189 - The Invisible Intelligence Gap

189 - The Invisible Intelligence Gap

Author: Brian T. O’Neill from Designing for Analytics March 5, 2026 Duration: 25:26

I’ve worked with a lot of teams building analytics and insights products and decision-support systems. The pattern I keep seeing isn’t that the math is wrong or the ML / AI models are weak. Much of the time, the technology is fine.

 

 

The challenge is that all that [not always artificial!] intelligence is not surfacing as value to your customer. Dashboards look impressive. AI features demo well. Pilots get strong reactions. And then… usage stalls. Sales cycles drag. Teams quietly revert to spreadsheets. Buyers, or rather, prospective buyers, say they “like the vision,” but deals don’t move into the “closed” stage.

 

 

If your gut tells you the primary blocker is not your sales process, pricing/packaging, procurement, data quality, or risk/compliance, then you may be suffering from what I call the Invisible Intelligence Gap. 

 

 

Your product’s intelligence simply isn’t visible to them. Three forces tend to amplify this gap. First, the value translation gap, which is when buyers and users can’t easily connect insights to their own goals. Second is the workflow alignment gap resulting from the product not fitting how work actually gets done. Third, the trust and control gap involves users lacking confidence in how the system reaches conclusions. My frameworks like CED, FOWA, and MIRRR are designed to close these gaps by making value obvious, workflows smoother, and AI more trustworthy.

 

 

Highlights/ Skip to:

  • The challenge of insights not providing value to buyers, end-users, and stakeholders (3:20)
  • How the invisible intelligence gap manifests itself (6:42)
  • Common symptoms of the invisible intelligence gap (8:10) 
  • Examples of how changes in human behavior cause the gap (10:00)
    • The (3) amplifiers of the invisible intelligence gap (11:47)
  • The CED framework for addressing the intelligence gap problem (18:28)
  • Addressing the invisible intelligence gap with FOWA (20:14)
  • Using MIRRR to solve the invisible intelligence gap (21:25)

For enterprise data and product leaders, the real challenge often isn't building the technology-it's getting people to actually use it. Experiencing Data w/ Brian T. O’Neill digs into that persistent gap between creating powerful ML, AI, and analytical tools and seeing them drive genuine business value and informed decisions. Host Brian T. O’Neill, from Designing for Analytics, moves past pure technical discussion to explore how design, product thinking, and strategic management can bridge that divide. This podcast lives at the intersection of data and human experience, making it essential for anyone who has ever wondered why a technically sound data product failed to gain user adoption or secure stakeholder buy-in. Across conversations with practitioners and through Brian’s own analysis, episodes unpack what a "data product" approach truly means in practice. You’ll hear concrete strategies for designing analytics that people want to use, framing data work in terms of business outcomes, and leading teams to create not just outputs, but impactful solutions. It’s a resource for rethinking how data work connects to the arts of communication, design, and leadership, all to ensure that data investments lead to tangible results. Tune in for a pragmatic, human-centered perspective that is often missing from the tech conversation.
Author: Language: en-us Episodes: 100

Experiencing Data w/ Brian T. O’Neill
Podcast Episodes
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