An AI prototype can produce an impressive answer and still be a poor investment. The harder questions concern the workflow around it: who needs the answer, what they will do with it, which sources it can use, and what happens when it is wrong. Answer those questions before choosing a model.
Start with an observable problem
Replace “we need an AI assistant” with a specific job. For example: “Support specialists spend time finding the policy that applies to a customer request.” This is an illustrative workflow, not a claim about a client project. It gives the team something concrete to investigate before deciding whether AI is appropriate.
Measure the current process with the people doing the work. Record how requests arrive, where the source material lives, which cases are difficult, and how specialists judge a successful resolution. A faster generated answer is not useful if the team spends longer checking it.
Write a one-page decision brief
- User and task: who needs help, and at which point in their day?
- Baseline: how is the task completed today, including review and rework?
- Allowed inputs: which data can the system access, and who owns it?
- Failure boundaries: which actions require approval or must remain manual?
- Acceptance evidence: what would justify a pilot, a wider release, or stopping?
Test the information path before polishing the interface
For a knowledge assistant, a fluent answer cannot compensate for the wrong source. Check whether relevant documents are current, accessible to the user, and retrievable for representative questions. Include missing information and conflicting documents in the test set. The product needs an honest “I cannot establish that” path.
For a prediction system, examine whether the available history represents future operating conditions. For an action-taking agent, test permissions and recovery when a connected tool fails. Different systems need different evaluation plans; a single accuracy score rarely describes the whole user journey.
Give the pilot an owner and a stopping rule
Assign someone who can resolve scope decisions and accept delivery. Agree which evidence will be reviewed at each milestone. If the data cannot support the task or a simpler search or rules-based solution works better, changing direction is a useful result—not a failed demonstration.
Before release, make monitoring, support ownership, and rollback part of the scope. A successful pilot is the beginning of an operating responsibility, not the end of the engineering work.
Your next step
Bring one workflow, a small set of representative examples, and the people who understand its exceptions. Our AI strategy and consulting service starts with those decisions. If the scope is already clear, explore product prototyping and engineering.
