This episode explores the infrastructure required to make intelligence repeatable, scalable, and operational. The key takeaway is that intelligent enterprises depend as much on infrastructure, integration, and operating design as they do on algorithms.

TLDR / At a Glance

• The three-layer intelligence stack
• Governed data as a reusable product
• Shared platforms for model lifecycle management
• Workflow integration as an adoption driver
• Decision environments that reduce friction
• Embedded feedback loops for continuous learning 

AI rarely fails at the idea stage. It fails after the promising pilot, when the organisation tries to execute the same “smart” decision again and again under messy real-world conditions. We dig into why intelligence only becomes real when it is operational and repeatable, and why infrastructure is the hidden engine of the intelligent enterprise. If your models look great in controlled tests but fall apart in production, the problem is often not the algorithm, it is the system around it.

We break the challenge down into a practical three-layer intelligence stack. First comes the data foundation: data that works for reporting can be unusable for decision systems, especially when you need early, probabilistic signals. We look at how inconsistent definitions, brittle pipelines, and manual reconciliation create “model problems” that are actually upstream quality and ownership problems, and why treating key datasets as products with clear accountability (often associated with data mesh thinking) changes what is possible.

Next is the platform layer, where MLOps stops being a buzzword and becomes a requirement for scale. A shared platform for development, deployment, monitoring, and version control helps teams spend time on decision logic rather than scaffolding, and it prevents the slow creep of shadow systems. 

Finally, we get to the application layer: the moment where behaviour either changes or stays the same. A slightly inferior model embedded inside the workflow, with next steps and audit trails, can outperform a better model sitting on a dashboard because integration removes friction and makes action routine.

If you want AI that people trust, use, and improve over time, start here.

Subscribe, share this with a colleague building AI in the enterprise, and leave a review telling us which layer is causing you the most pain.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how the Strategic Intelligence Loop converts organizational experience into compounding advantage.

TLDR / At a Glance

• Strategic Intelligence Loop
• Proprietary operational data moats
• Intelligence embedded in workflows
• Faster organizational learning cycles
• Operational maturity and strategic coherence
• Monetization, resilience, and trust 

Powerful AI is now cheap and widely available, so the old playbook of “pick the best model and win” is falling apart. We dig into what actually creates competitive advantage in the AI era: not access to algorithms, but the ability to improve decisions repeatedly and reliably. The centrepiece is the strategic intelligence loop a continuous cycle where signals drive decisions, decisions drive action, and action generates learning that strengthens the next round. That is what makes performance compound.

We also get practical about where your edge really comes from: organisation-specific operational data. Conversions, fraud outcomes, renewals, delivery delays, service records, call centre notes and exception tickets quietly build an operating history that competitors cannot rent. When that signal is organised and connected, AI predictions stop being generic and start matching the messy reality of your customers and processes.

Then we tackle the make-or-break move: workflow integration. Insights on dashboards are easy to ignore, but recommendations inside routing, underwriting, CRM, and collections systems change behaviour because they show up at the moment of decision. We talk through exceptions, decision rights, escalation, incentives, monitoring, and the cultural conditions that let teams report failures early so the system learns faster. We also touch on governance and trustworthy AI, including why approaches like the NIST AI Risk Management Framework treat risk management as a core design feature rather than a bolt-on.

If you want to scale AI beyond pilots, build operational maturity, and keep your AI portfolio aligned to strategy, this is a roadmap. 

Subscribe, share the episode with a colleague, and leave a review if it helps. What part of your organisation’s intelligence loop is weakest right now?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR/At a Glance

• The shift from Copilot as helper to Copilot as doer and why it lands on the leadership agenda 
• Home and Today as the new starting point for work and what that demands from processes and data quality 
• Co-work as task execution across email, calendar, documents, and systems with designed approval points 
• Usage based charging and the move from licence counting to cost per result thinking 
• Copilot Code for building apps in plain language plus the ownership and support risks it creates 

Copilot is no longer just a helpful sidebar that drafts emails and summarises meetings. Microsoft’s latest updates point to something bigger: AI that can carry out multi-step tasks, build lightweight apps from plain English, and soon keep working towards a goal while we are offline. That leap changes the conversation from training and adoption to operating decisions leaders cannot dodge: what work do we hand over, where do we keep human judgement, and who is accountable when an agent gets it wrong?

We walk through the major capabilities in plain terms, starting with Home becoming the single place to begin work, then moving into Co-work where Copilot takes actions across Microsoft 365 with explicit sign-off points. From there we explore Copilot Code, why “anyone can build an app” is both a gift and a governance risk, and what managed runtime means for IT oversight. We also unpack Autopilot, the most ambitious idea of all: persistent AI agents with their own identity, memory, and workspace, plus what independent evidence suggests about where agents are reliable and where they still overclaim.

Behind the headline features, we dig into the parts that make AI delegation real in an organisation: data access (including Power BI and business system connectors), identity and audit through Entra, and the shift to usage-based billing that forces a new metric: value per result. 

The episode closes with a five-question readiness check leadership teams can answer in a single meeting to decide what to try first and what needs fixing before more autonomous tools arrive.

You can also read this article on my website and LinkedIn pages, where I share regular insights on AI, strategy, and emerging technologies..

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Discover why AI transformation is not simply a technology or hiring challenge. It requires leaders to redesign skills, roles, workflows and career pathways so intelligence can become part of everyday work.

The episode explores AI’s rapidly advancing capabilities, its impact on knowledge work and the responsibility leaders have to prepare their organizations for a changing workforce.

Explore The Executive’s Guide to Strategic Intelligence and access the free preview:

https://kierangilmurray.com/strategic-intelligence/

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how vibe coding can deliver value when treated as a governed system for rapidly building internal tools with clear ownership and measurable outcomes.  It explains how to deploy vibe coding for controlled, high-impact internal use.

TLDR / At a Glance

• Issue to pull request agent workflows
• Low risk internal tooling focus
• Governance through permissions and review gates
• Integration via Model Context Protocol
• Organisational bottlenecks over model limits
• 30 day controlled pilot approach

Vibe coding is moving from curiosity to an operational advantage, but only for leaders who treat it like a governed capability rather than a shortcut. We dig into what shifted in March 2026 and why that month marks a practical turning point: AI coding agents are no longer sitting beside work, they are being embedded into workflow systems and enterprise development tools where tasks can move from an issue to a draft pull request with far less friction.

We also unpack the three developments that change the risk profile and the opportunity at the same time. First, tighter integration with work management makes agent output easier to review in familiar change-control paths. Second, autonomy controls are now explicit: plan modes, permissions, and autonomous execution settings become configuration choices that need policy, not vibes. Third, broader integration layers let agents connect to more tools and data sources, which boosts usefulness but increases the blast radius if access is too broad or ownership is unclear.

From there, we get concrete about safe starting points for AI coding agents and internal tool development: internal reporting and summarisation, workflow wrappers around ticketing and service desks, knowledge navigation across policies and procedures, engineering-adjacent helpers, and security or review support that keeps speed from becoming a bottleneck. We also draw a clear boundary around what to avoid by default, including unsupervised customer-facing outputs and regulated decision contexts that demand stronger governance, accountability, and tested review mechanisms.

Finally, we lay out a controlled 30-day pilot plan and the metrics that actually prove value: time from issue to pull request, time to merge, defect escape rate, rework frequency, portfolio hygiene, and whether every tool has an owner and remains in use after 30 to 60 days. If you found this useful, subscribe, share it with a leader who owns delivery risk, and leave a review with the first internal use case you would pilot.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

As AI moves from experimentation into core operations, governance becomes essential to scaling intelligence without scaling hidden risk. Effective oversight creates the accountability, consistency and trust needed for intelligent systems to influence consequential decisions safely.

This episode explores how governance protects Decision Aperture as AI becomes embedded across the enterprise.

TLDR / At a Glance

• Governance as infrastructure for scalable intelligence
• Strong oversight reducing operational friction
• Trust through explainability and accountability
• Fairness controls for historical bias
• Continuous monitoring across the model lifecycle
• Human oversight, ownership and intervention mechanisms 

AI that works in a demo can still fail spectacularly in the real world, not because the model is “bad”, but because the organisation never built the guardrails to scale it. We explore why governance is the foundation of responsible intelligence, especially once automated decision-making starts touching thousands of outcomes across credit, pricing, hiring, clinical triage, logistics, and compliance. When decisions become embedded in workflows, small inconsistencies don’t stay small. They propagate, interact with feedback loops, and can turn performance gains into systemic risk.

We challenge the assumption that governance slows innovation. From our perspective, weak or retrofitted oversight creates the longest delays: pauses, rework, and endless renegotiation after a model is already live. Strong AI governance speeds teams up by making expectations predictable, approvals repeatable, and incidents less chaotic. We then get practical about trust: it isn’t built through messaging, it shows up in how people behave when recommendations are probabilistic, accountability is fuzzy, and the cost of being wrong is personal. That’s where algorithm aversion thrives and where clear escalation paths, explainable reasoning, and fair review processes make adoption possible.

Fairness and bias risk sit at the centre of scalable AI. Historical data can encode exclusion and turn it into automated policy unless you manage disparate impact as rigorously as any other operational risk. 

We also dig into explainability and GDPR realities, then widen the lens to lifecycle governance: monitoring, validation under change, and treating AI as a living system. 

For high-impact decisions, we make the case for human oversight, clear boundaries, and kill switches, backed by strong documentation and named ownership.

If you found this useful, subscribe, share the episode with a colleague, and leave a review. 

What part of AI governance feels most underbuilt where you work?

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If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how organisations can turn governance into a strategic advantage.

TLDR / At a Glance

• AI risk scaling faster than oversight
 • Governance as executive responsibility
 • Regulatory pressure across EU, UK, US
 • Converging principles across frameworks
 • Audit logs, oversight, escalation controls
 • Governance enabling trust and scale

Strong governance transforms AI from a source of uncertainty into a system that can scale with confidence, accountability, and control.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

We sit down with Colin Levy, general counsel at legal tech company Malbek and an adjunct law professor, to get a grounded view of legal AI that goes beyond hype. 

His headline is simple and sharp: AI is a powerful but imperfect tool, and lawyers need to work with that reality rather than pretend it will disappear.

TL;DR / At A Glance

• why AI in law is “powerful but imperfect” and why that matters for risk
• what hallucinations look like in legal work and how to reduce them with context and grounded sources
• where AI is genuinely useful today such as first drafts, review, summarisation and knowledge creation
• why the real win is strategic productivity rather than raw efficiency
• how AI pressures the billable hour and what “value” can look like instead
• how to think about junior lawyer hiring and onboarding when AI can draft

We dig into the problem everyone whispers about and some people ignore: hallucinations. Colin explains why fabricated answers are a feature of how generative AI works, then walks through practical ways to reduce the risk, from giving better context and instructions to using tools trained on legal data for legal research. 

We also talk about what “collaboration” with AI looks like in real workflows: first drafts, document review, summarising large datasets, and creating usable knowledge, with humans still responsible for checking the work.

From there, we get concrete on leadership decisions: how to evaluate AI vendors without becoming a technical expert. We cover data handling and data sharing, security credentials such as SOC 2, privacy expectations including GDPR, and what to ask about model updates, transparency, and liability. 

We also unpack AI governance that actually works, training and permissioning, a sensible 90-day plan, and why measuring success by logins is meaningless. Finally, we tackle the future of junior lawyers, onboarding in the era of AI, and where practice areas like litigation analytics and contract work may shift next.

If you want a clear, practical approach to legal technology and AI governance that protects clients while improving output quality, press play, then subscribe, share with a colleague, and leave us a review. 

What’s the one AI rule your team refuses to compromise on?

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If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how culture, governance, workforce design, and accountability shape intelligent organizations.

