AI tools are accelerating productivity. Employees produce more, faster and with fewer errors. Drafting documents, summarising information, generating ideas or analysing data can be done in minutes rather than hours. For performance reviews, this creates a surface‑level impression of improvement: higher output, cleaner work, more polished communication. However, this can mask underlying gaps. An employee who leans heavily on AI may appear to have improving skills. For example, someone using AI to debug code may seem more technically proficient. An apprentice using AI for analysis may look more capable than their actual expertise. So, today we discuss AI and performance management for employees.
AI and performance management
Traditional performance reviews assume that output reflects competence. Unfortunately, AI breaks this assumption. Managers often cannot tell whether a strong deliverable reflects genuine skill growth, expanded knowledge, critical thinking or effective AI tool use. It also muddies the water for comparison against peers. In short, AI complicates the evaluation of employee capability.
Knowledge no longer a differentiator?
Knowledge used to be a core differentiator in performance reviews. Employees were rewarded for expertise, recall and depth of understanding. AI tools flatten that advantage. When anyone can generate a passable explanation of a complex concept, the value of memorised knowledge declines. When you can also ‘sell’ that knowledge, a new level of blagging becomes possible for those who drastically overestimate their competence.
This shift has two potential consequences:
- Knowledgeable employees may feel undervalued: Their expertise becomes less visible because AI can replicate the surface‑level output of their thinking.
- Inexperienced employees may appear too capable: AI fills gaps that would previously have been exposed in their work and in project responsibility.
The danger here is that we stop recognising the difference between knowing and AI prompts. AI can produce information, but it cannot replace the judgement required to interpret it, contextualise it or challenge it. Performance reviews that fail to distinguish between these layers risk rewarding superficial competence over genuine expertise.
Skill atrophy and AI dependency
One of the more critical concerns is skill atrophy. When employees rely on AI for tasks they once performed manually, their underlying skills may weaken. Writing becomes prompting. Analysis becomes summarisation. Problem‑solving becomes asking a model for options. Whilst many employees use AI thoughtfully, others may rely on it as a crutch. Performance reviews rarely capture this because they focus on output, not process. A manager sees the final report but not the fact that the employee used AI to generate 90% of it.
Over time, this creates a hidden fragility in workforces. Employees may struggle when AI tools fail, produce errors, require more training or additional human oversight. They may lose confidence in their own abilities and organisations may discover that their talent pipeline is thinner than it appears.
Bias, inequity and AI‑savvy
AI tools introduce a new workplace inequity i.e. employees who know how to use AI effectively and those who do not. Prompting, tool familiarity and experimentation become performance differentiators, which are rarely recognised explicitly in review frameworks.
This creates several distortions (in no particular order and not exhaustive):
- AI‑savvy employees appear more productive, even if their underlying skills are average and the output was not challenged.
- Avoiding AI makes them look slower or less capable, even if their work is more original, critical or more rigorously argued.
- Non-consciously rewarding AI‑generated polish, mistaking it for human excellence and deep subject matter expertise.
The issue is transparency. If AI use is not openly discussed, managers cannot fairly evaluate performance. Employees who quietly use AI may be rewarded for output that does not reflect their actual capability, while those who avoid AI may be penalised for genuine skill.
What should managers be evaluating?
Managers face a difficult question: should performance reviews measure the employee, the outputs, or the systems that they use? If reviews focus solely on output, AI‑assisted work will eventually dominate. If they focus on capability, managers must find ways to separate the human contribution from the machine. However, if they focus on tools, organisations must redefine what good performance looks like. Many organisations face a ‘Jekyll and Hyde’ hybrid model where they expect employees to use AI but evaluate them as if it does not exist.
An AI performance management approach
Organisations need to rethink how they evaluate workplace contribution in the age of AI. Here are a few suggestions for how to start that journey:
- Identify AI usage: Encourage disclosure of AI contribution for clarity.
- Evaluate judgement: The ability to interpret, refine and challenge AI output.
- Reward originality: Creativity, insight and novel approaches to solving problems.
- Assess proficiency: Prompting, workflow design and responsible AI use.
- Protect core skills: Analysis, critical thinking and knowledge still matter.
Performance reviews must evolve from measuring only what employees produce to understanding how they produce it. It should also consider hallucinations and accuracy.
Conclusion on AI and performance management
AI is transforming work but performance reviews have not caught up. The result is a growing disconnect between capability and perceived performance. Without deliberate action, HR teams and managers risk rating and rewarding AI tool dependency, relative AI-savvy and polished communication skills. At the same time, they may overlook real expertise, critical thinking, relationship management and collaboration skills.
We must recognise employee for how they use AI, not allowing them to hide behind it. Managers should assess judgement, originality and skill – not just polished output. HR must create and champion frameworks for meritocratic and equitable performance management. This does not begin to cover fair use, potential for data breaches, disclosure of confidential information and more. Ultimately, the concern is the scope to ‘game the system’ and to improve the what without scrutiny over the how. Whether this is a good thing or not, we can say with certainty that organisations have not yet fully understood all of the impacts.
The future of performance management with AI
Tech stacks, job roles and performance frameworks will all advance in the coming years. Firstly, where AI tools are absent or free versions have limits, organisations will have multiple tools and agents at hand. Secondly, where job roles scarcely mention AI tools, usage or the ability to critically challenge outputs, it will become integral. Thirdly, where performance management rewards output without asking how it was generated, it will become central. Unfortunately, just as education faces challenges in vetting coursework and assignments, so too will employers struggle to identify AI dependency and superficial knowledge without help.
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Finally, why not read related articles about AI prompting skills or challenges in getting AI ROI.