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The right process to automate with AI is one with a clear beginning and end, enough volume to matter, a real recurring pain point, and information an AI system can use. Start by mapping how the work actually happens, then assess AI fit separately from business impact and risk. If the process is already broken, improve it first: AI may amplify an existing problem rather than solve it.
What makes a business process a good candidate for AI?
A process is worth evaluating when it is frequent enough to matter, bounded enough to understand, and painful enough that improving it would produce a meaningful result. Repetition alone is not enough: the task also needs information that an AI capability can use, and the consequences of a wrong result must be manageable.
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The Australian Government’s National AI Centre recommends looking for processes that are high-volume, a pain point, data-rich, and contained, with clear start and end points. Its examples include returns, inventory stocking, enquiries, onboarding, proposal creation, invoicing, bookings, feedback, scheduling, patient intake, and referrals. These are prompts for investigation, not a ranked list or a guarantee that AI is appropriate for any particular organization. National AI Centre: Map your processes
- Contained: You can identify what triggers the work, where it ends, and which steps belong to it.
- Frequent or consequential: The work happens often enough, or matters enough, that a successful change could make a difference.
- A genuine pain point: People encounter delays, rework, inconsistent handling, or another specific problem—not merely a general desire to “use AI.”
- Information-rich: The relevant inputs are available in a form that can support the intended task.
- Understandable risk: You can identify who could be affected by an error, how serious it would be, and whether it could be reversed.
Use these traits as a screening lens, not a pass/fail score. A workflow can be frequent and still be a poor AI candidate if its information is inadequate, its process is unstable, or a wrong result could have serious consequences.
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For example, an organization could investigate enquiry handling or proposal creation because those processes appear in the National AI Centre’s examples. That does not establish that either is suitable in a particular business. First find out what information the task uses, how often it occurs, where the current process loses time or consistency, and what happens if the result is wrong. Likewise, patient intake and referrals may be worth mapping, but consequences for people make impact and oversight especially important considerations.
How should you find processes to evaluate?
Begin with a business problem, not a technology search. State what is going wrong, what outcome should improve, and how you will know whether the change worked. The National AI Centre advises addressing a weak process before applying AI because the technology can amplify problems already built into the workflow. National AI Centre: Prepare your business
Start with people who do the work
Talk with the employees who perform or support the process. They can explain workarounds and exceptions that a procedure document may not show, as well as who may be affected if the workflow changes. Ask open questions such as:
- What starts this work, and what tells you it is finished?
- Where does work wait, get stuck, or return for correction?
- What workarounds do people use, and why?
- What information is often missing, duplicated, or hard to find?
- Which cases are handled differently, and what makes them exceptions?
- What change would make the work easier or produce a better outcome?
Look for recurring friction such as bottlenecks, rework, waiting, duplicate manual data entry, inconsistent handling, information gaps, and over-processing. A complaint that occurs only in an unusual edge case may deserve attention, but it is not the same opportunity as a persistent source of wasted effort or poor outcomes.
Do not rely only on managers’ descriptions or written procedures. Compare the stated process with what people actually do, including handoffs and informal workarounds. Those differences can reveal that the apparent problem is not the whole problem: a delay at one step, for instance, may originate in missing information earlier in the workflow. Include the people who perform the work when checking the map and considering a change; they can identify where a redesign would affect daily responsibilities.
Make the problem measurable
Write the problem in plain language before deciding on a tool. For example, distinguish “staff spend time finding information across several handoffs” from “we need an AI assistant.” The first describes a workflow problem to investigate; the second presupposes a solution.
Choose an outcome that could show whether the problem improved, such as fewer repeated handoffs, less avoidable rework, or more consistent handling. The appropriate measure depends on the process; the cited guidance does not establish one universal metric or target. Record the current state before any change so a later comparison has a baseline.
Be specific about both the desired result and its scope. If the issue is repeated data entry, describe which handoffs or fields are involved. If the issue is inconsistent handling, identify the cases where variation occurs and why consistency matters. A clearly stated problem helps distinguish an improvement to the underlying workflow from an AI feature that may not address the cause.
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How do you map the process as it really works?
Document the actual workflow step by step, including informal decisions and exceptions. A useful map shows what happens, who or what performs each step, what information enters, what output leaves, and how long the step usually takes. The National AI Centre recommends mapping with the people responsible for the work and provides a process-mapping template and examples. See the National AI Centre’s process-mapping guidance
- Set the boundary. Name the event that starts the process and the result that marks its end.
- List the steps in order. Capture handoffs, checks, approvals, exceptions, and workarounds—not only the steps described in formal procedures.
- Identify the performer. For each step, note whether it is done by a person, an existing system, or a combination.
- Record inputs and outputs. Note what information or materials each step needs and what it produces for the next step.
- Capture time and friction. Record typical time where known, along with waiting, rework, duplicated entry, and points where information is missing.
- Check the map with the people involved. Confirm that it reflects real work and identify who might be affected by a redesign.
