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Build a living contribution record that shows what changed, what you did, and what another person can verify. For each meaningful AI-related change, capture the original task and baseline, the system or workflow introduced, the human skills you applied, your checks and decisions, the result, the evidence location and date, and a reviewer who can validate your account. Keep a separate AI-literacy log for training, guidance, experiments, and policy decisions.
This approach proves value beyond naming a tool. It gives a manager evidence for a performance review, gives you a defensible account when duties or staffing change, and helps you identify the digital, data, managerial, creative, and judgment skills to strengthen next.
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Why does an AI-related contribution record matter?
AI usually changes the composition of a job rather than replacing every part of it. A tool may draft, classify, summarize, or generate options, while a person still defines the problem, prepares data, checks output, handles exceptions, protects information, and accepts accountability for the decision. Your record should make those human contributions visible.
The International Labour Organization’s 2026 assessment says AI adoption is increasing demand for cognitive, socioemotional, digital, and AI skills, with AI literacy, adaptability, resilience, and human agency becoming especially important. OECD data from 2024 found that, in occupations most exposed to AI, 72% of vacancies requested at least one management skill, 67% at least one business skill, and 58% at least one digital skill. These percentages describe vacancies in the exposed group, not every job or every country.
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Documenting outcomes also keeps you from making an inaccurate claim that “AI saved time” or “AI made the decision.” OECD’s 2024 workplace survey found that four in five workers said AI improved their performance and three in five said it increased their enjoyment of work, while also identifying concerns about work intensity, data use, privacy, and inequality. A credible record shows both benefit and control: what the system contributed, what you contributed, and what risks you managed.
What should each contribution entry contain?
Use one entry for a material workflow change, project, recurring process improvement, or decision. The following fields create an auditable chain from the old way of working to the new result.
| Field | What to record | Why it matters |
|---|---|---|
| Before | The task, baseline period, time or quality level, backlog, customer or stakeholder outcome, and known constraints. | Provides a comparison instead of an unsupported improvement claim. |
| Change | The AI system, feature, or workflow; the date introduced; and the specific part of the task that changed. | Separates the technology from the work you performed around it. |
| Your contribution | Instruction or prompt design, data preparation, domain judgment, review, editing, testing, exception handling, coordination, or training. | Shows the skill and effort that are easy to miss when a tool is named. |
| Safeguards | Privacy checks, source verification, error or bias checks, disclosure, approvals, access controls, and escalation. | Demonstrates responsible use and accountability for consequential work. |
| Result | Time, quality, throughput, cost, revenue, risk, accessibility, customer, or team-capability change, with a comparison period. | Connects your actions to an observable outcome without assigning everything to AI. |
| Evidence | An approved dashboard, ticket, version history, feedback record, decision record, or training record, with its location and date. | Lets another person inspect the claim. |
| Validation | Reviewer name or role, review date, agreement or disagreement, and any limitation they noted. | Adds independent confirmation and preserves uncertainty. |
| Next skill | A gap, practice plan, completed training, confidence level, and the evidence you intend to produce. | Turns documentation into a development plan rather than a static archive. |
Use the smallest approved reference that proves the point. A link to a dashboard or ticket is usually safer than copying customer data or an entire conversation with an AI system.
How do I create the record step by step?
Start the record when a change is proposed, not months later when a review form is due. A short entry made during the work is more reliable than a retrospective description.
- Write the baseline and intended outcome. Describe the old process, its comparison period, and what “better” means. For example, the target might be faster issue triage without increasing the rate of incorrect escalations.
- Describe the bounded AI use. Name the system or feature, its approved purpose, the date, and the exact task it performed. Avoid treating a general-purpose system as if it owned the whole workflow.
- Save permitted working evidence. Retain representative instructions, input-preparation notes, revisions, test cases, approvals, and exception examples only when company policy allows it. Remove confidential, personal, regulated, or proprietary data from the record.
- Log your human decisions. Note what you accepted, rejected, rewrote, verified, or escalated, and the domain knowledge used to make those choices. Record a failed output when it changed the process or revealed a control that was needed.
- Measure the result against the baseline. Use a comparison period and state whether the figure is measured, estimated, or an observable change. If no numeric metric exists, describe a specific change such as fewer handoffs, clearer documentation, quicker triage, improved accessibility, or avoidance of a known risk.
- Ask for validation. Have a manager, peer, process owner, or stakeholder confirm your role and the result. Keep their date, role, and any qualification rather than editing the record to remove disagreement.
- Update the entry when conditions change. Revisit it when the model, process, role, data, or risk changes. Archive superseded versions so a reviewer can see which description applied at the time.
Which AI-related skills belong in a performance review?
List the capability and the evidence, not just the name of a software product. OECD’s 2026 analysis says fewer than 1% of workers need advanced AI-specific skills; most workers need digital skills, the ability to use, analyze, and interpret data, managerial skills, and human skills such as problem-solving, creativity, and innovation.
