Most companies in 2026 have an AI strategy. Far fewer have an AI budget they can defend.
This isn’t a failure of leadership. It’s a structural lag. Every new technology category goes through the same phase: experimentation runs ahead of governance, individual decisions accumulate faster than collective oversight, and somewhere along the way the organization realizes it has been spending serious money on something it doesn’t really know how to evaluate.
Email went through it. SaaS went through it. Cloud went through it. The gap between adoption and accountability is usually two to three years — long enough for the spend to become significant, short enough that nobody had time to build the systems to govern it.
AI is in that gap right now.
What the lag actually costs
When an organization can’t see something it’s spending on, three predictable things happen.
The first is duplication. Multiple teams pay for tools that do nearly the same thing — and because nobody is looking at the full picture, nobody knows.
The second is inertia. Pilots that should have been killed get renewed. Tools that should have been scaled get treated as experiments. Without a feedback loop, every decision defaults to keep doing what we’re doing.
The third — and most expensive — is opportunity cost. The conversation that should be happening at the leadership level (“where is AI actually creating value, and where should we invest more?”) never happens, because the data to have it doesn’t exist.
The way out is unglamorous
The organizations that get past this phase don’t do anything dramatic. They treat AI spend as a category that deserves the same rigor as any other meaningful line item: visibility first, then governance, then measurement.
That isn’t a transformation. It’s hygiene. But the companies doing it are pulling ahead, quietly — not because they’re using AI better, but because they finally know what better means in their own organization.
You can’t make good decisions about something you can’t see. That’s the whole point.
