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Keep a living contribution record that shows what changed, what you did, what result followed, and what evidence another person can check. For each meaningful AI-related change, document the original task and baseline, the new tool or workflow, your skills and decisions, the result, safeguards, evidence location and date, and a reviewer who can validate your role. Track training and AI-literacy activities separately. This makes your contribution clearer than a list of tools or course certificates—and helps you describe AI-assisted work without claiming the tool did your work for you.
What should I document when AI changes my job?
Document the change in the work, not just the name of the AI system. A useful record connects the task as it was before, the workflow as it is now, your contribution to that change, and evidence of the outcome. It should also show how you checked the work and handled risk.
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- The task and baseline: What work was being done, how was it handled before, and what was the relevant starting point for time, quality, backlog, or stakeholder outcomes?
- The change: Which AI system, feature, or workflow was introduced, when, and which part of the task changed?
- Your contribution: What did you do—such as prepare data, set instructions, apply domain knowledge, review or edit outputs, test the workflow, handle exceptions, coordinate approvals, or train colleagues?
- Safeguards: What privacy, accuracy, bias, disclosure, approval, or escalation checks applied?
- The result: What changed, compared with which period or baseline? Include a measurable outcome when one is available.
- Evidence and validation: Where is the approved supporting record, when was it created or reviewed, and who can confirm your role?
- What comes next: Which skill do you want to practise or strengthen, and what evidence would demonstrate progress?
This record is useful for a performance review, a role-change discussion, a skills inventory, or simply keeping an accurate account of work while responsibilities are shifting. Its purpose is not to make every task sound like an AI project. Include changes that matter to your work and describe their scale honestly.
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Which AI-related skills are worth recording?
Record the capabilities you used and the evidence that shows them in practice. For most workers, this means more than technical knowledge of a model: data interpretation, judgment, problem-solving, communication, management, and adaptability can all be relevant.
The International Labour Organization’s 2026 publication Changing landscape of skills in the age of AI describes growing demand for cognitive, socioemotional, digital, and AI skills, and highlights AI literacy, adaptability, resilience, and human agency. OECD’s 2026 brief AI and skills says fewer than 1% of workers need advanced AI-specific skills; most need digital skills, the ability to use, analyse, and interpret data, managerial skills, and human skills such as problem-solving, creativity, and innovation. That distinction matters: you do not need to present yourself as a model developer to document valuable work with AI.
OECD’s 2024 brief reports that among vacancies in occupations most exposed to AI, 72% demanded at least one management skill, 67% at least one business skill, and 58% at least one digital skill. These figures describe vacancies in those occupations, not the qualifications required for every job. They support documenting a broad mix of capabilities rather than focusing only on tool operation.
| Skill area | What to record | Useful evidence |
|---|---|---|
| Data use and interpretation | How you prepared, interpreted, or checked data or AI-generated analysis before relying on it. | An approved analysis, review note, decision record, or documented correction. |
| Problem-solving, creativity, and innovation | How you improved a process, explored alternatives, or made a better-informed decision. | A process change, documented options, or a decision with its rationale. |
| Management and coordination | How you prioritised work, organised handoffs, coordinated contributors, or communicated a change. | A project record, agreed workflow, or stakeholder feedback. |
| Domain expertise and judgment | How your subject knowledge helped set context, detect an error, interpret an answer, or decide when not to use it. | A review or decision record that explains the judgment, with sensitive details removed as needed. |
| Adaptability and learning | How you adjusted to a changed workflow, learned a new skill, or transferred an existing skill to new work. | A training record, updated process, or dated example of the changed work. |
| AI literacy and oversight | How you understood relevant capabilities, limitations, risks, privacy expectations, disclosure rules, and escalation routes. | Approved training or guidance records, plus examples of checks or escalations where appropriate. |
Choose the skills that match the work you actually did. A record that claims “AI expertise” but says nothing about judgment, checking, or outcomes is less informative than a specific account of a task and your role in it.
How do I build a contribution record?
