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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteBefore using AI to speed up a business process, map how the work actually gets done. A current-state map can reveal delays, handoff problems, repeated work, missing information, and workarounds—so you can decide whether to simplify a step, fix a handoff, use conventional automation, or apply AI to a specific task.
Which process should you map first?
Choose one contained process with a clear start and finish. Favor work that happens often, causes a recognized problem, and involves information an AI system might plausibly handle. Examples include customer returns, inventory stocking, customer enquiries, and client onboarding, as suggested by the Australian Government’s National AI Centre.
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These are prioritization prompts, not a scoring formula. A process is not automatically a good AI candidate just because it is slow or frustrating. The outcome you want, available information, and risks of the task matter too.
How do you map the process as it works today?
Build the map with the people who perform or oversee the work. Formal procedure documents can be a useful reference, but they may not show workarounds or the points where work regularly stalls. The National AI Centre’s guidance recommends recording the process name, date, owner or overseer, and participants.
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- Set the boundary. Write down what starts the process and what counts as completion.
- List the actual steps. Record what people do, what information they use, who receives the work next, and what happens when something is missing or unusual.
- Ask where the work gets stuck. Find out what people repeat, wait for, enter manually, or do outside the documented procedure.
- Identify missing information. Ask what people wish they had at each step and what they need to complete the work without chasing someone.
For each step, note the responsible person or role, the information or materials needed, and what the step produces. This gives the team a practical view of inputs, activities, and resources—the elements NIST Baldrige guidance says to examine when assessing operations.
What problems should the map expose?
Mark the problems on the map rather than assuming they all call for automation. Look for:
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- Bottlenecks where work waits for one person or team
- Repeated work, re-entry, or corrections
- Waiting between steps or while information is collected
- Manual data entry and transfers between systems
- Inconsistent handling of similar cases
- Information gaps that trigger follow-up
- Over-processing: checks, approvals, or activities that do not improve the result
The National AI Centre’s client-onboarding example illustrates the kinds of friction a map can expose: custom quotes, repeated requests for incomplete documents, spreadsheet tracking, manual transfer of customer details, a one-person bottleneck, and an incorrect portal link. These are illustrative issues, not evidence about how common they are.
How do you decide what to change?
Diagnose the causes before selecting technology. The NIH Office of Research Services describes process mapping as a way to find bottlenecks and duplicate or unnecessary activity, and to consider whether activities should be automated, combined, modified, or relocated. That leaves several options besides AI:
- Remove a step that adds no useful value.
- Simplify or combine activities that create needless effort.
- Clarify responsibility at a handoff or change who handles a task.
- Improve the quality or availability of information before work begins.
- Use conventional automation for a predictable, rules-based task.
- Use AI for a bounded task only when it fits the intended purpose and context.
For example, if a process repeatedly stalls because required documents arrive incomplete, first examine how requirements are communicated and checked. Adding AI before fixing that information gap may leave the underlying cause untouched.
How do you assess whether AI fits?
Define the proposed AI task in plain language: what the system would do, for whom, in what setting, and toward what outcome. Then consider the information it needs, the assumptions it relies on, its limitations, and who could be affected by its output.
NIST’s AI Risk Management Framework Core describes the Map function as establishing context for managing risks related to an AI system. 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 and recommends weighing risks against benefits.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11In practice, compare candidate tasks using factors the guidance supports: how often the work occurs, how serious the pain point is, whether usable information is available, how clearly the task is bounded, the outcome you expect, and the risks and impacts in context. The sources establish no universal threshold, score, or ranking formula.
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How should people stay in control?
Redesign the workflow around the task AI might perform, rather than simply inserting a system into the old sequence. The National AI Centre recommends keeping people in control of key decisions. One illustrative pattern is AI drafting a customer email for a person to review and approve before it is sent.
That pattern is not a universal control prescription: the appropriate review and safeguards depend on the task and its context. Make clear who checks an output, what they should verify, and what happens when it is incomplete, unsuitable, or uncertain. For consequential work or outputs that affect customers, retain meaningful human review rather than treating generated content as ready to use.
What should the team record before moving ahead?
Keep a concise record that connects the problem to the proposed change:
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- The process boundary, owner, participants, and current steps
- Observed delays, rework, workarounds, and information gaps
- The underlying cause the team intends to address
- The desired outcome and how the team will tell whether it improved
- The proposed intervention and why it fits better than other options
- For an AI task, its intended purpose, users, deployment setting, assumptions, limitations, risks, and human oversight
This makes it easier to see whether a proposal addresses the mapped problem or merely adds technology to a process that still needs repair. NIST identifies AI RMF 1.0 as a 2023 framework version, and its pages note that the framework is being updated; check the current applicable version when using it for risk management.
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