AI Workflow Documentation Template: What to Write Before You Automate

A prompt can instruct a model to produce a polite reply or summarise a document. It cannot make absent business facts available or settle a disagreement about who approves the result. If the process depends on a rule that exists only in one employee’s head, the tool may produce a convincing response while missing that rule. In a public accounting-owner account, proposal automation struggled with pricing information that had not been captured and systems that were difficult to connect. This is a practical example of an input problem, not evidence that every proposal workflow is unsuitable for AI. An existing standard operating procedure is a useful starting point, but check it against what staff actually do. Instructions may omit judgement, workarounds or the cleanup after a task appears complete. Watch one example all the way to its final record and ask why each decision was made. The brief becomes a shared agreement between the owner, operator, reviewer and implementer. It should identify what the first version will do, what stays manual and how the team will recognise an unacceptable result. Update it when a real exception changes the process, rather than letting the document describe a demonstration nobody uses.

Copy the fields into your own document and complete them with the person doing the task. Keep the first version short enough that an operator and reviewer can read it together. Use approved or fictional examples until you have agreed what business information may be used in the proposed tool. Do not fill an unknown field with a guess just to finish the document. Mark it unresolved, name who can answer and decide whether the pilot can proceed without it. A missing permission decision may block live testing; a missing formatting preference might be resolved during practice. The distinction belongs in the brief.

For each required fact, name the place the operator should trust. A customer’s contact details might be in the CRM, the approved price in a quoting system and the current process in a controlled document. If two systems disagree, record which one wins and who resolves the conflict. Avoid instructions such as use the client file when that file contains outdated documents, draft notes and unrelated personal information. Identify the particular record or document needed for the task. Limit access to what the process requires, and assess the tool’s actual configuration and terms before allowing sensitive information into it. Define what happens when a required input is missing. A useful rule might be: prepare a request for the missing information and return the item to the operator. That is more testable than an instruction to make reasonable assumptions. For some tasks, missing information should stop the process altogether. Record the date or version of changeable inputs, such as an approved price list. During review, you need to distinguish a model mistake from a correct response based on an outdated source. For an introduction to information boundaries and staff responsibilities, use the small-business AI policy template, then adapt it to your actual obligations and systems.

Suppose a fictional Australian accounting firm wants to reduce the time spent drafting document requests. The first trial prepares an internal email draft from an approved checklist. An accountant confirms the request and sends it using the firm’s ordinary process. The trial does not determine tax treatment or decide independently which records a client must supply. A test should include a complete input, a missing deadline and a conflict between the contact record and the request. The last two cases check whether the workflow stops or escalates as designed, rather than producing a fluent but unusable message. For the wider engagement and handover process, see accounting client onboarding automation. The point of this example is the boundary around one draft task. Keep any expansion into system writes or automated sending as a separate decision with its own approval requirements.

Start the exception list with the difficult cases experienced staff already recognise. Which customer should receive a different response? Which conflicting number requires investigation? Which document should never be used as an instruction? Record the action the operator takes, rather than simply labelling the item complex. For each exception, decide whether the workflow can continue with a warning, return the item for missing information or stop until somebody resolves it. Avoid leaving the model to choose that business policy. The appropriate response depends on the consequences of error and the people available to review it. An AI Collab discussion for small accounting and legal firms frames an AI workflow as a new process that needs the same care as an established procedure. This is adviser guidance, not a guarantee that documenting the process makes every use safe. Make checking instructions observable. Compare the recipient with the approved contact record; compare listed items with the checklist; confirm the deadline was supplied; reject unsupported claims. A reviewer who is told only to check that it looks right has no shared standard. Log the reason for each rejection so the next version can address the actual failure without weakening the check.

Record the baseline before the new workflow is used. Time preparation, drafting, review, sending and record keeping, not just the step the AI performs. Capture the number of items that need correction or escalation. A faster draft can still be a slower completed task. For an illustrative calculation, a task that takes 12 minutes manually and 4 minutes to draft plus 6 minutes to check saves 2 minutes per completed item. At 40 comparable items a week, that is 80 minutes of potential capacity. These are invented inputs for arithmetic, not measured savings, a forecast or a cash reduction. Setup, training, software and maintenance still need to be considered. Test the fallback deliberately. Ask an operator to complete an item when the tool is unavailable, the source record is missing or the draft must be rejected. Check that the team can find the ordinary template and knows how to record the work without duplicating the request. Agree what outcome would justify continuing. That might be acceptable quality with lower total effort and a manageable review queue. If the reviewer becomes overloaded, narrow the scope or reduce volume. Use the internal business-case template when you have enough observations to assess costs and payback.

A completed brief makes an implementation conversation more specific. The provider can inspect the systems, ask about access and identify which parts require deterministic rules, AI assistance or human judgement. It also reveals dependencies that must be resolved before a production commitment. Ask the provider to identify assumptions in their proposal. Who will prepare examples? Who approves the permitted information? Is system integration included? Who handles changes when the underlying software or business process changes? What will be handed over if the relationship ends? The brief should help both parties describe the same task. Keep the current version with the approved examples, checking instructions and change log in a location your team controls. Assign a maintainer and a review date. After a staff departure or software change, check that access, ownership and the fallback still work. Documentation has value when it remains connected to the task people perform. If the brief exposes uncertainty about the process, resolve that before commissioning a wider build. If the process is clear but the team needs practice, explore team training. If the work requires dependable connections between systems, discuss the automation scope. Bring the document and one difficult example so the next conversation can be concrete.

Is an AI workflow document the same as a prompt? No. A prompt instructs the model within one step. A workflow document also records inputs, permissions, rules, approvals, exceptions, system handoffs and ownership. It describes how the whole task is completed and checked. Can we use an existing standard operating procedure? Yes. Walk through it with an operator and compare it with a real completed task. Add the judgement, exceptions, information sources and recovery steps that the written procedure leaves out. Keep the approved version accessible to the team. How much detail does the first version need? Enough for the operator, reviewer and implementer to agree what the bounded task does and what happens when something is missing or wrong. Keep unknowns explicit. You can add detail as testing reveals a real dependency or exception. Does documenting a task mean it is suitable for AI? No. The brief may show that ordinary automation, a better checklist or a manual process is more appropriate. It can also reveal unresolved data permissions or judgement that should remain with a qualified person. That is a useful result of the exercise.