Use one entry for each meaningful change, then maintain it as the work evolves. The following fields make an entry reviewable without requiring a universal scoring system or a special certificate.
| Field | What to write |
|---|---|
| Before | The task, how it was handled, and the baseline period or starting condition. Depending on the work, this could include time, quality, backlog, or customer or stakeholder outcomes. |
| Change | The AI tool, feature, or workflow; when it was introduced; and the part of the task that changed. |
| Your contribution | Your specific actions: instruction design, data preparation, domain judgment, review, editing, testing, exception handling, coordination, or training others. |
| Safeguards | Relevant privacy checks, error or bias checks, source verification, disclosure, approvals, or escalation. |
| Result | The observed change in time, quality, throughput, cost, revenue, risk, accessibility, customer outcomes, or team capability, with the comparison period where applicable. |
| Evidence | An approved dashboard, ticket, version history, feedback record, decision record, training record, or other reference a reviewer is permitted to inspect. |
| Validation | The reviewer’s name or role, review date, and any disagreement or limitation that affects how the result should be described. |
| Next skill | A skill gap, relevant training completed, practice plan, or target evidence for a later review. |
Where possible, compare the new workflow with a defined baseline. Be clear about what the comparison covers: a single case, a project, or a period of work. A result without a comparison point may be worth recording, but it is harder to interpret. If you cannot quantify the effect, write down a specific observable change instead—for example, fewer handoffs, clearer documentation, faster issue triage, better accessibility, or a decision that avoided a known risk.
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Do not assign an outcome to AI alone when your judgment, checking, or implementation materially shaped it. A credible account separates what the system contributed from what you contributed and avoids claiming more certainty than the evidence supports.
How can I describe AI-assisted work accurately?
Use a sentence that identifies the bounded task, your human contribution, the result, and a safeguard or approval. This helps a reader see what you did without implying that the AI system made every decision or produced a verified outcome by itself.
A useful format is: “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].”
For example, as an illustrative format rather than a claim about a real workplace result: “I used an AI-assisted workflow to draft summaries of incoming issues, then checked and corrected them against the source records using my product knowledge. The team had clearer issue notes for triage, and I escalated cases where the source information did not support a confident summary.” If you have a documented measure, include it and explain its comparison period; if not, keep the result observable and specific.
Keep the original artifact, version history, or decision record where workplace policy permits. Your evidence should support the statement: if you say you corrected errors, retain an approved example or review record that demonstrates the check, not confidential content you are not allowed to keep.
How should I record AI literacy and training?
Keep a separate AI-literacy log for training, guidance, experiments, and policy decisions, then connect relevant entries to particular work when useful. A course certificate can show that training occurred; by itself, it does not show how you applied the learning in a specific role or system.
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The European Union’s AI-literacy Q&A says organisations should consider staff members’ technical knowledge, experience, education, and training, as well as the context and risks of the systems they use. It also says an organisation can keep an internal record of trainings and other guiding initiatives. The Q&A states that Article 4 does not require a specific certificate. So a record should reflect the person, role, system context, and relevant risks, rather than treating one credential as a universal proof of competence.
For your own log, note what guidance or training you received, when it happened, what role or system it covered, and what practice or follow-up you undertook. If your organisation maintains a central record, follow its process instead of creating a duplicate containing information you are not authorised to hold.
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A short, repeatable review helps keep entries contemporaneous and useful. Set up the record when a workflow changes, not months later when the details are harder to confirm.
- At the start of a change: Write down the baseline, the task being changed, and the intended outcome.
- While doing the work: Save approved examples of instructions, checks, revisions, approvals, and exceptions that policy allows you to retain.
- At the end of the cycle: Record the observed result and the period or baseline used for comparison.
- Ask for validation: Have a manager, peer, or stakeholder check the result and your role. Note a disagreement or limitation rather than silently presenting a disputed account as agreed.
- Update your skills matrix: Add the skill, the evidence, your confidence, the next practice step, and any training need.
- Protect information: Remove confidential data from your personal record and keep only approved references.
- Revisit after a change: Review the entry when the AI system, process, role, or risk changes.
A contribution record is not a substitute for a company’s performance-review process, data-handling rules, or approval requirements. Use the record to make your account clearer within those boundaries.
How do I show value when there is no clear metric?
Use a concrete observable change and explain its scope rather than inventing a number. Not every contribution produces a reliable before-and-after metric, and an unsupported figure can weaken an otherwise strong account.