TLDR / At a Glance

• Creators, translators, and operators
• Psychological safety under uncertainty
• Clear authority for human and model decisions
• Reskilling focused on judgment
• Incentives aligned with decision quality
• Decision-centric talent and expertise models 

The key takeaway is that effective AI adoption depends on designing human systems that support informed judgment, timely action, and continuous learning.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how integration turns local intelligence into coordinated enterprise capability.

TLDR / At a Glance

• System intelligence over isolated capabilities
• Connected signals, decisions, and actions
• Customer journeys as tests of coherence
• Shared authority and accountability
• Incentives aligned to enterprise outcomes
• Integration across data, interpretation, and execution

Smart models can still produce a stupid organisation. When signals sit in one function, interpretation happens in another, and action lands somewhere else entirely, the strategic intelligence loop slows at every handoff. We explore what it really takes to become an intelligent enterprise where intelligence operates as a system, not a set of isolated analytics wins.

We walk through concrete examples from banking, industrial operations, telecommunications, insurance, and supply chain. You’ll hear why banks can simultaneously treat the same customer as high risk, high value, and marginal, and how predictive maintenance can improve plant uptime while the wider network still misses delivery commitments. The thread running through every case is coherence: shared signals are not enough if the logic, authority, and accountability to act remain fragmented.

We also flip the lens to the customer experience, because customers become the first systems integrator. They feel the seams in broken journeys, missing context, and inconsistent decisions long before executives see the pattern in dashboards. From there, we get practical about integration: why platform consolidation and enterprise data programmes plateau without operating model change, and how control towers only deliver resilience once cross-functional decision rights are real.

If you care about AI strategy, decision intelligence, governance, and building a joined up organisation that can learn and adapt, this is for you. Subscribe, share with a colleague who owns “the handoffs”, and leave a review with the one integration barrier you want to fix next.

Enterprise intelligence scales when technology, governance, decision rights, and incentives work together as one connected system.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Subscribe, share this with a leader who owns outcomes, and leave a review: what is the biggest blocker to AI ROI where you work?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TLDR / At a Glance

• Addition as a leadership signal
• Cognitive bias against subtraction
• Organisational systems that reward launches
• Busywork, meetings, and execution drag
• Strategic subtraction across meetings and governance
• Protecting capability while removing waste

We lay out the case for strategic subtraction: the leadership discipline of removing work, process and activity that no longer creates enough value. Along the way, we unpack why subtractive solutions are so easy to miss, especially under pressure, and why most organisations are built to launch things but not to stop them. We also borrow from Peter Drucker’s planned abandonment to ask a blunt question that should sit at the heart of strategy: knowing what we know now, would we start this again today?

From there, we connect the dots to measurable realities like busywork and coordination overhead, decision latency driven by unclear decision rights, and the way constant fragmentation undermines deep work, innovation and wellbeing. We then bring it to life with practical examples of subtraction at different levels, from cutting recurring meetings to reducing bureaucracy and even using product constraint as a strategic advantage.

If you’re trying to lead in the AI era, this is a sharper, kinder way to create focus without burning people out. Subscribe, share the episode with a colleague, and leave a review, then tell us what you would subtract first.

The key takeaway is that stronger leadership often means removing work that no longer creates enough value.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how leaders can make subtraction a recurring management discipline.

TLDR / At a Glance

• Quarterly stop-doing reviews
 • Subtraction gates for new work
 • Portfolio pruning and consolidation
 • Visible measures of complexity removed
 • AI-assisted identification of waste
 • Balanced scorecards and risk guardrails

Strategic subtraction works when stopping, simplifying, pausing, and consolidating become normal parts of how the business operates.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how Decision Intelligence closes the gap between insight and operational action.

TLDR / At a Glance

• Insight-to-action gap
 • Decision Intelligence operating models
 • Embedded AI decision logic
 • Authority and accountability design
 • Incentive and workflow alignment
 • Decision environments built for action

The key advantage comes from designing organizations where credible intelligence consistently shapes decisions and behaviour.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TLDR / At a Glance

• Gen Z entering management at typical rates
• Importance of clear cohort definitions
• Status incongruence in age diverse teams
• Confidence versus capability gap
• Role design as root cause of challenges
• Governance risks from AI in people processes

We start by getting precise about what “Gen Z” even means, because inconsistent birth-year definitions can quietly distort workforce statistics, leadership pipeline forecasts, and executive decisions. Then we look at what the data actually shows: Gen Z is not avoiding management, their progression looks broadly similar to previous generations at the same age. What has changed is the context and the support gap, with new managers facing high expectations around decision making, conflict resolution, prioritisation, and workload management much earlier in their careers.

From there, we explore where friction shows up in day-to-day work: status tension when younger authority meets older experience, misread communication styles in hybrid teams, and the confidence-versus-capability gap that can lead to delayed decisions and unnecessary escalations. We also tackle a fast-growing issue for people leaders: AI governance in hiring, performance documentation, and employee monitoring, including why audit trails, transparency, and human accountability matter across the UK, EU, and US landscapes. If you want practical leadership development, better manager training, and role design changes that boost performance and retention, this conversation is for you. 

Subscribe, share with a manager, and leave a review with the one policy or practice you would change first.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TLDR / At a Glance

• AI access versus AI advantage
 • Workflow redesign and decision quality
 • Organisational complements and strategic fit
 • Coordination across data, controls, and workflows
 • Learning loops and institutional memory
 • The Human AI Operating System

We unpack the strategic divide showing up across industries: plenty of firms can access AI and many can point to adoption, yet far fewer can translate that into reliable AI capability inside real workflows, and even fewer reach AI advantage that compounds over time. Along the way we connect the dots to well-known strategy ideas like complementary assets and strategic fit, and we explain why general purpose technologies often require hard-to-measure organisational investment before productivity shows up. We also look at coordination as the differentiator, including why enterprise integration signals more than a long list of isolated tools.

To make this practical, we introduce the human AI operating system as a five-layer map of where AI programmes succeed or break down: work, decisions, capability, governance, and value. We then finish with five tests leaders can run now to see whether AI is improving workflow performance and decision quality, whether learning is compounding, and whether the system still creates value even if competitors use the same model. 

If you want AI strategy that goes beyond pilots and usage stats, subscribe, share this with a colleague, and leave a review with the biggest organisational blocker you are seeing.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the Work Layer of the Human AI Operating System.

TLDR / At a Glance

• Task gains versus workflow outcomes
 • Workflow redesign as the value lever
 • Limits of tool-first AI strategies
 • Ownership, handoffs, and judgement points
 • Embedded AI in execution systems
 • Measurement at workflow level

AI adoption is booming, yet the results feel strangely uneven. We keep hearing about dramatic productivity gains, but when you zoom out to the enterprise level the impact often fades into the noise. Our core claim is simple and uncomfortable: AI does not scale through tools, it scales through redesigned workflows, and most organisations are still confusing faster tasks with better work.

We unpack why “tool-first” AI strategy so often disappoints. Time saved on a document, a meeting summary, or a customer call can look impressive, but it can also hide the real costs of execution: rework, coordination overhead, exception handling, and quality checks that sit between steps. We define the work layer in practical terms (task decomposition, sequencing, handoffs, judgement points, exception paths, and quality standards) and explain why this is the true unit of change for enterprise AI, operating model design, and governance.

We also explore what “real value” looks like when AI is embedded into an execution system rather than floating as an optional assistant. Examples such as IBM’s client zero approach and Verizon’s customer service assistant show that the breakthrough is not just speed, but changing what people can focus on inside the workflow and linking redesign to measurable outcomes. We close with a clear playbook: map the workflow, separate routine from judgement, redesign ownership and handoffs, and measure workflow outcomes such as cycle time, error rates, rework, and consistency.

The key takeaway is that AI scales when organisations redesign how work flows, connects to decisions, and delivers measurable outcomes.

If you want a grounded way to turn generative AI into business performance, subscribe, share this with a colleague, and leave us a review with the workflow you most want to fix.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how leaders can use strategic subtraction to create value through better operating design rather than crude cost reduction.

TLDR / At a Glance

• Strategic subtraction versus cost cutting
• Efficiency, resilience, and prominence
• Waste versus useful organisational slack
• Elimination, substitution, and consolidation
• Pausing, hiding, and abstraction
• Protecting capability while removing drag 

The key is to remove what no longer deserves attention while strengthening the system that remains.

Learn more and access the free 2-chapter preview at https://kierangilmurray.com/strategic-intelligence/

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TLDR / At a Glance

• AI access and adoption are not advantage
• Competitive advantage is shifting from tools to systems
• Coordination matters more than model novelty
• Organisational learning compounds performance
• The organisation around AI is hardest to replicate

This article concludes The Human AI Operating System series. Read the other articles on the website, listen on Spotify, or download the complete collection from the series landing page.

Next, we turn to Strategic Intelligence and examine what it takes to lead, decide, and compete in an AI-shaped world.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

We examine why strategy must come before technology and why AI initiatives should be treated as investments rather than experiments.

TLDR / At a Glance

• Focus intelligence on decisions that matter
• Put strategic priorities before tools
• Treat AI initiatives like capital investments
• Govern the portfolio, not isolated projects
• Scale or retire initiatives based on value

Many organisations invest in AI before defining the outcomes they want to improve. The result is activity without direction, fragmented initiatives and uncertain value. We explore how portfolio discipline helps leaders concentrate resources, strengthen accountability and turn intelligence into measurable action.

Adapted from Chapter 4 of The Executive’s Guide to Strategic Intelligence.

Buy the book here

Access two free chapters here

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

We explore why organisations with similar tools achieve different results, how feedback compounds decision quality, and the four conditions that keep intelligence moving: Operating Philosophy, Operating Mechanics, People Capability, and a focused Execution Portfolio.

TLDR / At a Glance

• Connect signals to action and learning
• Choose reliable signals over more noise
• Combine models with human judgement
• Use feedback to improve future decisions
• Make earlier adjustments while options remain open

Your dashboards might be brilliant and still be useless. We dig into the uncomfortable truth we keep seeing across organisations: performance diverges not because one team has better data or smarter models, but because one team has a decision loop that actually moves. When insight stops at a slide deck, intelligence decays. When it cycles through real decisions, real execution, and real feedback, it compounds into an advantage that looks like “instinct” from the outside.  

We walk through the strategic intelligence loop in plain terms: sense, interpret, decide, execute, learn. That starts with deliberately choosing clean, timely signals rather than drowning in noise, then using models to produce probabilistic guidance that points to what is most likely to matter next. The make-or-break moment is decision and execution: pricing, inventory, staffing, maintenance, risk choices, and operational trade-offs that people approve, refine, or override using context. Learning closes the loop by turning outcomes, errors, and exceptions into better models and better judgement, so each cycle improves the next.  

We also break down why the same AI tools can lead to very different results, using four practical dimensions you can diagnose: operating philosophy, operating mechanics, people capability, and the execution portfolio of decisions where intelligence is applied. Along the way, we ground it in real-world cases such as aviation maintenance, fraud detection, and dynamic logistics routing, showing how feedback quality makes or breaks data-driven decision-making. If you want strategic intelligence that survives pressure, builds organisational learning, and reduces “shock” through continuous adjustment, this is for you. 

Subscribe, share with a colleague.

Learn more: https://kierangilmurray.com/strategic-intelligence/

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how leaders can build a continuous navigation system for decision-making and connect intelligence to judgement and action.

TLDR / At a Glance

• Strategic subtraction and reclaimed decision space
• Continuous sensing over retrospective reporting
• Decision Aperture as an intelligence foundation
• Probabilistic views of emerging conditions
• Earlier detection of risk and opportunity
• Converting signals into timely action 

The fastest way to make bad decisions is to stay endlessly busy. We talk about why modern leaders must create space to think, and why that space collapses the moment it is filled with meetings, reports, and reactive choices. Cutting noise is only step one. The bigger question is what you put back into that reclaimed time so judgement improves rather than merely catching its breath.

