How to compare HR AI training vendors for recruitment and people analytics

HR is one of the weakest places to buy generic AI training. The risks are too specific. Recruitment, people analytics, workforce planning and worker monitoring all affect people in unequal power relationships.

That does not mean every HR AI tool is automatically high-risk in every implementation. It does mean HR teams need training that reflects the work, the data and the human impact.

When comparing HR AI training vendors, use a sharper scorecard.

Start with the HR use cases

Ask each vendor to map training to real HR AI use cases:

• CV screening and ranking;
• candidate matching;
• interview support;
• vacancy text generation;
• people analytics;
• retention prediction;
• workforce planning;
• worker monitoring or productivity analytics.

If the vendor cannot talk concretely about these workflows, the training will probably stay generic.

Recruiter judgement

Recruiters need to understand AI output without blindly trusting it. A good vendor should include scenarios on:

• proxy bias in CV data;
• keyword bias in matching;
• human review of shortlists;
• explaining AI use to candidates;
• logging overrides;
• escalation when the model output looks unfair.

Ask for a sample recruiter scenario. If it is only a general quiz about "what is AI?", it is not enough.

Hiring manager behaviour

Hiring managers are often forgotten. They may not operate the AI tool, but they act on the shortlist. Training should cover automation bias, score interpretation and second-review triggers.

Useful question: "How do you train managers not to treat AI rankings as objective truth?"

The answer should include realistic shortlist cases, not just policy reminders.

People analytics fairness

People analytics training should teach HR teams when dashboards become risky. Variables such as absence, commute distance, engagement scores, shift preferences and manager ratings can carry proxy meaning.