpowered products fuel skepticism. You’ll hear about university collaborations targeting mood, opioid craving, and neuro recovery, plus why standardized protocols and dosing are the bridge from anecdotes to indications.

If you’ve wondered whether red light is hype or a true modality, this conversation gives you the science, the specs, and the stories to tell the difference. Subscribe, share with someone who needs relief today, and leave a review with the one condition you’re most curious to see treated next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Trophies are easy to buy; credibility is not. We sit down with Russ Fordyce, Chief Recognition Officer at Business Intelligence Group, to unpack how modern awards can be rigorous, transparent, and genuinely valuable for marketers under pressure to prove results. From volunteer judges with track records to blockchain certificates that verify logos across the web, Russ explains how trustworthy recognition turns into search-friendly proof that buyers and even AI systems can validate.

We dig into why the old models struggled—small panels, popularity contests, and murky criteria—and how a distributed, community-led approach fixes scale and fairness. Judges review a manageable set of entries, earn micro-credentials for service, and are themselves evaluated for quality. That structure keeps standards high while opening doors to niche categories that big shows ignore. Along the way, Russ shares memorable wins, from a solo founder behind BenjiLock turning recognition into Shark Tank momentum, to TD Bank’s creative use of enterprise tech across immersive learning and Roblox engagement.

For marketers navigating the shift from classic SEO to generative engine optimization, this conversation hits the heart of what works now: white-hat proof. Verified awards create durable authority signals that LLMs and search systems can trust, supporting your brand story with evidence rather than ad spend. If you’re building in B2B tech and feel drowned out by bigger budgets, you’ll find a pragmatic path forward through community, validation, and disciplined storytelling.

Ready to earn visibility instead of renting it? Subscribe for more conversations like this, share with a teammate who needs a credibility boost, and leave a review to help others find the show. Want to get involved? Go to we love techawards.com or visit bintelligence.com and check out my awards program at welovetechawards.com

Support the show

More at https://linktr.ee/EvanKirstel

Most companies call it innovation when they add a feature. Real disruption is when you build the thing that makes the old one irrelevant.

That’s where this conversation starts. JJ de la Torre, founder and CEO of **Raven**, joins us to talk about what leaders consistently get wrong about digitalization, transformation, and “innovation theater”—and how to re-anchor teams around revenue and outcomes instead of decks and demos.

We dig into why the classic services model keeps breaking down. Strategy writes slides. Product ships features. Marketing wins awards. No one owns the result. JJ walks through Raven’s end-to-end model where think, build, and sell operate as a single accountable system. It sounds obvious. It’s not. That shift rewires how ideas get vetted, how products are built, and how success is measured.

Then we tackle AI—minus the hype. The real opportunity isn’t cost cutting. It’s redesigning the business model. Where can AI remove an entire step in the value chain? Enable performance-based pricing? Create revenue streams that didn’t exist last year? If AI isn’t changing how you make money, it’s just a faster expense line.

At the core of the conversation is design as a discipline for transformation. JJ’s book, **Transformation Designed**, frames design as a blueprint for results: center on the customer, validate with behavior, and turn insight into something teams can actually ship. We talk about why design-led companies outperform, how to avoid CES-style “innovation” nobody needs, and the hardest move of all—detachment. Killing today’s winner to create tomorrow’s business, the way Apple let the iPhone eclipse the iPod.

We wrap with what’s next for Raven: co-investment and variable models that share risk and upside with clients across the US, Europe, and Latin America. If you’re planning a 2026 reorg, here’s the gut check: does it change what you build, how you sell it, and how you get paid? If not, you’re just rearranging furniture.

Subscribe. Share it with someone who actually owns a P&L. And leave a review telling us which legacy product you’d sunset first.

Support the show

More at https://linktr.ee/EvanKirstel

What if every meter in a city became a tiny base station? That simple shift unlocks coverage where towers fail, slashes operating costs, and floods the grid with the data it needs to stay resilient under the weight of electrification. We sit down with Wirepas CEO Teppo Hemiä to unpack how a fully decentralized mesh lets devices make local decisions on channel, power, and routing—no single point of failure, no shouting across long distances, and no fragile tuning from a central controller.

We trace the journey from early cellular breakthroughs to an edge-first architecture built for smart grids, smart metering, and industrial IoT at massive scale. Tippo explains why electrification flips the data problem on its head: rooftop solar, EV charging, and dynamic tariffs demand millions of reliable data points and two-way control. Real-world case studies from Oslo’s million-node deployment to India’s 300-million-meter landscape show how the mesh holds up in basements, metal cabinets, slums with extreme density, and even container ships where multipath overwhelms conventional radios. By embedding backhaul into a fraction of meters, deployments get simpler, cheaper, and faster—devices self-form the network and keep it healthy.

We dig into economics and standards too. With NR+ aligning the technology to the 5G family, utilities avoid lock-in while gaining a path to sub-second reads and grid-grade reliability. A new low-power milestone—mesh routers sipping around 20 microamps on Nordic’s latest silicon—opens the door to battery-powered routing and multi-year autonomy. The takeaway is clear: decentralization, spectrum efficiency, and autonomy are the keystones for a resilient, data-rich energy future. Subscribe, share with a colleague building smart energy systems, and leave a review with your biggest question about scaling IoT for the grid.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

CES is about to flood every feed with AI demos and wall-sized screens, but the real breakthroughs are quieter: teams turning bold ideas into measurable results. We sat down to unpack how the We Love Tech Awards celebrate that kind of progress—transparent judging, practical innovation, and stories you can actually learn from.

We revisit standout winners who proved utility at scale: TD’s immersive learning programs that used VR and AR to deliver real training outcomes, a playful banking experiment on Roblox that still respected user value, and enterprise CX leaders who applied generative AI to increase satisfaction while cutting handle time. Along the way, we talk about why recognition programs matter when news cycles skew negative, and how celebrating builders can lift morale, validate products, and sharpen a brand’s narrative.

You’ll also get a behind-the-scenes look at our judging model. Thousands of volunteer experts—engineers, PMs, and operators—score entries and share actionable feedback, turning a trophy into a roadmap. We dig into categories spanning AI, cloud, cybersecurity, SaaS, IoT, and leadership, then outline how to craft a winning nomination: define the problem, show the implementation, quantify outcomes, and include the lessons learned. With CES days away and nominations due by late March, there’s time to gather the right metrics and stories that prove your work scales.

Before we sign off, we share a few gear picks for creators tackling noisy events and remote workflows—from minimalist e-ink dashboards that cut phone time to compact wireless mics that make a phone feel like a pocket studio. Ready to spotlight tech that works? Subscribe, share this with a teammate, and leave a review with the one metric you’re proudest of from the past year.

Support the show

More at https://linktr.ee/EvanKirstel

Forecast calls shouldn’t feel like theater. We sat down with People got ai’s new CEO Jason Ambrose to unpack how turning raw activity—emails, meetings, call transcripts—into crisp answers can transform forecasting, end CRM fatigue, and give leaders the clarity they actually need. Instead of forcing reps to feed fields, the platform reads what is already happening and returns guidance you can use right now: which deals are at risk, which stakeholders are missing, and where to act to hit the number.

We trace People ai’s path from early activity capture to training models on billions of interactions across years of market shocks. That history pays off when patterns shift; the system sees signals static dashboards miss. Jason explains why most forecasting rituals only assign accountability, not risk, and how a risk-first approach reframes the conversation: timing, probability, engagement, and next best actions. The result is a plan you can execute, not a spreadsheet you defend.

We also dig into the CRM reality. Systems of record still matter, but the monolithic UI is giving way to flexible experiences, agents, and chat surfaces that pull answers into the tools your teams already use. Executives want to ask open questions—what’s happening in Japan, where a product stands—and get a directionally correct answer in minutes, not days. Agents are set to automate internal churn like account plans, win wires, and pricing checks, so sellers spend more time with customers and less time in tabs. Jason’s vision is simple and ambitious: make accurate, actionable answers available anywhere, integrate with the stack you have, and scale from pilot wins to trusted operations.

If you’re ready to replace hunches with clarity, tune in, share with a teammate, and leave a review so we can keep bringing you conversations that cut through the noise.

Support the show

More at https://linktr.ee/EvanKirstel

A bright pair of pink socks walked into HIMSS and walked out as a global symbol of connection. We sit down with Nick Adkins to unpack how a playful gesture turned into 300,000 gifts, a nonprofit rooted in kindness, and a book that invites all of us to love more and fear less.

Nick takes us back to 2015, when he and cofounder Andrew Richards skipped the booth, packed a backpack with socks, and discovered the power of intentional gifting to break silos in healthcare. From viral photos and a TEDx talk to collaborations with hospitals, schools, and even astronauts, the Pink Socks movement shows how a small token can open big conversations. The new book, Pink Socks: How A Pair Of Socks Became A Symbol Of Love And Connection, weaves ten years of stories for three audiences: longtime community members, the Pink Socks-curious, and anyone craving a simple path back to empathy.

