Imagine a manager is asked six months from now why a supplier was shortlisted, a candidate was screened out or a customer was offered a particular response. If an AI tool played a part, the honest answer is often “I am not sure exactly what it said, and I did not keep a note”. That gap is a problem for accountability, for learning and sometimes for fairness to the people affected.
In this guide you will learn how to document AI-assisted decisions in a way that is proportionate and practical. You will see what is worth recording, how to decide how much detail a decision needs, how to capture the human reasoning that matters most, how to store records safely and how to use them to improve. There are templates, tables and a checklist. This is general information, not legal advice, and requirements differ by sector and place.
Key takeaways
- The purpose of documentation is to show who decided, on what basis and with what checks, so the human reasoning must be recorded, not only the AI output.
- Scale the detail to the stakes: routine drafting needs almost nothing, while decisions affecting people’s rights, money or safety need a proper record.
- A good record states the question, the inputs, the tool, the output, the checks made, the decision and the reason, plus who made it and when.
- Keep records secure, keep only what you need, and take care not to copy sensitive data into places it should not go.
- Review the records periodically to spot patterns of error, bias or over-reliance, and feed what you learn back into your process.
Why document AI-assisted decisions
People have always documented important decisions: minutes, file notes, approval emails. AI changes little about that principle, but it adds new reasons to be careful. AI output can look authoritative, can vary from one attempt to the next and can be wrong without any sign. If nobody records how it was used, the organisation cannot explain or learn from its decisions.
The main benefits
- Accountability. A named person owns the decision, not the tool.
- Explainability. You can give a clear account to colleagues, customers or reviewers when asked.
- Quality. Writing down the checks makes people actually do them.
- Learning. Records show where the tool helps and where it misleads.
- Continuity. Others can pick up the work if someone leaves or is absent.
- Fairness. For decisions about people, a record helps show that the process was consistent and reasoned.
What documentation is not
It is not a promise that the decision was right, and it is not a way of passing responsibility to the AI. It is evidence of a sensible process. A record that says “the AI recommended it” with no human reasoning is a weak record.
Tools do not make decisions
Treat AI as an input to a decision, in the same way you would treat a colleague’s draft, a report or a calculation. The decision-maker remains a person, and the record should make that plain.
Which decisions need a record
Not every use of AI needs paperwork. Writing up a quick brainstorm is a waste of time. Use a simple triage to decide.
| Question | If yes, lean toward a fuller record |
|---|---|
| Does the decision affect a person’s job, pay, access, eligibility or treatment? | Yes, document thoroughly. |
| Does it involve significant money, contracts or commitments? | Yes, document. |
| Does it affect safety, health or legal position? | Yes, document thoroughly and involve a qualified person. |
| Will it be hard or costly to reverse? | Yes, document. |
| Will someone else rely on it or ask about it later? | Yes, document. |
| Is it a one-off personal drafting or brainstorming task? | Usually no record needed. |
Examples by level
| Level | Examples | Record |
|---|---|---|
| Minimal | Reworking the wording of an internal note; brainstorming headline ideas. | None, or a one-line note. |
| Standard | Using AI to draft a client proposal that a person then edits and approves; summarising a report to inform a plan. | Short record of purpose, tool, checks and approver. |
| Enhanced | Using AI analysis to choose a supplier, set a budget or prioritise customers. | Full record with inputs, alternatives considered and reasoning. |
| High-stakes | Anything influencing hiring, firing, discipline, credit, health or legal matters. | Full record, qualified review and compliance with applicable rules. |
Check the rules that apply to you
Some sectors and places have specific obligations about automated or assisted decisions, particularly where individuals are affected. These vary and change. Ask a qualified adviser what applies to you. This guide offers practical habits, not legal advice.
What a good record contains
A useful record answers the questions someone would ask later. Keep it short, structured and consistent.
| Element | What to write |
|---|---|
| Decision and date | What was decided and when. |
| Decision-maker | The named person accountable. |
| Question or purpose | What you were trying to decide or produce. |
| Role of AI | What the tool was used for: drafting, analysis, summarising, recommending. |
| Tool and settings | Which tool, which plan or version if known, and any relevant settings. |
| Inputs | What information you gave it, in summary, without copying sensitive data unnecessarily. |
| Output used | What it produced that you relied on, or a link to where it is saved. |
| Checks made | How you verified facts, figures and reasoning, and what you found. |
| Alternatives considered | Other options and why they were not chosen. |
| Human judgement | Where you agreed or disagreed with the AI and why. |
| Risks and limits | Known uncertainties or caveats. |
| Approval | Who signed off, if anyone else did. |
Write in plain language
Records should be readable by someone who was not there. Avoid jargon and shorthand. A colleague, manager or reviewer should understand what happened in a couple of minutes.
Record at the time
Write the record when the decision is made, or soon after. Memory fades quickly, and reconstructed explanations tend to be tidier than the truth.
Capturing the human reasoning
The most valuable part of the record is the reason the person decided as they did. That is also the part most often left out.
