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Build a living contribution record that shows what changed, what you did, and what evidence proves the result. For each AI-assisted task, record the original baseline, the system or workflow introduced, the human skill you applied, your checks and decisions, the measurable or observable outcome, where the evidence is stored, and who can validate it. Keep a separate AI-literacy log for training, experiments, guidance and policy decisions.
This approach prevents two common errors: claiming that a tool produced an outcome it could not produce alone, and underselling the judgment, domain knowledge and oversight that made the change safe and useful. It also gives managers a defensible record for performance reviews, promotion discussions, role changes and reskilling plans.
What should an AI contribution record prove?
A credible record connects a work change to a baseline, your actions and inspectable evidence. The tool name is only one field; the value usually comes from how you framed the problem, prepared data, checked outputs, made decisions and handled exceptions.
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Use one record for each meaningful change rather than one vague list of every prompt you have tried. A meaningful change might be a new triage workflow, an AI-assisted drafting process, a quality-control step, a reporting method, an accessibility improvement or a team practice you introduced.
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The seven essential facts
- Original task and baseline: Describe the work before the change, including time, quality, backlog, customer impact or another relevant starting measure.
- Changed system or workflow: Name the AI capability or process, when it was introduced and which part of the task changed.
- Your skill and contribution: State the instruction design, data preparation, domain judgment, testing, editing, coordination, training or exception handling you performed.
- Human controls: Record checks, source verification, privacy reviews, bias checks, approvals, disclosures and escalations.
- Result: Compare the new state with the baseline using time, quality, throughput, cost, revenue, risk, accessibility, customer or team-capability measures.
- Evidence location and date: Point to an approved dashboard, ticket, version history, decision record, feedback item or training record.
- Validator: Identify a manager, peer or stakeholder who reviewed the account, when they reviewed it and any disagreement or limitation.
If a result cannot be quantified, document a specific observable change: fewer handoffs, clearer documentation, faster issue triage, improved accessibility or a decision that avoided a known risk. Do not attribute the whole outcome to AI when your judgment or implementation materially shaped it.
Which skills should I document when AI changes my job?
Document the skills that make an AI-enabled workflow reliable and useful, not just the ability to open a model. International Labour Organization guidance from 2026 describes rising demand for cognitive, socioemotional, digital and AI skills, with AI literacy, adaptability, resilience and human agency becoming more important.
OECD figures show why a broad skills record matters. In occupations most exposed to AI, its 2024 analysis found that 72% of vacancies requested at least one management skill, 67% requested a business skill and 58% requested a digital skill. An OECD 2026 brief says fewer than 1% of workers need advanced AI-specific skills; most workers need digital skills, data use and interpretation, managerial capability, problem-solving, creativity and innovation.
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Show how you selected data, checked its quality, interpreted an AI-generated analysis and explained uncertainty. Useful evidence includes a data dictionary you created, a reconciliation of model output with source records, or a documented correction to a misleading chart.
Problem-solving, creativity and innovation
Describe the problem you reframed, alternatives you considered and why the adopted workflow was better. A prompt by itself is weak evidence; a tested process that reduced rework or exposed a previously hidden risk demonstrates applied problem-solving.
Management, prioritisation and communication
Record how you set priorities, coordinated contributors, communicated limits and obtained approval. AI often changes who handles a task and when; your decisions about queues, service levels and handoffs are part of the contribution.
Domain expertise and judgment
Explain the context you supplied and the errors you detected because you understood the subject. Include examples of an output you rejected, a source you substituted or a recommendation you changed.
Adaptability, resilience and transfer
Capture how you learned a new workflow, recovered from a failed experiment and transferred the method to another task or colleague. A short practice plan and evidence of its use are more informative than a generic statement that you are adaptable.
AI literacy, oversight and accountability
Record your understanding of capabilities, limitations, privacy, disclosure, bias and escalation. For consequential decisions, identify the human decision-maker and the point at which the AI output stopped being authoritative.
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How do I fill in an evidence template?
