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Keep a running record of what changed, what you personally contributed, and what evidence shows the result. When AI alters a task or workflow, document more than the tool you used: capture your decisions, setup, review, corrections, and the observed effect, while clearly labeling outcomes as measured, estimated, or qualitative. This makes your work easier to explain accurately at review time without claiming that AI caused results you cannot substantiate.
Why should I document contributions as AI changes my role?
AI can change the shape of a job before a job title, performance plan, or review form catches up. A task may take a different amount of time, require new checks, or make a kind of work possible that was previously impractical. If you record only the final output—or only that you used an AI tool—you may leave out the judgment, process changes, and team support that explain your actual contribution.
A useful record gives a reviewer a traceable account of the work: what needed to be done, what you did, what changed, who benefited, and what evidence supports the result. That is different from keeping a list of busy tasks. The U.S. Department of Commerce puts the distinction plainly: “Accomplishments are the products or services (the RESULTS) of doing your job (not tasks or activities).” Its guidance recommends relating accomplishments to organizational goals and considering quality, quantity, timeliness, and cost effectiveness where relevant. U.S. Department of Commerce: Writing Up Your Accomplishments
There is no universal AI productivity figure or standard attribution method that can show what a particular employee achieved. A time change in one workflow does not establish the same result for another person or organization. Your record should therefore make the basis for each statement visible instead of presenting estimates or illustrative comparisons as measured impact.
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What should I record as work happens?
Start with an employer-approved place you can maintain throughout the review period. It might be an existing notes file, calendar, journal, or another permitted method; the useful choice is the one that lets you capture an example promptly and retain its supporting evidence. The Department of Commerce recommends keeping notes or a file through the appraisal period, and UC Davis advises recording accomplishments and feedback rather than trying to reconstruct them at the annual review. UC Davis Human Resources: Writing a Self-Evaluation & Goals
Make a short entry when a meaningful event occurs. Record significant work products, outcomes, milestones, positive feedback, new skills, changed responsibilities, challenges, and relevant context. A brief note with a link to an approved artifact is usually more useful later than a polished paragraph written from memory.
A reusable contribution record
- Date or review period: When did the work happen?
- Goal, expectation, or changed responsibility: What work or outcome was expected, and did that expectation change?
- Work product or problem: What did you produce, improve, or resolve?
- My specific contribution: What decisions, actions, coordination, or expertise came from you?
- Result and beneficiary: What changed, and who or what benefited?
- Evidence: Which approved deliverable, feedback, metric, or milestone supports the account?
- AI-related effect, if observed: Did time, quality, volume, or capability change?
- Measurement basis: Is the statement measured, estimated, or qualitative?
- Human review and judgment: What did you check, correct, or decide?
- Context and next step: Were there collaborators, constraints, blockers, or remaining work?
You do not have to fill every line for every entry. Use the fields that clarify the contribution, and avoid recording sensitive information in a place or format your employer has not approved. The point is to preserve enough context to verify and explain the example later.
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No single app or method is best for every workplace. Compare the options your employer permits by four practical criteria: how easy it is to capture a note while work is fresh, whether you can retain a link to supporting evidence, whether the location meets privacy and retention rules, and whether you can sort entries by goal or review period.
- Notes file: A simple document can hold dated entries, links, and headings for each goal. It works well if the file is stored in an approved location and you can keep it current.
- Calendar or project log: A calendar entry or project system can anchor the note to a date, milestone, ticket, or deliverable. Use a separate approved summary if the system does not preserve enough context.
- Journal: A recurring journal can capture feedback, changed expectations, and lessons learned. Make entries specific enough to connect them to a work product or outcome.
- Employer performance system: If your organization provides a review workspace, it may make goal alignment and access easier. Check whether drafts are visible to managers or retained after the cycle before entering sensitive details.
The format matters less than consistency and traceability. Do not move confidential material into a personal application simply because it is more convenient, and do not assume that a tool’s search or summarization feature is permitted.
