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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Yes, a manager might use AI to help write a performance review, but that does not necessarily mean AI evaluated your work. Editing a manager’s completed assessment is different from a system that scores employees, recommends promotions, or otherwise influences a career decision. To understand what is happening, ask what the tool does, what information it uses, and whether a manager independently decides what to include and what action to take.
“AI wrote my review” can mean several different things
The phrase covers a range of uses. A manager might ask a writing assistant to make their own notes clearer, use software to summarize work records, or rely on a system that generates an assessment or recommendation. Those uses are not equivalent: the key question is whether AI changes only the wording or also influences the judgment about your performance.
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| AI’s role | What it may do | What to find out |
|---|---|---|
| Language editing | Revises or polishes text after a manager has decided what the review says. | Did the tool only edit wording, or did its suggestions change the substance? |
| Record summarization | Compiles information such as attendance or work records for a manager. | Which records were included, and could relevant work or context be missing? |
| Evaluation or ranking | Scores, compares, or ranks employees against selected measures. | What measures and data drive the result, and can the employee or manager correct errors? |
| Recommendation or decision support | Suggests who should receive a promotion or otherwise influences an employment decision. | How much weight does the recommendation carry, and can a manager reject it in practice? |
The European Commission AI Act Service Desk illustrates this distinction with examples: a tool that revises language after a manager has completed an evaluation is supportive editing; a preparatory attendance-reporting system that makes no evaluations or recommendations is also different from a system that influences an employment decision. The tool’s actual function matters more than the label “AI.”
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How AI can influence who gets promoted
A promotion process may involve software at several points: gathering work records, summarizing them, assessing performance, comparing employees, or recommending a decision. A tool need not make the final choice to matter. If its score or recommendation shapes what a manager sees, which candidates receive attention, or how a decision is justified, it can still influence the outcome.
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The European Commission’s Joint Research Centre describes “algorithmic management” as the use of computer-programmed procedures to coordinate labour input in an organisation. It can include work organization, performance assessment, and rewards or penalties. Algorithmic management can use AI, but it does not have to; not every automated workplace process is an AI system.
In the EU, recital 57 of Regulation (EU) 2024/1689 identifies employment systems used for evaluating people at work and making decisions affecting promotion among the uses classified as high-risk, given their potential impact on career prospects, livelihoods, and workers’ rights. That does not mean every AI writing assistant is automatically high-risk: the system’s intended purpose and role in the decision are central to the distinction.
How widespread is AI use in reviews and promotions?
There is not enough evidence here to say what share of employers use generative AI to draft performance reviews or AI systems to recommend promotions. A broad workplace-use figure should not be mistaken for a measurement of either practice.
The Joint Research Centre reported in 2025 that one third of EU workers surveyed said they used AI for work-related purposes. Its AIM-WORK survey took place in 2024–2025 across EU Member States, but it measured broad work-related AI use, not the prevalence of AI-written reviews or AI-supported promotion decisions. The European Commission’s study of algorithmic management, published on 28 March 2025, covers developments only through September 2023, so it is not a survey of current generative-AI review practices in 2026.
Where unfair or misleading assessments can come from
AI does not make an assessment fair simply by making it appear consistent or data-driven. The OECD identifies several ways bias can enter: historical ratings can reflect earlier disparities; records may be incomplete or unrepresentative; and design choices determine which variables or measures count as performance.
For example, a system built around recorded output may fail to capture work that is less visible in the data, such as mentoring, collaboration, or handling unusual cases. That is a practical possibility, not proof that any particular workplace system has made an unfair decision. The relevant questions are what evidence the tool uses, what it leaves out, and whether its output has been checked against the work being assessed.
The OECD also notes that well-designed tools could support more consistent assessments. Consistency alone, however, does not establish that the criteria are valid or that the results are fair. The OECD says more empirical research is needed to determine how workers’ perceptions compare with company outcomes.
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You can seek a concrete explanation without assuming that AI made the decision. Ask questions that distinguish assistance with wording from influence over an evaluation or promotion:
- What did the tool do? Did it edit language, summarize records, evaluate performance, rank employees, or recommend an action?
- What information did it use? Ask which work records or measures informed the output, and whether relevant work or context might be missing.
- How did a manager use the result? Did the manager review it independently, change it, or have the practical ability to reject it?
- How can an error be corrected? Ask how to flag inaccurate or incomplete information and who reviews a challenge.
- Was the output tied to a consequence? Clarify whether it affected a rating, promotion consideration, task assignment, reward, or penalty.
- What workplace process applies? Ask for the employer’s relevant policy or explanation of the review process. Rules and disclosure practices vary by jurisdiction; the sources discussed here do not establish a universal employee right or notice requirement.
Keep the distinction between a tool’s assistance and its influence in view. A polished paragraph does not, on its own, establish that AI assessed your work; a manager’s final approval does not, by itself, show that an AI recommendation had no effect.
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Why the rules depend on where you work
The EU AI Act provides a specific legal framework for certain employment uses, including systems used for evaluation and promotion decisions. The legal treatment depends on what a system is intended to do and how it is used; the Act’s classification should not be generalized into a claim that every automated or AI-assisted HR task is covered in the same way.
Elsewhere, policy may be different. In a November 2024 parliamentary response, Singapore’s Ministry of Manpower addressed automated employment decision tools in hiring or promotion and said the government would monitor trends and assess whether existing guidelines and regulations remained adequate. That statement describes Singapore’s position at that time, not a universal rule or a complete account of current employee rights everywhere.
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If a consequential decision appears to have relied on a system, identify the jurisdiction and ask what local employment and data rules apply. The EU and Singapore examples show why neither an employer’s practice nor an employee’s rights can safely be inferred from another country’s approach.
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