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)