Most organizations do not have an AI problem. They have unclear workflows, inconsistent data, undocumented decisions, and manual work that has never been measured. Adding AI before fixing those foundations produces faster confusion.

Begin with the workflow

Choose a process that is frequent, rule-driven, time-consuming, and easy to evaluate. Good candidates include classification, summarization, draft generation, information retrieval, routing, reconciliation support, and structured extraction from documents.

Use a practical screening framework

Business value

Estimate current hours, delays, rework, error rates, and customer impact. If the organization cannot describe the current cost, it will not know whether the pilot created value.

Data readiness

Confirm that the required data is available, permitted for use, accurate enough, and controlled. Sensitive or regulated data demands stronger restrictions, logging, retention rules, and vendor review.

Task suitability

AI works best when the task has recognizable patterns and the output can be evaluated. Avoid using it as the final decision-maker for high-impact actions unless governance, validation, and human accountability are explicit.

Operational fit

A useful model output still fails if it does not fit the existing workflow. Define who receives the output, who reviews it, what happens when confidence is low, and how corrections improve the process.

Define controls before the pilot

• Specify approved data sources and prohibited data
• Require human review for consequential outputs
• Log prompts, sources, outputs, and corrections where appropriate
• Test for inaccurate, incomplete, biased, or unsafe responses
• Define escalation and shutdown criteria
• Confirm contractual, privacy, security, and retention requirements

Measure the pilot honestly

Compare the pilot with the existing process. Track cycle time, human effort, accuracy, exception rate, rework, adoption, and total operating cost. Include the time required to review and correct AI output. A fast draft that requires extensive repair is not an efficiency gain.

A sensible first sprint

Document one workflow, establish the baseline, select a narrow task, prepare approved data, build a controlled pilot, and run it with a small user group. Review failures as carefully as successes. The goal is to learn whether the use case deserves expansion, redesign, or rejection.