That reframing, introduced by a senior commerce leader at one of the world's largest consumer goods companies, sets the tone for a conversation that goes well beyond the usual retail media talking points. 

The discussion features Jenna Levin, Senior Director of Global Digital Commerce at Colgate-Palmolive, who oversees the company's e-commerce capabilities and Amazon go-to-market strategy across global markets. Nagarajan Chakravarthy, Chief Digital Officer at iOPEX Technologies, whose mandate spans agentic AI enablement and helping brands build advertising as an operational discipline. Danilo Tauro, Co-Founder of CartographAI, hosts the conversation.

Fragmentation Is Not a Strategy Problem

The conversation opens with a tension every brand-side operator recognizes: investing across six or seven retail media networks, each with its own attribution logic, measurement methodology, and definition of success.

Naga's framing cuts through the noise immediately. "Don't misdiagnose fragmentation as a strategic problem," he said. "It's a fundamental infrastructure problem." Each network has its own attribution secret sauce. Treating that as something to be solved at the planning level, rather than the data layer, is where most brands waste the most time.

His prescription is direct: build your own measurement layer, own your KPIs, and stop outsourcing the interpretation of your performance to the networks that generate it. The shift is from media buying to media operating.

Stop Searching for One Metric. Start Aligning on One Outcome.

Jenna extended the measurement argument in a direction that will resonate with anyone managing retail media across a complex internal stakeholder structure. The search for a single number that satisfies every team, every market, and every budget owner is a dead end.

"Fundamentally, it is about accepting the multimetric reality," she said. Different teams will track different inputs. What cannot differ is the North Star output metric, whether that is market share growth, sales lift, or a media ROI figure that the whole organization agrees to stand behind.

What makes this actionable rather than theoretical is the technology now becoming available to support it. Jenna pointed to AI and clean rooms as the infrastructure that will soon enable brands to test correlations between input and output metrics in real time, rather than waiting for an annual marketing mix model to confirm what the team suspected 12 months earlier.

The Question Nobody Is Asking: Does AI Trust You?

The sharpest insight in the episode comes when Jenna introduces a concept that reorients how brands should think about the next phase of commerce.

"We talk a lot about consumers trusting AI. But I also think we need to talk about AI trusting brands."

The example is concrete. If Amazon's Rufus or Walmart's Sparky cannot verify a brand's product data, the agent will not recommend it. It is not a question of consumer preference or media spend; it is a matter of data structure. An AI agent operating without confidence in your product information will route around you entirely.

"We need to begin thinking about treating AI as one of our most valuable customers."

That means designing PDPs for both human shoppers and AI agents, structuring product data to be API-accessible with rich attributes. Jenna also identifies the second shift: from keywords to context. Consumers are no longer searching in two or three words, they're using eight, building full-sentence queries like "best whitening toothpaste and build me a routine for sensitive teeth." Brands that embed this context

Hosted by Danilo Tauro, Co-Founder of Cartograph AI, the conversation features Juuso Alho, Global Head of Product for Retail Media at JustEat Takeaway, and Nagarajan Chakravarthy (Naga), Chief Digital Officer at iOPEX Technologies. Together, they dissect the structural breaks, product tradeoffs, and operational realities that leaders must navigate to scale commerce media successfully.

Fixing the Foundation: Avoiding "Middleware Hell"
 
A recurring theme in the episode is the danger of skipping fundamental groundwork in a rush to launch a Minimum Viable Product (MVP). Juuso Alho highlights that new retail media networks often face internal skepticism from colleagues who view ads as detrimental to the organic user experience. Beyond cultural buy-in, the technical hurdles are steep; integrating ad servers is far more complex than just dropping a JavaScript tag onto a page.

Nagarajan Chakravarthy points out that the real challenge, and where most "scars" occur, happens in the execution or "run phase" rather than the initial build phase. Nagarajan put a sharp edge on that last point: "Using yesterday's data for tomorrow's decision is absolutely useless" when your account managers are trying to optimize live budgets. The lag between what happened and what your team can act on is where early revenue dies. 

He identifies "middleware hell" as a critical operational trap. When campaigns take weeks to activate due to manual legal approvals, fragmented ad servers, and siloed data, organizations suffer from severe "time to revenue realization" delays. To combat this, Naga advises that building a strong "operational tissue" and execution velocity from day one is far more critical than simply buying big-rock components. Ultimately, failing to standardize data pipelines and processes leads to a compounding loop of "data debt" and "operational debt".

Build vs. Buy: The "Shoe" Analogy 

When deciding whether to build or buy core technology, Juuso offers a highly practical framework: selecting a vendor is like buying a pair of shoes. You can buy many different pairs, but if you don't know whether you are going to a fancy party or preparing to run a marathon, you will make the wrong choice. Companies must evaluate their in-house engineering skills and specific go-to-market needs before locking into a technology stack.

His point was that digital ad servers are now commodity software. The differentiation comes from how you connect that tool to your proprietary first-party data. If your engineering team is not ready to build a custom ad server from scratch, partner with an established vendor and invest engineering effort in the data integration layer that creates a competitive advantage.

Naga echoes this sentiment, noting that ad servers and measurement tools are largely available as commodities today. The real differentiator is how well these components are orchestrated, intertwined, and made native to your platform without leaving behind dozens of manual reporting steps.

Danilo reinforced this from the investor side. The networks attracting capital are the ones that ship working products quickly, not the ones that spend 18 months perfecting proprietary infrastructure before generating a single campaign dollar.

The Evolution of AI: From Prediction to Agentic Operations 

Cutting through current industry hype, the speakers clarify what AI actually means in today's ad tech landscape. While machine learning and predic

Episode 1 brings together three operators who are living that reality from very different angles: Leora Kelman of BCG, who advises the world’s largest networks on where the market is actually heading; Danilo Tauro of CartographAI, who sits at the intersection of investment, technology, and activation; and Nagarajan Chakravarthy of iOPEX Technologies, who has led enterprise-scale digital transformation across retail, media, telco, and high‑tech. Instead of debating buzzwords, they focus on a single question: what does it really take to build a retail media business that lasts when AI, agents, and hybrid commerce are rewriting the rules in real time.

Across the conversation, you’ll hear why prediction-led plans are breaking down and how scenario planning, no‑regret moves, and signal‑tracking give leaders a more resilient operating system in a world where over 60% of consumers already use AI to help them shop. You’ll see how the economics of incrementality are changing as sophisticated advertisers press for margin-aware measurement, not just ROAS, and why infrastructure readiness, proof‑of‑concept velocity, and agent‑friendly design have become table stakes rather than edge cases.

Explore the practical framework for organizing demand - big rocks, pebbles, and sand - that helps networks prioritize strategic suppliers, long‑tail merchants, and programmatic partners without diluting what makes their audience unique. Finally, the discussion closes the loop at the top of the funnel, unpacking how retail media is starting to win bigger brand budgets by connecting first‑party purchase data to CTV, streaming, and creator ecosystems, proving influence across the full path to purchase rather than just the last click.

If you’re building or funding a retail media network, leading digital transformation for a retailer, or simply trying to separate durable strategy from 2026 hype, this episode gives you a clear vantage point on where the real opportunities and risks are next.