Where AI Actually Helps Inside a People Function

Two years ago I ran an experiment I did not tell anyone about. I had a model score 200 CVs for a role we had already hired for, then compared its ranking against how those candidates actually performed in interviews.

It ranked our eventual hire 74th.

It was not stupid. It was doing exactly what it was asked: matching surface features against a job description. Our hire had switched industries, had a two-year gap and had none of the listed keywords. She was also, obviously, the best person in the pipeline within about eight minutes of talking to her.

That result shaped how I have used AI in people work ever since. The line is not between simple and complex tasks. It is between tasks where the text is the work and tasks where the text is evidence about a human being.

Where it genuinely earns its place

Drafting anything that starts from a blank page

Job descriptions, interview guides, rejection notes, onboarding checklists, policy first drafts, offer explainers. A model gets you to a solid 70 percent draft in two minutes instead of forty.

The gain is not just speed. It is that things which used to get skipped now get done. Before, a structured interview guide existed for our top three roles because that is all anyone had time to write. Now every role has one, because the first version takes four minutes. Consistency across 30 roles beats brilliance across three.

Retrieval over your own documents

This is the most underrated use and the least discussed. Our policy handbook, benefits documentation and process guides run to a few hundred pages. Nobody reads them. Everybody asks a person instead, and that person is usually in HR.

Putting a retrieval assistant over those documents cut routine policy questions to my team by roughly 60 percent. Two conditions made it work: every answer cites the source document and section, and anything about pay, termination or immigration is deliberately routed to a human. It answers "how many days of leave carry over" and refuses to answer "am I being paid fairly".

Summarising and structuring what humans produced

Turning your own interview notes into a structured scorecard. Clustering 300 free-text engagement survey responses into themes. Summarising a long performance conversation into agreed actions.

Note the direction of travel: a human generated the judgement, the model organised it. That is the safe orientation. When it runs the other way, you are in trouble.

Removing scheduling and coordination toil

Unglamorous and worth real money. Availability matching, reminder sequences, document chasing, interview logistics. Our coordinator went from spending most of her week on calendar work to spending most of it on candidate experience — the part that actually differentiates us.

Where it does not belong

Ranking or scoring candidates

I have tested this repeatedly and I will not do it in production. Three reasons.

It optimises for legible signals — brand-name employers, keyword density, tidy career paths — and those correlate with access and privilege far more than with capability. The candidate who took two years out, or studied somewhere you have not heard of, or changed fields at 34, gets filtered by a system that cannot see the thing that makes them good.

It is confidently wrong in a way humans are not. A recruiter who is unsure says so. A model returns 8.4 out of 10 with the same fluency whether it is right or badly out of its depth. False precision is worse than acknowledged uncertainty because it stops the conversation.

And in most jurisdictions you must be able to explain an adverse decision. "The model ranked them lower" is not an explanation, and in the EU AI Act framing, employment screening sits firmly in the high-risk category. Building on that foundation is a legal position, not just a product choice.

Anything close to a termination, promotion or pay decision

These are the decisions where being wrong changes someone's life. They require context that is nowhere in your data: the six months someone carried a struggling team, the manager change that derailed a quarter, the medical situation nobody wrote down. A model sees the record. The record is not the person.

Writing the message that should have cost you something

An AI-drafted note declining a candidate after five rounds is technically fine and emotionally hollow, and people can tell. The same is true of a condolence note, a difficult performance message, or a company-wide announcement about layoffs.

If the point of a message is to show that a person spent time on you, outsourcing it defeats the message. Write those yourself. They are a small fraction of your volume and nearly all of your reputation.

Culture and values work

Ask a model to write your values and you get a competent, generic list that could belong to any company. Values are the record of trade-offs a specific group of people actually made under pressure. They have to be extracted from your own history, not generated.

The rules I actually operate by

After enough iterations, this is what we hold to:

1. AI drafts, humans decide. Any output that affects a person's status is reviewed and owned by a named human before it goes anywhere.
2. Never the sole basis for an adverse decision. No rejection, no performance rating, no termination traceable to a model output.
3. Cite or refuse. Anything answering a policy question links to the source. No source, no answer.
4. Candidates are told. If AI touches a hiring process, we say where and how. Nobody has ever objected to a clear explanation.
5. Sensitive data stays out. Health information, immigration status, compensation records and grievance details do not go into general-purpose tools.
6. Audit quarterly. We sample outputs and look for drift. Twice this has caught tone problems in candidate communications that nobody would have flagged individually.

What this is really worth

Honest accounting. AI has not reduced my headcount. It has moved where the hours go.

Roughly 30 percent of our people-operations time was administrative production — drafting, formatting, answering the same question, coordinating calendars. Most of that is now assisted, and the recovered time went into candidate experience, manager coaching and actually talking to employees.

That is the real return, and it is bigger than it sounds. The work that was crowded out is the work that only humans can do, and it was always the work that determined whether people stayed.

If you are starting, start with retrieval over your own documents. It is the lowest risk, the fastest to show value, and it teaches your team where the technology is trustworthy before you point it anywhere near a decision about a person.