We go inside the moment that changed everything: a raw, post-talk conversation at Stanford’s MedX with a lung transplant recipient whose story turned socks into a practice of holding space. Nick explains why connection, not just connectivity, heals teams and cultures, and how leaders can model empathy in ways that shift entire companies—sometimes starting with a poem. Practical ideas abound: put down the phone, introduce yourself to the person next to you, use a small, visible token to spark dialogue, and watch a tense room—say, a long post office line—soften into patience.

If your heart is ready for a clear, human reset, this conversation offers a map. Explore pinksocks.life, discover how the nonprofit supports schools and community projects, and consider gifting the interactive hardcover with QR codes that bring stories to life. If this story resonates, share the episode with a friend, subscribe for more human-centered conversations, and leave a review to help others find us.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if the secret to scaling AI agents has nothing to do with better prompts and everything to do with smarter integration? We sit down with Rich Waldron Co-founder & CEO of Tray dot ai to unpack how an integration-first architecture turns agent hype into measurable business outcomes. From cloud-native orchestration to resilient API handling, Rich explains why the hardest part of agents isn’t connectivity—it’s everything behind it: concurrency, retries, governance, logging, and security.

We dig into the real reasons enterprise pilots stall and the pattern he sees among programs that succeed: IT-led, department-partnered builds with clear ROI, tight scopes, and fast iteration cycles. Rich shares a standout customer story migrating hundreds of integrations off a legacy vendor while launching agentic workflows on a single governed platform—delivering 60% lower integration costs and three times faster builds. Along the way, we explore how engineering teams use AI coding tools to prototype faster and offload grunt work, freeing time for architecture and testing without threatening roles.

If you’re choosing where to start, accelerators for ITSM, HR, support, and knowledge make time-to-value tangible and create a structure teams can adapt to proprietary data and processes. We also cover the growing priority of enterprise governance: controlling which tools agents can access, how data moves, and how identities map across systems. Rich outlines Tray’s Agent Gateway for MCP—adding authentication and permissioning and exposing Trey-built tools to other services in a controlled way—so CIOs can move quickly without giving away the keys to the kingdom.

Ready to move from pilot to production with confidence? Follow, share, and leave a review to tell us where your organization is on the journey—and what’s blocking your next agent from going live.

Support the show

More at https://linktr.ee/EvanKirstel

Vegas turns into a living blueprint for the future as we sit down with CTA’s Gary Shapiro and best-selling author Jim Harris to unpack why CES 2026 is more than a showcase—it’s where strategy gets decided. We dig into how AI has moved from headline hype to the connective tissue of every sector, shaping robotics, health, mobility, and enterprise workflows. Gary lays out the big picture: a record wave of innovation colliding with real constraints like energy supply, fragmented regulation, and national industrial strategies. The tension is exciting—and actionable.

We walk through what’s truly new this year: a surge in humanoid and wearable robotics, patient-centered health tech powered by continuous sensing, and a startup scene in Eureka Park designed to compress months of business development into days. CES Foundry expands into AI and quantum with live demos, while new categories highlight enterprise tech, filmmaking, logistics, and travel. Keynotes—from Dr. Lisa Su to global brands at the Sphere—frame where compute, platforms, and partnerships are headed next. It’s a global stage, with 40% of attendees from abroad, and a clear signal that innovation is now a team sport.

If you’re an enterprise leader, this isn’t a gadget tour. Nearly half the action is B2B: partner summits, private suites, and tracks that turn tech trends into operating plans. We share practical tips to win your week—map your days by venue, leverage the upgraded AI-driven app, hydrate, and focus on one theme per day to avoid context switching. Looking ahead, we explore how quantum computing, agentic AI, and autonomy could reshape supply chains and healthcare, and why energy and policy choices will determine how fast the future arrives.

Subscribe for more deep dives from the front lines of innovation, share this with a colleague who’s planning their CES agenda, and leave a quick review to tell us which track you’re most excited to explore.

Support the show

More at https://linktr.ee/EvanKirstel

A journalist screams, “I’m wasting four hours a day transcribing,” and a product is born. We sit down with Lasse Finderup, CEO of Good Tape to unpack how a newsroom pain point turned into a privacy-first platform used by millions—and why saying no to feature bloat matters more than chasing every shiny AI trick.

We trace the spin-out origin story and the “instant product-market fit” that came from building for colleagues who needed reliable, fast transcripts yesterday. Lasse explains the decision to never train on user data and to host models in-house, trading flashy add-ons for deep security, ISO-grade compliance, and trust. We explore global AI adoption gaps—from Denmark’s “I’ll just ChatGPT this” culture to regions where automated speech-to-text still feels like magic—and why context matters when you’re designing tools for journalists, podcasters, and creators handling sensitive sources.

From a tech perspective, we dive into an open-source stack centered on Whisper V3 Large and the heavy lifting around the model: optimization, infrastructure, and the real costs of self-hosting LLMs. Lasse lays out a sharp distinction between “record-everything meetings” tools and workflows where the transcript is the output itself. That sets the stage for Good Tape’s next big leap: an “artificial memory” that surfaces relevant past notes at the right moment, with user-controlled reminders that feel helpful, not invasive. We also touch on multilingual transcription’s surge across contact centers and newsrooms, market consolidation on the horizon, and founder advice: build for real needs, not just because AI makes it possible.

If you care about accuracy, confidentiality, and simple tools that get out of your way, this conversation will sharpen how you evaluate transcription tech and where the industry is heading. Subscribe, share with a teammate who fights transcripts, and leave a quick review to help more builders and storytellers find the show.

Support the show

More at https://linktr.ee/EvanKirstel

Copilots can suggest the next click, but they rarely deliver the finished job. We dive into a different path: enterprise AI agents that integrate with your systems, understand your business rules, and execute end-to-end workflows with governance, accuracy, and reliability. Rob Bearden co-founder and CEO Sema4.ai shares how their platform moves beyond brittle scripts and UI macros to reasoning-driven automation that adapts to changing contracts, policies, and supply constraints—turning strategy into repeatable, measurable outcomes.

We trace the journey from big data to autonomy: insights and KPIs used to point the way, but humans still had to do the work across dozens of apps and tabs. Agents close that last-mile gap by reading documents, joining data across ERPs and CRMs, and following rule-bound reasoning paths to finish the task. You’ll hear concrete wins like multi-page invoice reconciliation done in minutes with higher accuracy, AP help desk cases resolved without swivel-chair searches, and quote-to-cash automated across fragmented systems. The result is less toil, fewer errors, and outcomes you can audit and scale.

If you’re stuck in AI pilot purgatory, the way out is a platform strategy and tight guardrails. We break down a crawl-walk-run approach: pick a high-leverage use case, define the outcome, run a focused proof, measure ROI, then rinse and repeat. We also scan the broader agent ecosystem—Salesforce, ServiceNow, and hyperscalers are leaning in—while making the case for an enterprise-wide layer that spans SaaS apps, data warehouses, and data lakes. Finance operations lead early adoption, but healthcare, insurance, and manufacturing are close behind, wherever people juggle multiple systems to make a decision.

Ready to trade tab hell for trained agents and predictable outcomes? Follow the show, share this episode with your ops and finance leaders, and leave a review to help more teams find it.

Support the show

More at https://linktr.ee/EvanKirstel

Big leaps in AI rarely come from one breakthrough. They emerge when hardware design, open software, and real workloads click into place. That’s the story we unpack with AMD’s Ramine Roane: how an open, developer-first approach combined with high-bandwidth memory, chiplet packaging, and a re-architected software stack is reshaping performance and cost from the edge to the largest data centers.

We walk through why memory capacity and bandwidth dominate large language model performance, and how MI300X’s 192 GB HBM and advanced packaging unlock bigger contexts and faster token throughput. Ramin explains how Rocm 7 was rebuilt to be modular, smaller to install, and enterprise-ready—so teams can go from single-node experiments to fully orchestrated clusters using Kubernetes, Slurm, and familiar open tools. The highlight: disaggregated and distributed inference. By splitting prefill from decode and adopting expert parallelism, organizations are slashing cost per token by 10–30x, depending on model and topology.

The conversation ranges from startup-friendly workflows to hyperscaler deployments, with practical insight into VLLM, SGLang, and why open source now outpaces closed stacks. We also look ahead at where inference runs: the edge is rising. With performance per watt doubling on a steady cadence, AI PCs, laptops, and phones will take on more of the work, enabling privacy, responsiveness, and lower costs. Ramin shares a sober view on quantum computing timelines and a bullish take on the broader compute shift—moving once-sequential problems into massively parallel deep learning that changes what’s even possible.

