An AI project needs a clearer starting point than “we should use AI.” Begin with a task people already understand, a result they can evaluate, and a reason to change how the work happens.

Describe one task from input to output

Write down what arrives, what a person does with it, and what should happen next. For example, a team might receive documents, identify a few fields, and route uncertain cases for review. That is a possible use case to investigate, not evidence that a particular model will handle your documents reliably.

Keep the scope narrow enough that you can compare an automated result with the way the work is done today.

Compare simpler approaches

A fixed calculation, a known lookup, or a straightforward rule may not need AI. If a supplier code always maps to one internal identifier, a maintained mapping table might solve the problem. If the inputs vary in language or structure, an AI approach might be worth testing.

The question is whether the approach improves the workflow at an acceptable cost, including the work needed to check its output.

Decide what an error means

A wrong suggestion in an internal draft is different from an incorrect payment instruction. Identify the consequences of false matches, missing information, and confident but incorrect answers. Define which outputs need human approval and what should happen when the system cannot produce a usable result.

Build an evaluation set before a convincing demo

Collect representative examples you are permitted to use. Include awkward formats, missing values, ambiguous requests, and exceptions—not only easy cases. Write down the expected result and how a reviewer will judge it.

Measure task success alongside review time, response time, and operating cost. A polished demonstration on a handful of examples does not establish production reliability.

Decide whether to proceed

Use the results to choose the next step: improve the inputs, narrow the task, keep a simpler approach, or develop a bounded prototype further. Agree on acceptance criteria before expanding the work.

ZipLogic’s AI/ML solutions service starts with this kind of use-case and evaluation planning. You can also map the workflow first or discuss your project.