Risk Has an Owner · aaddrick.com

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Where This Came From

I dropped some comments on a post in r/artificial revolving around AI’s use in procurement. My points were tangential to the original post, but I wanted to collect them here as something a bit more cohesive.

This article smells a bit like I’m pitching an AI governance platform or the latest AI procurement framework. Sadly, I am not. These are just some thoughts I wanted to get out.

It Depends

I do equipment and facility CAPEX procurement, deployment, and implementation for a living. It’s all different phases of procurement, but generally executed by different people. CAPEX procurement isn’t an optimization problem, though that’s part of it. It’s horse-trading under uncertainty and managing risks.

Here’s what a real situation looks like. $2M piece of bespoke kit that’s part of a larger project is running late. What do you do?

The answer is the one everyone hates most… it depends.

Maybe you can throw money at it. Expedite fees, overtime authorization, whatever it takes to pull the schedule in.

Maybe the delay doesn’t actually matter because the downstream isn’t ready anyway and you can absorb it in the schedule.

Maybe you take the floor model as a temporary solution and make it work until the real unit arrives.

Maybe you subcontract that capacity from somewhere else to bridge the gap while you wait.

Maybe you go back to the supplier and ask them to shoulder some of the cost. Or maybe you don’t, because they’re a small shop in some little German village1 with a couple of brilliant engineers and it’s not worth poisoning the relationship over a number that isn’t going to move anyway.

There isn’t a right answer unless you understand what the risks are and what the appetite for them is. The logical answers are only logical based on that context. But they all come back to the same thing: risk. Who owns it. How much of it someone is willing to take. And what that’s actually worth.

That’s where the post’s framing, “what is it optimizing for?”, lands a step short. The metric matters, but fixing the metric doesn’t fix the ownership problem. Those are two different gaps.

Risk Has an Owner

The load-bearing claim Risk is owned by a person. That’s not a preference or a best practice. It’s structural.

At any meaningful scale, when a bet goes bad, nobody accepts “the AI did it.” Whoever owned the risk owns the outcome. If they’re competent, they were doing the math themselves the whole time. Not just trusting the AI’s output, but pressure-testing it against their own read of the situation. The AI is advisory. Even when it’s being pitched by some Sales team otherwise.

The original post’s central scenario is an agent that works perfectly. It does exactly what it was told and still causes harm. A cost-minimizing agent renegotiates supplier terms flawlessly. Margin gets squeezed. The supplier, already thin, collapses six months later. Nothing malfunctioned.

A procurement agent that makes a decision at machine speed isn’t accepting the risk. It’s making a recommendation that executed without a human checkpoint.

Those are different things.

The governance failure there wasn’t that the agent optimized for the wrong metric. The failure was that someone let the agent act above the accountability line without owning the outcome themselves.

The agent is structurally advisory. That doesn’t change when it gets faster.

The Accountability Line

The obvious pushback: you can’t personally own 10,000 decisions. And that’s true. Volume is real. Speed is real. Nobody is reading every PO at a hyperscaler. But the accountability line scales with capital at stake, and that’s the key.

A mom-and-pop shop doesn’t want to mess up a $10k piece of equipment. A hyperscaler doesn’t want to mess up a $3B infrastructure build. The line is in a different place, but it exists for both of them. Below it, automation is fine. Above it, a person owns the outcome.

What a well-built AI procurement system actually does is move the line. It extends how far down the automation can go safely. That’s valuable. But it doesn’t erase the line. And it doesn’t transfer ownership.

CAPEX procurement in particular is going to be safe for a while on this front. It’s not because the AI can’t be useful. It already is. It’s because the outcomes are too large and too visible for anyone to accept “the model decided.” The tolerances get narrower the higher the capital gets. That’s not going to change.

The Part That Was Never a Target

Say you get the governance model exactly right. Clear owner above the line, auditable actions below it, reward functions designed as joint metrics across commercial,...