How do I describe an accomplishment rather than a task?
Write the result and your role, not just the activity. Commerce suggests a cause-and-effect pattern: “Drafted A that resulted in B which contributed to C.” Use it as a reasoning aid, not a formula that forces a claim of causation. Connect the outcome to a role expectation, team goal, or organizational purpose when the connection is supported.
Build a review-ready statement
- Name the work product or problem. Be specific enough that a reviewer can understand what was delivered or changed.
- State your part. Distinguish your contribution from work performed by colleagues, the team, or an AI system.
- Explain the result. Describe what followed, for whom, and why it mattered to a relevant goal.
- Attach the evidence and its limits. Identify the artifact, feedback, indicator, or milestone; say if a number is measured or estimated.
A practical sentence structure is: “I [specific contribution] to [work product or workflow], which [documented result] for [stakeholder or goal]. Evidence: [artifact, feedback, or measured indicator].” If the result is not yet known, describe the completed milestone and mark the outcome as pending. Do not convert an unfinished project into a completed impact claim.
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For instance, “Used AI for a report” says little about the work or outcome. A stronger entry would identify the report workflow, explain what you set up or changed, name your review or corrections, and point to an observed result—such as a verified quality change, an actual time measurement, or stakeholder feedback. If you do not have evidence of an effect, document the work you completed without claiming one.
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Consider quality, quantity, timeliness, and cost effectiveness only when they are relevant and supportable. A metric is not automatically stronger than a concrete description: an unverified number can make an otherwise sound account less credible. Commerce and UC Davis both emphasize clear, concise, honest self-assessment; Commerce also cautions against laundry lists. Commerce accomplishment-writing guidance and UC Davis self-evaluation guidance
What evidence is worth preserving?
Save the smallest amount of approved material that lets another person understand and verify the claim. Depending on your work, that might be a final deliverable, a version history, a ticket showing a resolved issue, a project milestone, a quality check, a documented customer response, or feedback from a colleague. A link to the location and the date can be enough when the underlying file is already retained in an official system.
For quantitative claims, record the period, scope, and method. “Reviewed 40 cases” is clearer when you can say which reporting period and what counted as a case. A before-and-after comparison is useful only if the tasks, conditions, and definitions are reasonably comparable. If the metric came from a dashboard, note which dashboard and date you used. If you counted manually, say that. If a number is an estimate, label it as one.
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How do I document AI-related changes without overstating impact?
Describe the task or workflow, the change you made, and the effect you actually observed. Relevant evidence can include elapsed time, output quality, volume, error or rework rates if they were measured, stakeholder feedback, or work newly made possible. The key distinction is between using a tool and contributing to a changed process or result.
Lean In recommends making a short weekly note about meaningful AI-related changes and illustrates describing a task’s time change. Those examples are illustrative, not general findings or guaranteed results. CIPD likewise recommends tracking time saved or quality improved and developing the ability to evaluate AI output. Lean In updated its guidance on July 30, 2026, and CIPD published its practical guidance on April 14, 2026. Lean In: How to Get Credit for Using AI at Work and CIPD: AI skills planning
Label the evidence, not just the outcome
- Measured: You have a defined basis for the figure, such as recorded elapsed time or a consistently counted output. State what was measured and avoid implying broader results than the measurement supports.
- Estimated: You have a reasoned approximation, but not a direct measurement. Say it is an estimate and explain its basis if that would help a reviewer assess it.
- Qualitative: The evidence is descriptive, such as feedback or a newly enabled capability. Name the source or observed change instead of turning it into an unsupported percentage.
