Someone on your team has tried an AI tool. Someone else has a list of tasks it might automate. You have seen a convincing demonstration, but you are still unsure where it would help your business enough to justify the effort.
To choose your first AI use cases, identify recurring tasks, check data suitability and human review, then compare value, feasibility and effort. Shortlist three distinct candidates but pilot one first. A promising score does not override a missing reviewer or unsuitable data.
Photo by Scott Graham on Unsplash.
To choose your first three AI use cases, build a shortlist around those conditions. Then pilot one before committing your team to all three.
1. Name the problem before naming the tool
“We need an AI assistant” is a solution in search of a job. “Account managers spend too long turning meeting notes into a checked action list” gives you something specific to assess.
Ask the people doing the work where they repeatedly read, summarise, classify, compare, or draft information. For each candidate, record:
The task and who currently does it.
How often it happens and what makes it difficult.
The input available and the output needed.
The person who could judge the quality of that output.
What happens if the output is wrong.
Check whether a simpler change could address the problem. A required field, a shared template, or a standard reminder may be enough for a predictable task. Use AI where its ability to work with variable information serves a clear purpose.
If the workflow is unclear, start with our guide to mapping business processes. It is difficult to assess a tool when the task itself changes depending on who you ask.
2. Apply a few checks before scoring anything
For an initial pilot, look for work where an error can be caught before it causes a problem. Drafting an internal summary for review is a different proposition from sending a commitment to a client automatically.
Pause a candidate if the necessary data cannot be used in the proposed tool, nobody can verify the result, or the consequences of a mistake exceed the controls your team can provide. Resolve those questions before treating the use case as ready.
NIST's AI Risk Management Framework treats trustworthiness and risk management as considerations across the design, development, use and evaluation of AI systems. For a small business, a practical starting point is to identify what could go wrong, who would be affected, and who will check the result.
3. Compare the candidates across six areas
The following scoring exercise is a discussion tool, not a certified assessment or a prediction of return. Give each candidate 1, 2, or 3 points in each area and record the reason.
Value: 1 means an occasional inconvenience; 2 means a recurring problem; 3 means a problem with a clear effect on delivery, capacity, or client experience.
Feasibility: 1 means major integration or an unstable task; 2 means some setup is needed; 3 means you can test a narrow task with tools and skills already available.
Data readiness: 1 means missing or unsuitable inputs; 2 means inputs need cleaning or approval; 3 means the required inputs are accessible, consistent, and approved for the proposed use.
Risk and oversight: 1 means difficult-to-detect errors or weak controls; 2 means manageable issues with additional checks; 3 means a reviewer can verify the output before it is used.
Adoption: 1 means no clear owner; 2 means an interested owner with limited time; 3 means an owner and users are ready to test it in their normal workflow.
Time to test: 1 means the idea depends on a larger project; 2 means several dependencies need resolving; 3 means you can run a small, bounded test soon.
Compare the totals, but keep the reasons visible. A high score for value must not cancel an unresolved data or risk issue. Put blocked candidates in a separate “resolve first” list.
Microsoft's guidance on evaluating AI use cases considers business value, user needs and technical feasibility. This exercise adapts those broad considerations to a small team's first decisions; the six-area scoring above is a suggested working format.
4. Build a shortlist of three distinct use cases
Choose candidates that solve real problems and are practical to evaluate. The following is an illustrative shortlist for a small service business, not a recommendation that every company should begin here.
Candidate one: turn meeting notes into a draft action list
Input: approved meeting notes. Output: a proposed summary with actions, owners and dates. Reviewer: the person who attended the meeting.
Check for invented commitments, missed actions, and incorrect owners. Measure the time needed to produce a usable action list, including review and corrections.
Candidate two: prepare draft answers to routine enquiries
Input: an enquiry and an approved set of service information. Output: a suggested response. Reviewer: the account or service owner before anything is sent.
Check factual accuracy, tone, pricing references, and promises. Define when the tool should leave a question unanswered for a person to resolve. A polished answer that invents a commitment is not a successful result.
Candidate three: extract fields from a standard document
Input: an approved document type. Output: proposed values in a draft record. Reviewer: the person responsible for that record.
Start with a bounded document set and check every extracted field against the original. Define what happens when a field is missing or unclear. During the pilot, keep updates to live business records under human control.
Your shortlist may look different. The important feature is that each candidate has a defined input, output, reviewer, and quality test.
5. Pilot one and measure the whole task
Choose the most suitable candidate, name an owner, and agree the test before starting. Record a baseline for the current task. Then compare the assisted version using the same definition of a completed, acceptable result.
Include setup, review, corrections, and exception handling. Counting only the seconds it takes to generate an answer can hide work that has simply moved elsewhere.
Decide in advance when to stop or adjust the test. Examples include unacceptable factual errors, too much correction work, unsuitable data handling, or users finding the workflow harder to maintain.
A useful pilot ends with a decision: proceed within the tested limits, improve and retest, or stop. It does not have to end with a rollout.
Your first AI roadmap can fit on one page
For each shortlisted use case, record the problem, owner, expected benefit, approved inputs, review controls, test plan, success measure, and next decision. Mark one as the current pilot and the other two as candidates.
Our article on what AI should automate and what your team should still own gives further context for keeping judgement and accountability clear.
What should go on the AI use-case shortlist?
Write one card per candidate: current task, frequency, approved input, proposed output, reviewer, baseline effort and stop condition. Keep blocked ideas in a separate list with the issue and owner. Three candidates that all depend on the same unavailable data are one shared dependency, not three ready projects.
If you want help turning scattered ideas into a practical sequence of tests, discuss your AI implementation roadmap with Hili Consulting. Start with one operational problem and a clear definition of what a useful result would look like.