Our answer is strategic intelligence: a leadership discipline that continuously turns signals into insight, insight into direction, and direction into action. We break down why this is not “more analytics” or “better dashboards”. A dashboard tells you what happened. Strategic intelligence behaves like a navigation system, updating as conditions drift, building probabilistic views of what might happen next, and helping you test assumptions before commitments harden. Along the way we unpack decision aperture, the idea that better decisions come from defining what matters and selecting the signals that should shape choices.

We also tackle the failure mode of traditional business strategy. Annual planning and quarterly reviews were built for stable environments; today, customer behaviour, regulation, pricing dynamics, technology, and risk can change in weeks. That lag turns coherence into irrelevance. Strategic intelligence replaces retrospective planning with continuous sensing, earlier questions, and calmer moves while options remain open.

You will hear concrete illustrations from organisations that spot pressure forming before it becomes a crisis, from Netflix-style signal detection to portfolio sensing in consumer goods, early warning intelligence in financial services, supply chain risk detection in aerospace, and public sector preparedness under heavy scrutiny. 

If this helps, subscribe, share it with a colleague, and leave a review so more leaders can trade noise for direction.

Learn more and access the free 2-chapter preview at https://kierangilmurray.com/strategic-intelligence/

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores strategic subtraction as a disciplined approach to removing work and complexity that no longer create proportional value.

TLDR / At a Glance

• The cognitive bias toward additive solutions
• Why organisations reward launches over retirements
• Execution drag from meetings, handoffs, and governance
• Planned abandonment and sharper decision rights
• Practical examples from Shopify, ING, and Costco
• Protecting resilience, trust, compliance, and capability
• Strong leadership requires knowing what to stop, simplify, or remove so essential work has room to succeed.

Every time work feels messy, the instinct is to add: another meeting, another approval, another dashboard, another KPI, another programme. It looks like action, but it often creates the very complexity we are trying to escape. We unpack why additive leadership is so tempting, why subtraction feels risky, and how “planned abandonment” turns stopping work into a serious strategic choice rather than an act of neglect.

We connect the psychology to the system: organisations are brilliant at launching things and far less mature at retiring them. The result is coordination overhead that eats the week, slower decisions driven by unclear decision rights, and a steady build-up of execution drag. We also explore the human cost, from fragmented attention that kills deep work and innovation to the burnout signals that come with unmanageable workload and constant alignment.

Then we get concrete. We look at real-world examples of strategic subtraction across different levels: Shopify tackling meeting overload, ING cutting bureaucracy and handoffs, and Costco using constrained product range as a competitive advantage. Finally, we add a crucial warning: not all complexity is bad. Smart strategic subtraction protects resilience, compliance, and safety while removing approvals, reports, and legacy initiatives that no longer earn their place.

If you want faster execution and clearer focus without breaking what keeps the organisation safe, press play. 

Subscribe, share with a leader who keeps adding, and leave a review, then tell us: what would you stop or simplify first?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores strategic subtraction, automation, Decision Intelligence, and organizational clarity as foundations for adaptive strategy.

TLDR / At a Glance

• Temporary competitive advantage
• Strategic subtraction and organizational friction
• Automation as a foundation for consistency
• AI-driven information and decision overload
• Decision Intelligence and explicit trade-offs
• Clear authority, incentives, and accountability

The central takeaway is that organizations adapt faster when leaders reduce complexity, clarify decisions, and preserve capacity for judgment.

Strategy doesn’t fail because leaders cannot plan; it fails because the world the plan was built for stops existing. We unpack what it means to operate in an environment that never slows down, where market signals move faster than traditional organisational structures, and where agentic AI accelerates experimentation while shrinking response time. The big shift is mental: competitive advantage is often temporary, so endurance comes from how quickly we spot signals, make decisions, and execute with both human and digital labour.

From there, we get practical and a bit uncomfortable. Under pressure, most organisations accumulate: more meetings, more reports, more tools, more layers. The result is congestion that erodes performance quietly rather than collapsing loudly. We explore strategic subtraction as a leadership discipline, using Shopify’s choice to cancel most recurring meetings and Amazon’s two-pizza teams as concrete examples of reducing coordination overhead, sharpening ownership, and keeping judgement close to the work.

We also follow the path from automation to AI and the hidden requirement underneath both: consistency. Automation exposes messy processes, unclear ownership, and poor data quality before it delivers efficiency. When organisations do the unglamorous basics well, like RFID-driven inventory accuracy or Toyota-style continuous improvement, analytics becomes trustworthy and deviations become real signals. AI then adds power and risk: more insights can mean more overwhelm, and trust breaks down when recommendations collide with incentives or intuition. That’s where decision intelligence comes in, linking analysis to explicit choices, assumptions, and trade-offs, and forcing alignment through clear decision rights.

If you want AI strategy that actually lands in day-to-day decisions, listen now, share it with a leader who’s drowning in coordination, and leave a review so more people can find the show.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This will provide the material needed to identify the episode’s main themes and insights.

TLDR / At a Glance

• Full article or narration script
 • Core topic and argument
 • Key frameworks and concepts
 • Important supporting insights
 • Executive-relevant implications
 • Concise episode takeaway

Once the script is provided, it can be converted into the required 90 to 140 word episode description.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TLDR / At a Glance

• AI authority and decision rights
• Assist, recommend, execute, never delegate
• Governance beyond tool approval
• Automation bias and human accountability
• Bounded autonomy for routine workflows
• Management as decision architecture

AI can draft, summarise, analyse, and even run parts of a workflow, but that is not the real problem leaders need to solve. The real problem is authority: which decisions should AI support, which can it execute within strict limits, and which must never be delegated because legitimacy and accountability still belong to humans.

This episode explores a practical model for AI decision delegation.

We walk through a practical decision delegation model built around four levels: assist, recommend, execute, and never delegate. Along the way, we ground the conversation in modern AI governance thinking, including the NIST AI Risk Management Framework and the EU AI Act’s focus on risk-based obligations and human oversight. The key move is simple but often missed: classify decisions first, then pick tools and controls that match the authority you are willing to delegate.

You will hear concrete examples across the ladder, from strategic scenario planning where AI strengthens preparation, to fraud detection and compliance triage where AI recommends but humans stay accountable, to high-volume operational tasks where “bounded autonomy” can outperform slow approval chains. We also tackle automation bias, why confident-looking recommendations can weaken human judgement, and the safeguards that keep decision-making honest: explainability, monitoring, challenge mechanisms, audit trails, escalation routes, and override rights.

Finally, we look at how management changes in AI-enabled organisations, shifting away from routine checking towards decision design, threshold setting, exception handling, and risk supervision. If you are building an enterprise AI strategy, redesigning an operating model, or setting AI governance, this is the missing lens. 

The key takeaway is that effective AI governance starts with deciding which decisions can be delegated, under what limits, and who remains accountable. 

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how leaders can redefine management as routine coordination becomes increasingly automated.

TLDR / At a Glance

• Supervision giving way to orchestration
• Workflow redesign as the value driver
• Five-part Orchestration Stack
• Rising skill demands in junior roles
• Governance as a management responsibility
• Exception handling and decision ownership

Flattening structures without redesigning management risks relocating friction rather than removing it, while deliberate orchestration creates clearer accountability and stronger AI-enabled performance.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the Value layer of AI scale.

TLDR / At a Glance

• Activity versus value
 • Stronger AI measurement chains
 • Output quality and workflow performance
 • Business outcomes and economic impact
 • Risk adjusted value metrics
 • Workflow level evidence

The key takeaway is that AI becomes defensible when leaders can connect usage to measurable performance, financial impact, and controlled risk.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how finance can move from scorekeeper to decision engine.

TLDR / At a Glance

• Decision speed and quality
• Sense, predict, judge, act
• Trusted finance data
• Human accountability
• AI Auditability
• Forecast accuracy measurement

We break down a simple, practical model for an AI-enabled finance decision engine: sense, predict, judge, act. AI strengthens sensing and prediction by turning live signals into analysis at speed, but we are clear about the boundary: judgement stays human, because accountability cannot be outsourced to a model. That shift changes the skills finance needs, moving the centre of gravity from preparation towards challenge, narrative, and commercial decision-making.

We also tackle the hard constraints that stop teams from getting measurable value from AI in finance and FP&A. Trusted data is the bottleneck, not the model, and poor definitions create “confident errors”. We explain how to build a minimum trusted data foundation for a specific decision, then scale from there. Finally, we cover why controls, audit evidence, and decision-quality measurement are not red tape but the mechanisms that create trust and let AI move into material work.

If you want practical guidance on how CFOs and finance leaders can redesign the loop, choose the right decisions to rebuild, and measure what matters, listen now. Subscribe, share with a finance leader who’s stuck in pilot mode, and leave a review with the one decision you would want 10% faster or sharper.

AI creates the possibility, but leadership design turns finance transformation into measurable business value.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores why workflow redesign determines whether AI improves organisational performance.

TLDR / At a Glance

• Task productivity versus enterprise value
• Five points of workflow leakage
• End-to-end process redesign
• Agentic automation and orchestration
• Human judgement and decision rights
• Outcome-based performance measures

AI creates greater value when leaders redesign workflows, clarify accountability, and measure business outcomes instead of adoption. 

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores governance as execution infrastructure.

TLDR / At a Glance

• AI scale and operating control
 • Weak governance risks and rollback
 • Excessive approval friction
 • Trust as a deployment constraint
 • Runtime monitoring and escalation
 • Risk tiering, ownership, and review

Effective AI governance gives leaders enough clarity, accountability, and confidence to move into higher value use cases safely. 

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance:

• rebranding HR as a signal only when behaviours and systems change
• people strategy, business strategy and technology strategy as one joined model
• future skills that stay constant alongside AI literacy and data judgement
• role clarity and updated expectations as the foundation for performance
• hiring and managing for outputs rather than clinging to job titles
• workflow mapping to decide what to automate and what must stay human
• HR business partner model shifting into consulting and diagnosis
• limits of self-service and why empathy still matters at work

We unpack the future skills people need now, not five years from now. Yes, AI literacy matters, but we also call out the capabilities that never stopped being essential: communication, curiosity, resilience, systems thinking, analytical decision making, and financial literacy. We talk about why AI is “lifting the lid” on gaps that were already there, and why quality control of AI output and critical thinking are becoming non-negotiable human skills as automation expands.

Then we get practical: stop starting with a grand HR transformation and start by mapping one real workflow end to end. We explore hiring and managing for outputs rather than titles, what the HR business partner role should look like as a consulting and diagnostic partner, and where self-service and automation should stop so employee experience does not collapse at the moments that matter. 

If you care about HR transformation, people strategy, AI at work, and building high-performing teams without overloading your managers, you will leave with a sharper model and clear next steps. 

Subscribe, share this with a people leader who needs it, and leave us a review, then reply with your take: what would you rename, and what would you redesign first?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores why a human-AI operating system is becoming a durable source of advantage. 

TLDR / At a Glance

• Adoption versus firm capability
 • Six operating model pressure points
 • AI-driven apprenticeship redesign
 • The eight-part Delivery Spine
 • Governed knowledge and quality controls
 • Pricing, measurement, and client trust

Sustainable AI value depends on building an integrated operating model around technology, professional judgement, and accountability.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance

• why AI errors in mental health can sound plausible and caring
• a suicide related failure pattern and why escalation matters
• how mania can be validated by chatbots and why that is dangerous
• what clinicians notice beyond words and why history matters
• the case for a mandatory human layer for diagnosis, risk, and treatment plans
• what to look for in safer tools including regulated medical devices and NHS use
• how AI can help clinicians with research, admin, scribes, and medication timelines
• why mental health presentations vary and do not match textbook prompts
• privacy risks when sharing intimate mental health data and how prompts get “tweaked”
• where to seek help in the UK including NHS 111 option 2 and Samaritans

We unpack real scenarios, from suicidal thinking to classic mania, where a general purpose LLM may validate and energise the worst possible next step. Dr. Hina Tahseen explains how clinicians assess far more than the text on the screen: behaviour, congruence of mood, intoxication, collateral history, safeguarding, and patterns over time. That leads us to a simple principle for AI in mental healthcare: a human layer is mandatory for diagnosis, risk stratification, and treatment plans, even if AI can help gather information or triage.

