Source: [AI process maps: distinguish recorded work from required steps — Skills for Business]() [← All articles](https://skillsforbusiness.ai/blog/) Workplace adoption · Guide # AI process maps: distinguish recorded work from required steps Review an AI-generated process map against event records, staff accounts and the approved procedure before turning its boxes and arrows into automation. [Skills for Business](https://skillsforbusiness.ai/blog/editorial/)4 November 20266 min read On this page 1. [Choose a bounded process and name the map’s purpose](https://skillsforbusiness.ai/blog/ai-process-maps-observation-policy-evidence/#choose-a-bounded-process-and-name-the-maps-purpose) 2. [Give each input a stated scope](https://skillsforbusiness.ai/blog/ai-process-maps-observation-policy-evidence/#give-each-input-a-stated-scope) 3. [Reconcile two fictional return requests](https://skillsforbusiness.ai/blog/ai-process-maps-observation-policy-evidence/#reconcile-two-fictional-return-requests) 4. [Review arrows as claims about sequence](https://skillsforbusiness.ai/blog/ai-process-maps-observation-policy-evidence/#review-arrows-as-claims-about-sequence) 5. [Check the map again after AI-assisted edits](https://skillsforbusiness.ai/blog/ai-process-maps-observation-policy-evidence/#check-the-map-again-after-ai-assisted-edits) 6. [Sources](https://skillsforbusiness.ai/blog/ai-process-maps-observation-policy-evidence/#sources) What to take away - Label whether each process-map claim comes from records, a procedure, an interview or a proposal. - Investigate missing events before calling a step unnecessary or declaring that it was skipped. - Check representative cases and exceptions before using the diagram to specify automation. Before using an AI-generated process map to specify automation, establish what each box and arrow claims. A diagram assembled from a procedure, staff interviews and system records can quietly combine three different things: what should happen, what people say happens and what the available records show. For an operations lead, the decision is whether the map is a reliable description of the workflow being changed. A clear diagram helps discussion, but clarity alone cannot resolve a missing approval, an undocumented exception or an activity that happens outside the system. Our proposed method is to keep the evidence behind the diagram visible and reconcile a few complete cases before approving automation requirements. The return-request example below is fictional. No business process, AI diagramming product or process-mining system was tested for this article. ## Choose a bounded process and name the map’s purpose Start with one decision about the map. Are you documenting the approved procedure, investigating recorded work or designing a future workflow? Write that purpose beside its title and date. A single picture can support several discussions, but readers need to know when its claims change. The Australian National AI Centre recommends choosing a contained, data-rich process and involving the responsible people. Its mapping guide records actions, roles, inputs, outputs and time. This is practical adoption guidance, rather than evidence that an AI-generated map accurately describes a particular business. Use those fields to organize the review. [Read the guide](https://www.ai.gov.au/planning-ai/prepare-your-business/map-your-processes). Define the start and end explicitly. For a customer return, the start could be receipt of a request and the end a recorded resolution. Receiving the parcel, approving a credit and issuing a refund are separate events. Choosing one as the endpoint changes which remaining work the diagram must show. Also define the unit being followed. One customer can have several return requests, and one request can contain several items. Combining records solely by customer identity can create a sequence that no individual request actually followed. Agree which identifier represents a case before asking the assistant to arrange events. ## Give each input a stated scope Provide the assistant with an input register. For the procedure, record its version, effective date and applicable products or teams. For an event export, record the system, selected period, included event types and known omissions. For interviews, identify the role being described and the date of the account, using the organization’s existing access rules. A staff member’s explanation can reveal work that software never logs. It can also describe an intended habit rather than every actual case. Keep that distinction in the review notes. A statement such as “we normally check eligibility first” is valuable context, but does not prove that a check occurred for a particular request. Research on process mining makes data preparation a separate concern. Suriadi and colleagues’ 2017 paper describes recurring event-log quality issues and a pattern-based approach to documenting them. The accessible university record summarizes its case study and practitioner evaluation. We inspected that abstract and publication metadata, while the full paper was unavailable in the retrieved material. It supports questioning log quality, without validating this proposed AI review method. [Read the research record](https://research.tue.nl/p/en/research-outputs/event-log-imperfection-patterns-for-process-mining-towards-a-syst). Do not ask the assistant to silently repair gaps while producing the diagram. Request a list of assumptions and unresolved relationships alongside the