Experiencing Data w/ Brian T. O’Neill

Yoni Leitersdorf's company, Solid, sells a semantic layer: the plumbing that helps AI agents understand an enterprise's data well enough to do effective, valuable work that passes a customer's evals. As platform software, this is about as invisible as a product gets, and by Yoni's count there are probably 100 semantic layers a buyer can choose from right now, including the ones Snowflake and Databricks already ship. So I wanted to know how a startup like Solid can get market share in a crowded category, and how it decides which deals are worth chasing. One of his answers: the prospect who shows up saying "we know we need a semantic layer" is not the deal he's hoping for.

• What a POC has to prove before a data leader can take it through the finance/procurement gauntlet

If your product is a horizontal platform or infrastructure your buyer can't see, I hope this episode sheds some light on the overlap between product, UX, and GTM for enterprise AI.

• GetSolid.ai

Full transcript and show notes: designingforanalytics.com/resources/episodes/205

Logan Havern's company, Datalogz, sits on top of an enterprise's BI platforms (Power BI, Tableau, Qlik, Databricks Genie, and the SharePoint spreadsheets nobody admits to) and finds the duplicated, conflicting, and unused reports that AI is now generating faster than anyone can clean up. At one large pharma customer, executives "talking to their data" get wrong answers and then argue with the data team, because they believe Claude over the humans. The product that fixes that, which Logan calls semantic intelligence, is the one he thinks makes Datalogz a multibillion-dollar company. However, this is not what he sells first—and this is the topic we dug into today on this episode of Experiencing Data.

What Logan sells first is a 30-minute connection and a cost-savings business case finance can sign off on. That wedge took him a long time to find, and it sets him apart from other companies trying to sell “better AI answers” and “talk to your data” solutions. We also dove into the manual work [of 5-to-30-person admin teams] his product replaces, and my skepticism that usage data on a dashboard can tell you anything about whether it delivered value. If your analytics product's biggest value is also the hardest to explain, this one is for you.

What happens when your product team suddenly moves six times faster at delivering features, but your sales and business traction is unchanged? AI, and particularly AI for coding, has made it easier than ever to ship features, but faster development doesn’t automatically create better products or better outcomes. The real question is whether what you’re building changes anything for the people who buy, use, and benefit from your product. 

Today, I get into why AI evals and acceptance criteria measure the product, not its impact, and why your buyers are probably judging your POC on success metrics your team never wrote down. Toward the end, I make the concepts concrete by applying them to a fictional AI-powered pricing tool to show what defining quality looks like for both the fiscal buyer and the end user, and what to do with all that extra engineering velocity instead.

Your goal? Better; not just faster. 

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What makes an analytics or intelligence product indispensable when technical sophistication alone isn’t enough to drive adoption, renewals, or sales? Why do POCs stall, why does adoption stay flat, and why aren’t prospects nearly as excited about your (impressive) analytics tech as you are? 

It’s that the value never becomes obvious to the humans in the loop who do the buying, using, and justifying.

In this episode, I provide a framework for identifying the personas (roles) and experiences that can turn sophisticated analytical capability into perceived value. We’ll explore the five essential control surfaces you’ll need to address in your product and why founders and product leaders need to look beyond technical development (e.g., the delivery of AI models, dashboards, MCP connectors, agentic capabilities, and traditional interfaces). 

I also examine the tensions that can emerge when incentives and goals for end users are not aligned with those of management and executive/fiscal buyers. Why does the efficiency ROI you sell to a management champion get read as a job threat to the operator/user whose adoption you need to succeed? I’ll answer this, plus the reasons AI can complicate those dynamics, how to prevent your product from creating an Invisible Intelligence Gap for customers, and why one past podcast guest’s “shipping the meter” matters much more than another feature. (That will also explain why I don’t think dashboards are dead in the age of AI.) 