We also cover the genuine benefits of AI for access and capacity, including support for people facing stigma, isolation, and cost barriers, and the practical upside for clinicians using AI scribes and summaries to regain time and eye contact. Finally, we tackle AI governance, regulation, and privacy, because mental health data is deeply intimate and users often do not realise how exposed it can be.

Subscribe, share this with someone who uses chatbots for wellbeing, and leave a review. 

What rule do you think should be non negotiable when AI touches mental health?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how workflows, decision rights, data, governance and incentives determine AI ROI.

TLDR / At a Glance

• The AI Adequacy Threshold
 • Models as operational components
 • Workflow and decision bottlenecks
 • Data access and integration
 • Governance as value infrastructure
 • Agentic AI operating requirements

Leaders should diagnose the binding constraint, redesign the operating model and define how efficiency gains will translate into revenue, cost, quality or capacity.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how confirmation bias, hierarchy and weak decision architecture can turn analysis into a defence mechanism.

TLDR / At a Glance

• Evidence filtered through executive preference
 • Hidden costs of silenced expertise
 • Decision architecture and explicit assumptions
 • Integrated data, judgement and operational knowledge
 • Strategic Intelligence as an organisational capability
 • AI’s role in scaling insight and bias

Better outcomes depend on leaders creating systems where evidence, expertise and constructive challenge shape decisions before valuable options disappear.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the shift from pilot activity to managed AI systems.

TLDR / At a Glance

• Pilot signals and scale risk

• Repeatability as the real test

• Workflow embedding and ownership

• Monitoring, feedback, and controls

• Reusable patterns over fragmented tools

• Leadership discipline in AI portfolios

The key takeaway is that AI scale depends on building managed systems that make repeatability operational.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how leaders can convert information, AI capability and operational insight into measurable enterprise value.

TLDR / At a Glance

• Information volume versus decision clarity
• The Sense, Interpret, Decide, Execute, Learn loop
• Strategic subtraction and focused attention
• Workflow redesign for AI value
• Decision rights, accountability and leadership
• Enterprise coherence with local judgement

Lasting advantage comes from recognising meaningful change early, acting while options remain and learning faster from outcomes.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance

• shifting workforce planning from roles and headcount to work, skills, and capability
• why layering AI on top of old workflows creates faster output but not better outcomes
• the middle manager squeeze: quality control, bias checking, and coaching under pressure
• preserving entry-level learning by designing deliberate practice and critical thinking
• training as part of the operating model rather than a once-a-year development event
• building internal talent pools and smarter hiring for hybrid AI plus domain roles
• psychological safety, fear of job loss, and the burnout risks of removing “breathing space”
• using AI to improve decision quality by 1% every day across the organisation

We dig into the hard truth we see across sectors: AI often gets layered on top of the usual way of working, creating a “fast car in traffic” problem. The result is pressure in the middle, with managers acting as the buffer between executive promises of efficiency and the reality of nervous teams, messy processes, and quality risks. 

We talk about “AI slop”, why managers end up checking accuracy, relevance, and bias, and how juniors can lose the learning loops that build judgement, resilience, and professional confidence.

From there, we move into what actually helps: redesigning workflows, planning for skills not job titles, and treating learning and development as part of the operating model. 

We explore internal talent pools, smarter hiring for hybrid AI plus domain expertise, and the role of psychological safety when staff fear that “efficiency” really means job cuts. 

The big takeaway is simple: use AI to augment thinking, create time for deep practice, and improve decision quality by 1% every day across the business.

If you want a clearer, more human approach to workforce planning, people leadership, and AI strategy for 2030, listen now. 

Want to learn more about human centred leadership? Then go to my new 8 part series on the Human Operating Model Human AI Operating System a guide to how modern businesses need to be shaped to win in the era of AI.

Subscribe, share with a manager who is feeling the squeeze, and leave us a review with the one work process you would redesign first.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores why productivity gains disappear and how leaders can build a deliberate Conversion Chain.

TLDR / At a Glance

• Task speed versus enterprise value
 • Workflow bottlenecks that absorb gains
 • Purposeful reallocation of freed capacity
 • Human judgement and verification controls
 • Outcome-focused measurement across five layers
 • Leadership ownership of value conversion

AI creates capacity, while management determines whether that capacity improves throughput, quality, risk, customer outcomes, or financial performance.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance:

• a gap between asking for ownership and creating the conditions for it
• leadership avoidance and the cost of dodging hard conversations
• why candid feedback should be normal not dramatic
• feedback as continuous coaching through better questions
• defining leadership from the board to the front line
• perception gaps where senior intent does not match junior experience
• role modelling openness by acknowledging challenge and changing your mind
• psychological safety built by how we respond to feedback

Kieran Gilmurray and Laura Lawless get provocative about “adults in the room” and why accountability often collapses in modern organisations. Teams are told to take ownership, speak up, and challenge decisions, yet many systems still run on outdated rules, meeting theatre, and unspoken consequences. We talk about leadership avoidance, the politics that punish honesty, and why no amount of AI, strategy decks, or new buzzwords can fix poor management behaviours.

From there we get practical. We unpack radically candid feedback that strengthens performance without turning every conversation into vinegar, and we explore psychological safety as something you can observe in real time: how a leader reacts when challenged, whether they defend immediately, and whether they act on what they hear. We also dig into curiosity and decision intelligence, why great leaders ask better questions, and how small changes in language can unlock better thinking, better engagement, and better results.

You will leave with experiments you can run fast: design a “bad leadership meeting” on purpose to surface patterns, introduce a red card rule to stop avoidance in the moment, and replace autopilot check-ins with questions that create clarity. 

If you are stuck in a toxic culture, we also share ways to make a small difference while you build credibility and plan your next move. 

Subscribe, share with a leader who needs to hear it, and leave us a review with the question you want your workplace to ask more often.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

We sit down with Brian Syring, Director of Sales at TechD, to get practical about what enterprise AI adoption actually demands and why trust becomes the deciding factor when GenAI still feels like a black box to many leaders. 

We cover: 

• Unpack what it means to be an IBM Gold business partner and how TechD works as an extension of IBM across pre-sales, delivery, and ongoing management. 
• Brian shares what he’s seeing in the market, especially the surge of interest in IBM Watsonx and the wider generative AI platform approach, where organisations want flexibility, security, and strong governance rather than a one-size-fits-all tool. 
• Aligning AI programmes to business outcomes such as time saved, cost reduced, and better decisions.
• Replacing spreadsheet-driven operations with secure, scalable systems to improve auditability and resilience.
• We also dig into the cultural shift: aligning senior stakeholders, defining the ROI, and ensuring employees truly adopt the new ways of working. 
• We finish with a look at what’s next on IBM’s roadmap, including orchestration capabilities and the longer-term excitement around quantum. 

The most actionable part is the reality check on why AI programmes stall: the data. Clean, secure, trustworthy data is the foundation for reliable outputs, and without it you get “garbage in, garbage out” at scale. 
 
If you found this useful, subscribe, share it with a colleague who owns AI delivery, and leave a review with the biggest blocker you’re facing in taking GenAI from pilot to production.

Learn more about IBM Partner Plus: https://ibm.biz/~xQR9HYClh  

Watch this on YouTube: https://youtu.be/NHZ4Bz9NZR0    

#IBMPartnerPlus #AIAdoption #ArtificialIntelligence #HybridCloud #Automation

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the Capability Layer of scalable AI adoption.

TLDR / At a Glance

• Access versus true capability

• Role clarity and judgement

• Limits of standalone training

• Managers as the conversion layer

• Workflow fit and execution discipline

• Capability as scalable performance

AI capability becomes real when people, managers, and workflows are equipped to use AI consistently under operating pressure.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how organisations can grant autonomy progressively while protecting performance, economics, and governance.

TLDR / At a Glance

• Organisational readiness over model capability
 • Demo-to-operating-model gap
 • Authority, access, and accountability
 • Five-stage Authority Ladder
 • Evidence-based increases in autonomy
 • Proportionate human oversight

A flashy agentic AI demo can make almost any workflow look solved, right up until it hits real data, real users, real risk, and real cost. We dig into Gartner’s headline prediction that more than 40% of agentic AI projects may be cancelled by 2027 and explain why that number is less interesting than the mechanisms behind it: escalating spend, fuzzy business value, and controls that never kept pace with the authority being granted.

Agentic AI succeeds when leaders choose suitable workflows, establish clear ownership, and expand authority only when performance and controls justify it.

The central shift is moving from “can the agent act?” to “may it act?” That question forces an operating model conversation: permissions and least privilege access, monitoring and logging, roll-back paths, escalation rules, and a named human owner who is accountable when the system takes action. We also challenge the sloppy use of the word “agentic”, where assistants and scripted automation get sold as autonomy, leaving teams to pay an autonomy premium while inheriting governance risk they did not design for.

To make this practical, we introduce the authority ladder: observe, advise, act with approval, act within limits, then higher autonomy under continuous monitoring. The goal is not maximum autonomy; it is the right level of authority for the workflow, earned through evidence that performance holds, controls hold, and unit economics work at volume. Along the way, we look at the kinds of workflows where agents already succeed, and why “human in the loop” only counts when the human has the information and power to say no in time.

If you’re building an agentic AI strategy, listen for the tests that kill weak projects early and the governance patterns that let strong ones scale. 

Subscribe for more, share the episode with a colleague who owns AI delivery, and leave a review: what workflow are you most tempted to automate, and that rung of authority has it truly earned?

We design and deploy autonomous agents that operate inside defined workflows. Built around your processes, integrated with your systems, and governed for reliability, they deliver operational leverage without increasing headcount.  Learn more here - Autonomous AI Agents - Kieran Gilmurray

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

The first chapter in my book 'Agentic AI: A Business Leader’s Guide to the Future of Work and Digital Labour' unpacks the rise of autonomous AI agents and why “today’s AI is the worst it will ever be” is not hype but a warning for leaders, teams, and anyone building a career in a fast-changing market.

TL;DR / At A Glance:

• the speed of AI progress and why capability keeps compounding
• what agentic AI means and how autonomy changes work
• the core building blocks behind autonomous agents, including LLMs, cloud and APIs
• practical examples across finance, healthcare, manufacturing and customer service
• how job roles evolve towards oversight, strategy, creativity and judgement
• the new baseline skills, including AI literacy, data analysis and ethical decision making
• governance-first deployment, bias, privacy and the need for explainability
• why competitive advantage shortens and organisations must stay agile

We walk through how agentic AI emerges from real breakthroughs: large language models that understand natural language, cloud computing that makes scale cheap, and API integrations that let software connect to software. When those pieces come together, an AI agent stops being a chatbot and starts becoming an operator, able to monitor, decide, and execute across workflows. We also explore why investment has accelerated and how tools like copilots and next-generation models push autonomy into everyday productivity apps.

Then we bring it down to earth with concrete use cases. We look at financial services where agents can adapt trading strategies and improve fraud detection, healthcare where proactive monitoring supports faster diagnoses and follow-ups, manufacturing where supply chains and maintenance become more autonomous through IoT data, and customer service where hyper-personalised interactions raise expectations for speed and empathy.

Finally, we tackle the hard parts: workforce transformation, reskilling, AI literacy, and the ethical and legal risks around bias, privacy, and transparency. We argue for a governance-first approach and a mindset shift where competitive advantage arrives in shorter cycles and organisations must learn to reconfigure human and agentic labour quickly. 