Depending on the work, useful observations may include fewer handoffs, clearer documentation, quicker issue triage, better accessibility, or a decision that avoided a known risk. Specify what changed, who noticed or used the change, and what evidence supports it. A dated stakeholder comment or approved process record can help a reviewer understand the result even if no dashboard measures it.
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If you cannot tell whether the workflow caused the outcome, say so. Other changes may have contributed, and the system’s output may have needed substantial human review. Distinguishing an observed outcome from a proven causal effect is part of documenting your work accurately.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I compare two roles, projects, or contribution records?
Compare the quality and significance of the contributions, not just the amount of AI used. A shared set of dimensions helps avoid giving undue weight to tool names or headline productivity claims.
| Comparison dimension | Question to ask |
|---|---|
| Outcome | What measurable or observable change followed, and how sound is the comparison? |
| Quality and error control | How were mistakes found, corrected, or escalated? |
| Human judgment and accountability | What decisions did the person make, and who remained accountable for the work? |
| Privacy and bias safeguards | What relevant safeguards were applied, and is there evidence of them? |
| Transferability | Can the skill be applied to a different task, system, or workflow? |
| Evidence quality | Can another person inspect the supporting evidence under workplace policy? |
| Learning and coaching | Did the contribution build capability or help colleagues adapt? |
These dimensions are useful for self-review and discussion, not a universal personal AI-readiness score. The evidence should fit the role, system, and risk rather than forcing every contribution into one ranking.
What privacy and attribution mistakes should I avoid?
Keep the record useful without copying sensitive material into a personal file or overstating what the system did. Workplace AI use can raise concerns about data use, privacy, work intensity, and inequality, alongside potential performance benefits.
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- Do not claim the AI produced a result you materially shaped. Identify your review, edits, decisions, and implementation.
- Do not describe an unchecked output as verified. Say what you checked and what remained uncertain.
- Do not imply causation from timing alone. Identify the comparison and acknowledge other factors where relevant.
- Do not hide exceptions or limitations. An escalation, failed attempt, or need for human intervention can be evidence of judgment when described accurately.
- Do not use a course or tool name as a substitute for evidence. Explain what capability you applied and where a reviewer can see it.
OECD’s 2024 report Using AI in the workplace says four in five workers surveyed reported that AI improved their performance at work, while three in five said it increased their enjoyment of work. The same OECD discussion also identifies concerns involving work intensity, data use, privacy, and inequality. Those findings are not a guarantee of benefit in every role; they are a reason to document both the result and the safeguards around the work.
What does a strong contribution entry look like?
A strong entry is specific, balanced, and verifiable. It identifies the change and your part in it, gives a baseline or observable comparison, and points to evidence another person is allowed to review.
Before submitting an entry for a review, check that it answers these questions:
- Can a reader tell what task changed and when?
- Have I distinguished the system’s output from my own work?
- Is the stated result supported by a comparison or a concrete observation?
- Have I noted relevant checks, approvals, or escalations?
- Can a reviewer locate the evidence without receiving confidential data?
- Have I named a limitation or uncertainty that changes how the result should be read?
If the answers are clear, the entry can support a more useful conversation about performance, skill development, and how your responsibilities are changing.
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Frequently Asked Questions
What if my manager does not agree with my description of the result?
Keep the account factual and mark the point of disagreement, including which part is disputed. Ask whether an approved record or a different comparison period can clarify it; do not present a contested interpretation as a shared conclusion.
Should I include an AI experiment that did not work?
You can include it when it shows a meaningful decision, risk check, or lesson that informed later work. Describe the objective, what you observed, and what you changed or stopped, without presenting an unsuccessful test as a successful outcome.
What if I used AI informally and there is no official project record?
Follow your workplace’s rules on AI use and disclosure before recording the activity. If it was permitted, use only approved evidence such as a dated work artifact or reviewer confirmation, and be clear that the activity was informal rather than an authorised process change.
Can I use my contribution record in a job application?
Yes, but convert it into a concise, non-confidential example and remove internal names, customer details, proprietary information, and restricted metrics. Preserve the distinction between your actions and the AI system’s role, and use only claims you can support if asked.
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