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

About this episode

The episode discusses the challenges teams face in discovering valuable analytics use cases for their products, emphasizing the importance of starting with customer decisions rather than just available data.

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…

People in this episode

Host: Brian T. O’Neill

Topics covered

• analytics
• AI products
• decision-making
• data context
• user behavior
• product design

Keywords

• analytics use cases
• AI
• data quality
• decision-making
• user behavior
• product intelligence
• contextual data

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