Use the following fields as a repeatable form. Write plain language that a reviewer outside your immediate project can understand.
| Field | What to record | Strong evidence example |
|---|---|---|
| Before | Task, baseline period, time, quality, backlog and stakeholder outcome | Average triage time and error rate for the previous four weeks |
| Change | System or workflow, introduction date and changed task boundary | Classification step added on 6 May; human approval remained required |
| Your contribution | Instructions, data preparation, judgment, review, editing, testing, exceptions, coordination or training | Defined categories, tested edge cases and rewrote the escalation rules |
| Safeguards | Privacy, bias and error checks, source verification, disclosure, approval and escalation | Removed personal data, sampled outputs and routed uncertain cases to a specialist |
| Result | Metric or observable change with comparison period | Median handling time fell from 18 to 11 minutes while the audit sample stayed within the existing error threshold |
| Evidence | Approved link or reference, owner and date | Dashboard view, ticket range and version history dated 30 June |
| Validation | Reviewer role, review date, agreement and limitations | Operations lead confirmed the measurement and noted two excluded cases |
| Next skill | Gap, practice, training and target evidence | Improve uncertainty communication; run a monthly calibration review |
Keep the original artifact, version history or decision record where policy permits. If retention is restricted, store an approved reference, a redacted excerpt or a description of the evidence owner instead of copying sensitive content.
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How should I describe AI-assisted work without overstating it?
Use a four-part sentence that separates the bounded machine task from your human work and the measured result:
“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: “I used an approved text-classification workflow to sort incoming support cases, then I reviewed low-confidence labels and rewrote the routing rules using product knowledge, reducing median triage time from 18 to 11 minutes over four weeks while retaining specialist approval for safety-related cases.”
This wording avoids two misleading extremes. It does not imply that the model owned the decision, and it does not hide the productivity gain. State whether figures are estimates, sampled, one-time or recurring, and identify the comparison period.
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Say exactly what the system did and did not do. “Generated a first draft” is more accurate than “wrote the report” when you selected sources, checked facts, changed the recommendation and obtained sign-off.
Separate contribution from correlation
If several changes occurred together, identify what you can attribute confidently and what remains uncertain. You might report a shorter cycle time while noting that a staffing change and a new intake form also affected the result.
Make review visible
List the checks that prevented an error from reaching a customer or decision-maker. Review logs, rejected outputs, exception queues and approval records can demonstrate expertise more clearly than a polished final document.
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What should go in a separate AI-literacy log?
Keep a second log for capability building and governance activities that do not belong to one project. It can cover training, guidance, experiments, policy choices and coaching.
- Training or briefing title, provider, date and topics covered.
- System context, role and risk level for the work you perform.
- Practice completed and the artifact or observation that demonstrates it.
- Guidance you wrote, such as a data-handling checklist or disclosure rule.
- Experiment hypothesis, test conditions, result and decision to adopt, revise or stop.
- People coached, questions raised and changes made after feedback.
- Policy or escalation decisions, including who approved them.
The European Union’s AI-literacy guidance says organisations should consider staff technical knowledge, experience, education and training together with the context and risks of the systems they use. It also says an organisation may keep an internal record of training and other guiding initiatives. That means a tailored internal log can be useful evidence; a course certificate alone does not establish competence.
How do I build a before-and-after comparison?
Start measuring before the workflow changes whenever possible. Define the baseline period, the unit of work, the quality rule and the population included. Then use the same definitions after implementation.
Choose a meaningful measure
- Time: cycle time, handling time or time to first response.
- Quality: defect rate, review score, rework or accepted-output rate.
- Throughput: completed cases or decisions per period.
- Risk: privacy incidents, escalations, control failures or avoided exposure.
- Customer and access: satisfaction, abandonment, readability or accessibility findings.
- Team capability: fewer handoffs, shorter onboarding or successful coaching outcomes.
Do not quietly change the denominator. If you exclude unusual cases, state which ones and why. If you have only a small sample, label it as an early observation rather than a stable trend.
Use observable outcomes when numbers are unavailable
A documented reduction in handoffs, a clearer decision record or faster issue triage can be valid evidence. Describe what changed, who observed it and when, then identify what you still cannot conclude.
What is a practical monthly routine?
A short monthly cycle keeps the record current without turning it into a diary.
- At the start: Write the baseline, intended outcome, scope and risk level.
- During the work: Save policy-approved examples of instructions, checks, revisions, approvals and exceptions.
- At cycle end: Enter the result and comparison period using the same definitions as the baseline.
- Validate: Ask a manager, peer or stakeholder to confirm your role and the measurement; record disagreements.
- Update the skills matrix: For each skill, note current level, evidence, confidence, next practice and training need.
- Redact and minimise: Remove confidential data and retain only approved references.
- Revisit on change: Recheck the entry when the system, process, role or risk changes.
How should I present this evidence in a performance review?
