The short version
Most corporate AI training teaches tools: prompts, features, menus. The gap is mapping, which means teaching people where AI fits in their actual workflows. This interactive piece walks through mapping AI capability to marketing function, the step field research links to finding 44% more viable use cases.
- Tool training without workflow mapping produces demos, not adoption.
- Map the tasks first, then choose the tools. The reverse order is where budgets go to die.
- Training should end with a build list, not a certificate.
The one thing AI training
programs get wrong
Most AI adoption programs teach people how to use the tools. A field experiment across 515 startups found that was never the constraint. The firms that pulled ahead did so because they learned where to deploy AI, not how. Seeing what reorganized production actually looks like, in concrete detail, across other firms, was worth 1.9x more revenue and $220K less capital needed. Access was never the problem. Discovery was.
Based on: Kim, Kim & Koning / "Mapping AI into Production" / INSEAD Working Paper 2026/20/STR
Both groups had identical tools, API credits, and technical training. Access was equal. Outcomes were not. The constraint was the cognitive work of discovering where AI creates value in a specific production process.
Abundant evidence shows AI improves individual tasks. What remained unresolved: does that compound to the firm? Yes, but only when firms solve the search problem first. Without it, gains dissipate before reaching the bottom line.
+30%
flat
Control firms typically reported zero or one new use case per week. The gap emerged after week 3 and widened each week. Cumulative discovery, not a one-time knowledge transfer.
The largest differentials were in areas that required rethinking how work is organized. Research, writing, and sales barely moved. The intervention shifted firms from adopting tools to redesigning their production process.
Not marginal efficiency improvements. Treated firms completed more tasks, acquired more paying customers, and generated substantially higher revenue over the same ten weeks.
Revenue effects are small through most of the distribution and spike at the 90th-95th percentile. AI raises the ceiling of what the best-positioned firms can reach. It does not lift the floor.
Treated firms demanded $220K less in outside capital (39.5% reduction) with no change in labor demand. The capital reduction was sharpest above the 60th percentile.
Technical founders did not benefit less. High-traction firms did not benefit more. The constraint was search scope, not skill or prior performance. Tap each group.
An eight-step AR process with one automated step still has seven human handoffs. Because firm activities are complementary, partial integration preserves the original constraint. The full sequence must be redesigned.
As AI capabilities expand, the space of possible applications grows larger. A firm that has mapped today's AI into production will face the same discovery problem when the next generation arrives. The bottleneck is managerial, not technological.
Related reading
- Point of View
Microsoft Copilot Has a Pick Me Problem
Why the race to embed AI everywhere is producing tools that optimize for adoption metrics instead of outcomes.
- Interactive
What's Your AI Philosophy?
A diagnostic quiz mapping your approach to AI against four distinct philosophies.
- Book Analysis
The AI Counterculture Is Your Procurement Manual
A buyer's read of Josh Tyrangiel's AI for Good: the six repeatable moves that separate AI rollouts that ship from the ones that end in bankruptcy.
By Thomas Rogers ·
Want to talk about this?
If something here resonated with a problem you're working on, let's spend 15 minutes on it.
Schedule a Discovery Call