The Tool Desk
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Start with a recurring business problem, not an AI product. A useful AI use case names the work, who does it, what information it needs, and the result you expect; then you check whether the value, data, feasibility, risks, and human oversight make a pilot worthwhile. That gives your team a short, defensible list of workflows to test before it spends money or commits to a tool.
How do I identify AI use cases for my team?
Work backward from a specific source of friction: repeated manual effort, missed service expectations, slow approvals, or another problem people can describe plainly. Keep the observed problem separate from the proposed solution. “Our team spends time finding approved answers in internal documents” is a problem statement; “we need an AI assistant” is a proposed solution that still needs to be tested.
This distinction prevents a product demonstration or feature list from defining the work for you. Microsoft’s AI adoption planning guidance likewise starts with business needs and considers value, feasibility, resources, data, and capabilities before selection.
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Ask the people who perform or depend on the work where time is lost, where queues build up, which tasks are repeated, or where service outcomes fall short. A promising candidate has an identifiable activity and a consequence worth improving—not merely a general desire to “use more AI.”
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Write the problem in neutral terms before mentioning a technology. For example: “Support agents spend time locating relevant answers in internal documentation, which may be slowing resolution.” This is specific enough to investigate without assuming that AI is the right remedy.
Check whether the work recurs often enough
A workflow that happens rarely may not justify the effort of implementation, integration, review, and upkeep, even if it is frustrating when it occurs. Record how often the activity happens and who performs it. Do not assume frequency or savings: establish the current pattern with the team that owns the work.
What should a good AI use-case statement include?
A concise statement identifies the activity, its user, the information or input involved, and the result the team wants. Include how often the work occurs and what observable measure could show whether the result improved.
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For [user or team], help with [specific activity] using [input or information] so that [expected outcome] improves, measured by [baseline and measure].
For example: “For support agents, help find answers in approved internal documents so that agents can resolve requests more efficiently, measured against current resolution time and answer quality.” Microsoft uses a related support-agent example in its adoption guidance. Treat this as a candidate to investigate, not proof that a particular system will improve performance.
Keep the result measurable but appropriate to the problem. A time measure may fit a slow process; an error or rework measure may better fit a quality problem. If the intended outcome cannot be observed, the team will have difficulty distinguishing a useful change from a convincing demonstration.
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- Activity: What work is being done?
- User: Who performs it or relies on its result?
- Input: What documents, data, or context would be needed?
- Outcome: What business or service result should improve?
- Measure: What baseline and evidence would reveal a change?
- Frequency: How often does the activity occur?
What kinds of work may be a good fit for AI?
Consider the nature of the input, the acceptable variation in the output, and what happens if the result is wrong. Generative AI is a more plausible early hypothesis when information is unstructured and variation in the response is acceptable; a structured, predictable task requiring repeatable outputs may fit a deterministic approach better.
This is an initial fit check, not a final technology decision. Some workflows combine methods, and business automation may require integration with other systems as well as AI. The right question at this stage is whether the work’s characteristics justify investigating AI—not which product has the most features.
| Workflow characteristic | Possible direction to investigate | Question to resolve |
|---|---|---|
| Unstructured information; some variation in a useful response is acceptable | Generative AI may be worth testing | Can a person check the response against dependable sources before it is used? |
| Structured, predictable task; output needs to be repeatable | A deterministic approach may be a better fit | Does the task actually need AI, or can an existing rule-based process do it? |
| Routing, forecasting, or another multi-step business process | May require automation, system integration, or a combination of methods | What systems, data, and owners are needed for the full workflow? |
Classify the source of value, too. An individual-work improvement—such as help with drafting or meeting preparation—differs from business automation, such as routing work or forecasting. The latter can depend on connected systems, operational controls, and a broader implementation effort. A task that sounds small in a prompt may therefore be much larger as a dependable end-to-end workflow.
How should we prioritize candidate workflows?
Compare each candidate’s expected business value with its feasibility and the resources available to deliver it. A high-value idea is not ready simply because it sounds impressive: it also needs usable data, practical ownership, appropriate capabilities, and a realistic path to implementation.
Use the same qualitative questions for every candidate. There is no universal weighting formula established by the cited guidance, so avoid presenting a made-up score as objective proof. If your organization chooses to score candidates, document the criteria and why the weights fit its priorities.
| Comparison area | Questions to record | Why it matters |
|---|---|---|
| Outcome and value | What result should change, and why does it matter to the team, customers, or organization? | A clear benefit lets the team judge the use case against the problem rather than against product novelty. |
| Frequency and repeatability | How often does the task occur? Does it follow a recognizable pattern? | Recurring work may make implementation effort more worthwhile; repeatability can affect suitability and review. |
| Data and access | What information is needed? Is it available, sufficiently complete and reliable, and accessible for this use? | A workflow cannot be evaluated realistically if its required information is missing or unsuitable. |
| Feasibility and resources | What infrastructure, integrations, staff time, skills, and implementation work are required? | Operational constraints can make an otherwise valuable idea impractical or not yet ready. |
| Errors and oversight | How harmful could an error be? Can someone detect and correct it before it matters? | The consequences and detectability of errors help determine whether AI should assist, automate, or stay out of the workflow. |
| Risk and accountability | Who owns the use? Who could be affected, and what safeguards or records are needed? | Risk depends on the particular use and context, not just on the general capability of a tool. |
| Capability and cost | What capability is actually required? Can an existing tool do it? What total cost and skills would a new approach require? | These checks keep a purchase decision tied to a validated need and available alternatives. |
ACT-IAC’s AI Playbook for the U.S. Federal Government, released in 2020 and written for a government context, is older process guidance rather than current vendor advice. Its emphasis on defined problems and requirements, suitable teams and skills, budget, technology, and complete, quality data supports the practical checks in this shortlist.