If you care about real performance, total cost of ownership, and developer velocity, this conversation brings a grounded blueprint: open ecosystems, smarter packaging, and inference architectures built for high utilization. Subscribe, share with a colleague who cares about LLM throughput and cost, and leave a quick review to help others find the show.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

A billion-dollar talent marketplace doesn’t happen by accident—it happens when access, scale, and experience come together. We sit down with  Michael Morris the head of Torc at Randstad Digital to unpack how a once-independent platform now matches millions of professionals with work, why the talent market feels like a “teenage” phase, and how AI is reshaping recruiting without removing humans from the loop.

The conversation gets practical fast. Automation now handles the busywork—sourcing, screening, scheduling—so recruiters and hiring managers can focus on what actually drives outcomes: culture fit at the team level, long-term growth, and a great candidate experience. We challenge sacred cows like resumes and rigid job descriptions, and explore how a next-generation marketplace will let customers express needs via prompts, voice, and examples. The result is faster, clearer matching that opens doors for more diverse, AI-enabled talent, from Python developers to marketers fluent in prompt-driven workflows.

We also dig into the skills that matter most as enterprises chase AI readiness. Coding is no longer the bottleneck; user experience and domain fluency separate good from great. That’s why soft skills and communication sit alongside credentials from Microsoft, Google, and OpenAI, and why personalized learning beats one-track training. Whether you’re building a data team in healthcare, prototyping fintech apps, or scaling a platform, the path forward is the same: invest in people, modernize how you describe work, and use AI to upgrade—not replace—the human touch.

If this resonates, follow the show, share it with a colleague, and leave a quick review. Your support helps more builders, leaders, and learners find the ideas—and opportunities—that move their careers forward.

Support the show

More at https://linktr.ee/EvanKirstel

What if your hotel could keep checking guests in even when the primary PMS goes down? We sit with Jason from SNET Communications to unpack how a true all‑in‑one stack—networks, SD‑WAN, security, UCaaS, CCaaS, and a native PMS—eliminates finger‑pointing and keeps operations moving when it matters most.

We walk through the realities of legacy PBX in hospitality and why a cloud approach finally fits the property workflow. SNET’s platform delivers over 120 PMS integrations and a unique twist: a built‑in PMS that serves as a live failover. That means front desks continue check‑ins, wake‑up calls, and room inventory while the main PMS is offline, then resync automatically. The economics make sense for boutique hotels and big brands alike—reuse analog room phones with ATAs, retire maintenance-heavy hardware, and unlock new revenue from modern in‑room devices and connected TV experiences. Reliability isn’t just promised; redundant data centers in Chicago and Sacramento provide resilience even when hyperscalers stumble.

The conversation travels beyond hotels. We break down a path from fixing broadband contention with SD‑WAN to deploying POS integrations and UCaaS across 1,500+ pizza locations, plus wins with dental groups, spas, auto dealers, and an energy company that needed precise call flows. Custom CRM integrations—often at little or no cost—turn rigid software into tailored workflows. Looking ahead, the roadmap brings practical AI to the front lines: a low‑cost chatbot bundled with UCaaS, a voice bot to deflect routine calls, and real‑time transcription with sentiment analysis to coach teams and protect the guest experience.

If you care about guest satisfaction, uptime, and real ROI from technology—not just new tools for their own sake—this is a deep dive into what modern multi‑location communication should look like. Subscribe, share with an operator who needs a smarter stack, and leave a review with the one feature you want most in your hospitality or retail tech.

Support the show

More at https://linktr.ee/EvanKirstel

Most people think of SD cards as camera accessories. We pull back the curtain with the SD Association to show how SD Express turns that tiny card into an SSD-class powerhouse for creators, PCs, and AI at the edge—without losing the flexibility of removable media. By bringing PCIe and NVMe to the SD form factor, SD Express delivers nine to forty times the performance of legacy cards and far better random I/O, so burst capture, rapid transfers, and on-device inference all feel instant.

We trace how this shift changes product design and user workflows. Creators get faster shoots and simpler post; device makers can build slimmer hardware that still upgrades in seconds. On the capacity front, SDUC pushes the horizon beyond two terabytes toward a 128TB ceiling, with 4TB microSD and 8TB full-size cards emerging. That makes it practical to keep large datasets, video archives, and full AI model versions local, then rotate or update them in the field, no downtime required.

Security is maturing alongside speed. The SD 9.0 specification adds encrypted drive capabilities and host binding, helping enterprises protect data at rest and perform secure firmware or model updates. We highlight a wave of student projects on NVIDIA Jetson Orin Nano using microSD Express as primary storage, proving how edge AI benefits from fast, reliable, swappable media. And we share where the standard heads next: endurance, reliability, and fair, transparent performance metrics that help buyers match cards to real workloads.

If you’re building cameras, drones, robots, or compact PCs—or you just want your gear to feel faster—this conversation maps the road ahead for removable storage. Subscribe for more deep dives like this, share with a friend who loves hardware, and leave a quick review to tell us what you’d build with SD Express.

Support the show

More at https://linktr.ee/EvanKirstel

Want a clear view of where global voice is headed and how AI changes the math? We sit down with Barbara Dondiego CEO at Avoxi to unpack the shift from legacy telephony to a modern cloud voice backbone that unifies numbers, routes across multiple CCaaS platforms, and puts intelligence at the very top of the stack. If you’ve wrestled with six vendors, inconsistent quality, and opaque billing, this conversation shows how enterprises regain control, visibility, and speed.

We trace the evolution from regional servers and PBXs to a software-first model that treats voice like programmable infrastructure. Barbara explains why agentic AI is driving voice minutes up, not down—once simple calls are automated, brands stop throttling phone support and let customers choose the fastest path. We dig into real deployment patterns: AI answering known intents, clean escalations to skilled agents for revenue moments, and omnichannel journeys where long text threads pivot to a quick call for instant clarity. Along the way, we cover the mobile-first reality—most inbound traffic now hits from cell networks—and why the phone remains the most universal, regulated, and low-friction gateway to natural language systems.

Under the hood, we get practical about interconnects and standards. Avoxi’s role in the ecosystem is to simplify the last mile with direct carrier connections worldwide while staying open to your stack—Amazon Connect, Genesys, Five9, NICE, Webex, and beyond. That openness lets teams consolidate on a single global voice layer while mixing applications by region or line of business. We also look ahead to what’s next: voice-native security as AI increases call volumes, anomaly detection at the phone-number level, end-to-end encryption, and smarter defenses against automated floods that pretend to be people.

If you’re planning your CX roadmap, this is a grounded guide to consolidating providers, automating with confidence, and securing the future of voice. Subscribe, share with your team, and tell us: where would AI at the top of your stack make the biggest impact?

Support the show

More at https://linktr.ee/EvanKirstel

Forget the old playbook of carrier hotels and cross-connects. We sit down with Dave Ward, Chief Technology and Product Officer at Lumen, to map the real shift to Cloud 2.0—an AI-driven rearchitecture of how data moves, where compute lives, and how enterprises keep control as speed and scale explode. Dave explains why data movement has become the bottleneck that decides AI ROI, and how distributed on-ramps, 400G to 1.6T connectivity, and network-as-a-service can shrink time-to-first-token while cutting operational drag.

We unpack the rise of neo clouds—GPU-first data centers with new commercial models—and what that means for planning training and inference. Instead of buying vague capacity, teams now rent defined GPU clusters for six to 36 months, often in nontraditional metros with power and cooling to match. That shift demands a new connectivity strategy that bypasses hourglass on-ramps and drives data directly to AI factories and hyperscalers. Dave makes the economics tangible: when moving a petabyte takes hours instead of days, GPUs stay busy, costs drop, and models get to work faster.

Control doesn’t have to disappear as DIY fades. Dave outlines how design, price, order, provision, and assurance can live in one digital platform, giving IT the same topology and policy control without the burden of racks and cross-connects. We cover why many SD-WAN and SASE deployments need deterministic bandwidth channels, how to build a data fabric across 30+ sources, and the practical first steps: inventory workloads, map your data flows, and match bandwidth to business outcomes. If you’re plotting an AI strategy without a network and data plan, you’re leaving value on the table.

If this conversation helps you think clearer about Cloud 2.0, follow the show, share with your team, and leave a quick review to help others find it. What’s the first workload you’d accelerate with a true high-speed data fabric?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

If you could see customer issues before they happen, would you still measure success by hold time? We dive into how CX is transforming from a reactive cost center to a proactive, revenue-positive engine—and why the shift depends on a new orchestration layer that acts as the brain across every channel. With Alorica’s co-CEOs, Max and Mike, we unpack the practical architecture of AI-first customer journeys, the role of empathy in automation, and the real-world metrics that prove impact, from fewer complaints to higher NPS.