The complete book is available globally on Amazon and Audible:
Amazon.co.uk : Kieran Gilmurray

Subscribe for more clear thinking on AI and work, share this with a colleague, and leave a review with your biggest question about autonomous agents.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the gap between AI ambition, leadership readiness, workforce reality, and governance expectations.

TLDR / At a Glance

• Executive AI fluency
 • System steering
 • Direction, design, guardrails, proof
 • Talent readiness gaps
 • Hidden employee AI adoption
 • Leadership modelling and legal duty

The key takeaway is that AI value depends on leaders who can govern, redesign, and measure AI across the organisation. 

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Nobody signs up for a half marathon and expects to wing it on the day, so why do we promote people into leadership and then act surprised when they struggle without training, coaching, or recovery?

TL;DR / At A Glance:

• running as a clear model for planning, consistency and recovery
• leaders as business athletes supported by a real performance team
• why constant pressure creates exhaustion and weaker decisions
• routines, habits and flexibility when curveballs hit the diary
• building a high-impact calendar with big rocks and peak flow
• deliberate practice, feedback and the limits of “experience”
• recovery as a performance lever and the risks of always-on culture

Kieran Gilmurray and Laura Lawless explore the idea of leaders as business athletes, and what that metaphor reveals about modern organisations. We talk about the unseen teams behind elite performance and why workplaces often do the opposite: deliver at 120%, transform in 90 days, hit the numbers, repeat. 

They dig into routines that actually hold up in the real world, including high-impact calendars, time blocking the “big rocks”, and working with peak flow rather than fighting it. We also get blunt about the signals leaders send when coaching and development are always the first things dropped.

From there, they challenge the always-on culture that normalises burnout, and we discuss how AI can unintentionally accelerate the “more tech, more work” loop, with a real psychosocial impact on agency and wellbeing. We push on the tension between personal responsibility and organisational responsibility, and we unpack what HR can enable versus what leaders and individuals must own. The thread that ties it all together is simple: performance is an outcome, but practice, support systems, and recovery are the foundations.

If you want practical, grounded ideas for leadership development, executive coaching habits, sustainable performance, and building a culture where people can do their best work, press play. 

Subscribe, share this with a colleague, and leave us a review with the one habit you want to build next.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the Work layer of The Human AI Operating System.

TLDR / At a Glance

• Workflow as the unit of change
 • Task gains versus enterprise value
 • End-to-end execution redesign
 • Ownership, handoffs, and judgement points
 • Workflow-level success measures
 • Alignment across decisions, capability, governance, and value

The key takeaway is that AI scales when organisations redesign how work gets done, measure outcomes across workflows, and connect execution to the wider operating model.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the practical tests boards can use to assess real AI oversight.

TLDR / At a Glance

• Board ownership of AI
 • Strategy, risk and disclosure alignment
 • EU AI Act readiness
 • Ownership, visibility and assurance
 • Investor expectations on AI governance
 • Value creation through disciplined oversight

The key takeaway is clear: AI governance is now central to board accountability, confidence and durable business value.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how AI changes roles, judgment, trust and organisational capability.

TLDR / At a Glance

• Work redesign before workforce reduction
 • Productivity versus realised value
 • The Reshape Sequence
 • Verification as the new bottleneck
 • Human trust in AI-enabled service
 • Junior roles and leadership pipelines

The quickest way to get AI wrong is to make it a headcount story. We keep hearing the same boardroom question surface within minutes: “How many people can we let go?” It feels rational when generative AI is expensive and ROI pressure is high, but it puts the hardest and least reversible decision first, right when the work is still changing shape.

We argue that AI’s first and most consequential impact is on workflow design, not workforce size. When machines take on routine drafting, triage, and summarising, the job is not simply smaller. Tasks get rebundled, handoffs move, and new responsibilities appear around prompting, review, correction, and AI governance. That is why productivity is easy to generate but harder to convert into real value: unless the operating model changes, time saved leaks away instead of becoming customer impact, quality, or margin.

We share a clear four-step “reshape sequence” for leaders: redesign the workflow, redefine the human role, rebuild the surrounding system (controls, incentives, career paths, measures), and only then resize. Along the way, we dig into why verification becomes the new bottleneck, why customer trust makes skilled human support more valuable, and why cutting junior roles can quietly break the pipeline that produces future judgement.

The key takeaway is that leaders should redesign workflows, redefine human roles, rebuild systems and resize only when the new shape of work is clear.

If you’re building an AI strategy, use this as a practical guide to redesign work, redeploy people, and avoid capability cuts you will regret. Subscribe, share with a colleague, and leave a review telling us where AI is reshaping your work most right now.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Kieran Gilmurray and Laura Lawless  promote smart, capable people into leadership roles, then pile on performance management, feedback, meetings, reporting, and “just one more responsibility” without ever teaching the fundamentals or redesigning the job. No wonder even experienced managers admit they avoid the hard conversations until they absolutely have to.

TL;DR / At a Glance

• why capable managers avoid performance and feedback conversations
• defining what a manager is responsible for now
• reframing “soft skills” as operating skills that make work work
• why “better communication” often means clearer thinking and decisions
• AI changing the value of writing versus judgement
• moving from one-off workshops to practice loops and habits
• HR shifting from process owner to capability builder
• the time problem and the case for slowing down to speed up
• strategic subtraction using technology to remove low-value work
• three practical takeaways for leaders and HR teams

Kieran Gilmurray and Laura Lawless  unpack what a manager is actually for today: getting work done through other people, developing the team, and removing obstacles rather than being the hero-doer. 

From there, they dig into the skills that really move performance, engagement and retention. “Better communication” sounds obvious, but we challenge it: more messages do not fix unclear priorities, vague decisions, or inconsistent expectations. 

As generative AI starts drafting emails and feedback, the human edge becomes clearer thinking, sound judgement, and the courage to be direct.

Then Kieran Gilmurray and Laura Lawless get practical about leadership development and HR transformation. They argue for replacing tick-box training with operating standards for meetings, feedback and decision making, plus simple practice loops that build habits in real work. Kieran Gilmurray and Laura Lawless  also debate time, coaching culture, and strategic subtraction: if AI can do the admin, managers should not be doing it, and HR should stop being the fixer and start building capability across the business.

If you know the pain of meeting overload and constant “more”, this conversation will help you see what to remove, what to standardise, and what to practise so managers can lead well again. 

Subscribe, share with a manager who needs this, and leave a review with the one task you would subtract first.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how decision rights shape AI scale.

TLDR / At a Glance

• Fragmented AI accountability
 • Decision rights over job titles
 • Centralised, federated, and hybrid models
 • Governance without slow consensus
 • Executive sponsorship and escalation routes
 • Ownership maps for enterprise scale

The key lesson is that AI scales when authority, accountability, and value ownership are explicit.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance

• retiring transactional HR work that technology can automate
• why rebranding HR does not fix purpose, trust, or credibility
• employee trust gaps and the case for advocacy or ombuds roles
• shifting people management back to leaders with HR as architect
• the leadership capability gap in coaching, feedback and difficult conversations
• AI in performance feedback and why AI plus human coaching matters
• redesigning jobs for humans and AI, not just adding tools
• moving HR metrics from compliance to organisational capability

We pull apart the case for retiring the term HR and the case for keeping it, from the Ulrich model to today’s rebrands like People Ops and employee experience. Then we get into the real issue behind the labels: trust. 

Many employees assume HR represents the organisation first, which creates a credibility gap. We explore whether advocacy and ethics roles should sit outside HR, and what that means for fairness, transparency, and day-to-day employee experience.

From there, we go straight at the uncomfortable fix: leaders must own people management. That only works if we stop promoting accidental managers and start building leadership capability in feedback, coaching, and difficult conversations. 

Finally, we debate AI and performance feedback. AI can be candid and consistent, but it cannot replace human judgement and empathy, so we argue for “AI plus a human coach” and, crucially, redesigning jobs for humans and AI rather than layering more tech onto broken work.

If you want a practical, future-of-work conversation about people strategy, agentic AI, organisational design and leadership development, press play.

Subscribe, share with a colleague, leave a review, and tell us in the comments: should we retire HR, or reinvent it?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance

• broadening from graduate hiring to workforce diversity and capability
• why digital fluency is not determined by age
• AI, agency, and the potential mental health impact
• the gap between what we say, what is heard, and what we mean
• adapting to different communication styles beyond picking a channel
• using AI as a coaching tool for clearer stakeholder communication
• the real skills gap: onboarding, financial fluency, data and AI literacy, curiosity, resiliency, communication
• psychological safety as the condition for challenge and growth
• capability swaps and pairing by strengths rather than age

Kieran Gilmurray and Laura Lawless also push back on the lazy comfort of generational stereotypes. “Gen Z are digital natives” sounds neat until you see who is genuinely excited to learn, who is anxious about losing agency to algorithms, and who has the curiosity to keep improving. We talk about mental health risks, why learning agility beats age, and why a multi-generational workplace works best when leaders focus on capability, not labels.

From there Kieran Gilmurray and Laura Lawless  get practical: how communication breaks down between what we say, what people hear, and what we meant, plus how to adapt to different styles without dumbing anything down. We explore psychological safety as the foundation for healthy challenge, then move into concrete team design ideas like capability swaps and cognitive diversity roles (challenger, translator, integrator). Finally, we unpack why microlearning often fails and how to build continuous learning into the flow of work so skills actually stick.

If you care about AI in the workplace, hiring strategy, learning and development, and building high-performing teams, this conversation is your reset. 

Subscribe, share it with a colleague, and leave a review with one change you are making this month to help your team learn faster and speak up more.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Google Notebook LM agents break down the learning performance paradox, where AI can make you look brilliant in the moment while quietly preventing the mental work that builds memory, judgement, and real competence. If you have ever “understood” something with AI help and then blanked the next day, you will recognise what we mean. 

TL;DR / At a Glance

• the learning performance paradox and why speed can mask absent learning
• cognitive offloading and metacognitive laziness in AI-assisted study
• productive struggle, desirable difficulty, retrieval practice and the generation effect
• scaffolding done right through hints, worked examples and calibrated challenge
• ConMigo and CodeHelp as contrasting designs for preventing shortcut learning
• adaptive AI that captures microinteractions to model misconceptions and emotions
• shared regulation to protect learner autonomy and avoid black box tutoring
• responsible foundations: explainable AI, privacy-by-context and inclusive personas

Google Notebook LM agents explore what a true AI learning companion should do differently, grounded in learning science: productive struggle, desirable difficulty, retrieval practice, and the generation effect. Instead of handing over solutions, the companion should ask you to explain, apply, and generate answers in your own words. It should also help with metacognitive calibration, so your confidence starts matching your actual understanding, not just the smoothness of the chatbot’s output. 

From there Google Notebook LM agents get practical, using real case studies. We look at ConMigo’s shift from strict Socratic tutoring to smarter scaffolding with hints and worked examples, and CodeHelp’s “sufficiency check” that trains students to troubleshoot by providing proper context. 

Google Notebook LM agents also unpack adaptive learning systems that remember your patterns over time, why shared regulation protects autonomy, and what responsible AI in education requires: explainable recommendations, privacy that fits the learner, and inclusive design that reflects diverse classrooms and lived experience. 

If you care about AI in education, learning how to learn, or building skills that last, listen now.

Subscribe, share with a friend who relies on AI to study, and leave a review with the biggest change you are making to your prompts.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the Human AI Operating System as a framework for repeatable AI value.

TLDR / At a Glance

• Five-layer AI operating model
 • Workflow redesign for adoption
 • Decision rights and accountability
 • Capability beyond basic training
 • Embedded governance and controls
 • Value tracking linked to outcomes

The central takeaway is that AI scales when work, decisions, capability, governance, and value operate as one aligned management system.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores six lessons for leaders scaling AI beyond pilots.

