The line this pilot draws
People operations teams carry a lot of administration. Employees ask how much leave they have left, how to claim an expense, when benefits enrolment closes, what the parental leave policy says. New joiners need equipment, accounts, contracts countersigned and induction booked. All of it is necessary, much of it is repetitive, and most of the answers already exist in a policy document somewhere.
AI can help with that. It must not be allowed to drift into decisions about people. Screening candidates, scoring interviews, rating performance, choosing who is made redundant: these are decisions with legal, ethical and human consequences, and in several jurisdictions they are regulated as high-risk uses of AI. This pilot is limited to administrative support only, and that boundary is written into the scope before work starts.
What the work involves
Approved policy information. We identify the policies that generate the most questions and confirm that each has a current, approved version and an owner. The assistant answers employee questions only from those documents and always shows the source section. If the policy does not cover the question, it says so and points to HR rather than guessing.
Data minimisation. With your data protection lead, we write a checklist: which categories of staff information may enter which tool, which never may (health, grievances, disciplinary records, anything special-category), how long anything is retained, and how the tool is configured. The policy workflow is designed to need no personal records at all.
Human escalation. We map the topics that always go to a person, who receives them and how quickly. The assistant recognises those topics and routes rather than answers.
One onboarding workflow. Using information HR already holds, the assistant drafts the joiner checklist, reminders and access requests for HR to review and send. It does not decide anything about the joiner.
This follows the principle I have published as the Substrate Pattern: decide in advance what an AI system can touch and where a person must approve, and enforce it in the design rather than by asking people to be careful.
The signature deliverable
You end with an approved policy-information workflow, a data minimisation checklist and a human escalation map. Illustrative example of an escalation map extract:
| Topic | Assistant behaviour | Routed to |
|---|---|---|
| Leave balance or policy wording | Answer from approved policy, show source | Not routed |
| Health, sickness or reasonable adjustments | No answer; give HR contact | Named HR adviser |
| Grievance, bullying or discrimination | No answer; give confidential route | HR business partner |
| Pay dispute or individual contract terms | No answer; give HR contact | Payroll / HR adviser |
| Question not covered by any policy | Say so; no guessed answer | HR inbox |
Illustrative example. Topics and routes are set with your HR team and data protection lead.
How acceptance is judged
The HR operations owner accepts the pilot against measures agreed at the start: the share of routine policy questions answered from an approved source with the correct section, the number of sensitive queries correctly escalated (and any that were not), HR inbox time spent on routine questions, and onboarding checklist completion on time. Any escalation miss is reviewed the same week. The adoption measure is whether employees use the policy route instead of emailing HR or turning to public tools.
Ownership and handover
HR owns the policy library, the checklist and the escalation map. Policy owners keep their documents current; the assistant follows them. Your data protection lead keeps approval of the tool configuration. The handover note explains how to add a policy, change an escalation route and review the log.
Boundaries
If you are looking for AI to make or support decisions about candidates or employees, this is not that engagement and I will not scope one. For the wider picture of AI use across business teams, see practical AI enablement for business teams. For an employee-facing knowledge assistant that serves customers rather than staff, see AI enablement for customer support.