Lead with outcomes, then show the human work that made them dependable. A concise review entry can use this order: business or stakeholder problem, baseline, changed workflow, your decisions and safeguards, result, evidence owner and next capability goal.
Group entries by transferable skill rather than by product name. For example, place a reporting automation project and a customer-support triage project under data interpretation, quality control and escalation judgment if those are the capabilities that transfer.
Use the reviewer’s language where possible: service-level target, audit requirement, accessibility standard or risk threshold. Avoid claiming that a single project proves a permanent skill; describe the conditions under which you demonstrated it and what practice will make it repeatable.
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What evidence shows value beyond the AI tool?
Compare contributions on seven axes so a reviewer can see more than speed:
| Axis | Questions to ask |
|---|---|
| Outcome | What changed for time, quality, throughput, cost, customers or risk? |
| Quality and error control | How were errors found, corrected and prevented from escaping? |
| Judgment and accountability | Which decisions required your expertise, and who remained accountable? |
| Privacy and bias safeguards | What data restrictions, fairness checks or escalation rules did you apply? |
| Transferability | Can the method work in another task, team or system? |
| Evidence quality | Can another person inspect the source, date, method and limitations? |
| Learning and coaching | Did you build capability in yourself or others? |
This framework also helps distinguish a one-off shortcut from a durable professional capability. A durable contribution leaves behind a tested process, clear controls and evidence that another person can use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What pitfalls weaken an AI contribution claim?
Listing tools instead of skills
A catalog of model names says little about judgment. Replace it with the task, decision and result, and mention the system only to define the workflow.
Claiming an unverified productivity number
Do not report a precise percentage without a baseline, period and measurement method. Use an observable change or label the figure as an estimate when that is all you have.
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Speed without error, privacy and bias checks can be a liability. Record the controls and any cases escalated to a human specialist.
Saving sensitive prompts or outputs
Follow organisational retention rules. Remove personal, confidential and proprietary data; keep a redacted reference or evidence owner instead.
Treating a certificate as proof of competence
Training proves participation, not reliable application. Pair the course record with a work artifact, observed practice, review or coaching outcome.
Hiding uncertainty
State what the evidence cannot establish, such as whether an improvement will persist after a model update or whether other changes contributed to it.
How do privacy, disclosure and accountability affect the record?
Privacy and accountability are part of the contribution, not administrative extras. Record what data was allowed, what you excluded, whether disclosure was required and who approved the use. For high-impact or consequential work, identify the human owner of the final decision and the escalation route.
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Workers in an OECD 2024 survey commonly reported performance benefits from AI: four in five said it improved their performance and three in five said it increased their enjoyment of work. The same OECD work identifies concerns about work intensity, data use, privacy and inequality. Your record should therefore show both benefit and control, rather than presenting adoption as unqualified success.
Is there a universal AI-readiness certificate or score?
No universal personal AI-readiness score or certificate is established by the cited authoritative guidance. The European Union’s AI-literacy guidance does not require a specific certificate; evidence should fit the person’s role, experience, the system and the risks involved.
Use the contribution record, AI-literacy log and skills matrix together. They show applied capability, not just attendance, while leaving room for different standards in a low-risk drafting role and a high-risk decision-support role.
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How can I keep the record useful when systems change?
Describe the enduring skill separately from the temporary interface. “Validated model-assisted classification against source records” will remain meaningful after a vendor changes its menus, while a screen-by-screen description may become obsolete.
Version the workflow, note the system change date and rerun a small quality and risk check after a major update. If the role changes, add a new baseline rather than editing away the old one; the history shows how you adapted.
Frequently Asked Questions
Should I include the exact prompts I used in a performance review?
Include prompts only when policy permits and when they help a reviewer understand your method. Otherwise, summarise the instruction strategy, inputs, checks and decision logic, and point to a redacted or access-controlled record.
What if my employer has no approved way to measure AI-assisted work?
Start with a small, agreed baseline such as cycle time, rework or escalation counts and ask your manager to confirm the definition in writing. If measurement is impossible, document a dated observable change and the limitation instead of inventing precision.
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Update it when you complete training, run a meaningful experiment, issue guidance or encounter a new risk. A monthly review is a practical minimum for active workflows, with an extra entry after a system or policy change.
Can team contributions be documented collectively?
Yes, but separate the shared outcome from each person’s role. Name the workflow owner, reviewers, trainers and decision-makers so credit and accountability are not merged into an uncheckable team claim.
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