Keep the map practical. Its purpose is to expose where effort and delay occur and to make each candidate task specific enough to assess—not to create a polished diagram that hides the exceptions.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor each step, note what information arrives, what decision or action follows, and what gets passed onward. Record duration where people can estimate it reliably; distinguish time spent actively working from time waiting for a response or approval when that distinction is useful. The point is not to force every workflow into a complicated diagram, but to make the sequence and its friction visible enough to discuss.
A process-mapping workbook can be a useful search term for organizations that prefer a guided format, but buying one is not a requirement. A free template or a simple document may be enough to capture steps, owners, inputs, outputs, time, and pain points. The National AI Centre provides a template and examples to get started.
How should you score AI fit and business impact?
Assess AI fit and business impact as separate questions. A task can look technically suitable while still being too consequential to automate without strong safeguards; conversely, a low-risk task may not merit AI if another method would work just as well.
The National AI Centre recommends scoring pain points for AI fit and then prioritizing candidates using AI fit and business impact as two axes. It identifies impact as high when an incorrect result could harm people or be difficult to undo. Its published scoring ranges—low AI fit of 5–12 and high AI fit of 13–25—are guidance only, not independently validated benchmarks or universal thresholds. There is no source-backed score that proves a process is “right” for AI. National AI Centre: Identify opportunities
| AI fit | Business impact | How to interpret the candidate |
|---|---|---|
| High | Low | It may be a contained quick win to investigate, provided the expected benefit exceeds the risks. |
| High | High | Potential fit does not remove the need for careful validation, oversight, and risk controls. |
| Low | High | Consider process improvement before reconsidering AI; the stakes do not make poor fit acceptable. |
| Low | Low | AI may not be worth pursuing; compare with a simpler process or tool change. |
The matrix is a prioritization aid, not a substitute for judgment. For each candidate, describe what a wrong result could do, who might bear the consequences, and how difficult it would be to detect and reverse. Keep the reasoning visible instead of reducing a consequential choice to a single total.
Use the scoring in two stages. First, assess whether the AI capability and available information match the pain point. Then compare business impact: what could happen if the output is wrong, and how much harm or difficulty would follow? A high-fit score answers neither the impact question nor the question of whether AI is better than a simpler alternative.
When comparing a shortlist, record the reasoning behind each rating rather than treating a numerical total as a decision. The Australian guidance gives ranges of 5–12 for low fit and 13–25 for high fit, but calls them a guide. Do not read a score at either end of those ranges as proof of readiness, safety, or likely return. A candidate that is high fit but high impact may warrant further evaluation and stronger safeguards; one with low fit and high impact is a reason to improve the process before considering AI again.
Business impact is about the consequences of error, not simply how much a workflow is visible or important to a department. Ask who could be affected, how serious an incorrect result could be, whether a person can spot it, and how readily it can be reversed. These questions help keep risk distinct from technical fit.
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How can you tell whether AI is the right tool for a specific step?
Match an available AI capability to a specific task in the mapped workflow. Do not assume that every step needs AI, or that AI is preferable to an existing method. The National AI Centre advises defining boundaries and weighing expected benefit against risk. National AI Centre guidance on identifying opportunities
For each step under consideration, ask:
- What exact task would the AI system perform, and what would remain with a person or existing system?
- What information would it need, and is that information relevant and sufficiently reliable for the intended use?
- What output should it produce, and how would someone check whether that output is acceptable?
- What happens when the input is incomplete, unusual, or outside the defined boundary?
- Would a process redesign or a non-AI tool address the same problem with less risk or effort?
These questions help separate an AI-enabled task from a vague goal to automate an entire department. A process may contain some steps suited to AI and others better left unchanged or handled through a different approach.
Be precise about what “automate” means in the proposed use. The candidate might be one task within a larger process, rather than the whole workflow. State which steps AI would touch and what remains with people or existing systems. That boundary makes it possible to assess the information required, the expected output, and the checks needed before anyone relies on the result.
Consider the exceptions as well as the routine cases. If an input is incomplete, unusual, or beyond the intended scope, the process needs a defined way to handle it. If the organization cannot explain how an output will be checked or what happens when it is unacceptable, it has not yet established a workable use for that task.
What risks and oversight should you assess?
Risk assessment is ongoing, not a one-time approval before launch. The UK Government’s AI Risk Management Toolkit describes identifying, assessing, treating, monitoring, and reporting risks across the AI lifecycle, with attention to matters such as data quality, understanding the model, validation, and reassessment when circumstances change. UK Government: AI Risk Management Toolkit guidance
Before committing to a candidate, identify who could be affected, what could go wrong, how the risk could be mitigated, and how the system’s performance would be monitored. Consider whether the organization can carry out those checks and respond if the system behaves differently than expected.