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Recently, I met David Krauza, VP of Enterprise Data Strategy and Products & Governance at Comcast, at the 2026 CDOIQ symposium, and after chatting for a bit, he agreed to come on the show to talk about how he, as an enterprise buyer, thinks about B2B software purchases in the age of AI. As vibe coding makes internal development more accessible, David explains why the buy-versus-build decision isn’t simply about whether a company can build a solution itself. Leaders need to weigh long-term roadmaps, maintenance, integrations, and whether they want to take on the responsibility of becoming a software company. The real question is not just what can be built, but what makes the most strategic sense to own.

David also highlights an important consideration vendors frequently overlook: the data their own products create and the possible importance of that to an enterprise data leader. This is particularly true when the product’s primary intended purpose is not analytics itself. In a complex sales environment, which may include a senior data leader as a champion or decision maker, product metadata, or what you might currently be thinking of as a byproduct, might actually be a primary decision point in an enterprise purchasing conversation. David then outlines the pre-implementation work required before any of this can be evaluated: establishing shared definitions, explicit success metrics, and a documented “before-picture” you can run at renewal time to understand the ROI of the product.

We also explored the hidden costs that can undermine an otherwise compelling product. A polished UI may still create UX friction if users have to constantly move between systems, while complex data integrations can introduce additional labor and operational burdens. This friction should be considered during the evaluation rather than discovered after development. David says vendors can stand out by demonstrating that they understand where a customer’s business is headed and how their roadmap supports that direction. He also dropped some real gold about the difference he sees as a buyer when being pitched by a founder vs. a B2B salesperson—and what the latter is missing when they pitch him.

I'm talking to Nishkam Prabodh of Venture Guides, an early-stage venture capital firm focused on infrastructure software, cybersecurity, and data. Nishkam explains why strong technology alone rarely determines whether a startup succeeds. Many technical founders struggle when transitioning from founder-led sales to a repeatable go-to-market motion because the founder's deep customer understanding often does not translate into a scalable sales process. The challenge isn't just building better technology, but connecting technical capabilities to clear business outcomes.

Nishkam breaks down the communication gap that often appears between technical products and enterprise buyers. Rather than focusing only on technical outputs, products need to demonstrate business value through experiences designed for different stakeholders, including end users, executives, compliance teams, and budget owners. Whatever ROI the product shows has to relate back to improving revenue, reducing cost, or reducing risk. Nishkam also discusses how AI is changing product organizations, with execution-focused tasks becoming increasingly automated while judgment, domain expertise, and strategic decision-making become more valuable.

On buy versus build, he notes that two-thirds of the failures in the MIT study on enterprise AI deployments were internal builds, while the successes skewed toward buying. While traditional advantages like proprietary technology and architecture still matter, he believes the strongest defensibility increasingly comes after deployment. Products that learn customer context, retain institutional knowledge, improve workflows, and generate organization-specific intelligence can create compounding value over time. AI models may become commoditized, but the surrounding product layer of memory, retrieval, context, tooling, and governance will determine long-term differentiation. He closes with the discipline that makes all of this possible: pick one customer, one industry, one revenue band, and build repeatability there before worrying about coverage.

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I’m talking to Bhaskar Sunkara, CEO of bicycle.AI, which provides an AI analyst product designed to monitor revenue-critical KPIs, investigate the business and technical drivers behind KPI changes, and take a “governed next step.” Bhaskar explains why analytics products often fail when they overwhelm users with telemetry instead of focusing on the signals that matter. Drawing from his experience as founding CTO of AppDynamics, he shares how his team moved from low-level technical monitoring to business transactions like logins, checkouts, and bookings. The key lesson? Start with the right metric at the right level of granularity, then use deeper technical analysis to explain why something changed.

Bhaskar also breaks down how bicycle.AI serves multiple audiences inside an enterprise. Business leaders want measurable outcomes, KPI owners need answers about what changed and what to do next, and data teams require trust, governance, and traceability. He explains how, in order to support these different users, Bicycle separates product experience into four core surfaces: pull features like dashboards and chat, and push features like alerts and data stories. Alerts further help operational users respond quickly to KPI changes and data stories provide executives with strategic narratives around trends, causes, and business impact. During our chat, Bhaskar also draws a line most AI products blur: be explicit about which findings are deterministic and which are only a theory. He connects this directly to my CED framework, separating the conclusion from the evidence from the underlying data, and argues that how much you automate should be governed by one question: how costly is being wrong?

