Framework 7 · Intelligence Abundance Toolkit
Most organizations track AI tool usage rates. High usage equals successful adoption. This metric is wrong. Some of the worst AI use looks great on usage dashboards. Measuring effort instead of capability development is the most common measurement mistake in AI transformation.
What goes wrong
Measurement systems tracking effort instead of capability development. People generating volumes of mediocre output because the tool is easy (not because they're using it effectively) show up as successful adoption. Leadership sees green dashboards. The transformation isn't working. High usage does not equal good outcomes.
Are outputs on the third attempt better than outputs on the first attempt? Improving trajectory matters more than absolute quality at any given moment. A team whose AI-augmented outputs are getting better each week is building capability. A team whose outputs are flat despite high usage is not.
How to track: have managers score work product quality on a 1-5 scale weekly. Benchmark against prior week, not against a fixed standard. Direction, not level.
Are people catching AI mistakes before they reach stakeholders? This skill improves with practice, but only if people are using their judgment to evaluate AI output rather than passing it through uncritically. A team with a high error detection rate is a team that has learned where AI fails in their domain.
How to track: ask teams to log AI errors caught before submission. Make error detection visible and valued, not punitive. The goal is to make catching failures a rewarded skill.
Time from information availability to decision implementation. AI should move this number. If AI is in use but decision velocity isn't improving, something in the workflow is broken: either AI output quality is too inconsistent to trust, or the verification process is adding more time than AI is saving.
How to track: pick 3-5 decision types your team makes regularly. Time them from trigger to implementation for one month. Repeat at months six and twelve.
How often do teams need leadership input for decisions within their defined scope? This should decrease over time as constitutional interpretation matures. If escalation frequency is flat or increasing despite AI deployment, the constitutional principles aren't clear enough to support autonomous decision-making.
How to track: count leadership escalations per team per week. Look for a downward trend starting around month six.
The Intelligence Abundance Toolkit includes the AI Transformation Measurement Guide: a tracking template for all four metrics, baseline-setting instructions, a measurement calendar tied to the transformation timeline, and a dashboard replacement template that removes effort metrics and replaces them with capability indicators.
Seven frameworks. Seven worksheets. 90-min session guide, 12-month roadmap, and 11 AI prompts. $97.