TLDR / At a Glance

• Predictable AI and governed execution
• Outcome based AI cost control
• Closing the strategy to execution gap
• Orchestrating agents through approved workflows
• Legacy modernisation as AI readiness
• Enterprise discipline in AI assisted development

AI agents have had years to impress us. PegaWorld 2026 forces a tougher standard: prove you can run inside complex enterprises without cost surprises, compliance gaps, inconsistent decisions or another layer of fragmented tech. That shift matters if you own regulated operations, customer outcomes, technology risk, or a budget that has to hold up when usage scales from a pilot to millions of interactions.  

We dig into six takeaways that keep agentic AI trustworthy. The big one is predictable AI: do the heavier reasoning upfront when redesigning workflows and operating models, then keep live execution tight by using lighter AI to understand intent, select an approved workflow, and follow it consistently. We also unpack why ambiguity is the real project risk, and how tools like Pega Blueprint aim to turn business intent into build-ready workflow designs that can be governed, reused and audited.  

Cost becomes a board-level conversation when token-based pricing meets long context windows and multi-step processes. We argue for measuring AI economics by outcomes such as cost per completed case, not prompts, tokens or model calls, and explain how deterministic workflows can narrow agent scope to reduce spend and risk. From orchestration and Model Context Protocol through to legacy COBOL modernisation with AWS Transform, we connect the dots between workflow automation, AI governance, and true AI readiness. 

If you care about enterprise AI that lasts, subscribe, share this with a colleague, and leave a review with the one workflow you would redesign first.

#PegaPartner

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how boards define and operationalise an explicit AI risk posture.

TLDR / At a Glance

• AI as enterprise level risk category
 • Risk appetite, tolerance, capacity distinctions
 • Board versus management responsibilities
 • Red line AI use cases
 • Escalation thresholds and governance flows
 • 30, 60, 90 day implementation roadmap

A clear AI risk posture enables controlled innovation while maintaining accountability, resilience, and regulatory readiness.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores why AI scale depends on organisational absorption.

TLDR / At a Glance

• Pilot to scale gap
 • Organisational absorption
 • Workflow redesign
 • Decision ownership
 • Governance and monitoring
 • Value measurement

The key takeaway is that AI scales when leaders redesign the operating model around trusted, repeatable execution.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance

• the core problem with direct Likert ratings and why LLMs collapse to neutral threes
• how semantic similarity rating converts free-text responses into numerical scores using embeddings and cosine similarity
• why follow-up AI grading helps but still trails the embedding-based approach
• what 57 real product surveys and 9,300 human responses reveal about accuracy and distribution matching
• how persona prompting reproduces real demographic patterns across age and income constraints
• why zero-shot LLM methods can beat supervised machine learning models trained on the same domain

The shocker is that the first attempt fails badly. When you make models like GPT-4 or Gemini answer a classic Likert scale with a single number, they hedge and pile up on neutral “3” ratings. The fix is not “better AI”, it is better questioning. 

Google Notebook LM Agents help us unpack semantic similarity rating: let the model respond in natural language, convert that text into embeddings, and map it to five anchor statements using cosine similarity. You get fast, automated scoring without stripping away the model’s reasoning.

From there, we pressure-test the method against thousands of real survey responses across dozens of personal care product concepts, then look at whether AI personas actually reflect real constraints like age and income. 

We also compare the approach with traditional machine learning models such as LightGBM, and dig into an underrated advantage: synthetic consumers can produce richer, more candid qualitative feedback than many human panels.

If you care about product testing, consumer insights, or the future of focus groups, listen through and tell us where you’d trust this and where you wouldn’t. 

Subscribe, share with a colleague, and leave a review with your take: would you let synthetic consumers influence a real launch?

Paper: http://arxiv.org/abs/2510.08338

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

If you have spent time counting tokens or worrying that one enthusiastic pilot will burn through a month’s AI budget, this conversation is for you.

David Vidoni, CIO at Pega, shares why predictable cost matters as much as model capability and how “charging for outcomes” changes the way you govern AI. 

We talk about the practical tension between creativity and cost control, and why leaders should pause and ask whether AI is genuinely the best tool for a given challenge. 

The goal is not to slow innovation down, but to stop wasting energy on spend anxiety and refocus on measurable business value.

We also get concrete on delivery: how Blueprint supports a design-first approach that clarifies what you are building before you build it, reduces costly mistakes, and speeds up time to first release. 

You will hear real internal stats, plus what it takes to deliver secure, compliant, repeatable outcomes rather than variable answers. 

Finally, we explore agentic AI wins in legal and contract work, including significant hours saved and major ticket deflection.

Listen, then subscribe, share with a fellow CIO or product leader, and leave a review with your biggest AI cost or governance challenge.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR:

• why AI investment keeps rising while production success stays low 
• the scaling wall: latency, compute cost, fragile error handling, messy data 
• the trust gap when autonomous agents can touch procurement, payments, and live systems 
• process inertia and the trap of paving the cow path 
• pragmatic AI mindset: hyper-specialised utility over sci-fi general intelligence 
• six pillars of agentic AI: tool use, action, memory, perception, planning, orchestration 
• multi-agent systems as modular digital specialists that isolate risk and raise accuracy 

We use Google Notebook LM Agents to take insights from a Deloitte AI Institute report produced with Google Cloud to unpack why scaling enterprise AI is so hard and what actually changes when you build goal-oriented agents.  Google Notebook LM Agents break down the practical architecture behind autonomous digital workers, including memory and reflection, multimodal perception, and planning that turns an ambiguous goal into an executable workflow. They also dig into multi-agent systems, where specialised agents work like a kitchen brigade rather than one giant generalist model, and why that modularity improves accuracy while reducing the blast radius when something fails.

Autonomy without governance is just risk at speed, so we get specific about controls: an agent OS hub-and-spoke model for visibility, FinOps guardrails and kill switches to stop runaway compute spend, and a defence-in-depth approach to security. That includes linguistic guardrails against prompt injection, sandboxing, semantic checks with constitutional AI auditing before actions execute, and infrastructure-level threat hunting. We also cover IDAMA, identity and access management for agents, so permissions stay least-privilege and accountability stays human-owned.

Finally, we bring it back to reality: change management, process redesign, and data gravity. You will hear concrete case studies in accounts payable automation and an agentic knowledge assistant with citations, plus why Apache Iceberg and cross-cloud lakehouse patterns matter for querying data where it lives. Subscribe, share, and leave a review if this helped, and tell us what task you would trust an agent to run first.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR:

• extending AI-driven development into the platform with coding agents such as GitHub Copilot, Codex, and Cloud Code
• deploying agents that run predictably against rules, regulations, and compliance needs
• shifting from token-based consumption to outcome-based agentic pricing for predictable ROI
• why vendor pricing changes can flip an AI use case from profit to loss
• using AI to analyse legacy systems, translate code into natural language, and guide modernisation
• combining AWS legacy analysis with Blueprint to support mainframe exit and reimagined journeys
• building enterprise-ready apps that are explainable, secure, scalable, and consistently developed

We talk about AI-driven development and the growing role of coding agents in everyday work, including tools such as GitHub Copilot, Codex, and Cloud Code. Speed is great, but Matt explains why it can also create apps that aren’t explainable, hide vulnerabilities, and struggle to scale. The goal is to keep the acceleration while making the output enterprise-ready: transparent, deployable at massive scale, compliant, secure, and built consistently.

Cost control is the other make-or-break topic. Token-based pricing sounds simple until reasoning agents start consuming unpredictably and vendors change their models. Matt lays out an outcome-based approach to agentic pricing that focuses on work done and value delivered, aiming for predictable costs and predictable ROI so promising AI use cases don’t suddenly turn unprofitable.

We also dig into Pega Blueprint’s progress on legacy modernisation, including how AWS-powered analysis of legacy languages like COBOL can produce natural language understanding that feeds transformation work. If you care about mainframe exit, cloud modernisation, and reimagining customer journeys rather than lift-and-shift, you’ll find plenty to take away. 

If you found this useful, subscribe, share it with a colleague, and leave a review so more builders and leaders can find the show.

#PegaPartner

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR:

• Infinity 26 as a major step forward for AI powered workflow automation
• Blueprint AI inside Infinity Studio and an AI assistant that builds rules behind the scenes
• Calling Pega workflows from different AI tools while keeping execution predictable
• AWS Transform plus Blueprint to modernise legacy code into production apps in three months
• Designing business rules and user experience earlier to cut rework later
• No token charging and a shift towards outcomes based pricing

We talk through what it looks like when AI is designed to strengthen workflow automation rather than replace it. Kara explains how Pega Blueprint has evolved from an early idea into a deeper application design experience where you can shape process flows, business rules and user experience before you build. 

We also dig into Infinity Studio with its built-in AI assistant, where you can chat and have the system generate Pega rules behind the scenes, opening the door for more people to participate in creating workflow applications. 

The conversation turns to two big enterprise concerns: modernisation speed and AI cost. Kara highlights the on-stage AWS Transform announcement, describing how AWS Transform plus the power of Blueprint can take organisations from a legacy code base to a production app in three months. 

We also cover Pega’s decision not to charge for tokens, focusing instead on outcomes and predictable cost in a world where tokenomics and model changes can feel chaotic. If you care about practical, governed AI, agentic workflows and faster legacy transformation, this one is for you. 

Subscribe, share with your team, and leave a review with the workflow problem you want to modernise next.

#PegaPartner

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how leaders decide what to remove, redesign, protect, or simplify. 

TLDR / At a Glance

• Strategic subtraction discipline
 • Automation before redesign risk
 • Workflow clutter and decision friction
 • The VITALS subtraction test
 • Capacity release and governance focus
 • Protecting trust, compliance, and learning

AI can make your organisation faster while quietly making it worse. If we use copilots and agents to accelerate reports nobody reads, approvals nobody trusts, and meetings that never end in a decision, we are not transforming anything, we are scaling clutter.

We take on the most common starting point for AI transformation and argue it is strategically dangerous: asking what can be automated. The better first question is tougher and far more useful: should this work still exist in its current form? From there, we explore why AI shifts the economics of production but does not fix the real constraint in many businesses, which is attention, coordination, and the ability to absorb information without drowning in it.

To make subtraction practical, we walk through a simple leadership tool: the Strategic Subtraction Test, built around six prompts on value, interference, duplication, assurance risk, liberation of capacity, and strategic fit. You will hear how to apply it to real work objects such as meeting series, dashboards, approval steps, governance forums, workflows, and tools, plus concrete examples of actions like simplifying low-risk approvals, consolidating overlapping governance, substituting decks with live views, and hiding specialist reports from default circulation.

We also get specific about what not to cut. Some work that looks slow is actually trust infrastructure: legal controls, cyber checks, privacy safeguards, incident reviews, escalation routes, and learning loops. If we remove those without redesign, we can damage compliance, resilience, and judgement. If you want AI strategy that delivers capacity release rather than work intensification, subscribe, share this with a leader who owns “AI rollout”, and leave a review telling us what work you would stop carrying forward.

The key takeaway is that effective AI transformation depends on removing low value work before accelerating the system around it.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how AI changes learning mechanics, performance outcomes, and risk management for junior talent.

TLDR / At a Glance

• Accelerated time to competence
 • Disproportionate gains for juniors
 • AI-driven feedback and scaffolding
 • Overreliance and accuracy risks
 • Enterprise access versus shadow tools
 • Leadership guardrails and training

AI can compress years of learning into months, but only when paired with structured oversight, calibration, and secure implementation.

Juniors reaching veteran-level productivity in a fraction of the time should make every leader curious and a little nervous. We dig into what recent evidence says about AI copilots, coding assistants, and AI tutors, and why the biggest performance gains consistently appear in the least experienced employees. When AI surfaces the right information at the right moment, it doesn’t just speed up tasks, it rewires the day-to-day learning loop.

