Everyone bought AI. Almost nobody got results.

A study published last month by the National Bureau of Economic Research surveyed nearly 6,000 senior executives across four advanced economies. Seventy percent of firms report using AI. More than 80% say it has had no measurable impact on productivity or employment.

PwC’s 2026 Global CEO Survey found that only 12% of CEOs report AI has delivered both cost savings and revenue benefits. Section’s AI Proficiency research: only about 15% of enterprise AI use cases generate measurable ROI.

And yet. BCG’s AI Radar 2026 found that 94% of companies plan to continue investing at current or higher levels, even if it doesn’t pay off. Ninety percent of CEOs believe this year will be different. Companies plan to double their AI spending.

Meanwhile, Block just cut more than 4,000 employees because AI made it possible. McKinsey estimates 57% of U.S. work hours could be automated with technology that already exists. The gap between what AI has delivered and what it could deliver is where most enterprise budgets go to disappear.

The most common AI strategy in enterprise today is a procurement decision dressed up as a transformation initiative.

Buy licenses. Distribute access. Wait. The Larridin Q1 2026 report found that 45.6% of organizations don’t even know their workforce AI adoption rate. The average organization has 23 AI tools in active use. Nearly half were adopted by employees on their own, outside any strategic framework.

The result is three compounding failures.

Strategy failure. No thesis on where intelligence creates compounding value in the specific business. Tools without architecture. Tool shopping doesn’t compound.

Tooling failure. Twenty-three disconnected tools, no coherent intelligence layer. The same pattern we saw in early SaaS before the industry learned that architecture beats a collection of point solutions.

Adoption failure. Organizations expecting their workforce to design the transformation from the inside. A mid-level knowledge worker wouldn’t be expected to architect an Azure AD deployment, but they’re expected to reimagine their entire operational workflow around intelligence?

People inside an organization optimize around existing processes and protect what’s familiar. When your job is at stake, you don’t design the system that eliminates it.

The companies seeing measurable impact brought in external expertise to answer the hard structural questions. What does this business look like when intelligence is foundational? What operating model emerges when you start from first principles instead of from the current org chart?

Every quarter spent buying tools and waiting is a quarter where the gap widens. Intelligence-native organizations compound their advantage. Organizations stuck in whack-a-mole AI churn. New tool, same structure, same results.

Three questions every executive should be able to answer right now. What is the measurable business impact of your AI investments to date? Who owns the AI strategy? What percentage of your workforce has fundamentally changed how they work?

If you can’t answer all three with specifics, the technology isn’t the problem. The approach is.