190 - Why Discovering Valuable Analytics Use Cases for Your Product Is So Hard (Even with AI)

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Mar 17, 2026

They unpack why starting with available data leads to polished but ineffective analytics. The conversation pushes teams to work backwards from the decisions customers actually face. It critiques data-availability bias and showy dashboards that shift interpretation onto users. They explore how conversational AI can help or hinder discovery and why prototypes should reveal problems, not validate assumptions.

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ADVICE

Work Backwards From Customer Decisions

Do discovery by working backwards from customer decisions instead of starting from available datasets.
Identify the specific decisions customers make, their uncertainties, and where workflow friction blocks action.

INSIGHT

Insights Don't Automatically Produce Decisions

Insights alone rarely change behavior because real decisions rely on intuition, incentives, and context.
Example: an analyst's strategic insight may not matter if the end user only needs to keep a metric above 41 to avoid alerts.

ADVICE

Use Prototypes To Find Problems Not Confirm Value

Use prototypes as conversation props for problem finding, not as validation of product-market value.
Ask about current behavior: how they solve the problem today, where they revert to Excel, and where time is wasted.

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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/