We walk through the mechanisms behind the jump in output and quality: tighter feedback cycles, just-in-time knowledge retrieval, and scaffolding that handles routine work so juniors can focus on judgement. But speed has a shadow side. When teams treat confident AI output as truth, accuracy can fall on complex tasks, and juniors can mistake AI fluency for genuine mastery. That “illusion of competence” becomes a long-term capability risk, not just a short-term mistake.

We also tackle the growing policy divide. Organisations that provide secure enterprise AI accelerate development safely, while blanket bans often push people into shadow AI tools, raising data privacy, compliance, and IP risks. Our practical takeaway is straightforward: give safe access early, train for prompting and verification, keep peer review, set clear guardrails, and measure more than productivity by tracking how often people verify and how they perform without AI.

If you found this useful, subscribe, share it with a manager or mentor, and leave a review. What guardrail would you put in place first?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how leaders can move AI from experimentation into structured execution.

TLDR / At a Glance

• Usage versus fluency
 • Fragmented adoption patterns
 • Workflow integration
 • Repeatable AI practices
 • Behaviour and judgement
 • Operating standards for AI

The key takeaway is that real AI fluency emerges when AI becomes embedded in how work is designed, delivered, measured, and improved.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores governance as an operating rhythm for scaling AI with control and confidence.

TLDR / At a Glance

• Policy-to-practice governance gaps
 • Cadence, monitoring, and escalation
 • Ownership across workflows and vendors
 • Dashboards linking risk and value
 • Proportional controls by risk level
 • Governance as performance infrastructure

The central takeaway is that AI governance works when leaders run it continuously through the same systems that manage the business.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TLDR / At A Glance

• the speed–safety paradox and why clarity wins
• regulatory shifts in the US, UK and EU
• shadow AI risk and the need for discovery
• risk tiering that matches control to impact
• machine-speed controls for access, data and monitoring
• cross-functional roles, stress tests and routines
• practical foundations for predictable approvals

We start by breaking down the speed–safety paradox: tools ship overnight, employees adopt them in hours, and traditional control gates buckle under constant change. Rather than slowing delivery, we show how clear guardrails become accelerators. You’ll hear why a living AI inventory is the first deliverable, how to write plain-language acceptable use rules that cut negotiation time, and where many organisations lose control by assuming they already have it.

From there, we map the regulatory squeeze shaping decisions right now: US momentum toward lighter-touch national alignment alongside new state-level obligations, UK calls for faster oversight and AI stress testing, and EU AI Act timelines that make transparency and risk management non-negotiable. 

We translate those pressures into concrete steps: risk tiering that aligns review depth to impact, machine-speed controls like least-privilege access, masking and tokenisation, centralised logging, and real-time anomaly alerts that can block unsafe actions before they become incidents.

Finally, we make governance operational. Fast, safe AI needs cross-functional roles with clear decision rights, repeatable processes, and service levels that keep work flowing. Think central oversight platforms, continuous monitoring, stress tests modelled on cybersecurity, and a culture where compliance is built into code patterns, not stapled on at the end. By the close, you’ll have a crisp foundation to implement now—inventory, tiering, acceptable use, and enforcement—that turns governance into the way you say yes quickly and confidently.

If this helped reframe your approach, follow the show, share it with a colleague who owns AI delivery, and leave a quick review telling us which control you’ll implement first.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TLDR: 

• Domain expertise remains crucial even as AI capabilities expand
• The best value from business education comes from networks and brand, not just knowledge
• AI doesn't replace human judgment but requires it to evaluate output quality
• Relationships still "compound like money" over decades in business
• Knowledge workers must focus on "not urgent but important" learning to stay relevant
• Future competitive advantage comes from combining human expertise with AI capabilities
• The best technologists and managers are already using AI to amplify their skills
• Human-to-human relationships remain irreplaceable in high-level decision making
• Strategy still requires "go and see" - physically understanding customer needs first hand

"Management consultants should be very afraid," warns Bunch, revealing that AI can now produce better market analyses in minutes than top consulting firms deliver in weeks—for $20 a month instead of millions. 

This isn't science fiction; it's happening right now in boardrooms worldwide. But rather than painting a dystopian picture, Bunch offers a practical roadmap for navigating this new landscape.

The secret lies in understanding what machines can't replace. While knowledge has become commoditized, relationships still "compound like money" over decades. 

A coffee at a trade show might lead to a $100 billion deal twenty years later—something no algorithm can replicate. The distinction between domain experts who use AI as a powerful tool versus those who compete against it makes all the difference between thriving and merely surviving.

Particularly fascinating is Bunch's analysis of business education's evolving value proposition. "If you just want knowledge, go to the cheapest business school," he advises, explaining that networks and brand recognition now outweigh information transfer as education's primary value. 

This reframing challenges institutions and individuals alike to reconsider what truly matters in professional development.

Whether you're a C-suite executive, entrepreneur, consultant, or knowledge worker concerned about your future, this episode provides essential insights for making AI your partner rather than your replacement. As William Gibson famously noted, "The future is already here—it's just unevenly distributed." Will you be among those who embrace this transformation and shape the future, or will you be left behind?

The call to action is clear: embrace AI as a partner rather than fearing it as a threat. 

Those who adapt will thrive while those who resist risk obsolescence.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how different AI bets create value across different time horizons.

TLDR / At a Glance

• Efficiency gains in existing work
 • Transformation through workflow redesign
 • Optionality as future strategic flexibility
 • Category errors in AI business cases
 • Portfolio metrics by investment type
 • Governance matched to value logic

The key takeaway is that strong AI portfolios classify investments first, then apply the right proof standard for each category.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance:

• • agent employees with names and roles, from Slack monitoring to autonomous customer support
• why automation shifts jobs upwards into oversight, routing, boundaries and edge cases
• the human sandwich model of co-working, framing then generating then judging
• the hidden maintenance burden, drift, integrations breaking and compute token costs
• visible residue of human competence, abundance, and the flood of generic work
• agent versus agency, why goals still need a framer and a reason to care
• the closing challenge, finding the unframeable part of your work and leaning into it

Google AI Agents walk through a concrete case study from Dan Schipper’s “After Automation” and unpack two modes of human and AI collaboration. 

First are agent employees: named, role-based systems living inside tools like Slack and customer support, drafting proposals, collecting ideas, and even closing tickets autonomously. 

Second is co-working in a shared document, the “human sandwich”: we set the frame, AI does the heavy lifting, then we judge, correct, and extend the output.

From there we get honest about the hidden costs: maintenance, drift, brittle integrations, and compute token spend that can turn “simple automation” into a complex system that constantly needs care. 

Then we zoom out to the economics of generative AI. 

When cheap competence floods the market, we get slop: perfectly fine, deeply generic work. The result is a new premium on difference, live context, and human judgement. 

We also tackle benchmark hype and “chart psychosis”, why a benchmark is a frozen frame, and why the most valuable skill is often choosing the frame in the first place.

If you’ve been feeling that tightness in your chest about AI and job security, this is a practical way to think clearly and act. 

Subscribe, share with a colleague, and leave a review with your answer: what is the unframeable part of your work that you should lean into next?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Google voice agents unpack the latest 2026 IBM Institute for Business Value CEO study, conducted with Oxford Economics, and translate its biggest signals into plain language you can use in strategy meetings, operating reviews, and career planning.

TL;DR / At A Glance

• the 2026 IBM Institute for Business Value CEO study and why it matters at global scale
• AI shifting from productivity tool to operating model rewiring
• moving from siloed leadership to real-time “basketball team” execution
• decision latency changing from information bottleneck to authority bottleneck
• productive friction enabled by shared dashboards and predefined decision rights
• the rise of the Chief AI Officer and the growing influence of the CHRO
• the AI flywheel: reinvesting savings to compound automation and speed

We start at the top: why AI isn’t a productivity layer any more, but a rewiring of how decisions get made. The old relay-race org chart breaks when intelligence is real-time, so authority moves closer to the work, new roles like the Chief AI Officer surge, and “productive friction” replaces pointless spreadsheet battles. Then we follow the AI flywheel as leaders reinvest early savings into deeper automation, aiming for a world where AI executes a huge share of routine operational decisions while humans design the rules, exceptions, and ethical guardrails.

From there, we dig into competitive advantage: the shift from generic foundation models to a hybrid AI strategy that blends LLMs with smaller specialised models trained on proprietary data, plus the idea of AI sovereignty and why it matters for security, governance, and differentiation. We also explore what reskilling really means in an AI first workplace, including systems thinking and the reality of AI as a day-to-day workflow orchestrator. Finally, we lift our eyes to the quantum horizon, where ecosystem partnerships and clean, portable data architecture become the real preparation.

If this helps you think more clearly about where business is heading, subscribe, share it with a colleague, and leave a review so more people can find the conversation.

Source: IBM Rewriting the C-Suite, The Fast Track to 2030

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores why workflow governance now matters as much as model performance.

TLDR / At a Glance

• Coordination risk in AI workflows
 • Agentic AI and cross-system exposure
 • Ownership gaps and escalation failures
 • Handoff design as a control point
 • Workflow inventories over model registers
 • Metrics for operational AI governance

The key takeaway is that scalable AI depends on strong coordination systems around every workflow.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how leaders can avoid automating inefficient processes.

TLDR/ At a Glance

• Workflow redesign over task acceleration
• Cycle time, handoffs, and bottlenecks
• Human judgement and AI role clarity
• Autonomy matched to risk
• Metrics beyond adoption and usage
• Governance embedded in operations

If AI is making everything faster, why does work still take forever? 

The uncomfortable answer is that most organisations use AI to accelerate tasks inside the same broken process, then wonder why cycle time, quality, and customer outcomes barely change. We unpack the difference between task productivity and workflow performance, and why “faster drafting” can still leave you waiting days for reviews, approvals, escalations, and missing information to be chased down.  

We walk through a practical approach to enterprise AI transformation that starts with the outcome rather than the tool: define the business result, map the workflow from trigger to completion, and find the real constraint. From there, AI becomes a design decision: place it where it reduces waiting, improves first-pass quality, lowers coordination overhead, and shortens the path to a decision. We also tackle “automating the mess”, the common failure mode where AI increases upstream output and quietly creates more downstream rework and exception handling.  

Autonomy gets special attention because agentic AI is not automatically more valuable. We break down when AI should assist, when it should recommend, and when it can take bounded action with the right controls. To keep things grounded, we share the workflow metrics that actually prove ROI: cycle time, throughput, quality, rework, exceptions, cost per case, stakeholder outcomes, and human review effort, plus what good governance looks like when it is built into everyday work rather than left in a policy document. If you found this useful, subscribe, share it with a colleague redesigning a process, and leave a review with the workflow you would fix first.

The key takeaway is that AI creates durable value when organisations improve the flow of work, not simply the speed of isolated tasks.

Read the written version on my LinkedIn page 

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how leaders should define accountability before agent autonomy scales.

TLDR / At a Glance

• Agentic AI as workflow actor
• Limits of traditional ownership models
• Four ownership layers
• Decision rights and autonomy boundaries
• Governance through distributed accountability
• Value, risk, and intervention rights

The key takeaway is that agentic AI can only scale responsibly when organisations clearly assign ownership for the system, workflow, risk, value, and authority to intervene.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores the gap between AI adoption and true organisational absorption.

TLDR / At a Glance

• Adoption versus absorption
• Usage metrics and false confidence
• Task productivity versus system productivity
• Shadow AI and governance risk
• Workflow redesign as value driver
• Board-level absorption dashboards

The core takeaway is that AI value appears when workflows, controls, decisions, accountability, and outcomes change in measurable and governed ways.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Are you ready to see what that actually looks like?

PegaWorld 2026 is where enterprise AI moves from pilots to production, with a clear focus on agentic AI, workflow transformation, and modernising legacy systems without disruption.

This is not theory. This is how organisations are starting to operationalise AI at scale.

