How to Choose Your First AI Use Case
Choose a first AI use case that is valuable enough to measure, narrow enough to evaluate and reversible enough to stop. Prefer an observable workflow over a broad ambition such as “automate marketing.” A pilot should resolve a specific uncertainty about quality, cost or adoption.
Do not begin by scoring every idea. First remove candidates that lack essential data rights, an accountable owner or a credible way to identify a wrong result.
Observe the current task
Watch people complete ordinary and difficult cases. Record the trigger, inputs, steps, result and handoffs. Measure how often the task occurs and how long usable completion takes. Include checking and corrections; typing time alone is an incomplete baseline.
Ask why the task is slow. Missing source information, conflicting responsibilities and waiting for a customer may dominate the delay. AI cannot solve those problems merely by producing the next paragraph faster. A checklist, better form or clarified approval rule may be the appropriate intervention.
Screen for feasibility and risk
| Test | Evidence | Decision |
|---|---|---|
| Input access | Permission-cleared representative examples | Pause if essential information is unavailable |
| Output standard | A person can explain correct and unacceptable results | Clarify before prototyping |
| Error containment | Review and fallback before harm | Reduce authority if failures cannot be caught |
| Workflow owner | Named person with time to operate the pilot | Defer until ownership exists |
| Volume and value | Baseline time, frequency and consequences | Compare with implementation effort |
For candidates that pass, use rough categories such as low, medium and high. Avoid turning uncertain guesses into precise rankings. Two candidates with similar apparent benefit may have very different review requirements and implementation dependencies.
Worked example: compare three first projects
Illustrative shortlist: a startup considers summarizing internal meetings, drafting help responses and automatically granting refunds. The same general-purpose model might support pieces of each, but the operating requirements differ.
Meeting summaries use a repeatable format and have an internal reviewer, but the team needs appropriate recording permissions and a way to confirm action owners. Help-response drafting has approved source documents but needs checks against outdated policies. Refund automation affects money and customer commitments and depends on reliable account state.
The team chooses help-response drafts limited to a small set of general questions, with no sending authority or account changes. This is a decision based on its particular source quality and owner capacity, not a universal recommendation. Another team could reasonably choose meeting notes if those conditions were stronger.
The pilot specification names the permitted topics, sample cases, reviewer, budget and stop conditions. It measures time to a correct response, unsupported claims and escalation workload. It excludes billing disputes. Any unauthorized account access pauses the pilot immediately, while minor wording defects enter the improvement backlog.
Write a short pilot brief
Use this structure: “For [user], when [trigger], propose [output] using [approved inputs]. A person checks [criteria] before [action]. We will compare against [baseline] using [cases and measures]. The owner is [person], the cap is [budget or volume], and the pilot ends or pauses when [condition].”
Choose the smallest test that could change your decision. That might be an offline evaluation of historical requests before any live integration. If the prototype cannot meet the output standard, there is no reason to build the entire workflow around it first.
Estimate total cost with review and maintenance. Released staff time may be useful capacity, but it becomes cash savings only if spending changes. Keep those outcomes separate in the business case.
Move from the brief to vendor evaluation and the current tool shortlist. For deciding where AI fits founder work, read applications and limits. The integration overview explains the later release stages.
Estimate the benefit range before scoring ideas
For each candidate, calculate current monthly effort from volume and complete handling time. Then estimate how much work would remain after assistance. Use a conservative range for review, corrections and exceptions, rather than assuming the entire manual task disappears.
Illustrative comparison: drafting internal meeting summaries occurs forty times monthly and takes fifteen minutes each, or ten hours. If AI-assisted preparation still requires eight minutes of checking and correction, the potential reduction is about 4.7 hours before maintaining recordings and handling exceptions.
A second candidate processes 300 help questions at seven minutes each, or thirty-five hours. If assistance reduces complete handling to four minutes, it releases fifteen hours before maintenance. The second candidate has a larger apparent opportunity, but only if its source quality, review capacity and permission controls are acceptable.
A third candidate may have large theoretical savings but require private account access and automatic financial actions. Those dependencies can make it an unsuitable first project. Use the automation calculator to explore assumptions, not to override a failed essential requirement.
Use an evidence-based comparison sheet
| Candidate field | What to record | Weak evidence to avoid |
|---|---|---|
| Frequency | Recent observed volume | A guess based on the busiest day |
| Handling effort | End-to-end time across varied cases | Typing time only |
| Quality standard | Examples of acceptable and unacceptable work | “Looks professional” |
| Data readiness | Actual permitted inputs at task time | A curated demo file alone |
| Reviewer capacity | Available hours and backup owner | An approval button with no staffing |
| Recovery | A tested manual route | “We will fix failures later” |
You can use low, medium and high categories after gathering evidence. If numbers help, keep the underlying observations beside each score. A ranking should reveal disagreement, not hide it in a weighted total.
Ask what would change the ranking. Perhaps the meeting-summary workflow wins if the support knowledge base needs months of cleanup. Perhaps support drafting wins if an experienced owner already maintains the sources. Record those dependencies and choose the test that can resolve the important one cheaply.
Match authority to the first experiment
Separate four possible scopes: offline analysis of old records, live draft suggestions, internal record changes and external actions. A first use case does not need to start at the most consequential scope to learn something useful.
For the help-response candidate, an offline evaluation can test whether the system finds supported answers. A live draft pilot can then measure reviewer effort. Automatic sending is a further decision with different consequences. Do not grant it merely because the first two stages produced good examples.
A pilot can also begin without integration. A person manually supplying approved inputs may establish output fit before engineering connects applications. Record that the manual prototype has not yet tested authentication, current-state checks or failure recovery.
Design a test pack around failure consequences
Include representative cases to estimate ordinary workload and targeted cases to challenge boundaries. Keep those groups distinguishable in reporting. A deliberately difficult test set is useful for finding weaknesses, but it is not necessarily representative of normal frequency.
For a first support-drafting pilot, include a straightforward question, an outdated source, conflicting documents, no supported answer and a request for restricted account information. The expected behavior differs: answer, prefer the current approved source, flag conflict, ask or escalate, and preserve access restrictions.
Define what stops the pilot immediately and what becomes an improvement task. A missing punctuation mark and a private-data disclosure should not be averaged into the same score. Use the output evaluation guide to make those distinctions explicit.
Decide what success will authorize
Write the decision that follows a positive result. It may authorize another evaluation, a limited workflow pilot or a purchase for a small group. It should not silently authorize a company-wide rollout.
Specify spending and workload caps, who reviews results and what evidence remains missing. Keep a stop date so an exploratory tool does not become a permanent dependency without review. If the pilot does not meet requirements, distinguish an unsuitable task from a fixable implementation problem before trying another vendor.
Finally, record the projects you declined and why. A future change in volume, data or staffing may make them suitable. That backlog is more useful when it preserves conditions for reconsideration than when it merely contains attractive AI ideas.
Sources and further reading
- Google: understand the problem, including comparison with simpler approaches
- NIST AI RMF Core, for organizing context and risk decisions
- Google: ML development phases