The Brief on Endeavor Intelligence

The Friction Layer: Where Structural AI Pressure Actually Lands

Tue, 05 May 2026 00:00:00 +0000

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

Wed, 29 Apr 2026 00:00:00 +0000

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

Fri, 27 Mar 2026 00:00:00 +0000

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

Wed, 18 Mar 2026 00:00:00 +0000

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.

Thu, 12 Feb 2026 00:00:00 +0000

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

Thu, 15 Jan 2026 00:00:00 +0000

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.