The AI value gap is a management problem
Most organizations now use AI. Few can find it in their financial results. Three independent studies point to the same cause, and it is not the models.
4 min read
AI Insights
Where AI creates value, how to choose use cases and how to measure return.
Where AI creates value and how to prove it: why most organizations use AI without capturing its value, how to measure productivity honestly, what AI-ready data means, and how to write contracts and choose models that keep options open.
Start with the guide: The AI Management Office guide.
Most organizations now use AI. Few can find it in their financial results. Three independent studies point to the same cause, and it is not the models.
4 min read
The model is rented, changes without notice and sits inside someone else’s supply chain. The contract is where those risks are either allocated or ignored.
4 min read
Self-reported time savings overstate some gains and miss others, and real gains often arrive after a dip. Two pieces of research explain what to measure instead.
4 min read
Stanford’s annual report runs to more than 400 pages. Six figures from it belong in every AI strategy discussion this year.
4 min read
Gartner expects organizations to abandon most AI projects that lack AI-ready data. “Ready” has a practical definition, and five controls that make it true.
4 min read
McKinsey’s 2025 survey shows near-universal experimentation and thin financial results. The organizations that report impact share three management habits.
4 min read
A certificate is an input, not a verdict. The vendor questions that matter are the ones a sound supplier answers easily and a fragile one cannot.
4 min read
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