I also probed Bhaskar about their moat. He’s learned that enterprise adoption requires winning over both executives who care about revenue impact and analytics teams that need confidence in the system’s recommendations. Bhaskar also explains why their long-term advantage comes from the DEAL framework: Detect, Explain, Act, and Learn. By continuously incorporating validated decisions, business context, and customer-specific knowledge, the platform becomes more useful over time. We finish up with his advice for fellow analytical AI product founders, including why AI makes user experience more important, not less: it is the connection between agents, decisions, humans, and accountability.

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Today, I'm talking to Rana Gujral, CEO of Behavioral Signals, which provides AI that interprets human behavioral cues in speech to help route call center conversations more effectively, improve customer service performance, and detect voice-based fraud. Their moat is a decade of voice data tied to real business outcomes, not the model itself, as Rana explains.

During our conversation, Rana shares his practical framework for making the value of their AI obvious to the various humans in the loop that the product needs to “touch,” and he argues that a one size [UI] doesn’t fit all. In Rana’s product, they discovered that customer service reps need ambient assistance, supervisors need aggregate patterns, compliance teams need audit trails, and executives need outcome metrics tied to business results.

He also explains why having measurable ROI isn't enough. Early renewals for Behavioral Signals suffered because the people signing the checks couldn't actually see the product's impact. Rana's solution? “Ship the meter” alongside the intelligence. If your AI works quietly in the background, you still need reporting UIs that clearly communicate the product’s value.

For founders struggling with stalled POCs, Rana breaks down the three-stage evaluation journey his team developed after repeatedly seeing deals fail at predictable moments. By designing the customer experience around those milestones, his team transformed how buyers gained confidence throughout the evaluation process.

Finally, we explored why great B2B AI products don't succeed by becoming another dashboard. Rather, they succeed by closing the loop between decisions, outcomes, and learning. Rana also fills me in on his upcoming book, The AI Instinct, which focuses on how AI changes human judgment rather than simply advancing model capabilities. And his parting advice? Listen to find out!

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I've been seeing teams heavily invest in copilots, agents, semantic layers, governance frameworks, and increasingly sophisticated models, yet many still hear the same feedback from sales prospects: “We may just build this ourselves?" Or they don’t hear it, but suspect the customer is doing just that. 

Whether they actually can DIY the solution is the wrong question. The bigger question is *why they believe they can.* Your product may have a genuine competitive advantage, but your real challenge is that this advantage isn't obvious to buyers. The moat exists, but it is invisible.

What makes this relevant is that many capabilities once considered differentiators are rapidly becoming normalized. AI copilots, agentic analytics, governed data, semantic layers, and broad integrations now appear across nearly every platform in the category. As AI accelerates development, sophisticated engineering alone becomes harder to defend as a lasting advantage.

So what actually creates a durable moat if the engineering and product seems easy to copy? I explore four areas: proprietary data, trusted relationships, and products that accumulate institutional knowledge remain difficult to replicate. And finally, user experience itself as a strategy. As users increasingly access your intelligence through AI agents rather than dashboards, their experience may become the moat that competitors can't copy.

I've been seeing a recurring pattern with companies selling APIs, MCPs, data feeds, and other developer-focused AI products. While the technology is often sound if not impressive, sales momentum sometimes slows when prospects have to imagine how the product will create value in their own environment. My perspective on this is that the flexibility that makes these tools powerful can also make them harder to evaluate.

Flexibility can adversely increase the Invisible Intelligence Gap, and I think certain types of AI-based solutions (LLM) may actually increase this because the boundaries of the product are often so much wider than ever before (if not invisible to the buyer). So, how to close this gap? Well, one way is to build a visual UI that showcases what’s possible with your API/feed/data solution. You take the buyer out of the conceptual space and make things concrete. So today, that’s what we dig into: when to consider adding a UI, how far you need to go with it, how you can use Copilot/AI agents to help customize these example implementations, and the benefits you might see.