How to Reward Better Work With AI
An AI usage count shows activity. A pay review needs evidence of better work and individual contribution, with a fair account of the tools, training, and help available to the employee.
The short version
- AI rewards should reflect verified improvements in quality, time, and business outcomes.
- Proficiency depends on each role's tasks, risks, and responsibilities.
- Comparable access to approved tools, training, and practice should precede financial consequences.
The work on the table
In a hypothetical appraisal, a research note turns up on a manager's desk alongside drafts from a colleague. Both staff members used the same AI tool. The colleague used it more frequently and produced more drafts. The analyst spent hours checking sources and correcting weak conclusions after AI had helped with the first draft of the note. The contribution to the business is the bit that's harder to quantify.
The Selby Jennings survey offers some context for that decision. It found that 25% of respondents said their organization considered AI proficiency as one factor in decisions on pay rises, promotions, and bonuses. 30% of the respondents said their organization required employees to disclose their AI use in formal reviews. Censuswide surveyed 500 US finance HR professionals between 19th and 26th August 2026.
The responses describe reported appraisal practices. For the manager at this desk, the next step is still to examine the note and the work behind it. A reward needs a result the reviewer can check, with a fair account of the opportunity and help the employee had.
The hours behind the note
OSVARD's assessment begins with the note and its review record. Supported conclusions, clear assumptions, delivery by the agreed deadline, and sources a colleague could check give the assessment substance. The record of AI use explains the method, while the finished note shows the result.
The time record starts before the draft and runs through the handoff. Preparation, drafting, checking, and correction all count. A fair comparison would use comparable work and clearly defined start and end points. An analyst might save an hour but leave a reviewer with two hours of repairs. In that case, the work has moved to a colleague; the total effort has not fallen.
A quality checklist determines whether the time claim qualifies for credit. It looks at accuracy, completeness, usefulness, and adherence to approved procedures. If a critical check fails, the work must be corrected before any time savings can count. Completion times are compared only after the work passes its critical checks.
A defensible route from AI skills to rewards
- 01Work definition
The manager and employee agree on the role, work product, quality checks, and who is responsible.
- 02Tool and training access
Employers provide approved tools, relevant training, and practice time, and record material differences in opportunity.
- 03Quality review
Reviewers check accuracy and usefulness. Time comparisons include corrections and any later rework.
- 04Contribution verification
Managers compare similar completed tasks, check whether improvements repeat, and record what the employee contributed.
- 05Reward calibration
Managers review similar cases, explain the reward decision, and show employees how to have errors corrected.
The limits of one score
A study of 5,172 customer support agents found that AI access increased the number of issues resolved per hour by an average of 15%. The uplift varied according to experience and skill. That was in one operational context, though, and provides neither a benchmark for finance staff nor guidance on an appropriate bonus size.
Task choice mattered in a randomized study involving 758 consultants. Participants did better on work suited to the tested AI. On a separate managerial problem outside its capabilities, those using AI were about 19 percentage points less likely to give the right answer. That experiment used GPT-4. It does not establish how every current AI system will perform, and a fluent answer still needs to be checked.
A research note, a service case, and a piece of code all leave their own performance record. For the analyst, the key metrics are supported conclusions and review time. For client service, it's about resolution accuracy and the number of avoidable repeat contacts. For the developer, it's about code quality (maintainability) and post-release defects. Criteria agreed before the review period can identify better work within an existing responsibility, better work on a harder task, help provided to colleagues, and good decisions to exclude AI from the wrong sort of task.
The opportunity behind the result
Software, suitable data, time for experimentation, restrictions, and workloads can all differ by staff member. That record helps reviewers assess the results. A paid tool provided by the employer is a resource; its availability doesn't necessarily mean that the employee is any more capable.
Tool access alone does not guarantee training. The OECD's April 2025 brief on AI skills notes that supply may not keep up with demand for general AI literacy. The brief also examines ways to make training more accessible and inclusive.
An employer’s contribution starts with approved tools and time to learn. Staff need examples of suitable work, instruction they can access, and supervised practice. The rules should say what is allowed and where to get help. If access differs, a reviewer can assess learning and judgment while putting off comparisons driven mainly by resources. A reasonable accommodation or an approved restriction does not, by itself, show a lack of ambition.
Evidence that fits the role
| Role | Useful outcome | Required check |
|---|---|---|
| Research analyst | Supported note within agreed time | Sources, assumptions, review effort, and corrections |
| Client-service specialist | Accurate issue resolution; fewer avoidable repeat contacts | Case complexity, resolution quality, and customer experience |
| Software developer | Maintainable code with less total effort | Security, tests, later defects, and maintenance work |
| HR operations specialist | Accurate case completion; clear explanations for employees | Privacy, exceptions, correction rates, and employee access |
The contributions a review can miss
The final note may reveal little about earlier attempts. Linking pay to every attempt could give an employee a reason to omit the ones that didn't work. A learning record will contain the attempt, the failure, and the changes. A separate performance record will document improvements that passed review.
A limited trial lets the company test its criteria before using them in reward decisions. Employees and reviewers will start with comparable tasks and an agreed baseline. They then record the work completed, the time taken, the corrections, and any relevant constraints. A number of examples will be used to help establish whether the improvements are repeatable. Managers will have thresholds in place in advance and will explain how the issue of uncertainty will impact the decision.
The name on the note doesn't necessarily identify everyone who contributed. One staff member may have developed a method that others can then use to produce their own work under their own name. The documentation would track the original contribution so credit can be given to the original contributor. If no credit is given, then individual bonuses may encourage staff to keep the useful method to themselves.
The decision the employee can understand
The review brings together business outcomes, clear decisions, and responsible AI use. A manager can start with the work already on the table and ask what improved, what it took to get there, and who contributed. That gives the employee a practical basis for understanding the decision.
Prior to the policy having any effect on pay, the employee is given the criteria, examples of acceptable evidence, and a route by which they can correct an inaccurate assessment. A common review process will assist managers in treating similar contributions in a consistent manner. HR will check whether any differences in rating reflect quality, role, access, or inconsistent judgment. The employee will then be given a clear explanation.
At the end of the imagined appraisal, the manager can put the note beside its time record and the account of who helped produce it. The employee can see what improved and why it deserves recognition. Shared learning and the judgment to leave AI out of an unsuitable task can count as contributions too.