We break down what “participation over problem solving” looks like when AI learns a customer’s preferences and adapts each step—more relevant touches, fewer dead ends, and seamless handoffs between bots, systems, and people. You’ll hear how an airline used real-time detection to proactively rebook travelers, cutting complaints by over a third and lifting satisfaction. Beyond the headlines, we talk through the nuts and bolts: why data freshness matters more than perfect data, how to avoid 75 conflicting policy answers, and where to start when AI evolves weekly and the vendor map is overwhelming.

Forget seat-based contracts and vanity metrics. We explore outcome-based partnerships, resolution time by type, model effectiveness, and customer satisfaction as the north star. We also share a simple playbook to get moving now: launch a narrow bot with current policies, pilot orchestration on one high-friction journey, use a lab to capture edge cases, and deploy AI-embedded tools across sales and legal to bank quick wins. Finally, Max and Mike offer a candid look at Alorica’s 2026 roadmap—greater tech adoption, deeper investment in people, and a firm bet on blending AI with empathy to raise the bar for loyalty and lifetime value.

Join us, then tell us: what’s the first journey you’ll orchestrate? If this episode sparks ideas, follow the show, share it with a colleague, and leave a quick review so more builders can find it.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if you could see the internet the way attackers do—before the headlines, before the breach, before the phish hits your inbox? We sit down with Aidan Holland, senior security researcher at Censys, to unpack how daily global scans reveal the live shape of the web: assets you didn’t know you own, services you thought were private, and the malicious infrastructure gearing up for its next move.

Aidan explains how distributed scanning nodes in eight regions capture real banners, certificates, and configuration details, then stitch that telemetry into a searchable index. We dig into two high-value outcomes: attack surface management that links stray assets back to your org through DNS and certs, and threat hunting that tracks bulletproof hosting, brand impersonation, and the flood of fake captcha kits. You’ll hear why internal inventories miss internet-facing systems—rotating IPs, scattered cloud accounts, mergers—and how external vantage points and AI assistants help teams query in plain English, triage vulnerabilities, and fix what matters first.

The stories are gripping and practical: wastewater controls left on the open web, shipboard networks forwarding every port over Starlink, and navigation systems exposed to anyone who could find them. We also talk about the quiet shift back to on-prem and the renewed pressure to patch Exchange-class systems on a tight cadence. Looking ahead, Aiden shares how IPv6 changes the game—no brute force, smarter traversal—and why faster, more diverse scanning is key to catching ephemeral threats.

If you care about cybersecurity, visibility, and measurable risk reduction, this conversation gives you tools and perspective you can use today. Subscribe, share with a teammate who wrangles shadow IT, and leave a review with the biggest “unknown asset” you’ve uncovered lately.

Support the show

More at https://linktr.ee/EvanKirstel

What happens when only 30% of your data fuels decisions and the rest sits idle? We dig into the shift from traditional storage to intelligent data infrastructure and reveal how that change powers AI, real-time insights, and rock-solid resilience across industries. Cesar Cernuda President of NetApp walks us through exabyte-scale growth, why outcome-driven design beats tool-first thinking, and how to prepare data so AI actually delivers measurable impact.

The sports arena offers the clearest stress test. From the NFL’s international push to a historic regular-season game in Madrid, we break down what it takes to deliver a flawless fan experience: seamless ticketing, instant replays, and real-time stats that don’t stutter under peak demand. Behind the scenes, teams analyze player workload and strategy with the same rigor financial institutions apply to transactions and healthcare providers bring to critical care. The common thread is a platform that moves data quickly, protects it continuously, and scales without drama.

Security now sits at the center. If data is the new gold, then ransomware is the modern bank heist. We unpack practical defenses: immutable backups, anomaly detection, automated responses, and policy-driven controls that travel with your data across hybrid and multicloud. Then we get tactical about AI readiness. Don’t haul data to AI and hope. Prepare data for AI with clear lineage, smart placement across clouds, and governed access that enables rapid experimentation. Cap it off with a leadership playbook: define near-term outcomes, run tight measurement loops, and invest in a platform that won’t crumble when milliseconds matter.

If you’re ready to turn raw data into competitive advantage, hit follow, share this with your team, and leave a quick review telling us the one data problem you want solved next.

Support the show

More at https://linktr.ee/EvanKirstel

What happens when a global tech leader turns its entire company into an AI lab? Our conversation with TCS’s CIO Janardhan Santhanam opens the door on a culture-first transformation powered by the world’s largest AI hackathon—half a million ideas, 55 million lines of code in days, and a wave of agentic apps built from no‑code to pro‑code. The result isn’t just energy; it’s a blueprint for scaling AI responsibly, measurably, and fast.

We break down the five pillars guiding TCS’s shift to an AI‑led services company: internal reimagination where every function becomes an AI practitioner; new client services from AIOps to AI‑driven development; talent acceleration through code assist, platform boot camps, and AI dojo programs; sector solutions spanning retail, supply chain, finance, and life sciences; and a robust partner ecosystem across hyperscalers and niche platforms. Along the way, we dig into what surprised the team—equal participation from HR and finance, agentic applications shipped by early‑career talent in hours, and the use of generative AI to evaluate submissions at unprecedented speed without losing human judgment.

We also tackle a big product question: are apps dying? Not quite. Routine tasks move to conversational agents, while apps double down on deep work—research, modeling, and analysis—exposing services that agents can orchestrate. To make any of this scale, governance comes first: security, IP, privacy, and legal built into the SDLC, plus a hard pivot from scattered POCs to a true path to production. And the scoreboard is business value, not hype: faster month‑end close, shorter deployment cycles for people, higher learning satisfaction, better margins, and happier customers.

If you’re mapping your own AI journey—how to democratize tools safely, design for outcomes, and keep talent growing—this conversation offers concrete steps you can apply now. Subscribe, share with a colleague who’s stuck in POC purgatory, and leave a review with the first process you’d reimagine with AI.

Support the show

More at https://linktr.ee/EvanKirstel

Stop juggling one-time codes and clunky authenticator apps. We sit down with Eddie DeCurtis, CEO and co-founder of Shush, to unpack how carriers can quietly verify users in milliseconds using standards-driven, privacy-guarded data. Think number-to-IP matching, SIM swap checks, and location signals—all done behind the scenes so sign-ins feel almost invisible while fraud defenses tighten.

Eddie explains why the real barrier hasn’t been demand from banks and brands, but supply from mobile operators. His team built a platform that bundles three hard problems—API exposure, network integration, and business operations—so carriers can go live fast without buying new equipment or hiring armies of consultants. A recent deployment with Dito in the Philippines moved from signature to launch in under 90 days, supporting a wide set of Camara APIs and 40+ endpoints that future‑proof brand integrations.

We also cover the big turn away from SMS OTP. Prices swing, delivery fails, and attackers intercept. Network-based authentication changes the math with “intrinsic data” that supports multiple checks per user journey—verify number, confirm no recent SIM swap, and only then grant access. That creates higher value for brands and a durable revenue stream for carriers. Consent and compliance get full treatment too: how US CPNI rules enable fraud use cases, why EU Recital 47 differs, and where countries like Poland apply stricter limits. The practical answer is market-specific consent modules baked into the platform.

If you’re building login flows, running fraud operations, or operating a mobile network, this episode maps the path from legacy OTP to carrier-grade security that users barely notice. Subscribe for more deep dives, share this with a teammate who owns authentication, and leave a quick review to help others discover the show.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Tired of pretty charts that don’t change your strategy? We go straight at the hard truth: social media is evolving from follower-first tactics to value-first systems, and the winners are those who align a crisp ICP with content that solves real problems at scale. Emeric Ernoult is the Founder and CEO of Agorapulse, a serial entrepreneur with two decades of experience in social media and SaaS and breaks down how brands can prove impact, not just activity, and why AI is the lever that turns expertise into consistent output across formats.

We dig into the new playbook for 2025: narrow your audience, clarify the problem you solve, and let AI help you repeat your best ideas in fresh ways. From short videos and carousels to text posts and long-form explainers, the goal is a content spine that compounds trust. We talk practical moves like transforming raw transcripts into standout explainers, building a studio in your pocket, and using agentic workflows that shift you from doing tasks to approving smart recommendations.

Analytics gets a reboot too. Instead of handing your boss a PDF full of colors, use AI to extract what worked, what didn’t, and what to try next. We cover how small teams can operate like pros with a smart inbox, suggested replies, prioritization by sentiment and urgency, and reporting that actually prescribes action. Emeric also shares a peek at the 2026 roadmap: LinkedIn-first thought leadership for B2B, then expanding to visuals and video, all grounded in sharply defined use cases and personas.