When comparing before and after, make sure the comparison is meaningful. Note what task or period you are comparing and any important difference in scope or conditions. If there was no comparable baseline, say so; do not create a precise “time saved” figure from memory and present it as measured.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsBe careful with attribution. If a team result improved after an AI-assisted workflow changed, that timing alone does not prove the tool caused the improvement. Record what you personally did—such as selecting a task, designing or improving the workflow, checking outputs, correcting errors, or applying professional judgment—and distinguish your contribution from the tool’s output and the team’s result. This approach follows evidence-focused appraisal guidance and the human-oversight principles emphasized by OpenAI Academy and Australia’s National AI Centre. OpenAI Academy’s guide was published July 20, 2026, and updated August 4, 2026; the National AI Centre guidance was published May 5, 2026. OpenAI Academy: Conducting performance reviews with ChatGPT Work and National AI Centre: Guidance for AI adoption
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How often should I update the record?
Capture an entry close to the event rather than waiting for the annual review. Add a note after a meaningful deliverable, milestone, feedback exchange, changed assignment, or problem you helped resolve. A short weekly review can help you notice AI-related workflow changes that are easy to overlook, but the appropriate cadence depends on your workload and employer process; no universal schedule has been established.
During a weekly check, scan for four things: work completed, outcomes or feedback, changes in responsibility, and evidence that still needs to be saved. Remove duplicate entries and add the missing context while you still remember it. This keeps the final self-assessment selective instead of forcing you to reconstruct an entire year from email and memory.
How do I show changed responsibilities and development?
Record how the expectation changed, not only that it changed. Note the new task, why it was assigned, what you learned, and how you applied the skill. Examples might include learning to evaluate AI output, creating a review checklist, training colleagues on a revised workflow, or taking responsibility for a quality or risk check that did not previously exist.
Challenges and constraints belong in the record when they explain the result. Note an incomplete dependency, a privacy restriction, a changing requirement, or a decision to stop using a workflow after quality concerns. Include the action you took and the next step. This gives a reviewer context without turning the self-assessment into an excuse.
Less visible work can be important evidence: mentoring, coordinating handoffs, preventing an error, documenting a process, or enabling a colleague to complete their part. Describe the beneficiary and the artifact or feedback that makes the contribution visible. OpenAI Academy’s 2026 guide specifically encourages attention to enabling work and missing context.
How do I show my contribution when the work was shared?
Describe the team outcome, then make your own part explicit. A shared result does not mean every employee made the same contribution, and a self-assessment should not imply sole credit for work delivered by others. Commerce recommends identifying the individual’s contribution within team efforts and reporting meaningful milestones even when a project remains unfinished.
Record enabling work where it helps explain the result: coordination, mentoring, review, risk prevention, or work that let colleagues move forward. Include customer or colleague feedback, blockers, changed expectations, and relevant constraints when they affect how the contribution should be understood. OpenAI Academy’s 2026 performance-review guide also calls attention to less visible enabling work and the need for reviewers to check for missing context. OpenAI Academy performance-review guide
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What mistakes weaken an AI-related contribution record?
- Making the tool the headline: Naming a model or application does not explain your judgment, setup, or accountability.
- Using false precision: A precise percentage or time saving is not credible when it was recalled informally or based on incomparable tasks.
- Claiming causation from timing: An improvement that followed a new workflow may have several causes. Describe your contribution and the evidence rather than assigning all credit to AI.
- Listing activity without a result: Prompts, meetings, drafts, and experiments matter only when they explain a deliverable, decision, capability, or lesson.
- Ignoring review work: Checking accuracy, correcting errors, protecting sensitive information, and deciding not to use an output are contributions when they affected the work.
- Overlooking team context: Do not claim sole ownership of a shared result, but do not omit your specific enabling work either.
- Hiding uncertainty: Mark outcomes as pending and numbers as measured, estimated, or qualitative. Honest limits make the rest of the account easier to trust.
How should I turn my notes into a self-assessment?
At review time, group the strongest examples by performance-plan goal, role expectation, or changed responsibility. Choose examples that show meaningful results and the evidence behind them; do not paste every entry from your running record into the form. UC Davis advises employees to be clear, concise, and honest, while Commerce recommends concise bullets rather than a laundry list. UC Davis self-evaluation guidance
- Review your notes and sort them under the goals or expectations used by your employer.
