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Every week the AI press produces approximately 4,000 articles. Most of them are some variation of “AI is either going to save us all or end civilisation as we know it, here are five quotes from people who haven’t shipped anything.” I skip those. Here’s what actually mattered.
The One Thing You Should Have Noticed
The real story this week wasn’t any model release or funding round. It was the gap between what AI companies are announcing and what enterprise buyers are actually deploying.
Every major analyst firm is revising their enterprise AI adoption projections: numbers down for year one, up for years three through five. Translation: companies bought the narrative in 2023–2024, hit the wall of actual implementation complexity in 2025, and are now slowly figuring out what they actually need to do to make this stuff work.
This is healthy. The hype cycle is compressing into something more useful. Companies that did the boring work — understanding their data infrastructure, documenting their processes, planning for change management — are starting to see real ROI. Companies that bought enterprise AI platforms because the board asked about AI strategy are quietly extending contracts while deploying nothing.
The winners in the next eighteen months aren’t the companies with the biggest AI budgets. They’re the ones where someone in operations actually understood what needed to change before the software arrived.
Three Things the Coverage Got Wrong This Week
“[Model X] will replace [profession Y].” It won’t. At least not in the way or on the timeline described. The pattern holds without exception: volume work at low-to-medium complexity, yes. Judgment under uncertainty with real accountability, no. Take any “AI will replace [profession]” headline, divide the claimed timeline by three, and add “at the junior level.” Now it’s probably accurate.
“OpenAI’s valuation means AI is overvalued.” Private company valuation in a winner-take-most market where the underlying capability is still improving rapidly tells you almost nothing about whether the technology is overvalued. These are different questions. The press treats them as the same question, constantly, and it’s a lazy argument either direction.
“AI is hitting a wall.” The “scaling laws are ending” narrative keeps cycling back. It keeps being premature. The honest answer is we don’t know. Neither does the person writing that headline with certainty in either direction. If anyone tells you they know how this curve develops over the next five years, they’re either guessing or selling something.
The Number Worth Knowing This Week
€847 billion — IDC’s estimated total addressable market for enterprise AI software by 2030. For context: the entire global ERP software market — SAP, Oracle, everything — is roughly €50 billion annually.
Either the most important technology market in history, or the most overhyped forecast in decades. Probably both, with the actual number landing somewhere between them and arriving two to three years later than predicted.
What You Should Actually Do
If you run a business and you’re trying to figure out where to start with AI: ignore the weekly headlines. They’re mostly noise designed to keep you reading, not to help you make decisions.
Focus on one question instead: which task in your operation do people spend the most time on that produces the least differentiated output?
That’s your entry point. Not “AI strategy.” Not “digital transformation.” One task. Automate it. Measure the result. Then do the next one.
The companies winning at this are not the ones with the most sophisticated AI vision. They’re the ones with the most boring, systematic implementation practices. That’s always been how technology adoption actually works. This time is not different.
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