The Brief
Short, direct pieces on what I'm seeing, what I'm thinking, and what it means for the leaders navigating this.
A monthly update collects the latest pieces and research. Get it here.
The Classification You Don't Do, a Court Will
You can delete a bad output. You cannot delete a bad action. A draft can be rewritten, a recommendation ignored. An email that has already gone, a payment that has cleared, cannot be recalled. That is the line between software that proposes and software that decides and acts, that has agency. Organisations are stepping over it now without quite noticing they have done so, which is what makes most of the governance advice on offer in 2026, sensible as it sounds, beside the point.
The Talent Inversion: Why the People Most Ready for AI Leave the Organisations Least Ready to Change
Earlier this year I was working with a COO and their Chief of Staff on an AI rollout that had reached just over 3,000 employees. The numbers all read well: adoption was up, the tools were live, satisfaction was high. So I asked what had actually changed about how decisions get made. There was a pause. “Nothing,” the COO said. “We’re faster at the same things.”
The Decisions Your AI Is Making Without You
The AI deployed in your organisation made decisions this week. Your records say you sanctioned every one of them. Both statements are true. Neither one means what most leaders believe it does.
The Friction Layer: Where Structural AI Pressure Actually Lands
TechWolf’s Skills Intelligence Index analyses over two billion job postings from 1,500+ companies worldwide, mapping the tasks people perform and scoring each for AI impact using the Stanford Human Agency Scale.
Shadow Agents: The AI Your Governance Wasn't Built to See
There was a moment in a conversation with a Chief Learning Officer a few months ago that I haven’t been able to set aside. They told me their organisation had deployed AI agents across four functions. Healthy adoption. Strong metrics. The board was pleased. I asked what had changed in how those functions made decisions. They said: the tools are there, people are using them, adoption is strong. I said: that’s not what I asked. The room went quiet.
When Oversight Becomes Forensics
The pilot failed quietly. The agent won’t. The last two years had a logic to them. Open the door, watch what walks in, learn. Shadow AI proliferated outside procurement, outside IT, outside anyone’s line of sight. Governance lagged. It always does. But the outputs were containable. A bad summary stayed a bad summary. A hallucinated competitor analysis sat in a deck until someone caught it. Damage was bounded by the nature of the thing producing it: a system that generates text, not one that acts in the world.
The Fluency Trap Revisited
I’ve been at UNLEASH America in Las Vegas this week, sitting in sessions, talking to senior leaders in the corridors, listening to what people are actually saying about AI right now. Not what they say in press releases. What they say when they’re thinking out loud between panels. The thing I keep hearing is some version of this: we’re past the early problems. The models are solid now. They reason through things. They catch their own mistakes.
They Costed The Role. Not The Person.
When I’m brought in after the fact, after the automation is live and something isn’t working the way it should, the first thing I ask is: what do we have to work with? Every time, the room moves toward the same things. The system architecture, the data, the governance documents, the codebase. People start pulling up diagrams. Someone mentions the platform vendor. Someone else opens a laptop.
The Timestamp Problem
When someone reviews an AI output before it goes out, what are they actually checking? I’ve been asking that near the end of these meetings, after the frameworks and the policies. Someone stares at the mug in front of them. Someone else sees an urgent Teams message pop up on the screen, types two sentences, looks up.
