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If AI-related tasks have become a regular part of your job, make the case for a raise by showing how your role has changed, what results you can substantiate, and how your pay compares with relevant benchmarks. Using AI is not, by itself, proof that a raise is due—and there is no established universal percentage for taking on AI work.
Build the case around a changed role, not just a new tool
Start with the work you are now expected to own. The case is stronger when AI tasks have become recurring responsibilities that require judgment, review, decisions, or accountability—not merely occasional use of a tool to complete the same work.
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Compare your duties before and after
Write down your previous responsibilities, then list what has been added or materially changed. Include the work AI supports, what you decide or review, and what you are accountable for. Note whether the new work replaced existing duties or sits on top of them. The U.S. Department of Labor’s TEAMS Salary Negotiation Participant Guide (2026) recommends preparing your skills, experience, and added value to support a compensation request.
Keep an evidence log
Record dates, examples, and outcomes as the work happens. Useful evidence might include a verified reduction in turnaround time, more work completed, improved quality or service, less rework, or risks you identified. Explain how you measured each result. Label estimates as estimates, and do not claim a productivity gain you cannot support.
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Use only material you are permitted to share. Do not paste confidential company information into an external AI service to prepare your case.
Find a pay comparison that fits your work
Use pay data as context for your request, not as a personalized promise of what your employer should pay. Match the comparison to the work and level of responsibility you actually have, then account for location, industry, experience, education, and the rest of your compensation package.
Use BLS wage data carefully
The U.S. Bureau of Labor Statistics’ OEWS salary-negotiation guidance points readers to Occupational Employment and Wage Statistics (OEWS) data by occupation, state or area, and industry. The BLS page describes May 2025 estimates across about 830 occupations and includes wage distributions as well as averages. Averages and percentiles describe groups of workers; they do not determine an individual employee’s appropriate salary.
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Choose the closest occupation and responsibility level you can defend. If you use more than one comparison, explain why each is relevant and where it differs from your role. Avoid choosing a high-paying title just because it mentions AI if its duties or seniority do not match yours. The BLS also describes compensation-rate research for work with different duties and responsibilities through its National Compensation Survey data usage page.
Do not treat an AI wage-premium study as a raise formula
An IZA Discussion Paper 17607 from January 2025 examines AI developers in 29 European countries and reports an average wage premium relative to comparable workers, including an unexplained component. That population, geography, and analysis do not establish a raise percentage for someone in another job who has taken on AI-related tasks.
Decide what to ask for and how to frame it
Before the meeting, choose a specific figure or a defensible range and connect it to your role evidence and pay comparisons. Think about total compensation, not just base salary: relevant components may include bonus, benefits, or other terms. Decide which outcomes would be acceptable to you, but keep the central request clear.
The DOL guide recommends preparing your evidence and considering the full compensation discussion. A concise opening could sound like this:
“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?”
Best Value
Replace each bracket with facts you can substantiate. If you do not have a defensible benchmark or result, do not invent one to make the script sound more persuasive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prepare for the conversation and the next step
Rehearse likely questions
Practice explaining what changed, why it matters, how you measured results, and how you chose your pay comparisons. The DOL guide suggests using AI to draft salary scripts and practice negotiation scenarios. Treat generated wording as a rehearsal aid: verify every number and factual claim against reliable sources. Harvard Law School’s Program on Negotiation cautions that AI can introduce errors and implicit biases in its article, “How to Negotiate a Pay Raise or Starting Salary Using AI” (June 17, 2026).
If the answer is not now
If your manager cannot approve an adjustment immediately, ask what measurable expectations would support a future review, who makes the decision, and when you can revisit the request. This gives you a concrete next step; it does not guarantee that a raise will follow.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesNegotiation evidence can offer perspective, but not a forecast for your case. A 2025 NBER working paper, revised in July 2025, reports two field experiments involving over 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 the study, not current employees negotiating a raise, and do not predict an individual outcome. See NBER Working Paper 33903.
Quick Recap
A practical checklist before you ask
- Changed scope: You can name recurring new duties, decisions, review obligations, and accountability.
- Evidence: You have dated examples and can distinguish measured outcomes from estimates.
- Relevant benchmarks: Your comparisons fit your occupation, responsibility level, location, and industry.
- Clear request: You have chosen a specific amount or range and considered total compensation.
- Next step: You know what you will ask if an immediate decision is not possible.
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