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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →If AI-related work has become a recurring part of your job, make the case for a raise by showing how your responsibilities have changed, what outcomes you can substantiate, and how your pay compares with relevant roles. Using AI tools alone does not establish a case for higher pay; the stronger argument is that your role now includes valuable work, judgment, or accountability that your compensation should reflect.
Start with how your job has changed
Describe the difference between the role you were hired to do and the role you do now. Focus on recurring responsibilities and accountability, not simply the fact that you use an AI tool.
- Prior responsibilities: What work was already part of your job?
- New or expanded work: What AI-related tasks do you now handle regularly, such as designing workflows, evaluating outputs, integrating tools, or training colleagues?
- Judgment and oversight: What decisions do you make, what do you review, and what risks or errors are you expected to catch?
- Workload and scope: Did the new duties replace other work, add to it, or make you accountable for a broader process?
This before-and-after account connects AI work to the scope of your position. The U.S. Department of Labor’s TEAMS Salary Negotiation Participant Guide 2026 advises employees to prepare their skills, experience, and added value to support a compensation request.
Build evidence of your contribution
Keep a concise record of examples, dates, and outcomes. Useful evidence might include completed work, improved service, reduced rework, quality checks, or risks identified. Include time saved or increased throughput only when you have a credible way to support the estimate.
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- Separate measured results from estimates, and explain how each figure was calculated.
- Use examples that show your contribution, not just the capabilities of the tool.
- Choose evidence that you are allowed to share. Do not paste sensitive company information into an external AI service unless your employer has approved that use.
You do not need to claim a precise productivity gain if you cannot verify one. Specific, defensible examples are more useful than an impressive but unsupported percentage.
Benchmark pay against comparable work
Look for roles that match your occupation and level of responsibility, then account for location, industry, experience, and education. The U.S. Bureau of Labor Statistics’ Occupational Employment and Wage Statistics (OEWS) salary-negotiation guidance describes May 2025 estimates for about 830 occupations, including wage distributions and averages, with data by occupation, area, and industry. Its published figures provide context, not a personalized salary promise.
Where possible, compare more than one defensible match. Explain why each is relevant and note differences in seniority, duties, or industry rather than selecting only the highest figure. Consider total compensation as well as base pay. The BLS also describes the National Compensation Survey’s data uses as including comparisons of compensation for work with different duties and responsibilities.
AI wage-premium studies are not a shortcut to a raise figure. An IZA analysis of AI developers in 29 European countries reports an average premium relative to comparable workers, but the study population, geography, and jobs differ from an employee who has added AI tasks to an existing role. Its findings do not establish a universal premium or raise percentage for your situation. See IZA Discussion Paper 17607.
Choose a clear request
Decide what adjustment you want before the meeting and connect it to the change in scope, your evidence, and relevant pay comparisons. A specific amount or range makes the conversation concrete; use a figure you can explain rather than one derived from a general claim about AI pay.
Prepare to discuss the overall compensation package, not only salary. Depending on your circumstances, you might also ask about other terms or a shorter review period. These are options for the conversation, not guaranteed substitutes for a pay adjustment.
Make the case in the meeting
Keep the opening concise: explain what changed, provide one or two strong examples, state what you are seeking, and invite a discussion. For example:
“Over the past [period], my role has expanded to include [specific recurring AI-related responsibilities]. I’m accountable for [review, decisions, or outcomes]. Examples include [verified result and evidence]. I’d like to discuss adjusting my compensation to reflect this scope. Based on [relevant role, location, and industry benchmarks] and these results, I’m seeking [specific amount or range]. What would be the right process and timing to review this?”
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Replace every bracketed phrase with details you can substantiate. If your manager cannot approve the request or an adjustment is unavailable now, ask what measurable expectations would support a review, who makes the decision, and when you should revisit it. That gives you a clearer next step, though it cannot guarantee an increase.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use AI for rehearsal, not as your authority
The Department of Labor guide suggests using AI to prepare salary scripts, anticipate questions, practice scenarios, compare offers, and examine salary data. You can rehearse a concise explanation or ask for likely objections, then check every factual claim and number against reliable sources and your own records. Harvard Law School’s Program on Negotiation warns that AI can introduce errors and implicit biases; its guidance on using AI for pay negotiations is a reminder to treat generated advice as a draft, not a source of truth.
What negotiation studies can—and cannot—tell you
A 2025 National Bureau of Economic Research working paper reports two field experiments involving more than 3,100 U.S. tech job seekers. Light-touch encouragement increased negotiation attempts and compensation gains, while discounted coaching did not significantly affect attempts. Those results concern job seekers in specific experiments, not current employees asking for raises, so they do not predict the outcome of an individual conversation. See NBER Working Paper 33903.
For your decision, the most relevant case remains the one you can make about your own duties, outcomes, and comparable compensation—not a study-derived promise of a particular increase.
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