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Generative AI calls for particular care. NIST’s 2024 Generative AI Profile notes that some risks and longer-term performance may be less well understood, and organizations may need more human review, tracking, documentation, and management oversight. The appropriate level of oversight depends on the task and its consequences; generative AI is not a reason to remove people from decisions that could harm someone or be difficult to reverse. NIST: Artificial Intelligence Risk Management Framework, Generative Artificial Intelligence Profile (2024)
Think of risk work as a cycle: identify the possible problems, assess their likelihood and consequences, decide how to treat them, monitor the system and workflow, and report what is happening to the people responsible. Revisit that assessment when the data, process, intended use, or surrounding circumstances change. Validation and monitoring are not substitutes for understanding the task; they are part of managing it over time.
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Human review should be proportionate to the consequences and uncertainty. NIST’s guidance on generative AI points to the possible need for more review, tracking, documentation, and management oversight because risks and longer-term performance may be less understood. Keep people involved where a wrong outcome could harm someone or be difficult to undo, and make clear who is responsible for reviewing issues and responding to them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you document before choosing a process?
Write a short decision record for each serious candidate, including why you would use AI—or why another approach is preferable. The OECD’s due diligence guidance and the National AI Centre’s implementation guidance support a risk-based approach, in which more significant uses receive deeper assessment. OECD: Due diligence framework and practical examples · National AI Centre: Guidance for AI adoption
- The business problem and target outcome
- The mapped workflow, including affected people and known exceptions
- The specific task proposed for AI, alongside steps that will remain outside its scope
- The data required and what is known about its quality
- Potential risks, mitigations, human review, and plans for monitoring
- Success measures, an accountable owner, and the reason for choosing AI or another approach
This record makes the decision reviewable. It also gives the organization a basis for revisiting the choice if the workflow, data, risks, or intended use changes.
Keep the record concise enough to use, but specific enough that another person can understand the decision. Include the workflow boundary, the observed friction, the intended benefit, what information the task needs, and how performance and risk will be checked. Note who owns the decision and who is accountable for monitoring and responding to problems. A risk-based approach means that more significant uses receive deeper assessment, rather than requiring the same level of effort for every candidate.
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Document why AI is being considered instead of process improvement or another tool. If the information is not yet adequate, the process needs redesign, or the consequences are not manageable with available oversight, say so. The outcome of an assessment can be to improve the workflow, gather better information, pursue a different approach, or defer a decision—not just to automate.
Where should a business start?
Start with one contained workflow that employees recognize as a real pain point, rather than selecting the most visible or fashionable use of AI. Map it, address process problems first, and compare its fit, impact, data readiness, and oversight needs before deciding whether AI belongs in any step.
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A free process-mapping template may be enough to capture steps, owners, inputs, outputs, time, and pain points; a paid workbook is not necessary to do this work. The National AI Centre’s guidance includes a template and examples, so begin with those if they meet your needs. National AI Centre process-mapping template and examples
A practical first pass is to name the business problem, talk with the people who do the work, and map the process from trigger to finish. Use that map to identify specific friction and candidate tasks. Compare AI fit with impact, check the data and validation needs, and consider who would be affected and what oversight is realistic. If the case is unclear, the next step may be process improvement or gathering better information rather than deploying AI.
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Do I need special software to map a business process before using AI?
Not necessarily. A simple document or diagram can capture the workflow details needed for an initial decision; the National AI Centre also provides a template and examples that may be sufficient.
Should I start with a customer-facing process or an internal one?
The cited guidance does not rank customer-facing and internal workflows. Choose based on the specific process’s volume, pain point, information, boundaries, expected benefit, and potential consequences of error.
What if our process data is incomplete or inconsistent?
Treat that as a readiness issue to investigate before relying on AI output. Establish what information is needed, where gaps or inconsistencies arise, and whether the workflow or data collection can be improved before evaluating the use further.
Can we use an AI-fit score as approval to automate a task?
No score by itself establishes that a task is suitable or safe. The National AI Centre labels its scoring ranges as guidance, so the decision also needs a separate assessment of impact, risks, validation, and oversight.
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Not by default. Identify the specific task or steps under consideration, define what remains with people or existing systems, and assess the boundaries, information, risks, and checks for that use.
What should we do if a process is already unreliable?
Improve the process before applying AI. The National AI Centre cautions that AI can amplify problems in a process that is not working well; map the causes of friction and address them before reconsidering the technology.
What does a high AI-fit score tell us?
It suggests that the task may match AI capabilities, but it does not establish that the use is safe, beneficial, or ready. Assess business impact, data quality, validation, oversight, and whether a simpler approach would solve the problem.
How often should we reassess risks?
Risk management should continue across the AI lifecycle. Monitor and report risks, and reassess when the data, process, intended use, or other circumstances change.
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The Bottom Line
Choose a process because it has a specific, recurring problem—not because it is merely repetitive or prominent. Map the real workflow, separate AI fit from business impact, and make sure the information, validation, and human oversight are adequate. If the process itself is broken, fix it before asking AI to handle it.
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