- Select examples with clear personal contributions and useful evidence, including relevant milestones and enabling work.
- Draft each example as a result-focused statement, naming your part and the beneficiary or goal.
- Check every factual statement against its supporting artifact, feedback, or measurement.
- Label estimates and qualitative observations; remove or qualify any claim whose evidence does not support it.
- Add a concise account of development, challenges, or changed expectations where it helps explain the period.
Before submitting, ask whether a reviewer could tell what you did, what changed, who benefited, and how you know. If not, add context or narrow the claim. Local review criteria, forms, and employer policies differ. The cited employee guidance is from U.S. government and university contexts, so use your own organization’s performance plan and instructions as the controlling framework. The broader advice to keep evidence traceable is practical guidance, not a substitute for local policy or a legal requirement.
Can AI help me organize or draft my contribution record?
It can help sort approved material, identify themes, or produce a draft, but a person must verify the final account. Check every statement against the underlying record, restore missing context, and remove exaggeration, bias, or claims that the evidence does not support. Keep final judgment with people rather than treating generated prose as proof.
OpenAI Academy recommends reviewing generated performance-review output for exaggeration, missing context, bias, and unsupported claims. Australia’s National AI Centre guidance emphasizes accountability, documentation, and meaningful human oversight. These principles are especially important when a generated summary compresses team work into an individual claim or turns a rough estimate into a confident-sounding number.
- Use only tools and materials permitted by your employer.
- Do not enter confidential or sensitive information into a tool unless your organization allows it.
- Compare generated claims with original artifacts and feedback; correct omissions and misattribution.
- Retain the distinction between measured results, estimates, and qualitative observations.
AI can help with wording, but it cannot supply evidence that was never recorded. If a draft makes a claim you cannot trace to a work product, measurement, or feedback, revise it or leave it out.
Frequently Asked Questions
What if I don’t have a baseline for an AI-related time saving?
Do not invent a before-and-after number. Describe the workflow change and any other evidence you do have, and state that a comparable baseline was not recorded.
Should I ask my manager what evidence to include in a review?
Yes. Ask which goals, role expectations, and evidence types the review process uses, especially if responsibilities have changed. That helps you organize examples in the framework your employer will actually assess.
Can I include customer or colleague feedback in my self-assessment?
You can cite relevant feedback when it is appropriate to share and your employer’s process permits it. Identify its source and context accurately, and avoid presenting one person’s comment as a broader measurement.
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What if a work example contains confidential information?
Follow your organization’s rules for storing and submitting review evidence, and use an approved summary or reference when the underlying artifact cannot be shared. Do not move sensitive material into an unapproved notes file or AI tool.
How do I document an AI result that is only an estimate?
Label it as estimated, explain the basis briefly, and avoid presenting it as a measured organization-wide result. If the estimate is too uncertain to defend, describe the workflow change without a number.
What should I write when the project is unfinished?
Document the completed milestone, your specific contribution, and the evidence for that milestone. Mark the final outcome as pending instead of claiming the project’s eventual impact.
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Does using AI by itself count as an accomplishment?
Not by itself. Explain what you changed in the workflow, what human review or judgment you applied, and what documented effect or capability followed.
How can I show work that enabled other people?
Name the coordination, mentoring, review, risk prevention, or process documentation you provided, identify who benefited, and connect it to feedback, a milestone, or another approved artifact.
What if AI helped draft my self-assessment?
Use it only with approved information, then verify every statement against your record. Correct missing context, exaggeration, bias, misattribution, and unsupported numbers before submitting.
The Bottom Line
Document contributions as they happen, connect each example to a result and a goal, separate your judgment from AI output and team outcomes, and label evidence honestly. A concise, traceable record is more persuasive than a long list of tools or tasks.
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