The best first AI project is rarely “add a chatbot.” It is a narrow, repetitive job with clear inputs, a useful draft or recommendation, and a person who can judge the result.
Before choosing a tool, separate three kinds of work. That one decision prevents most bad automation projects.
Put the work in the right bucket
Use for calculations, validation, syncing, schedules, permissions, and exact business rules.
Use for drafting, summarizing, extracting meaning, classifying language, and research with review.
Keep for commitments, exceptions, sensitive judgment, safety, money, and employee or customer consequences.
If a task must produce the same correct answer from the same data every time, it probably does not need AI. A normal formula, workflow rule, or integration will be cheaper and easier to test.
Good first AI projects
- Summarize long inputs. Turn meeting notes, call transcripts, or service histories into a draft summary with links to the source.
- Draft from approved facts. Prepare a follow-up email, product description, proposal section, or internal update for a person to review.
- Classify unstructured requests. Suggest a category or route for inbound messages while preserving the original text.
- Extract fields from documents. Pull candidate values from invoices, applications, or vendor files, then validate them before they enter the system of record.
- Search internal knowledge. Help an employee find the relevant SOP or policy and show the source used for the answer.
Each example has a bounded job, an input the team can inspect, and an output that can be checked before action.
Bad first AI projects
- sending refunds, prices, approvals, or legal commitments without review;
- deciding who to hire, fire, discipline, insure, finance, or serve;
- changing inventory, accounting, or customer records without validation;
- answering safety-critical questions from memory instead of an approved source;
- giving an assistant broad access because narrowing the workflow felt inconvenient.
These uses combine uncertain output with meaningful consequences. A disclaimer does not repair a workflow that gives the model too much authority.
Run a small pilot with an escape hatch
- Write the job in one sentence. “Draft a reply from the approved customer record” is testable. “Improve customer service” is not.
- Collect real examples. Include normal cases, missing information, contradictory requests, and the examples employees hate handling.
- Define acceptance. State what must be present, what must never happen, and who reviews the output.
- Limit access. Give the workflow only the data and actions required for the job.
- Log the work. Keep the input, output, model or tool version, reviewer, and final action when the risk justifies it.
- Make failure visible. A stopped or uncertain workflow should create a review task, not disappear.
Five questions before buying another AI tool
- What exact task will stop or become faster?
- Why does this task need AI instead of a rule or integration?
- What information will the tool receive, retain, or send elsewhere?
- Who checks the result, and before which action?
- What happens when the tool is wrong or unavailable?
If the vendor demo cannot answer those questions, the business is being asked to buy novelty instead of an operating result.