If you care about making social media drive pipeline, retention, and brand equity, this conversation gives you a clear path forward—less noise, more signal, and a toolkit that scales quality without burning out your team. Subscribe, share with a teammate who runs social, and leave a review telling us the one insight you’re acting on this week.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if your enterprise could make the right decision, across every silo, in real time? We sit down with Paul Breitenbach, Founder & CEO r4 Technologies, to unpack how predictive AI goes beyond generative tools and actually drives outcomes: higher revenue, lower costs, and fewer bad events across the board. Paul explains why cross-enterprise management matters now, how it automatically pulls data from legacy systems, and how it sends decisions back without ripping anything out.

We walk through tangible examples that make the strategy real. In supply chain, predictive logistics ensure the right product hits the right shelf at the right time and price. In security operations, microlocation and environmental signals re-route guards on the fly, cutting incidents by 50 percent. And in a powerful “tech for good” case, R4’s Smart Food program identifies surplus food and matches it to SNAP demand, effectively doubling or tripling buying power while reducing waste—turning a national problem into a solvable coordination challenge.

Trust and adoption come from transparency and speed. Paul shows how confidence scores, explainability, and human-in-the-loop controls help leaders move from gut feel to measurable, auditable decisions in days, not quarters. We also explore what’s next: a more intuitive V5 interface, tackling higher-complexity domains like healthcare and energy transition, and why 2025 could be the tipping point where predictive AI becomes the enterprise standard.

If you enjoyed this conversation, subscribe, share it with a colleague wrestling with silos, and leave a review to help others find the show. What cross-enterprise challenge do you want AI to solve next?

Support the show

More at https://linktr.ee/EvanKirstel

Imagine asking your AI to book the flight, pick the hotel, and reserve the car—and it just does it. That convenience also opens a massive security gap when agents ask for your passwords, impersonate you online, and leave no clean line between what you intended and what was executed. We sit down with Peter Horadan is the CEO of Vouched, to map a safer path where digital identity, consent, and reputation tame the chaos without killing the magic.

Peter explains how mobile driver’s licenses bring cryptographic proof to everyday transactions, replacing fragile one-time codes and making phishing far harder. We dig into why physical IDs are increasingly easy to fake, how digitally signed credentials flip certainty in your favor, and what that means for ecommerce chargebacks, password resets, and high-value purchases. Then we pivot to Model Context Protocol, a fast-growing standard that lets websites speak “agent” natively, moving beyond clumsy screen-scraping into clean, auditable API calls.

The heart of the conversation is a pragmatic blueprint: redirect humans to grant permissions just like OAuth, scope what agents can do, log consent, and keep it revocable with one click. We talk about building reputation systems for agents so good actors rise and bad bots get blocked. Healthcare emerges as a powerful use case—patient access becomes smoother, and remote workforce fraud gets harder—while privacy stays intact thanks to standards that avoid phone-home tracking to the state. If your site has a login button, the future is knocking: agent-friendly APIs, digital ID checkpoints for risky actions, and clear rails that let assistants work for you, not against you.

Subscribe for more smart takes on AI security, digital identity, and the standards shaping how assistants act on our behalf. Share this with a colleague who wrangles logins or fights fraud, and leave a review to help others find the show.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if your marketing stack could think with you and act for you? We sit down with Christian Monberg Chief Technology Officer & Head of Product from Zeta to unpack Athena, a super intelligent agent designed not as a chat add-on but as the operating system for modern marketing. Built on a consolidated data fabric and a supergraph grounded in identity, Athena translates insights into action—turning questions into live segments, optimizing campaigns for incremental lift, and giving teams software on demand without the usual tool sprawl.

We explore how Athena’s identity-first approach unlocks trustworthy measurement, why incremental gains at the margins drive real revenue, and how “impossible products” like Advisor run always-on strategies in the background. Christian shares a real beta story where natural-language analytics didn’t just produce a report—it generated targetable audiences ready to activate in one click. We also tackle the realities of enterprise adoption: pairing probabilistic agents with deterministic governance, standards like MCP that help agents talk to services, and the cultural shift from skeptical pilots to production-scale trust.

From replatforming and consolidation to generative UI and on-demand app creation, this conversation charts a path from channel-centric silos to true consumer-centric engagement. Expect practical insights on cutting onboarding time with agent-driven migrations, simulating strategies before you spend, and building the measurement rigor that proves impact. If you’re ready to move at the speed of business, reduce total cost of ownership, and give your team more room to experiment, this one delivers the blueprint.

If you found value, follow the show, share it with a teammate who’s battling tool sprawl, and leave a quick review so others can discover it.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Hype is loud, but outcomes win. We sit down with Smartling CEO Bryan Murphy to explore why the center of gravity in AI is shifting from raw models to the application layer where workflows, data, and governance translate into real business value. If you’ve been stuck in pilot purgatory, you’ll hear how to move from experiments to production with clear ROI.

Bryan draws sharp parallels to earlier waves—the internet and cloud—and explains how today’s “chips and data centers” moment mirrors the 90s buildout, with a familiar follow-up: purpose-built apps that make the infrastructure usable. We dig into translation as a top-tier AI use case and unpack Smartling’s results: translating 3x more content at about 60 percent less cost and six times faster than traditional methods. The secret isn’t a flashy bolt-on; it’s re-architecting around an AI hub and agentic workflows that integrate directly with enterprise stacks.

We also challenge the “AI will kill SaaS” headline. Foundational models like GPT and Claude are treated as infrastructure, while the real differentiation comes from orchestration—selecting the best model for the job, routing data safely, learning from feedback, and guaranteeing quality. Bryan shares how governance protects brand voice at scale, from hallucination detection to automated language QA and custom-trained models honoring glossaries and style guides. With 7 billion words translated a year, reliability isn’t a feature—it’s the product.

Looking ahead, we explore multimodal translation across text, audio, and video, and a future where translation functions as a service embedded in everyday tools. For founders, Bryan offers practical advice: target a 10x problem, verify there’s real TAM, and build applications that customers will happily pay for. For enterprise leaders, the message is simple: stop chasing models, invest in orchestration, and pick solutions that measure and deliver outcomes.

If this conversation helps you think clearer about where AI value is headed, follow the show, share it with a colleague, and leave a quick review so others can find it.

Support the show

More at https://linktr.ee/EvanKirstel

Your most powerful product might also be your biggest liability: an AI agent making decisions you can’t see and answers you can’t predict. We sat down with Andre Scott of Coralogix to unpack how to make black-box systems measurable, accountable, and—most importantly—improvable over time.

We trace the journey from monoliths to microservices to LLMs and explain why old-school “index everything, analyze later” monitoring breaks under today’s data explosion. Andre introduces an analytics-first approach that processes telemetry in-stream and then stores what matters in your own object storage. That shift delivers cost control and true data ownership, turning observability from an insurance policy into a growth engine. We dig into open tooling like an LLM trace kit built on OpenTelemetry that captures prompts, responses, and metadata, so you can evaluate correctness, flag prompt injection, and enforce guardrails at runtime.

Bias and hallucinations don’t announce themselves; they creep in through context loss, retrieval misses, and model updates. The fix is continuous evaluation with small, purpose-trained models that run outside your app to score tone, safety, factuality, and leakage risks. Think of agents like employees: give them performance reviews, train them with real data, and escalate when risk spikes. We also explore Olly, CoraLogix’ agentic SRE that reads your telemetry, answers business-grade questions, and recommends alerts and remediations—especially handy when cloud outages ripple through your stack.

Regulation is coming fast, and accountability rests with the teams who ship AI into production. If you deploy it, you own the risk. The practical playbook is clear: embrace analytics-first observability, capture LLM telemetry, make evaluators your crown jewels, and keep the data that teaches your models to improve. Subscribe, share this with your engineering and product teams, and leave a review with the one place you’d add guardrails first.

Support the show

More at https://linktr.ee/EvanKirstel

Telecom doesn’t need another AI science project; it needs outcomes. That’s the thread running through our conversation with kishore bobbarjung from Tupl, where explainable, agentic automation is delivering real wins: faster resolution of customer issues at a US tier one and double-digit energy savings across more than ten European networks—without sacrificing KPIs. We walk through how connecting customer signals to network data shrinks time-to-fix, how policy-aware power optimization lets radios sleep smart, and why trust and transparency with engineering teams are the real force multipliers for scale.

We dig into the design choices that matter: building AI around workflows engineers already use, surfacing every step so fixes are understandable and safe, and promoting repeatable investigations into closed-loop automations. That’s how costs stay predictable and knowledge compounds. We also unpack the tough parts—workforce concerns, governance, security, and the surprise bill that can come from token-heavy agents—and show how Tupl contains exposure by localizing processing and turning one-off agent work into durable scripts.