Data use and interpretation
Show how you prepared data, checked an AI-generated analysis, tested a conclusion against source material, or explained uncertainty to a decision-maker. Evidence can include a reconciled data set, a documented validation check, or a decision note that records why an output was not accepted.
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Describe the problem you reframed, alternatives you generated, experiment you designed, or process you improved. The valuable act may be choosing a different workflow after the first AI approach failed, not producing the first draft.
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Management, prioritization, and coordination
Record how you set priorities, allocated review time, coordinated people around a changed process, or decided which exceptions required escalation. The 72% management-skill figure from OECD applies to vacancies in occupations most exposed to AI, so it is a signal about demand in that group rather than a universal benchmark.
Domain expertise and judgment
Explain the context you supplied, the errors you detected, and the decision rules you applied. A subject-matter expert who recognizes a plausible but wrong answer is contributing a control that a tool cannot provide by itself.
Adaptability, resilience, and learning
Document a transferred skill, a changed workflow you learned, recovery from a failed experiment, or coaching you gave to colleagues. Include practice evidence and the conditions under which the skill works; a course completion alone does not demonstrate reliable performance.
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Capture your understanding of capabilities, limitations, privacy, disclosure, bias, access, and escalation. Include how you explained those limits to others or changed a process to reduce risk.
Human oversight and accountability
State who had authority to approve the outcome and what you did before it reached a customer, employee, regulator, or other affected person. This is especially important where an error could cause financial, legal, safety, employment, or reputational harm.
How can I describe AI-assisted work without overstating it?
Use a four-part sentence that keeps the system’s role bounded: “I used [system or workflow] to [bounded task], then I [checked, interpreted, edited, or decided] using [domain skill], which changed [metric or observable result] from [baseline] to [new state], with [safeguard or approval].” Replace the bracketed terms with facts and identify whether the result is measured or observed.
Illustrative example: issue triage
“I used an approved classifier to group incoming support issues, then I reviewed uncertain cases and corrected the routing rules using product knowledge, which reduced the average number of handoffs from the baseline period while keeping a human approval step for priority incidents.” The example attributes the routing improvement to the combined workflow, not to the classifier alone.
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Illustrative example: document production
“I used a drafting assistant to produce a first outline, then I checked every factual claim against the source file, rewrote the analysis for the intended audience, and obtained the required approval, which shortened the drafting cycle without changing the publication standard.” Keep the source file, revision history, and approval reference if policy permits.
Illustrative example: data analysis
“I used a language model to suggest queries, then I tested them on a controlled sample, reconciled totals to the reporting system, and explained the limitations to the project lead, which made the analysis reproducible and prevented an unverified result from entering the decision record.”
Do not claim that an AI system made a judgment you made, that a draft was correct because it sounded plausible, or that a time reduction was caused only by automation when training, process redesign, or staffing also changed.
What evidence will make a contribution credible?
Evidence should be inspectable, proportionate to the claim, and retained under your organization’s rules. Match the evidence to the type of contribution rather than collecting everything.
| Claim | Useful evidence | Check to include |
|---|---|---|
| Faster work | Timestamped workflow records, cycle-time dashboard, or comparison-period report. | State whether volume, staffing, or scope also changed. |
| Higher quality | Sampling results, defect log, approval record, or stakeholder feedback. | Define the quality measure and who assessed it. |
| Better decisions | Decision memo, alternatives considered, source verification, and approval trail. | Show your reasoning and unresolved uncertainty. |
| Risk reduction | Privacy review, escalation record, bias check, access-control change, or avoided incident. | Describe the known risk and the control you applied. |
| Team capability | Training record, guidance document, office-hours notes, or evidence that colleagues adopted the process. | Record audience, date, and what they could do afterward. |
| Transferable skill | A second project or role where you applied the same method successfully. | Identify what transferred and what had to be relearned. |
When a result cannot be quantified, use a concrete observable change and identify who observed it. “Improved efficiency” is weak; “removed one manual handoff from the approved intake path, confirmed by the process owner on 15 May” is inspectable.
How should I maintain an AI-literacy log?
Keep the literacy log separate from project entries so learning and governance work is not lost when a project closes. Add training, internal guidance, experiments, policy decisions, and coaching, each tied to the systems and risks in your role.
EU AI-literacy guidance says organizations should consider staff members’ technical knowledge, experience, education, and training together with the context and risks of the systems they use. It also allows an organization to keep an internal record of training and other guiding initiatives. The guidance does not require a particular certificate under Article 4, so fit your evidence to the role, system, and risk instead of chasing a universal credential.
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- Training: title, provider, date, topics, assessment or practice, and the work situation where you applied it.
- Guidance: policy or briefing delivered, audience, questions raised, and changes made afterward.
- Experiments: hypothesis, approved data, test method, failure or limitation, and decision to adopt, modify, or stop.
- Risk decisions: system context, affected people, review required, approval, and escalation route.
- Coaching: who you helped, the task they practiced, and evidence that the capability was transferred.
Use a skills matrix alongside the log with columns for current skill, evidence, confidence, next practice, and training need. Review it monthly and whenever the AI system, process, role, or risk changes.