Questions the reasoning should answer
- What did the AI suggest, and what did I do with that suggestion?
- What other information did I use that the AI did not have?
- Did I verify the key facts, and how?
- Where did I disagree or adjust, and why?
- What would have changed my mind?
- What am I least sure about?
Show independent thought
A record that shows you weighed evidence, tested claims and used your own knowledge demonstrates real oversight. One that simply copies the AI’s conclusion suggests over-reliance.
Beware of automation bias
People tend to give too much weight to confident machine output, particularly when busy. Writing down your own reasoning is a useful guard against it, because it forces you to engage with the substance. For why outputs can be wrong, see what AI hallucinations are and how to reduce them.
Record disagreement
When you reject or change AI output, note that and the reason. These entries are some of the most informative when you later review how the tool performs.
Recording inputs and outputs
Deciding how much of the AI interaction to keep is a balance between usefulness and risk. Keeping every word can create a store of sensitive material, while keeping nothing leaves you unable to explain what happened.
Options
| Approach | Description | Best for | Drawback |
|---|---|---|---|
| Summary only | Describe the question and the output in your own words. | Standard decisions. | Cannot reproduce the exact wording. |
| Key excerpts | Save the specific output you relied on. | Enhanced decisions. | Must still protect sensitive content. |
| Full transcript | Keep the whole conversation. | High-stakes decisions. | More data to secure and review. |
| Prompt and settings log | Record the instructions and configuration used. | Repeatable processes. | Needs a consistent habit. |
Be careful with sensitive content
A record that copies personal or confidential data into a new document spreads that data. Use references or reduced descriptions where possible, for example “applicant A, details held in the HR file”, and apply the same protections to the record as to the original data. See how to protect company data when using AI tools.
Outputs can differ between attempts
Asking the same question twice may give different answers. That is one reason to save the output you relied on, not just the prompt. Re-running later may not reproduce it.
Note tool changes
Tools are updated often. Where it matters, record the tool name, the plan or version if available and the date of use, so differences in behaviour can be understood later.
Scaling the record to the stakes
A proportionate approach keeps documentation useful and prevents it becoming a burden nobody follows.
| Stakes | Minimum record | Review |
|---|---|---|
| Low | One line in a work log, if any. | None. |
| Moderate | Purpose, tool, checks and approver (about five lines). | Spot checks. |
| High | Full template including alternatives and reasoning. | Second person reviews before action. |
| Critical | Full record, qualified professional involved, compliance review. | Formal sign-off and periodic audit. |
Keep forms short
If a form takes twenty minutes, people will skip it or fill it with boilerplate. Make the standard version quick, and reserve long forms for decisions that deserve them. Reuse the same fields so people become fluent.
Make it part of the workflow
The easiest way to get records is to build them into where the work already happens: a field in your project tool, a section in the proposal template, a line in the approval email. Documentation that lives elsewhere is rarely completed. Ideas for building simple processes are in how to build your first AI workflow without code.
Decisions about people
When AI contributes to decisions about individuals, such as recruitment, performance, discipline, credit, access to services or customer treatment, the stakes and obligations are higher. People can be affected unfairly, and rules may apply.
Extra care points
- Human decision. A person with the authority and information to decide should make the decision, and genuinely consider it, not rubber-stamp a recommendation.
- Relevant criteria. Record the criteria used and why they relate to the decision.
- Consistency. Show that similar cases were treated similarly.
- Bias awareness. Consider whether the tool or inputs could disadvantage particular groups, and record what you did about it.
- Explanation. Be able to give an affected person a plain account of how the decision was reached.
- Challenge route. Ensure there is a way for someone to question or appeal the outcome.
- Legal advice. Take advice on obligations such as notice, consent and rights relating to automated or assisted decisions. This is general information, not legal advice.
Questions to ask before using AI at all
- Is AI appropriate for this decision, or should it be left to people?
- Do we understand what the tool does and what data it uses?
- Can we explain and justify the result to the person affected?
- Have we taken advice on the relevant rules?
Not everything should be assisted
For some decisions, the right answer is not to use AI at all, or to use it only for narrow supporting tasks such as formatting. Documenting that choice is also part of good governance.
Storage, access and retention
Records are only useful if they can be found, and only safe if access is controlled.
Where to keep them
- In a single agreed location, not scattered across personal drives and chat histories.
- Alongside the related decision file, project or case record.
- With clear naming and dates so they can be searched.
Who should see them
Limit access to those who need it: the decision-maker, their manager, reviewers and, where appropriate, auditors. Records about individuals may contain personal data and should be treated accordingly.
How long to keep them
Retention should match the importance of the decision and any rules that apply. Keep what you need for as long as you need it, then dispose of it securely. Check whether legal or sector requirements set minimum or maximum periods. This is general information, not legal advice.
Protect the chat history
Many AI tools store conversation history. Know what your tool retains, who can see it and how to delete it, and decide whether the history itself is part of your record or only a source for it.
