The Dollar Threshold Is Static. Risk Isn’t.

Most companies have a number at which they stop trusting you.

You might be able to approve a $5,000 expense but not a $50,000 one, offer a customer a small credit but not a large one, or approve an ordinary claim but send an unusual one upstairs. We call these approval limits, and there is nothing particularly wrong with them. They are one of the ways organizations control risk.

The logic is familiar. The larger the consequence of a decision, the smaller the group of people allowed to make it. When something falls outside the rules, authority moves upward until it reaches someone the company trusts to make the call.

In The Average Is Lying to You, I argued that AI changes the mix of work left for people. In The Side Door Is the Whole Building Now, I followed that into the exception processes that increasingly have to handle it. Put those two together and you get an uncomfortable problem: the work left for people requires more judgment, while the systems governing that work were built to constrain it.

Imagine a claims manager looking at a $75,000 claim. The documentation is messy. The policy doesn’t quite fit. She’s handled hundreds of claims and thinks this one should be paid.

Should the company simply raise her approval limit from $10,000 to $75,000 and tell her to use her judgment? Of course not. The approval limit wasn’t invented because somebody disliked judgment. It exists because the company is taking risk. More discretion can mean more inconsistent decisions, more mistakes, more opportunity for fraud and, occasionally, more damage from someone who simply isn’t very good at exercising judgment. So authority can’t move by itself. Control has to move with it.

For decades, hierarchy has helped solve that problem. The claims manager sends the decision to her supervisor. The supervisor sends the unusual one to a specialist. Large enough decisions go another level higher. The organization controls risk partly by controlling who is allowed to take it. That works surprisingly well when unusual decisions are unusual. It works less well when technology systematically removes the usual ones.

Here’s where I think the interesting possibility is, and it isn’t just giving people more visibility after they’ve already decided. It’s that the boundary itself can stop being static.

Right now, that boundary is a dollar figure. Under $10K, decide. Over $10K, escalate. But a threshold like that treats every $75,000 claim the same, and every $12,000 claim the same, regardless of what’s actually inside them. Suppose instead the system looks at the case itself: a $75,000 claim with a familiar fact pattern, strong supporting evidence, an experienced adjuster and no fraud indicators can be decided on the spot. A $12,000 claim with contradictory evidence, unusual claimant behavior and a departure from normal policy gets escalated, even though it’s a fraction of the size. The dollar threshold is static. Risk isn’t.

That’s a different kind of control than watching a decision after it’s made. The evidence, the comparable cases and the departure from normal pattern are what decide, before money moves, whether this case needs another judgment or not. What gets logged afterward — her reasoning, how her decisions compare to her peers’, whether she’s repeatedly overriding the same policy — isn’t a substitute for that control. It’s what keeps recalibrating where the boundary sits.

That distinction matters because hierarchy is a fairly blunt instrument for governing judgment. A manager approving a decision doesn’t necessarily know more about the case than the person who sent it upstairs. Sometimes the manager contributes experience or judgment that genuinely improves the decision. Sometimes the decision moved because the organization decided long ago that someone with one title could take the risk and someone with another title couldn’t.

AI gives us a chance to distinguish between a decision that needs another judgment and one that merely needs another signature.

The person closest to the work can potentially have more room to exercise judgment while the organization gets better visibility into how that judgment is being used. Instead of controlling every decision through prior permission, it can decide which decisions need permission, which can happen inside defined boundaries, and which can be reviewed after the fact. Once permission can respond to context rather than just hierarchy, permission itself starts to look less like a policy and more like an architecture.

I called this Offensive Permission Architecture, or OPA, in Migrating Scarcity. The idea is that permission itself can be designed: who can act, under what conditions, with what information, inside what boundaries, and what gets reviewed before versus after the fact. Most companies inherited their answers to those questions from an operating model built before this kind of visibility was possible. AI changes enough of the underlying economics that those answers are worth reopening.

Most of the AI conversation is about the falling cost of doing work. I think something else may be falling too: the cost of controlling who is allowed to do it. If AI can make the context around a decision visible, preserve the reasoning behind it, and identify risk before and after someone acts, delegation itself becomes cheaper. We built authority around the value of the transaction because we couldn’t cheaply measure the risk of the decision. AI may change that.

It doesn’t mean hierarchy disappears. Some decisions are important enough that another person should look at them before anything happens. Some require genuinely different expertise. Some risks should never sit with one individual. But perhaps decisions should move upward because another person’s judgment adds something, not simply because hierarchy is the only control mechanism we have.

For decades, we have used hierarchy to answer two different questions: who has the judgment to make this decision, and who are we willing to trust with the risk. AI may finally let us separate them. A decision doesn’t have to move upward because it crossed a number on an org chart. It can move because another person’s judgment would actually make it better. That’s a very different reason to have a hierarchy.