Fit for Purpose AI: Matching the Model to the Mission

Most organizations begin an AI initiative by asking which model to buy. The more productive question is which decision the organization needs to improve, and what that decision requires in accuracy, speed, explainability and data protection. The answer frequently points to a smaller, narrower or more tightly governed solution than the market's loudest offerings.

Start with the decision, not the tool

Define the decision, who makes it, how often, and what an error costs. A drafting aid for internal summaries tolerates mistakes that a benefits determination or a procurement evaluation cannot. The cost of error sets the standard of evidence the solution must meet.

Five tests of fitness

• Accuracy: Can performance be measured against the organization's own data and tasks, rather than a vendor benchmark?

• Explainability: Can a reviewer, auditor or affected person understand why an output was produced?

• Data exposure: Where do prompts, records and outputs travel, and who can retain them?

• Operating cost: What does the solution cost at production volume, including monitoring and human review?

• Maintainability: Who owns the solution when the model, the data or the regulation changes?

Common failure patterns

• Selecting a general purpose model for a task that a rules engine or a classical statistical method would perform more reliably.

• Piloting on clean sample data, then deploying against the organization's actual records.

• Omitting a defined human review step for consequential outputs.

• Treating a successful demonstration as evidence of operational readiness.

What to do next

Select one candidate use case and document its decision, its error tolerance and its data sensitivity on a single page. If the page cannot be completed, the organization is not yet ready to choose a model. A short working session on that page is a sound first step. To discuss one, submit a consultation request on the Get Started page.