The roadmap gets bold: a “no tools” North Star where an agentic co‑pilot interrogates data, guides deep investigations, and then codifies proven remedies back into Network Advisor for ongoing, low-cost execution. With AI moving deeper into the RAN, we explore early gains in radio design, optimization, and critical-site resilience, as major vendors push intelligence closer to the edge. If you care about cutting OPEX, boosting customer experience, and replacing tool sprawl with a coherent AI layer, this conversation lays out a practical playbook.

Enjoy the episode, then subscribe, share with a colleague, and leave a quick review to help others find the show. What would you automate first in your network?

Support the show

More at https://linktr.ee/EvanKirstel

Tired of chatbots that stall when customers need real help? We dive into how agentic AI flips the script by taking actions inside your systems—issuing refunds within limits, updating orders, authenticating users, and escalating with intent—while humans handle complex, high-empathy moments. Joe Anderson, Senior Director and Head of CX and Digital Transformation at TaskUs, breaks down a practical playbook: choose the right high-volume use cases, embed guardrails and escalation paths, and integrate platforms like Decagon and Regal to ship in weeks, not quarters.

We walk through safety by design, from prompt hardening and policy constraints to continuous human evaluation that tunes agents over time. Regulated industries like healthcare and financial services come up often, and we outline how to meet compliance standards while delivering faster resolutions and better customer outcomes. The conversation also tackles a persistent myth: AI won’t erase your support team. It will elevate them. As tier-zero and tier-one tickets automate, human experts shift to white-glove support, retention saves, and nuanced sales where judgment and empathy pay off.

The business model is evolving too. Instead of hourly rates, TaskUs leans into outcome-based pricing and gain share, aligning incentives around first contact resolution, containment, CSAT, and cost per resolution. For leaders just starting, we offer a clear path: pick a major contact driver, launch to a small percent of traffic, measure rigorously, and scale as accuracy climbs. You’ll learn how to convert insights from AI interactions into product fixes and journey improvements that reduce avoidable contacts and lift satisfaction across the board.

If you’re ready to transform customer experience with real action, not just conversation, this episode is your blueprint. Subscribe, share with your CX and product teams, and leave a review telling us which high-impact use case you’ll tackle first.

Support the show

More at https://linktr.ee/EvanKirstel

Trust has become the entry ticket to the internet, and users are now choosing safety over price and speed. We sit down with Raj from Trua to dig into the 2025 Trust Survey and why verification has shifted from a nice-to-have to a must-have. From shocking stats on consumer expectations to the real reasons fear and fatigue are rewriting behavior, we map out what it takes to build platforms people will actually use—and pay for.

We walk through the practical playbook: make safety the first thing users see, put verified badges and last-checked timestamps front and center, and adopt “friction by design” to deter bad actors without punishing good ones. Raj breaks down progressive checks that scale with risk—identity and address for low-stakes, licenses and criminal screens when necessary, and liveness for high-impact flows like dating and hiring. He explains why trust tech is emerging as its own category and how a reusable, tokenized credential can follow you across apps, slashing repeated data collection while boosting conversion and confidence.

AI and deepfakes add urgency. With bots posing as people, human identity verification becomes essential for high-risk interactions, and continuous authentication helps separate real users from synthetic scams. We also explore the regulatory landscape, why co‑opting consumers is cheaper and more effective than piling on rules, and how brands like Apple translate privacy into loyalty. The gig economy emerges as the flash point: tens of millions move across platforms that need fast, safe onboarding and fewer data silos to breach.

If you care about growth, retention, and brand resilience, now is the time to operationalize trust. Subscribe, share this episode with a product or security leader, and leave a review with one change you’ll make to bring safety to the surface.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if the most human-feeling teammates on your projects moved at machine speed and collaborated across every vendor you use? We sit down with Vijoy Pandey is GM and Senior Vice President of Outshift to unpack how agentic AI and quantum networking move from shiny demos to dependable, scaled systems that actually ship results.

We start by reframing innovation horizons around risk instead of time. Technology risk is where quantum lives today, with foundational challenges from hardware to protocol. Market risk is where agentic AI thrives, as teams test real enterprise use cases beyond coding assistants. Platform risk is the mandate for a company like Cisco: open, interoperable architectures instead of brittle point tools. That lens shapes everything OutShift builds.

You’ll hear a clear blueprint for the Internet of Agents: discover the right capabilities across vendors, grant task and transaction-based access that expires fast, enable real-time many-to-many messaging across voice and video, and close the loop with rigorous measurement that feeds reputation and policy. We spotlight Agency (AGNTCY), the Linux Foundation–hosted open source project backed by a broad industry coalition, designed to make multi-agent collaboration portable and trustworthy.

The proof shows up in production. A healthcare triage flow uses a Webex voice agent that coordinates with insurance and diagnostics agents to verify claims and route patients to the right human expert faster. In networking, a multi-agent pipeline built with Swisscom stress-tests configuration changes like a chess engine, exploring outcomes, generating tests, and catching cascading failures before they hit production. On the horizon, quantum networking becomes a scale-out fabric that links quantum computers, data centers, and sensing devices—accelerating timelines by five to ten years while delivering near-term classical benefits in security and precision.

If you care about enterprise AI, open standards, and the road to quantum-ready infrastructure, this conversation maps the path from uncertainty to impact. Subscribe, share with a colleague who’s building with agents, and leave a review with the one capability you want most from an Internet of Agents.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Cameras don’t just record anymore—they inform, alert, and help teams act before problems get expensive. We sit down with Jeff Corrall Chief Product Officer at March Networks, to unpack how AI-powered video is evolving from security footage to real operational intelligence you can search, measure, and scale without ripping out legacy systems.

Jeff explains why continuous video analysis is costly and how an image-first approach delivers the wins that matter. By capturing snapshots at set intervals or when simple edge analytics detect an event, their platform sends images to the cloud for deeper interpretation. That means retailers catch blocked fire exits before fines hit. Managers get texts when queues spike. Regional leaders track trends across hundreds of sites in real time. And teams can literally “Google” their stores for spills, cash left out, or outdated signage, fixing issues at the speed of a search.

We also dig into the AI3 camera, a 360-degree device that blends people counting, queue monitoring, and traffic flow with traditional video, replacing multiple sensors with one scalable unit. Beyond retail, transit agencies pair passenger counts with payment data to spot revenue leakage, while banks audit promotional compliance by uploading an example poster and finding any branch where it’s still displayed. The near-term horizon is especially exciting: fusing point-of-sale data with AI triggers to calculate conversion, surface anomalies with visual proof, and notify the right person to take action—before customer experience or revenue suffer.

If you care about operational excellence, loss prevention, and smarter use of your existing cameras, this conversation is packed with practical insights. Follow the show, share it with a colleague who owns store ops or security, and leave a quick review to tell us what you’d automate first.

Support the show

More at https://linktr.ee/EvanKirstel

What if your network could understand intent, adapt in real time, and deliver the exact experience you need—whether you’re at a packed concert, inside an ambulance, or running a factory floor? We dive into the shift from manual engineering to autonomous networks and how open standards are making AI practical, measurable, and global.

Claudia Muñiz, Global Head of Sales for Cognitive Network Solutions at Ericsson, explains why 5G’s real promise is differentiated connectivity and how the Service Management and Orchestration (SMO) layer unlocks it. We trace the journey from early self-organizing networks to an open RAN ecosystem where developers ship RAN apps once and deploy everywhere. Expect clear examples of digital twins and multi-objective reinforcement learning tuning antennas across massive configuration spaces, with live results: higher throughput, faster speeds, fewer dropped calls, and better spectral efficiency—proof that AI at the radio layer moves the needle when paired with the right data and processes.

We also separate hype from reality on large language models in telco. Lightweight, energy-aware models handle millisecond decisions at the edge, while LLMs shine in cloud workflows, documentation, coding, and natural language operations. With partnerships that blend domain expertise and elastic compute, engineers can ask plain-English questions and get guided actions, all built on a unified, secure data layer. The conversation lands on what matters most: time-to-first value, measurable gains in churn, revenue, energy, and effort; and the near future of intent-based networking that coordinates specialized agents to meet business goals across open interfaces.

If you care about 5G, open RAN, AI-driven operations, developer ecosystems, and the real-world path to autonomous networks, this is your roadmap. Subscribe, share with a colleague who lives in dashboards, and leave a review with the one question you want us to tackle next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What happens when thousands of retired Ethereum GPUs meet a tidal wave of AI demand? We sit down with Hidde Hoogland Founder at Decent to unpack how a former mining operation evolved into a decentralized edge provider and why connecting to Aethir’s GPU marketplace changed the math on utilization, cash flow, and scale. It’s a candid look at turning stranded hardware into a real business with clearer ROI targets and a pricing story that can undercut hyperscalers by 60–80 percent for many workloads.

