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AI is not a shortcut around a broken workflow. First document how work actually moves, find the causes of delay and rework, and decide what should change. Only then can you judge whether AI suits a specific task—or whether a clearer handoff, better information, or a simpler process is the better fix.
Why map a process before adding AI?
A process map makes the work visible from its starting point to its outcome. It can reveal bottlenecks, repeated work, waiting, manual data entry, inconsistent handling, missing information, and steps that add effort without value. Those problems may look like an invitation to automate, but they do not show by themselves that AI is appropriate.
The National AI Centre, an Australian government resource, recommends mapping current work before redesigning it. NIH’s process-mapping guidance likewise describes mapping as a way to identify bottlenecks and duplicate or unnecessary activity, then consider whether activities should be automated, combined, modified, or relocated. That means a good diagnosis may lead to a process change that uses no AI at all.
How do you map a business process before automating it?
1. Choose one contained process
Start with work that happens often, causes a recognized pain point, involves information that could plausibly be handled by AI, and has clear start and end points. Examples from the National AI Centre include customer returns, inventory stocking, customer enquiries, and client onboarding. These are useful prompts for choosing a candidate, not a universal scoring system.
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Keep the boundary specific. “Improve customer service” is too broad to map usefully; a process such as “handle a customer enquiry from receipt to response” is more manageable.
2. Record how the work really happens
Involve the people who perform or oversee the process. Formal procedures may not show the workarounds, informal handoffs, or missing information that shape the actual workflow. The National AI Centre’s mapping guidance suggests recording the process name, date, owner or overseer, and participants, then asking:
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- What starts the process, and what counts as completion?
- What does each person do, and what happens next?
- Where do people wait, repeat work, or use a workaround?
- What information is missing or needed at each step?
- Where does work usually get stuck or delayed?
3. Mark the friction points
As you trace the steps, mark bottlenecks, repeated tasks, waiting between handoffs, manual data entry, inconsistent decisions, information gaps, and over-processing. The National AI Centre’s client-onboarding example includes custom quotes, repeated chasing for incomplete documents, spreadsheet tracking, manual transfer of customer details, a one-person bottleneck, and an incorrect portal link. These illustrate possible issues; they are not evidence of how common those issues are.
4. Diagnose the cause before choosing a tool
For poor results, examine the process inputs, steps, and assigned resources. Ask whether the problem comes from defective information, unclear ownership, a needless approval, an awkward handoff, or a genuinely repetitive task. NIST’s Baldrige operations guidance and NIH’s mapping guidance both support examining how operations work before selecting improvements.
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Possible interventions include removing or simplifying a step, combining activities, changing or relocating work, using conventional automation, or applying AI to a bounded task. For example, if customer details are repeatedly retyped because two teams do not share a reliable handoff, fixing the handoff or data flow may address the cause more directly than adding a generative AI system.
How do you decide whether AI fits the mapped task?
Describe the task and its context before selecting a system. NIST’s AI Risk Management Framework (AI RMF) version 1.0 (2023) uses its Map function to establish context for AI risk management. The mapped context should inform whether to proceed with design, development, or deployment; NIST’s Manage guidance also says AI may not be the right solution for a business task.
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Write down the intended purpose and expected outcome, who will use the system, where it will operate, what information it will receive, what assumptions it depends on, and the possible limitations, risks, and impacts. Consider who could be affected if an output is wrong or incomplete. The sources do not establish a universal threshold or scoring formula for deciding that a workflow is ready for AI.
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NIST identifies AI RMF 1.0 as the 2023 framework, and its pages note that the framework is being updated. Check which version applies when using it to guide a current project.
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- Optimize Academic Success: Elevate your academic journey with our 2027 Academic Planner with a 2026-2027 Calendar, featuring a meticulously designed Daily, Weekly and Monthly Planner Calendar that aligns with your school term, making it the perfect student planner for managing assignments, exams, and academic goals.
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Redesign the workflow with people in control
Once you identify a suitable task, decide where AI belongs in the redesigned process and what happens around it. The National AI Centre gives an example in which AI drafts customer emails for a person to review and approve before sending. That is an illustrative pattern, not a rule for every use case: the appropriate review and safeguards depend on the task and context.
Make the review point explicit. Identify who checks the output, what they need to verify, and what happens when it is inaccurate, incomplete, or unsuitable. Where outputs affect customers or other consequential work, build human review into the workflow so people retain control of key decisions.
Compare candidate processes and interventions
When choosing what to map first, compare candidates using evidence your team can observe rather than an invented score. Consider how often the process runs, how serious its pain point is, whether relevant information is available in a form the proposed AI could handle, and whether the process has clear boundaries. Also define the outcome you want and how you would assess it, alongside the risks, limitations, and impacts in context.
For each mapped problem, compare possible changes: remove or simplify a step, combine activities, change or relocate work, automate with conventional technology, or use AI for a bounded task. The guidance supports considering these options; it does not establish that AI is always preferable or that mapping alone guarantees a successful deployment.
Sources and scope
- National AI Centre: Map your processes
- National AI Centre: Redesign your workflow
- NIST Baldrige Performance Excellence Program: Operations
- NIH Office of Research Services, Office of Quality Management: OQM Process Mapping
- NIST AI Risk Management Framework: AI RMF Core
- NIST AI RMF Playbook: Manage
The National AI Centre is an Australian government resource; its preparation guidance is practical guidance, not a universal legal requirement. These sources do not provide a universal automation threshold, comparative return-on-investment figure, or proof that mapping by itself causes successful AI deployment.
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