Why do your B2B analytics or AI Products not close? Learn more in this episode with Brian O'Neill.

More episode details coming soon.

If you’re hoping that adding AI to your analytics product or capabilities is going to unlock new revenue, sales, and greater user adoption, but you’re not sure what’s involved in this transformation, this episode is for you!

Today, I’m talking with Juan Sequeda today, an expert in knowledge graphs and ontologies who most recently was Head of the AI lab at data.world, which was recently acquired by ServiceNow. Juan and I met while speaking at CDOIQ a few years ago, and after being on his former podcast “Catalogs and Cocktails.” (With a name like that, I naturally had him out to my local tiki bar while visiting Cambridge!)

Talk-to-your-data products – effectively next-gen business intelligence applications – are a hot topic right now, and this has made much of Juan’s PhD work in semantics highly relevant right now as companies try to make analytics more user-friendly via natural language.

Juan is clear that the starting point for this transformation isn’t the model or the UI, but actually the customer’s workflow—and that was like music to my ears! Analytics only matters when it drives action, so the real challenge is not answering more questions, but enabling better decisions and outcomes.

A key theme is semantics, which, in product design language, I think of as making users’ mental models of their business or domain map logically to system and data models so that AI produces the right answers in the right context. Juan outlines a practical path to getting started with this: strong data modeling, a well-defined semantic layer, buy-vs-build considerations, and throughout, a constant focus on what the customer’s workflow and problem is.

• Juan Sequeda’s background (2:14)
• Is AI for BI the way to go for proprietary analytics products? (4:30)
• Bolted-on AI versus transformational AI, and what customers are doing with current reporting (8:26)
• Knowing your product’s boundaries and when extending into adjacent customer workflows stops making strategic sense (14:46)
• Setting proper expectations for non-technical founders around what AI can “answer” with analytics (18:43)
• The role of customer problems in informing the prerequisite technology and data decisions (24:37)
• What's the actual lift to add chat-with-your-data capabilities to a SaaS product: data foundation, semantic layer, and the build-vs-buy call (33:38)
• Why Juan thinks every company should become “AI-native” (41:20)
• AI might theoretically make for a better analytics UX, but are users ready to change their behavior or abandon the analytics tools they use now? (46:00)
• How to follow Juan Sequeda (49:03)

• Catalogs & Cocktails Podcast
• Juan Sequeda’s LinkedIn
• Juan Sequeda’s Substack

Speed is often confused with good product thinking. The idea is that if teams can ship prototypes, dashboards, and models faster, they will automatically learn faster. But execution speed alone doesn’t ensure a clearer understanding of what’s actually worth building.

Instead, teams often fall into a loop driven by demo feedback. They present working prototypes, and users respond to what they can see in the form of interface design, visualizations, or surface-level data behavior. While this feedback feels positive, it’s often misleading. Teams can end up reacting to presentation (UI) feedback only to find it does not change propensity to buy or increase user adoption.

The key idea today is that prototypes can either be used to clarify the problem space and user needs or to validate the solution presented. Where I see most teams fail is that every artifact or prototype is seen as a solution to validate, and they can miss the forest for the trees.

Another approach borrows from blue ocean thinking, which focuses on creating value by looking for overlooked opportunities in the empty space—beyond the known “problem space” your customer knowingly lives in now.

Because AI lets us move so fast with prototyping, I think there is an exciting possibility to explore the blue-ocean spaces where your product could evolve to produce value.

As always, we seek to go beyond building “technically right, effectively wrong”—which doesn’t make people buy, use, or refer your product. Today, we look at what AI can help us to do to see even farther beyond the immediate problem space.

• Where the idea for this episode came from (00:46)
• Why faster building of artifacts with AI doesn’t necessarily mean faster market validation (2:09)
• How understanding the problem space results in fewer prototypes being created (5:40)
• Using blue ocean strategy to arrive at new products worth paying for (08:23)
• Finding missed market opportunities blocked by cost, tech limits, or risk (12:39)
• How AI-assisted user research fits into blue ocean thinking (14:33)
• The big picture: winners will figure out what’s worth building before they build it (20:42)

• Contact Designing for Analytics