We trace the journey from proof-of-work mining to a three-part operating model: sourcing hardware directly from vendors, hosting and maintaining systems, and owning the facility layer. Hine explains how decentralized GPU-as-a-service works for both sides of the market: buyers get location choice, hourly rentals, and on-chain payments; providers get a single platform that aggregates demand. The kicker is stability. Aethir’s static lease fees create a baseline even when GPUs sit idle, letting operators scale into actual demand rather than guessing. That foundation supports a portfolio approach to revenue: pay OPEX in fiat, stack tokens for growth, and invest in GPU SKUs with clear utilization histories and 12–24 month payback windows.

We also zoom out to the European landscape. The Netherlands offers elite fiber and interconnect density but faces expensive power and a saturated grid. That reality shifts strategy toward storage-heavy growth while onboarding next-gen GPUs like 5090s where the numbers make sense. The medium-term vision is compelling: pair decentralized compute with distributed storage so teams can run inference, fine-tune LLMs, and manage data gravity without surrendering cost control or flexibility. If AI demand keeps rising, this “house of DPin” model could redefine how we think about cloud alternatives at the edge.

If this conversation sparked ideas about your own GPU strategy or you’re curious about decentralized infra economics, follow the show, share it with a teammate, and leave a quick review to tell us what you want to hear next.

Support the show

More at https://linktr.ee/EvanKirstel

Customers don’t call to chat with a bot—they want outcomes. In my chat with Jim Palmer Dialpad's Chief AI Officer we dive into how Dialpad’s AI-first platform turns real-time voice, video, and digital interactions into agentic workflows that act on behalf of users, learn from every result, and hand off seamlessly to humans when it counts. No black boxes. No bolt-ons. Just a unified stack where speech recognition accuracy, in-domain adaptation, and transparent evaluations compound into trust you can measure.

We talk through the nuts and bolts of building AI where it belongs: inside the media layer, not tacked on after the fact. That foundation unlocks higher-quality transcripts, reliable summaries, and precise suggestions that downstream models can use without drowning in errors. From there, in-domain training becomes the engine for better answers: when support questions share patterns across industries, the system adapts faster and resolves issues with fewer escalations. And when a case does escalate, the agent sees the distilled context—intent, key points, attempted steps, and recommended next actions—so the conversation keeps moving.

The flywheel is the real breakthrough. As new issues surface in a rolling window of conversations, the platform quantifies impact and proposes automations. Humans review, approve, and refine. The system executes, measures completion and containment, and folds learnings back into models. Layer on governance—clear data policies, observability, and rigorous evaluation—and you get AI that leaders can trust and teams love to use. We also share how internal hackathons and applied research push practical features over the line, keeping innovation and execution in lockstep.

If you care about customer experience, agent performance, and measurable ROI from AI, this conversation brings a clear blueprint. Subscribe, share with a colleague who owns CX or support, and leave a review with your biggest automation challenge—we might feature it next.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

Most brands pay again and again to win back customers they already won. We wanted to fix that. In this candid conversation with Sandeep Menon Co-Founder Auxia we unpack how an agentic AI platform can sit on top of rich first-party data and finally make it work: autonomous decisions about timing, creative, channel, and offer that meet people at the right moment across web, app, and owned media. No more brittle, rules-based journeys that spam everyone. Think continuous learning, clear objectives, and experiences that build trust.

We talk through the reality of today’s martech sprawl and why so many marketers feel like systems integrators. Sandeep shares a practical model for role compression, where a suite of agents handles the repetitive work and analysts’ chores, while the human team sets goals, brand guardrails, and strategy. He breaks down the “reacquisition treadmill” and why acquisition gets the spotlight while retention gets sidelined, even though the biggest gains often come from deepening relationships with people who already converted.

You’ll hear a tangible story: a large C2C marketplace used cross-category nudges to move fashion buyers into electronics, driving an 84% lift in customer lifetime value. We dig into how the decision agent and analyst agent work together, how marketers can query outcomes in plain language, and why the next two years of AI—especially improved reasoning—will reward teams that measure success by revenue impact, not pilot buzz. If you’re ready to turn first-party data into loyalty instead of noise, this one’s for you.

If you enjoyed this conversation, follow the show, share it with a colleague who lives in lifecycle or growth, and leave a quick review so others can find it.

Support the show

More at https://linktr.ee/EvanKirstel

AI is moving faster than any wave we’ve seen—and the hidden cost is complexity. We sit down with New Relic’s leadership to unpack how observability must evolve to keep up with agents, vibe coding, and probabilistic systems that don’t fail like traditional software. From the early SaaS days to hyperscale cloud and now AI-native architectures, we connect the dots on where value breaks, why pilots get stuck, and what changes when real users meet real AI in production.

We challenge the headline that “95% of GenAI projects fail” and explain the real blockers: adoption, verification, and integration into messy enterprise workflows. Then we dive into the rise of AI agents—microagents and nanoagents coordinating around-the-clock—to offload repetitive tasks and accelerate delivery. With that power comes a new ops reality: tracing prompts and tool calls, monitoring hallucinations, enforcing safety policies, and tying every hop back to user and business outcomes. You’ll hear practical guardrails for vibe coding, including evaluation suites, prompt versioning, canary releases, role-based access, and real-time model quality monitoring.

Finally, we look at outages through a clear lens: even the smartest teams can’t outpace system complexity without better visibility and faster remediation. New Relic’s two-pronged roadmap—observability for AI and AI for observability—aims to give teams both the data and the decisions: model-aware telemetry, agent state insights, automated incident summaries, and recommended fixes that protect revenue and trust. If you’re planning for 2026, take this as your cue to embrace the era with discipline: ship agents, instrument everything, and let observability become the backbone of safe, scalable AI.

If this conversation helps you think sharper about AI in production, follow the show, share it with your team, and leave a quick review so others can find it.

Support the show

More at https://linktr.ee/EvanKirstel

What if your contact center could speak 46 languages, automate millions of calls, and still hand tricky moments to the right human in seconds? We dig into the launch of Teneo 8 with CEO Per Ottosson and reveal how a hybrid AI approach—combining LLM-powered conversation with deterministic logic—delivers scale, governance, and measurable results without sacrificing warmth or control.

We walk through the architecture that makes this possible: long-form, human-style dialogue guided by LLMs, paired with rules and business processes that capture facts, validate entities, and keep flows on track. You’ll hear how a major global rollout built in English, then used language objects to expand into dozens of markets over a weekend, and why public APIs let providers embed high-scale automation under their own brands. Real outcomes anchor the story—like Medtronic’s virtual assistant in healthcare, which lifted customer satisfaction by eight percentage points and drove revenue gains while meeting strict compliance needs.

Beyond replacing legacy IVR, we explore omnichannel reality and persistent memory that unifies voice with channels like WhatsApp and iMessage. We share a practical stance on human-in-the-loop: AI first for routine steps, expert agents for edge cases, leading to happier teams and better resolutions. Looking ahead, we map the shift to voice-to-voice conversations supervised by a control layer that enforces compliance and intent understanding. If you’re evaluating Dialogflow, Genesys, or custom stacks, you’ll get a clear view of how to scale, how to avoid the “easy demo” trap, and how to measure value through CSAT and revenue—not just cost cuts.

If this resonates, follow the show, share it with your CX and engineering teams, and leave a review with one question you want us to tackle next.

Support the show

More at https://linktr.ee/EvanKirstel

Tired of chasing downloads that never turn into deals? We sat down with Refine Labs’ CEO Megan Bowen to unpack why so many B2B playbooks still assume the seller holds the power—and how to rebuild your go-to-market around the way buyers actually make decisions today. From redefining MQLs to recognizing meaningful intent, this conversation is a straight path from vanity metrics to qualified pipeline.

We break down the Brand–Demand–Expand model in practical terms. Brand ensures you land on the day-one consideration set with clear, memorable narratives. Demand translates attention into real sales conversations by removing friction and aligning to buyer timing. Expand focuses on adoption, retention, and account growth, turning customers into advocates who fuel word of mouth. Along the way, you’ll hear why aggressive outreach erodes trust, how to design self-serve paths that respect buyers, and where most teams underinvest when it comes to lifetime value.

Pulling it all together, we explore Refine Labs’ rigorous onboarding: multi-quarter revenue analysis, funnel diagnostics, and creative audits that produce a focused roadmap instead of a one-size-fits-all playbook. We also dive into an AI strategy that actually matters—AI-powered benchmarks from first-party data, automation that frees humans for strategy, and structured experiments that prove lift before scaling. Add in concrete steps for aligning sales and marketing around shared pipeline and revenue goals, and a people-first culture that compounds performance, and you get a blueprint for modern B2B growth that’s both human and high performing.

