Choose a small custom AI pilot by identifying one recurring task, defining acceptable and unacceptable outputs, and testing against real examples before connecting it to a critical workflow. The pilot should have an accountable owner, a human review path, and a clear decision rule for whether to stop, revise, or expand. If the task can be handled reliably with ordinary rules, or no one can verify the result, AI is probably unnecessary.
Start with a task, not an AI feature
Choose a small custom AI pilot by selecting one bounded, recurring task and defining what acceptable performance looks like before development starts. A pilot is useful when it tests a real workflow decision; it is not useful merely because a team wants to try a model.
Describe the task in operational terms: who supplies the information, what the system should produce, who checks it, and where the result goes next. A good first candidate is often a step that consumes staff time but still has a clear human reviewer and a recoverable error.
Look for a problem with observable outputs
A candidate task should have examples the team can inspect and a result that can be judged against a standard. For instance, extracting a few named fields from incoming documents is easier to evaluate than asking an assistant to make broad, subjective business decisions.
Check whether the work is frequent enough to provide meaningful test cases, whether inputs are accessible with appropriate permissions, and whether a wrong answer can be caught before it causes harm. If the process is rare, highly variable, or impossible to verify, first improve the procedure or collect better examples.
Do not use AI where a rule-based check is more dependable. A fixed calculation, required-field check, or simple routing rule usually belongs in ordinary application logic, not a model prompt.
Write acceptance criteria that expose errors
Acceptance criteria state what the pilot must do, what it must not do, and how the team will judge the difference. Avoid a vague target such as “good summaries.” Specify the required fields or properties, how missing information is represented, and which cases must be escalated for review.
Build a test set from representative past examples. Include clean inputs, incomplete records, unusual formatting, conflicting details, and cases outside the intended scope. Keep some examples separate from development so the team can check whether changes work beyond the cases used to tune the system.
Record errors by type, not only as a pass rate. A missing value, invented detail, misrouted item, and unnecessarily cautious response have different consequences and may need different remedies. Decide in advance which errors are unacceptable.
Set boundaries for data, access, and review
Determine which information the pilot needs and which systems or records it may access. The AI component should receive only the data required for its task, under the same role and permission rules that govern the surrounding application. Identify who owns the source data and who can approve changes to its use.
Keep a person in control when outputs affect money, eligibility, safety, legal commitments, or customer promises. The interface should make review practical: show the source material behind an extracted value, let staff correct it, and record whether they accepted or changed the suggestion.
Agree on what happens when the model is unavailable, returns an unusable result, or encounters an out-of-scope case. A safe fallback may be a manual queue or the existing process. Do not let an uncertain output silently become a final business record.
Measure the workflow, not model novelty
A pilot should answer whether the proposed workflow is fit for a next step. Track task completion, staff corrections, review time, escalations, and the kinds of errors that matter to the business. Establish a baseline for the current process where practical, but avoid promising a specific gain before real use is measured.
Separate model behavior from integration behavior. An output can be correct but fail to reach the right record; an integration can work while the suggested content is wrong. Log enough information to investigate failures while respecting data access and retention policies.
Set a review point and decision rule before launch: stop, revise the task, adjust the workflow, or test a broader scope. Verify any changing vendor terms, data handling options, or technical limits directly before committing to a design.
Keep the first implementation deliberately narrow
Limit the first build to one input path, one output shape, and one accountable reviewer. Connect it to a live system only if the pilot needs that connection; otherwise, a controlled review queue may reveal whether the task is worth integrating. Keep retries and duplicate submissions from creating repeated work or records.
Hypothetical example: an operations team wants help sorting supplier documents into a review queue. It tests a set of past documents, checks proposed categories against staff decisions, and requires review for uncertain or missing information. The team measures correction patterns and handling time, then decides whether to refine the categories or stop. This is a test plan, not a claim about results.
If the workflow has no clear owner, no reliable examples, or no safe way to check outputs, the next step is process discovery—not an AI pilot. A short engineering assessment can map the task, data access, system boundaries, and evaluation plan before implementation is scoped.
Pilot readiness checklist
- Name one recurring task, its users, and the decision or output the pilot will support.
- Collect representative examples, including incomplete, ambiguous, and unusual cases.
- Define acceptable outputs, unacceptable errors, and when a person must review or override a result.
- Assign an owner for source data, access permissions, and changes to the workflow.
- Test against a held-back set of examples and document results by error type.
- Set a stop, revise, or expand decision date before the pilot begins.
Questions about this approach
How many examples do we need to test a pilot?
There is no universal minimum. The set should reflect real input variation and include both ordinary and difficult cases. Keep a portion aside from development so evaluation is not limited to examples used to adjust the system.
Should the pilot make decisions without staff review?
Start with a controlled, human-reviewed workflow when the task involves consequential decisions, unclear source data, or outputs that are difficult to validate. Expand automation only after the team understands error patterns and has designed a reliable fallback.
When is a rules-based feature a better choice?
Not necessarily. If the process is governed by stable rules, a normal software feature may be simpler to test, explain, and maintain. Use AI only when it addresses a genuine ambiguity or language task that rules do not handle well.
For help applying this approach, explore our custom AI projects services.
Discuss your project with Full Blown Studio
Tell us what needs to work better and what you have tried. Discuss your project, email bob@fullblown.com, or call (661) 429-0940.