Building a lightweight process
A simple, repeatable process works better than an elaborate one that nobody uses.
A five-step routine
- Triage. Decide the stakes level using the table above.
- Work as normal. Use the tool and verify the output.
- Fill in the record. Use the short or full template as appropriate.
- Get approval. A second person signs off for higher stakes.
- File it. Save the record in the agreed place.
Who does what
| Role | Responsibility |
|---|---|
| Decision-maker | Makes the decision, verifies output and writes the record. |
| Approver or reviewer | Checks the reasoning and signs off higher-stakes decisions. |
| Process owner | Maintains templates, storage and guidance. |
| Leadership | Sets expectations and ensures time is available. |
Write it into your policy
Include documentation expectations in your AI use policy, so that people know when records are required and what they should contain. How to write an AI use policy for your team shows where this fits.
Train and model it
Run a short session with real examples, and have managers follow the routine themselves. People copy what leaders do more than what policies say.
Reviewing the records
Records have a second life as learning material. A short, regular review turns paperwork into improvement.
What to look for
- Frequent corrections. Tasks where the AI is often wrong signal where to tighten checks or stop using it.
- Rubber-stamping. Records with little human reasoning or no disagreement may show over-reliance.
- Skipped checks. Patterns of unverified claims.
- Inconsistent decisions. Similar cases treated differently without explanation.
- Near misses. Errors caught before they caused harm, and how they were caught.
- Good practice. Examples of clear, well-reasoned records to share as models.
Review rhythm
| Review | When | Who |
|---|---|---|
| Sample of standard records | Quarterly | Process owner |
| All high-stakes records | Each one, before action | Reviewer |
| Patterns and lessons | Twice a year | Leadership and process owner |
| Policy and template refresh | Annually or after an incident | Process owner |
Act on what you find
Update prompts, checks, training and policy based on the review, and tell people what changed and why. For checking methods, see how to check AI output for mistakes.
Templates and checklist
Short record (moderate stakes)
| Field | Entry |
|---|---|
| Date and decision-maker | [date, name] |
| Decision or output | [what was decided or produced] |
| Role of AI and tool | [what it did, which tool] |
| Checks made | [how verified] |
| My reasoning | [why this decision, any changes to AI output] |
| Approved by | [name or not required] |
Full record (higher stakes)
| Field | Entry |
|---|---|
| Decision, date, decision-maker | [details] |
| Question and context | [what was being decided and why] |
| Tool, version or plan, date used | [details] |
| Inputs given (summarised) | [description, references to source data] |
| Output relied on | [excerpt or location] |
| Verification steps and results | [facts, figures, sources checked] |
| Alternatives considered | [options and reasons] |
| Where I agreed or disagreed with the AI | [detail] |
| Risks, uncertainties and caveats | [detail] |
| People affected and how they were considered | [detail] |
| Review and approval | [name, date] |
| Retention and location | [where stored, until when] |
Checklist
- I decided the stakes level before starting.
- I verified the facts and figures I relied on.
- I wrote down my own reasoning, not only the AI output.
- I recorded where I changed or rejected the AI’s suggestion.
- I avoided copying sensitive data unnecessarily into the record.
- The record is stored in the agreed location with controlled access.
- Higher-stakes decisions were reviewed by a second person.
- I know how long the record will be kept.
Frequently asked questions
Do I need to document every use of AI?
No. Routine, low-stakes tasks such as rewording a note rarely need a record. Document decisions that affect people, money, safety, legal position or that others will rely on.
Should I save the whole chat?
For higher-stakes decisions, keeping the relevant conversation can be wise, with appropriate security. For standard decisions, a summary and the output you relied on are usually enough.
Who is responsible if the AI was wrong?
Responsibility sits with the people and organisation using the tool, not with the tool. That is why the record should show the human checks and reasoning.
Does documentation protect us legally?
A clear record of a sensible process can help you explain and defend a decision, but it does not guarantee any legal outcome. Take professional advice on your obligations. This is general information, not legal advice.
How do I keep the paperwork from becoming a burden?
Match detail to stakes, use short templates, build the fields into existing tools and keep the full process for the cases that need it.
What if I cannot explain why the AI recommended something?
You do not have to explain the model’s inner workings. You need to explain your own reasoning: what you checked, what you relied on and why. If you cannot support a recommendation, do not rely on it.
Should customers be told that AI was involved?
Practices and requirements vary. Being open is generally good practice, especially for decisions affecting individuals. Check any rules that apply and follow your organisation’s policy.
Conclusion
Documenting AI-assisted decisions is mostly ordinary good record-keeping applied to a new tool. Decide how much detail the stakes call for, capture what was asked, what the tool produced, how you checked it and, above all, your own reasoning. Keep records secure and proportionate, store them where they can be found and review them regularly so that patterns of error and over-reliance come to light.
A short template, a named owner and a clear line in your policy will take you most of the way. For next steps, read how to write an AI use policy for your team and how to check AI output for mistakes.