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How do I run a monthly documentation routine?
A recurring routine prevents evidence from disappearing into inboxes or private notes.
- At the start of a change, create the baseline and intended-outcome fields.
- During the work, save approved examples of instructions, checks, revisions, approvals, and exceptions.
- At the end of the cycle, enter the result and comparison period, labeling estimates and observations.
- Ask a manager, peer, or stakeholder to validate the result and your role.
- Update the skills matrix with evidence, confidence, next practice, and training need.
- Remove confidential data and retain only approved references.
- Revisit the record after a model update, process change, role change, or new risk.
How should I compare two roles, projects, or contribution records?
Compare the quality of the contribution, not the novelty of the tool. The same seven axes work for a performance review, an internal transfer, or a decision about which skill to develop next.
| Comparison axis | Questions to ask |
|---|---|
| Measurable outcome | Which baseline and comparison period show a change? |
| Quality and error control | How was accuracy assessed, and what happened to exceptions? |
| Human judgment and accountability | Who interpreted the output, made the decision, and approved the result? |
| Privacy and bias safeguards | What data controls, source checks, bias checks, disclosure, or escalation were used? |
| Transferability | Can the method or skill be applied in another workflow, or is it tied to one tool? |
| Evidence quality | Can an authorized reviewer inspect dated records rather than relying on recollection? |
| Learning and coaching impact | Did the work build capability for you or others, and what proves that? |
What privacy and governance pitfalls should I avoid?
- Copying sensitive data into an unapproved system: Use approved environments, minimize inputs, and record the privacy check without retaining the sensitive content in a personal file.
- Keeping a complete prompt or chat archive by default: Retain only what policy permits and what is needed to reproduce or review the decision.
- Hiding AI assistance: Follow disclosure rules for your organization, client, regulator, or publication. A short disclosure and an approval reference are better than an ambiguous claim.
- Treating fluent output as verified output: Check sources, calculations, permissions, and edge cases before relying on a result.
- Using one metric without context: Record changes in volume, staffing, scope, quality, and risk that could explain the result.
- Claiming ownership of a team result: Identify collaborators, the part you led, and the reviewer who can confirm it.
- Turning a course certificate into a competence claim: Add practice, an applied example, and an observed result.
- Leaving disagreements out: Preserve a reviewer’s limitation or dissent; it increases credibility and shows how uncertainty was managed.
- Keeping records indefinitely: Follow retention, access, deletion, and confidentiality rules for your employer and the people represented in the evidence.
How do I turn the record into a performance-review or job-application statement?
Choose two or three entries that show different capabilities and convert each into a compact evidence statement. Start with the business or stakeholder outcome, name the changed workflow, describe your human contribution, and finish with the control or validation.
A useful structure is: Outcome — changed workflow — my judgment or action — evidence and safeguard. For example: “Improved issue-routing consistency during the April–June comparison period by introducing an approved AI-assisted classification step; I prepared the categories, reviewed uncertain cases, and updated the escalation rules; the process owner validated the change and the approval record is linked in the team register.” Replace the illustrative wording with your measured or observed facts.
For a job application, emphasize transferable capabilities—data interpretation, prioritization, communication, risk control, and learning—rather than a list of brand names. For a promotion discussion, show increasing scope: a task you improved, a process you governed, and a capability you taught to others.
What does a strong record look like six months later?
It should let an authorized reviewer answer five questions quickly: What was different before? What changed in the workflow? What did this person do that required judgment or skill? What happened afterward? Where is the evidence, and who checked it? If any answer is missing, add the smallest factual note that closes the gap rather than adding promotional language.
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Keep the record living: update it when the system or risk changes, link it to approved evidence, and use the next-skill field to choose practice. This makes your contribution legible even when the tool is replaced, the team changes, or the task is redesigned.
Frequently Asked Questions
Should I include the exact AI tool name in my performance review?
Include the system name when it is approved, relevant to the risk, and allowed by policy, but do not make it the headline. Describe the transferable skill, the bounded task, your checks, and the outcome so the contribution remains meaningful if the tool changes.
What if my employer forbids saving prompts or AI chat history?
Do not keep prohibited content. Record the approved workflow, your decisions, checks, dates, and a reference to the authorized system or ticket, and ask the process owner what evidence can be retained.
How can I document a contribution when the result is confidential?
Use a restricted reference and describe the result at the lowest level of detail that still permits validation. A reviewer can confirm the outcome and your role without exposing customer, employee, financial, or proprietary data in your personal record.
Is an AI certificate enough to prove I am AI-literate?
No. A certificate records participation in training, not reliable performance in a particular role. Add applied examples, risk controls, reviewer feedback, and evidence that you can use, interpret, and explain the system responsibly.
What should I do if an AI-assisted project did not improve the metric?
Record the baseline, the experiment, the failure or limitation, and the decision to stop or change the approach. Showing that you detected an unsafe or ineffective workflow and escalated it can demonstrate judgment even without a positive efficiency result.
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