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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.
Subscribe today on all major platforms or browse the episode archive.
Get 1-Page Episode Summaries:
https://designingforanalytics.com/experiencing-data-podcast/
About the Host, Brian T. O'Neill:
https://designingforanalytics.com/bio/
Episodes
Tuesday Mar 17, 2026
Tuesday Mar 17, 2026
I’ve seen this pattern repeatedly with teams building analytics and AI products: the issue usually isn’t the quality of the models or the sophistication of the data. The technology often works just fine. The real breakdown happens earlier—when teams begin with the data they already have and try to figure out what to build, instead of starting with the decisions their customers need to make.
That approach often produces polished dashboards and compelling features that generate interest, but fail to drive real action. The missing piece is context. Decisions in the real world depend on incentives, habits, risk tolerance, and uncertainty—not just clean data. If your product doesn’t reflect that reality, it won’t meaningfully change behavior.
Another common trap is assuming all available data is *evidence* worth surfacing. This “more is better” mindset leads to cluttered analytics tools that offload interpretation onto users. Even conversational AI interfaces can fall into this, encouraging open-ended exploration without helping users reach decisions.
The analytics and AI products that succeed take a different approach. They’re designed around decision-making to reduce uncertainty, fit into real workflows, and guide users toward clear actions. In doing so, they bridge the gap between analytical capability and real-world value, making the product’s intelligence tangible, usable, and worth paying for.
Highlights/ Skip to:
- The core mistake I see people making during the discovery process of building an insights product (2:07)
- Improve your product strategy by working ‘backwards” and understanding what decisions customers are trying to make (6:06)
- Insights don’t equal decisions in the real world (7:39)
- Designing with a goal of improving the lives of users in mind (11:17)
- Prototypes as a means of discovery (vs. product/solution validation) (13:48)
- The bias of data availability (20:39)
- Using AI and LLMs for discovery and product UX (24:17)
- Why AI-assisted analytics products should shape UX around making structured decisions (31:03)
- Overcoming the Invisible Intelligence Gap (34:57)
- Final thoughts (37:21)
Links
- CED: My UX Framework for Designing Analytics Tools That Drive Decision Making https://designingforanalytics.com/ced
- Need my help finding the right use cases for your analytics or AI product? Book a complimentary 1x1 discovery call with me: https://designingforanalytics.com/contact/
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