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How do we decide what AI should do and what a person should review?
Use the workflow’s repeatability, impact, error detectability, and time sensitivity to decide how much responsibility AI can take. Routine work with visible, correctable errors may be a candidate for automation with human review; judgment-heavy work may be better served by AI assistance while a person leads.
Microsoft’s task decision framework uses these factors to help distinguish assistance from delegation. The balance depends on the workflow and the consequences of a mistake.
Keep a person in control where consequences demand it
Final approvals, high-impact decisions, sensitive communications, and ambiguous work call for human ownership or may be unsuitable for delegation. “A person reviews it” is not a sufficient plan by itself: name who reviews, what they check, and whether they have time and authority to stop or correct the process.
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When AI output informs a decision, recommendation, or communication shared with others, people remain accountable for validating it. Microsoft’s output validation guidance recommends checking source, verification, context, and resilience before acting. In practice, that means checking where a claim came from, confirming important details, judging whether the answer fits the situation, and considering whether it remains dependable when the prompt or circumstances change.
Set the oversight plan before a trial
- Name the person accountable for the workflow and the person or role that reviews its output.
- Define which errors matter, how a reviewer can recognize them, and what correction or escalation is available.
- Decide when a person must approve an output before it affects a customer, employee, decision, or external communication.
- Set conditions for pausing or revising the workflow if errors, unexpected effects, or other risks appear.
What governance checks belong before a pilot or purchase?
Assign an owner and assess the particular use, its possible impacts, and ways it could be misused before putting the workflow into operation. A tool’s general capabilities do not determine risk by themselves: the same tool can create different risks in different contexts.
The Australian Government’s National AI Centre foundations guidance highlights context, clear accountability, and keeping records of actions. The UK Department for Science, Innovation and Technology published its AI Risk Management Toolkit on 8 September 2026 for people involved in designing, procuring, operating, or delivering AI products; it frames risk assessment and treatment as part of responsible adoption.
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Before a trial, make sure the team can say who owns the use, who may be affected, what information the workflow uses, and what safeguards fit the context. Do not treat procurement as a substitute for these decisions. If responsibility or risk controls are unclear, the candidate is not ready to move forward as designed.
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How can we run a pilot that tells us whether a use case is worthwhile?
Set a baseline, target outcome, review method, and decision rule before the trial begins. A pilot is useful only if the team can judge the workflow against the problem it was meant to solve and can catch unacceptable errors along the way.
- Record current performance. Capture the relevant starting measure, such as time spent, service quality, or rework, using a method the workflow owner can explain.
- State the expected change. Define what should improve and how the team will observe it. Do not substitute a general impression that the AI “seems helpful” for the measure tied to the original problem.
- Choose a bounded workflow. Specify which activity is included, which users and information are involved, and what remains outside the trial.
- Assign review and accountability. Name who checks outputs, what errors matter, and who can stop, correct, or escalate the process.
- Agree on a decision point. Decide in advance what evidence would justify revising the workflow, stopping it, or considering a broader evaluation.
This pilot structure follows from the need to establish business value, feasibility, risk, and accountability before adoption. It does not imply a universal trial duration or a guaranteed result; those depend on the workflow and the evidence needed to judge it fairly.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should we compare tools or decide whether to buy?
Compare tools only after the team has a defined use case, a plausible value measure, an initial feasibility screen, and an oversight plan. Then evaluate required capabilities, data access, integrations, skills, cost, and risk against what the organization already has and against possible build-or-buy paths.
Write down the capabilities the workflow requires before reviewing product features. For example, identify which information the system must access, where a person must intervene, and what integrations are essential. This makes it easier to distinguish a real requirement from a feature that looks attractive in a demonstration but does not solve the stated problem.
Check existing tools as part of the comparison rather than assuming a new purchase is necessary. If no practical option meets the requirements, the candidate may need redesign, further feasibility work, or a different method. The result of this process can be a reasoned decision not to buy or not to use AI for that task.
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What should the final shortlist look like?
A useful shortlist is a small set of work-focused candidate records, not a catalog of products. Each entry should make it possible for another decision-maker to understand the problem, expected outcome, feasibility, risks, and next test without relying on the original brainstorming conversation.
- Problem and activity: What is happening now, and what work might change?
- User and frequency: Who does it, and how often?
- Input and outcome: What information is needed, and what result should improve?
- Measure and baseline: What evidence will show whether the change helped?
- Feasibility: What data, infrastructure, integrations, staff capacity, skills, and cost are involved?
- Review and risk: What could go wrong, who checks the output, who is accountable, and what safeguards apply?
- Next decision: Is the workflow ready for a bounded pilot, does it need more preparation, or is AI not the right fit?
That record keeps the order of decisions clear: define the problem, test whether AI is suitable and feasible, decide how people stay accountable, and only then assess tools.
Frequently Asked Questions
How long should an AI use-case pilot run?
There is no universal duration established by the guidance cited here. Make the trial long enough to observe representative instances of the workflow and assess the agreed measures, while keeping it bounded and subject to review.
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What if a pilot saves time but produces more errors?
Treat speed and error impact as separate outcomes rather than allowing one to conceal the other. Revisit whether the workflow is suitable for delegation, whether review catches mistakes reliably, and whether the process should be revised or stopped.
Can we test a use case with sensitive or restricted information?
Only after the organization has established that the information may be used in the proposed workflow and identified appropriate access and safeguards. If those conditions are unresolved, test the workflow without that information or defer the trial.
What if employees disagree about whether a task is a problem?
Clarify whose outcome is affected and gather observations from the people who perform the task and those who rely on it. If the team cannot agree on a meaningful problem or measure, it does not yet have a defensible use case to evaluate.
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