If you’re ready to move beyond form fills and start building buyer intent, hit follow, share this with your team, and leave a quick review to tell us what you’ll change first. Your feedback helps more leaders find these conversations and modernize their go-to-market.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

We’re joined by **Anand Eswaran**, CEO of **Veeam Software**, and **Rehan Jalil**, CEO of **Securiti AI**, following Veeam’s bold acquisition of Securiti—a move that fuses data protection, security, and AI governance into one platform. Together, they unpack why GPUs alone can’t fix untrusted data and why 80–90% of enterprise AI projects fail, exploring how unifying data security and resilience lays the groundwork for AI that’s safe, compliant, and genuinely useful.

GPUs don’t fix untrusted data. We dig into why 80–90% of enterprise AI projects falter and how bringing data security and data resilience together creates the foundation for AI that’s safe, compliant, and actually useful. Our guests break down a data command graph that maps files, tables, permissions, sensitivity, and regulations across on-prem, cloud, SaaS, and modern warehouses, then uses that context to enforce the right controls and power rapid recovery.

We explore how DSPM fits inside a larger platform that understands data relationships, flags toxic connections, and automates remediation. From ransomware and exfiltration to model poisoning, the graph approach speeds both prevention and incident response. You’ll hear how to prioritize the crown jewels, set dynamic resilience policies, and hit aggressive RPO/RTO targets so the business stays online. We also dive into safe AI enablement: find the sensitive “needles,” mask or tokenize them, and let the “haystack” fuel conversational search and agents without leaking secrets.

The conversation goes further into operational reality: low-friction adoption for existing environments, visualizing which datasets train which models, and rolling back AI pipelines with precision when bad data slips through. We talk timelines for integrating the platforms and why this unified view unlocks better security, stronger governance, and smarter storage tiering. If you’re ready to raise AI success rates, reduce blast radius, and move faster with confidence, this one lays out the playbook.

If this resonates, follow the show, share it with a teammate who owns AI risk, and leave a quick review telling us which challenge you want solved next.

---

Would you like me to format it as a LinkedIn or YouTube post (optimized for engagement and readability on those platforms)?

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel

What if your next high-performing hire never sleeps, knows your stack, and ships secure environments in hours? We dive deep with Chris Chapman CTO of MacStadium to map the shift from brittle automation to agentic AI running on fleets of Macs, where outcomes replace step-by-step scripts and digital employees take on repeatable, high-value work. Chris opens the hood on Orca, their software-defined Mac platform, and shares how customers virtualize thousands of machines to train agents, spin up secure developer workspaces, and deploy specialized small language models that thrive on Apple silicon.

We explore why early movers gain an edge: building agents around their policies and workflows instead of bending to generic tools. That means using standards like MCP and A2A for reliable tool use, embracing hybrid architectures that keep sensitive data sovereign, and bringing compute to the data rather than hauling data to the cloud. The result is practical speed: fleets that scale on demand, agents that verify progress at checkpoints, and environments that remain auditable and compliant. Add the efficiency of Apple silicon—high performance per watt, unified memory, and strong on-device inference—and you get meaningful cost control without sacrificing capability.

Security stays front and center. We unpack a security-by-design approach where agents inherit guardrails, act on alerts, remediate drift before rollout, and document every change. We also talk shop about the road ahead: M5-based clusters for training and inference, deeper graphics and AI integration in Orca, and Apple’s unique edge-to-cloud continuity that unlocks multimodal workflows from Vision Pro and iPhone to Mac mini clusters. If you’re weighing how agentic AI will reshape roles, budgets, and delivery, this conversation offers a clear blueprint: encode goals, codify guardrails, and let agents do the heavy lifting while your team moves upstream to architecture and strategy.

If this resonated, follow the show, share it with a colleague, and leave a quick review to help others find it.

Support the show

More at https://linktr.ee/EvanKirstel

What if your crypto wallet felt as safe as your bank account? We sit down with Crypto Insurer BDIC Insurance Founder and CEO Jeffrey Glusman to unpack a bold plan to bring deposit insurance to digital assets and unlock mainstream confidence. Instead of chasing whales, the model targets the everyday holder with clear, affordable coverage tiers, vetted wallets, and a focused list of insurable coins. The goal is to turn fear, uncertainty, and doubt into transparent rules and reliable protection that stands up when it counts.

We dig into how coverage works, what’s actually insured, and why solvency signals matter—sovereign fund commitments, bank guarantees, and established carriers. Exchanges are watching closely. With regulators weighing in and users demanding safety, supplemental insurance could become table stakes. Using the Bybit hack as a reality check, Jeffrey explains how an insured framework could have reimbursed most losses, protected AUM, and kept trust intact. Insurance can’t stop every breach, but it can turn catastrophic events into recoverable ones.

Education plays a starring role. We break down hot versus cold wallets, key custody, tokenization, and the small habits that prevent big losses. Jeffrey shares a painful personal phishing story and a recent large-scale fraud involving a spoofed bank and forged policies—then maps out the steps that stop most attacks: a dedicated wallet device with no SIM, no socials, no SMS 2FA; SIM locks at the carrier; $1 test transfers; and on-chain verification. Finally, we talk go-to-market: why Hong Kong and Pan-Asia come first, why Latin America is primed, and how a utility token and partner exchanges could kick off a domino effect toward industry-wide normalization.

If you care about crypto adoption, user protection, and credible risk management, this conversation brings clarity and a practical path forward. Subscribe, share with a friend who trades, and leave a review with your take: should exchanges be required to offer deposit-style insurance?

Support the show

More at https://linktr.ee/EvanKirstel

Most AI pilots impress in a demo and stall in production. We wanted to unpack why. Joseph Kim is the President and CEO of Druid AI joins us to break down the real blockers to AI readiness and the playbook that moves teams from wow to working systems. We talk candidly about market hype, sobering stats on failed deployments, and the ingredients that make enterprise agents safe, accurate, and compliant at scale.

We start with first principles: define the business case, choose the right capability for the job, and decide if AI is even necessary. Joe draws a clean line between generative tasks and agentic tasks, explaining why a passable answer is fine for search but dangerous for automated actions. He shows how Druid engineers fidelity with background subroutines that pre-check identity, history, and likely intent, so the first response is accurate and the downstream steps are auditable. That control layer—complete logs, policy dials, and compliance hooks—turns black-box models into systems you can govern and certify.

From there, we tackle data hygiene, hallucination traps, and how to measure success beyond vanity metrics. Joe lays out a practical path: start small with a high-value workflow, track NPS, deflection rates, and accuracy, then expand into adjacent use cases. For enterprises running dozens of agents from different vendors, we dig into orchestration. Druid’s Conductor acts as a control plane that routes tasks, mediates context, and prevents lock-in, so you can swap components without burning time and budget. We close with a grounded take on the AI bubble: yes, excitement inflates expectations, but the durable gains will mirror the early internet—messy at first, transformative over time.

If you’re ready to turn experiments into outcomes, this conversation will help you align technology with ROI, design for compliance, and scale with confidence. Subscribe, share with a teammate who owns an AI pilot, and leave a review with the one blocker you want us to tackle next.

Support the show

More at https://linktr.ee/EvanKirstel

What happens when agents can talk to agents—securely, asynchronously, and at scale? We sit down with Edward Funnekotter Chief Architect and AI Officer from Solace to unpack the architecture behind an open agent mesh and why event-driven design is the key to turning AI hype into dependable workflows. Instead of a single chatbot, imagine a network of specialized agents that advertise their skills, discover each other, and coordinate like a high-functioning team. They fetch data from Salesforce and support systems, compile a customer health report, rank the urgent accounts, and hand you the three actions that matter.

We get practical about where to start. Dynamic dashboarding and interactive analysis are low-lift, high-impact wins, and they translate smoothly into scheduled reports and triaged alerts. Using an event broker lets you subscribe to only the events you need, template them into agent requests, and run parallel tasks without disturbing existing integrations. The philosophy is simple and powerful: make LLMs do less. Reserve the model for judgment and planning, then let deterministic tools handle transformations, queries, and updates. You’ll cut latency and cost while improving reliability.

Security isn’t an afterthought. We dig into prompt injection, data leakage, and how to enforce tainted-context policies so confidential data never escapes to public tools—and external data never triggers risky internal actions. Lineage and audit trails make decisions explainable. We also explore the role of standards like A2A and MCP, why big-tech sponsorship accelerates adoption, and how open-source on-ramps paired with enterprise hardening create trust. Looking ahead, we see an AI org chart that mirrors the human one: layered roles, clear responsibilities, and shared context running on faster, cheaper infrastructure that prioritizes answer quality over raw speed.

Ready to build with confidence instead of cobbling in the shadows? Listen now, subscribe for more conversations at the edge of AI and architecture, and leave a review with the first agent workflow you’d deploy.

Listen on: Apple Podcasts   Spotify

Support the show

More at https://linktr.ee/EvanKirstel