How should firms review AI used in hiring and performance decisions?

Short answer: Treat AI as support, not as the decision-maker, when its output could affect who is hired, promoted or assessed. Define permitted tasks, check the information and output, and ensure a competent person can make an independent decision.

Why this matters to professional-services firms

In a professional-services firm or regulated SME, hiring and performance decisions affect people and the organisation’s ability to explain how it reached an outcome. AI-generated rankings, summaries or recommendations can look authoritative while omitting context or containing errors. A person who simply accepts an output is not providing meaningful review.

This applies whether a tool organises applications, ranks candidates, summarises interview notes, analyses employee activity or suggests a performance rating. The more an output shapes the outcome, the clearer the firm’s controls and human accountability need to be.

Separate administrative support from assessment

Define the task rather than approving a broad use such as “HR”. For example, a firm might permit AI to draft a job description or organise application materials, subject to checks. Ranking candidates, filtering who advances or recommending an employee outcome is different: those outputs can influence decisions even when a person formally signs them off.

Write down what the tool may do, what it must not decide, which outputs require review, and who can challenge or override them. Avoid a nominal human approval step that does not allow time or authority to examine the evidence.

Set practical review and record controls

• Define the decision: Identify the workflow, affected people and permitted role of AI before introducing or expanding use.
• Check inputs and access: Establish what information is used, whether it is relevant, and who can access it. Handle personal or confidential information under the firm’s applicable policies.
• Test and question outputs: Use representative examples to look for errors, omissions and patterns that merit investigation. Testing can inform a decision; it does not prove that every use is suitable.
• Assign an accountable reviewer: Name someone who understands the process, can inspect relevant evidence and has authority to reach a different conclusion.
• Keep a usable record: Record the tool’s role, the review performed and the decision in line with the firm’s record-handling practices.
• Provide an escalation route: Make it possible to pause use and refer unusual or disputed cases to a responsible person. Revisit the controls when the tool, data or workflow changes materially.

Plan implementation before choosing or expanding a tool

Start with the decision the firm wants to support, the information needed and the person who remains accountable. If the firm cannot explain how a reviewer will examine the evidence or how concerns will be escalated, pause expansion and resolve those gaps first.

Pattrn Data’s AI Governance Retainers can help firms establish and maintain practical governance controls, ownership and review routines for AI use. Discuss whether this support fits your implementation needs.

Source and provenance

Prepared in response to: “What happens when AI touches a decision about a person’s job, pay or career?” Background source: AI policy for employees. This article presents practical governance considerations, not legal advice or a claim that any particular control guarantees a fair outcome.