map. If two events share a timestamp and the export has no reliable ordering field, preserve their uncertain order instead of inventing a precise sequence. ## Reconcile two fictional return requests Imagine an approved procedure that requires an eligibility check before a credit is approved. The available export contains these invented records. Times are local times on the same fictional working day, and each row belongs to the request shown. | Request | Time | Recorded event | | --- | --- | --- | | R-17 | 09:00 | Request received | | R-17 | 09:12 | Eligibility checked | | R-17 | 09:15 | Credit approved | | R-18 | 09:05 | Request received | | R-18 | 09:30 | Credit approved | A generated map might draw a direct path from receipt to approval and label the eligibility check optional. That would turn a gap in R-18’s records into a policy claim. Alternatively, the assistant might insert a check into both requests because the procedure requires it. That would turn a requirement into an observation. Neither conclusion follows from the export. R-17 contains the expected event. For R-18, the eligibility event is absent from the supplied data. Possible explanations include a skipped check, an unlogged manual check or an export that omitted the relevant event. Those possibilities require different remedies. The reviewed map should retain eligibility checking as a required step when describing the procedure. Its accompanying case comparison should mark R-18 as unresolved against that requirement. The process owner then investigates the gap using authorized records and appropriate staff confirmation. If the investigation finds a manual check recorded elsewhere, link that evidence and explain the coverage limit. If it finds that the check was skipped, record the deviation. Avoid changing the approved procedure simply because an observed path was shorter. ## Review arrows as claims about sequence An arrow can imply more than the source establishes. Two events appearing consecutively in an export may mean only that no other included event falls between them. It does not necessarily mean the first event caused the second or that no work occurred in between. Ask the assistant to produce a compact review record for consequential connections. It should identify the cases supporting the connection, the relevant procedure rule, exceptions and unresolved evidence. Keep this record near the diagram so reviewers can inspect the basis without relying on the assistant’s confidence. Our editorial recommendation is to distinguish four bases in that record: recorded activity, required activity, reported practice and proposed change. Use accessible labels or a legend that survives export. Color alone is insufficient when someone prints the map or reads a text equivalent. For a proposed automation, require a separate decision about authority. A box saying “approve credit” does not specify who may approve it, under what conditions or which evidence is required. The existing guide to [approval-workflow policy cases](https://skillsforbusiness.ai/blog/ai-built-approval-workflows-policy-cases/) covers testing those rules before implementation. ## Check the map again after AI-assisted edits Treat diagram generation as an editing workflow with outputs to review. Microsoft’s documentation for process diagrams in Power Apps describes an AI-first experience and says validation can involve AI reviewing and merging manual changes. That is documented product behavior, not a claim that this article tested it or that another diagramming tool behaves identically. [Read the documentation](https://learn.microsoft.com/en-us/power-apps/maker/plan-designer/process-diagram). After an edit, inspect the resulting connections and conditions, not only the newly added box. A request to simplify the layout should preserve a required exception path. A request to merge duplicate labels should preserve genuinely different activities that happen to have similar names. Keep the reviewed revision and source register together. If the procedure changes or the export covers a different period, identify which claims need another review. Record who is responsible for approving the map in the actual business workflow. An AI-generated approval label cannot establish that a person reviewed it. Start with one normal case, one exception and one case with incomplete evidence. Ask whether the diagram can represent each without filling gaps or weakening the required procedure. A map that keeps the unresolved case visible gives the team a concrete investigation task before its arrows become automated behavior. ## Sources & further reading Sources checked for this article. Research findings and vendor claims are distinguished in the text. 1. [Map your processes](https://www.ai.gov.au/planning-ai/prepare-your-business/map-your-processes)Australian National AI Centre · Accessed 4 November 2026 2. [Event log imperfection patterns for process mining](https://research.tue.nl/p/en/research-outputs/event-log-imperfection-patterns-for-process-mining-towards-a-syst)Suriadi and colleagues; TU/e research portal · Accessed 4 November 2026 3. [Generate process diagrams](https://learn.microsoft.com/en-us/power-apps/maker/plan-designer/process-diagram)Microsoft Learn · Accessed 4 November 2026 Produced with AI-assisted research and writing. Examples and recommendations are our editorial synthesis unless attributed. This article does not claim independent product testing or human expert review. 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