What to expect at PegaWorld 2026:

🔹 Keynotes and real-world insights from leaders at companies like MetLife, Wells Fargo, Amazon Web Services (AWS), and Cognizant
🔹 80+ breakout sessions covering AI, automation, and customer engagement
🔹 Live demos and hands-on exploration in the Innovation Hub
🔹 Practical strategies for embedding AI into real enterprise workflows
🔹 Training and certification opportunities to build real capabilities
🔹 High-value networking across business and technology leaders
🔹 And yes, the chess tournament with Alan Trefler is back! 

Whether you are in Vegas or following remotely, this is where the next phase of enterprise AI becomes tangible. 

Let’s see what execution really looks like. 

🌎 PegaWorld 2026 | June 7–9, 2026 | Las Vegas 

#PegaWorld #AI #AgenticAI #PegaPartner #EnterpriseAI

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how leaders can judge AI by the quality and efficiency of completed outcomes. The key takeaway is that AI creates enterprise value when it improves controlled, repeatable outcomes with less friction and burden.

TLDR / At a Glance

• Hours saved is only a weak supporting signal
• AI value depends on completed outcomes improving
• More output can increase rework and risk
• Review, governance, and workload costs matter
• Workflow-level measures reveal real performance change
• Leaders should scale AI where outcome density rises

If your AI programme looks “successful” because prompts are up and hours saved are easy to quote, you might be optimising the wrong thing. We make the case that activity metrics are comforting but weak, because they don’t prove the business is delivering better outcomes, faster decisions, or stronger financial performance.

We walk through why hours saved became the default, and why it often evaporates inside the working day through coordination, review, and scattered time. Then we introduce a sharper idea for enterprise AI ROI: outcome density. It asks a simple, demanding question: are we producing more valuable, controlled outcomes per unit of total organisational input, including review effort, management attention, exception handling, and risk capacity? 

That shift exposes a common trap where AI increases output while quietly raising rework, escalations, and governance load.

To make it practical, we break down an Outcome Density Scorecard built around six dimensions: flow, quality, economics, workload, risk and control, plus learning and capability. We also show how leaders should apply these measures at workflow level, from document work and customer support to software engineering, finance operations, and agentic workflows where traceability and supervisory intervention matter even more. 

If you want AI measurement that stands up in the boardroom, this gives you a clearer dashboard and better decisions on what to scale, redesign, or stop.

If this helped, subscribe for more on enterprise AI strategy, share the episode with a colleague who owns your AI metrics, and leave a review telling us which scorecard dimension your organisation struggles with most.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how leaders can move AI from scattered usage into structured, repeatable workflows.

TLDR / At a Glance

• Usage versus true fluency
 • Overstated enterprise adoption
 • Fragmented experimentation patterns
 • Workflow integration and standards
 • AI maturity stages
 • Behaviour, judgement, and verification

Real AI fluency emerges when AI becomes part of the operating model, improving quality, cycle time, decision speed, and execution at scale.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how AI reshapes task volume, workflow structure, and performance measurement.

TLDR / At a Glance

• Effort reduction vs workload expansion
 • Task expansion across roles
 • Boundary creep and blurred ownership
 • Multitasking and reduced focus time
 • Verification burden as bottleneck
 • Workload accounting as control model

The key takeaway is that without deliberate work design and measurement, AI amplifies output and complexity, increasing pressure instead of reducing it.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores the risks, trade offs, and strategic implications of an AI saturated content ecosystem.

TLDR / At a Glance

• AI generated web content dominance
 • Explosion of synthetic images and media
 • Declining trust in generic AI output
 • Rise of automated content farms
 • Google penalties and SEO shifts
 • Hybrid human AI content strategies

The key takeaway is clear: scale alone no longer creates value, and organisations that combine AI efficiency with human authenticity will be best positioned to earn trust and long term relevance.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how CFOs and COOs can evaluate AI initiatives using a structured, outcome-driven framework.

TLDR / At a Glance

• Rising financial scrutiny on AI spending
 • Gap between pilots and scaled execution
 • Focus on process-heavy quick wins
 • Joint CFO and COO evaluation frameworks
 • Importance of data, workflows, and skills readiness
 • Governance, compliance, and risk as core criteria

AI success depends on combining financial discipline with operational feasibility to deliver measurable and scalable business outcomes.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

Google Notebook LM agents dig into the March 2026 Creating People Advantage Report from Boston Consulting Group and the World Federation of People Management Associations, drawing on insights from 7,000 HR and business leaders across 115 markets to show what is actually working inside organisations right now. 

TL;DR / At A Glance

• what the 2026 Creating People Advantage Report reveals across 7,000 leaders in 115 markets
• the widening gap between HR’s current strengths and future priorities
• why “digital solutions” rise fast while digital fluency and analytics lag
• the ROI of high-capability HR teams filling critical roles 17 to 18 days faster
• why GenAI adoption often turns into “party tricks” instead of transformation
• data privacy and compliance as the top barrier to scaled AI in HR
• replacing uncoordinated pilots with a secure AI service layer
• moving from job titles to skills-based matching and a real skills taxonomy
• lessons from a company mapping 350 skills into 60 clusters
• why SMEs prioritise culture, rewards, and upskilling over complex analytics
• regional differences in workforce strategy across Europe, Asia Pacific, and the Americas
• the looming challenge of agentic AI and a blended human plus digital workforce

We walk through the capability gap: HR remains strongest in compliance, employee relations, and the operational essentials, while “digital solutions” like HR process automation and digital employee experience surge in importance. 

The problem is that digital fluency and people analytics maturity are still near the bottom, so companies buy shiny HR tech and never build the skills or data culture to turn dashboards into decisions. From there, we unpack “capability value” and how to connect talent moves to business outcomes like ramp-up time, unit productivity, and profitability, including the striking data point that high-capability HR teams fill critical roles roughly 17 to 18 days faster. 

Then we tackle the GenAI paradox: widespread experimentation, low perceived relevance, and privacy fears that lead to uncoordinated pilots. We explain what a secure AI-enabled service layer looks like, why it boosts adoption, and how HR can step up to lead enterprise-wide AI change management, job redesign, and upskilling. 

Finally, we shift from job titles to skills-based organisations, explore why regions prioritise differently, and end on the question that changes everything: when agentic AI becomes part of the workforce, who “manages” the digital workers? 

Subscribe for more deep dives on HR strategy, people analytics, digital transformation, and the future of work, and if this sparked a debate, share it with a colleague and leave a review. 

Are you fixing the plumbing or designing the future?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explores how boards can establish effective oversight of AI in 2026.

• generative vs agentic AI distinction
 • rising board-level priority and oversight gap
 • regulatory pressure across EU US and UK
 • core questions boards must ask management
 • AI inventory risk ranking and governance design
 • case studies on controlled enterprise adoption

Strong AI governance requires clear accountability, structured oversight, and alignment with evolving regulation to manage risk while enabling value.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR/At A Glance

• generative AI adoption racing ahead of mental health safeguards
• KISSA and why people anthropomorphise chatbots
• short-term relief vs the isolation paradox over time
• reward-system hijack and cognitive offloading in daily use
• e-HARS and attachment anxiety vs attachment avoidance
• case study escalation from utility to romantic bonding
• cognitive dissonance and ChatGPT-induced psychosis reports
• suicide-linked examples showing crisis-handling failures

We dig into research and case reports describing how anthropomorphism and KISSA (computers as social actors) can escalate from polite interaction to full-blown attachment. We also explore the “isolation paradox”, where short-term comfort and reduced loneliness can quietly lead to social withdrawal, dependency, and cognitive offloading. When a system is always available and endlessly validating, it can train us to avoid the friction that makes human relationships real, and it can dull skills like emotion recognition, conflict navigation, and sustained attention. 

The hardest part is what happens when things go wrong. We talk through documented escalations including AI-induced delusions and what clinicians are calling ChatGPT-induced psychosis, plus tragedies where bots failed to detect or respond safely to suicidal ideation. We also look at why harms are not evenly distributed, with heightened risks for children, older adults, and people already living with mental illness or intense loneliness, and we explain the “stochastic parrot” problem: fluent language that mirrors a user’s state without a grounding in truth, empathy, or moral judgement. 

Finally, we ask what responsible next steps look like, from new diagnostic language in DSM and ICD updates to practical regulation such as mandatory disclosure, robust crisis detection protocols, and clearer legal liability. 

If you found this useful, subscribe, share it with someone navigating heavy AI use, and leave a review telling us what guardrails you want to see next.

Attribution

Source: Artificial intelligence vs. human expert: Licensed mental health clinicians' blinded evaluation of AI-generated and expert psychological advice on quality, empathy, and perceived authorship 

Authors: Ludwig Franke Föyen,Emma Zapel,Mats Lekander,Erik Hedman-Lagerlöf,Elin Lindsäter

Publication:  Internet Interventions

Publisher:  Elsevier

Date:  September 2025

© 2025 The Authors. Published by Elsevier B.V.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

It explores how leaders can design AI operating models that prioritise trust, accountability, and measurable performance.

TLDR / At a Glance

• Access outpacing governance and control
 • Pilot success versus scalable readiness
 • Shift from enablement to control layers
 • Workflow ownership and human handoff design
 • Telemetry, monitoring, and lifecycle management
 • Measuring outcomes beyond usage metrics

AI at scale succeeds when organisations build controlled, observable workflows with clear ownership rather than expanding tool access without structure.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

TL;DR / At A Glance:

• the core myth that enterprise AI is mainly a technical challenge
• invisible costs that dominate delivery including change management and process redesign
• why prior failed pilots often become the foundation for later success
• process fixes that make automation possible including invoice template standardisation and workflow mapping
• escalation based oversight versus approval based oversight and the productivity gap
• where internal resistance really comes from including legal HR risk and compliance
• • executive sponsorship as a mechanism for incentives and psychological safety
• security and privacy architectures that satisfy firewall constraints through anonymisation pipelines
• the productivity fork between cost cutting and growth investment
• using LLMs to unlock unstructured data instead of waiting for clean data
• agentic AI with guardrails and why autonomy drives the biggest gains
• why model choice is often a commodity and why proprietary data becomes the moat

We dig into the invisible costs that decide success or failure, like change management, process redesign, data quality, and organisational readiness. The most striking pattern is that many big wins are built on earlier failed pilots, with learning and iteration doing the heavy lifting while the sunk costs stay out of the ROI slide. You’ll hear why standardising workflows can matter more than upgrading models, and why escalation based human oversight beats approval gates that simply recreate the bottleneck.

Then we get practical about enterprise AI governance: who really blocks projects (often legal, HR, risk, and compliance), how executive sponsorship shifts incentives, and how privacy and security constraints can shape the architecture, from anonymisation pipelines to strict guardrails for agentic AI. We also challenge the obsession with model brand names, showing why model choice is often a commodity and why your durable moat is proprietary data plus the orchestration layer you build around it.

Subscribe for more evidence led AI strategy, share this with a colleague who is stuck in pilot purgatory, and leave a review if it helps. What “wiring” would you fix first in your organisation to make AI deliver real value?

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified. 

This episode explains how generative search engines retrieve, evaluate, and compose answers, and what drives inclusion.

TLDR / At a Glance

• Natural language understanding and query intent parsing
 • Retrieval augmented generation and evidence sourcing
 • Answer composition, reranking, and citation logic
 • Structured content and schema for extractability
 • Brand reputation signals across the wider web
 • New metrics including citation rate and sentiment

Generative search rewards structured, credible content and elevates brands that are easy for machines to understand, verify, and cite.

Support the show

If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.
 
🌎 Website: www.KieranGilmurray.com
📅 Book a call: https://calendly.com/kierangilmurray/catch-up
📘 Kieran Gilmurray | LinkedIn
🌐 Substack: https://kierangilmurray.substack.com
📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK 

AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.