People debt: what GenAI exposes, and what to do about it

The demo worked. The rollout didn't. That gap is where most People teams meet their people debt for the first time, and it usually costs them a workflow before they understand what happened. GenAI does not create people debt. It exposes it. The inconsistent levelling, the undocumented process, the decision rights nobody can name, all become legible the moment you try to build an AI workflow around them, because the model needs rules that were never actually written down.

Here is the shape of it. About three weeks into the first build, the team realises the problem is not the model. The inputs the workflow needs do not exist in any consistent form. Levelling varies by team. The hiring loop on paper is not the hiring loop in practice. The performance criteria mean three different things at three different levels. The model can only be as clear as the rules it is handed, and the rules turn out to have been carried in people's heads for years.

What people debt actually is

People debt is the accumulated cost of decisions the People function has deferred, fudged, or never got round to writing down. It comes in four flavours, and every function I have looked at carries some of each.

• Definitions that drift. What "senior" means. What counts as a high performer. What a Director does that a Senior Manager does not. What the hiring loop actually is, step by step. These started as shared understanding and quietly diverged.
• Decision rights nobody can name. Who approves a salary band exception. Who signs off a counter-offer. Who decides when a role gets opened. In well-run companies, written down. In most, transmitted by oral tradition and reconstructed under pressure.
• Undocumented processes. The promotion cycle, the performance review, the comp calibration. Each one runs on a mix of spreadsheets, memory, and a Slack thread from eight months ago.
• Drifted artefacts. Comp bands nobody has calibrated in two years. Job descriptions that stopped matching the work. Scorecards no one updates but everyone still fills in.

Like technical debt, people debt compounds invisibly. It rarely shows on a quarterly review. It shows the first time you try to build something new on top of it. And the reason it survives so long is that humans are extraordinary at absorbing it. A person hits a contradictory rule, shrugs, picks the interpretation that fits the case in front of them, and moves on. The debt is paid, silently, out of individual judgement, every single day. Nobody logs the cost.

Why GenAI is the harshest debt audit you will run

Every AI workflow worth building needs three things: structured inputs, clear rules, and a defined output. The moment you assemble those for a real People process, you find out what is missing.

The onboarding workflow needs a canonical handbook. Half of it lives in three Google Docs and one Notion page that contradicts the others. The HRBP triage workflow needs a routing matrix. There is no routing matrix. Requests get routed to whoever happens to be online. The performance summary workflow needs a single definition of "exceeds expectations". There are six, one per level, and four of them are mutually inconsistent.

A person tolerates all of this without a word. A model cannot. AI is unforgiving of ambiguity, and people debt is mostly ambiguity, so the collision is not a bug in the build. It is the audit doing its job. Where the model fails, it is pointing at something that was never written down. That is worth more than a clean first result, because you now know exactly where the function is soft.

Read that as the base rate, not an outlier. If you have never run an AI workflow against your own definitions, you do not yet know how much debt you are carrying. The first build is how you find out, and finding out early, on one workflow, is a great deal cheaper than finding out late, across a rollout the whole team is depending on. This is the same reason AI pilots stall at production: the demo runs on a clean export a person prepared, and production runs on the mess underneath.

No, you don't pay it all down first

The instinct, once the debt is visible, is to stop and clean everything before building anything. Resist it. Three reasons.

You will never pay it all down. Even good companies carry people debt, because the function evolves faster than the documentation ever will. Waiting for a clean slate means waiting forever, and while you wait the debt keeps compounding.

The window is open now and not for long. Teams that build real capability in the next eighteen months will be operating in a different gear two years from now. Teams that treat cleanup as a prerequisite project will still be scoping it while everyone else is shipping.

AI work is the cheapest forcing function for cleanup you will ever get. The team is motivated, the build surfaces the specific debt that matters rather than the debt that feels tidy, and the cleanup carries a visible reward: the workflow you wanted in the first place. A standalone documentation project has none of that and dies in a backlog. So the cleanup rides on the build. That is the whole move.

The discipline is picking the right first workflow, one where the debt underneath is smaller than the thing you are building. Run the candidate through this before you commit.

The order to repay people debt

Once you are inside the right workflow, repay the debt it surfaces in a fixed order. The order matters because the early items are inherited by everything downstream, so cleaning them once pays interest on every later piece of work. Get the order wrong and you build a fast, confident, wrong model.

Skipping definitions is the most common and most expensive mistake. Teams jump straight to automating the process, hand the model six contradictory definitions of the same word, and produce confidently wrong outputs at scale. The model does not resolve the ambiguity. It amplifies it, and now it does so in every case at once, in writing, with a tone of authority. Fixing "exceeds expectations" is one afternoon in a room. Not fixing it is a performance cycle that quietly breaks trust for a year.

Decision rights are the second trap. Almost every automated workflow eventually reaches a step where it does not know who to route to. If that answer lives in oral tradition, the automation stalls there every time, and you end up with a human manually unblocking the "automated" process, which defeats the point.

The four kinds of debt, and where each one bites

Not all debt costs the same, and not all of it gets repaid at the same moment. The kind determines the blast radius, and the blast radius determines the order. This is the map I run against a workflow before committing to it.

Read the table top to bottom and you have the repayment sequence. Definition debt sits at the top because it is inherited widest: a wrong definition poisons every workflow that touches it. Artefact debt sits at the bottom because it is local and cosmetic by comparison, and because it tends to repair itself as a by-product of doing the higher work properly. Pay in that order and the interest stops compounding. Pay out of order, starting with the tidy artefact work because it feels productive, and you have polished the scorecards while the definitions underneath them still contradict.

Paper over it, or pay it down as you build

There are two ways to respond when the first build exposes the debt, and they lead to very different places. One treats the model as a way to hide the mess. The other treats the model as the reason to fix it. Same tool, opposite outcome.

The right-hand column is the seductive one, because in the short term it looks like progress. You shipped something. It even demos well, on the cases someone hand-picked. But you have taken an undocumented, ambiguous process and bolted a confident machine to the front of it, so now the ambiguity ships at speed with a straight face. That is worse than the manual version, because the manual version at least had a human in the loop quietly catching the contradictions.

What paying it down actually leaves you

Here is the part teams underestimate. Pay down people debt while building the workflow and you end up with two assets instead of one. You have the workflow. You also have a documented, current, defensible version of the underlying process, with named owners, written rules, and clean inputs.

That second asset is worth more than the workflow. It is the foundation every later workflow stands on, and it is the thing consultants usually charge you for and then take with them when they leave. Build it into your own systems as you go and the capability stays. This is exactly what a Grain Audit is designed to produce: one process taken end to end, the debt under it repaid, and a plan you keep rather than a slide deck you file.

You can see the full arc in the FinEdge 90-day case. The first two weeks were a diagnostic that surfaced the definition, decision-rights, and process debt sitting under three target workflows. The next four weeks repaid the specific debt those workflows depended on, then built. By day 60 the workflows were running. By day 90 the cleanup had bled out into adjacent processes, because once a definition is written down it tends to stay written down. If you want the diagnostic that finds the highest-debt workflows first, the People Ops diagnostic toolkit is the same instrument, and diagnosing AI readiness in People Ops shows where debt shows up in the score.

The pattern repeats every time. The AI work creates the deadline. The deadline creates the cleanup. The cleanup creates the foundation for everything that comes next. Skip it and you build castles on sand. Do it as you build, and each new workflow makes the next one cheaper, which is the compounding you actually want, running in the other direction. This is the practical core of the AI workspace for People Ops: the workspace is only as good as the definitions and decision rights underneath it.