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Does the value of your insights, analytics, or automated intelligence product sometimes feel invisible to buyers and users? Does your product have impressive analytics and AI technology, but user adoption and sales still are not where you want them to be?
While it has never been easier to build data-driven products, why does it still seem so hard to build indispensable data products that users can't live without—and will gladly pay for?
I’m Brian T. O’Neill, and on Experiencing Data — a Listen Notes top 2% global podcast — I help founders and B2B software product leaders close the Invisible Intelligence Gap through solo episodes and interviews with leaders at the intersection of product management, UX design, analytics, and AI.
If you’re building analytics, BI, or automated intelligence (AI) products, this non-technical show will help you better connect your product to outcomes, value, and the human factors that still matter — even in the age of AI.
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https://designingforanalytics.com/experiencing-data-podcast/
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https://designingforanalytics.com/bio/
Episodes
Tuesday Apr 28, 2026
Tuesday Apr 28, 2026
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.
Highlights/ Skip to:
- 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)
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