Insights
June 24, 2026
Roughly 88% of enterprise AI pilots never reach production — and it's almost never the model's fault. The 12% that make it scope narrow, tie the build to one owned metric, deploy in their own infrastructure, and keep an engineer accountable past go-live.
Reports say Uber burned through its entire 2026 AI budget by April — a vivid symptom of a wider problem: AI spend that isn't tied to measurable return. The fix isn't spending less on AI. It's knowing the cost and the value of every task before you scale it.
When AI underperforms, the model gets blamed. The real culprit is almost always the data feeding it. The unglamorous work of getting data ready is the highest-leverage thing you can do before deploying AI — and a moat competitors can't easily copy.
Key obligations of the EU AI Act begin applying on 2 August 2026, covering high-risk uses like hiring, credit, and essential services — and it reaches you even from outside the EU. Compliance isn't a document you write at the end. It's an architecture you build in from the start.
The most common AI mistake isn't picking the wrong model — it's picking the wrong first project. The best first workflow is boring: bounded, repetitive, high-volume, and measurable, where inputs are structured and the outcome can be counted.
Most AI rollouts don't fail on the tech — they fail because the people it was built for don't adopt it. The fix isn't a better model or a mandate. It's embedding AI inside the existing workflow, and involving the people who'll use it before you build.
Your people are already pasting company information into consumer AI tools you didn't approve and can't see. Banning it doesn't work — it drives the behavior underground. The fix is to give people a sanctioned, governed path that's better than the shortcut.
The 'AI replaces jobs' framing is scary and mostly wrong for how enterprises actually deploy it. Well-built AI takes over the repetitive, low-value work — freeing skilled people for the work only they can do. That framing isn't just kinder, it's how you get real adoption.
Most companies pick one of two extremes: buy a generic platform, or build everything in-house. Both have poor odds. The path that fits is AI built around your actual workflows, owned by you, delivered by people who've shipped it before.
Most companies budget and plan for building AI, then treat deployment as the last step. It's actually where the real work and cost begin — and the difference between a system that survives and one that dies is often just someone accountable who stays through go-live.
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