AI Strategy for Executives: Where to Begin

Define the outcome before the solution

Choose a recurring decision or workflow with a clear owner. A useful starting point might be reducing the time needed to locate approved internal guidance, preparing a first draft of a routine report, or helping staff triage incoming requests. Describe the current process and the cost of getting it wrong.

Set a baseline before introducing AI. Measure completion time, rework, error rates, and the quality of the final output. A demonstration that looks impressive is not evidence that the business process has improved.

Assess readiness across people, data, and controls

Check whether the information needed for the use case is available, accurate, and permitted for that purpose. Identify confidential information and restrictions on sharing it with an external provider. If the process depends on undocumented judgment, capture that judgment before trying to automate it.

Readiness also includes management capacity. Name a business sponsor, a process owner, and a reviewer who understands the work. Security, privacy, legal, and procurement should participate early enough to change the design rather than merely approve it at the end.

• Define the decision or task and its accountable owner.
• Identify approved data sources and access restrictions.
• Record the baseline and a measurable success threshold.
• Specify where human review is mandatory.

Prioritize a small, reversible pilot

Compare use cases by expected value, feasibility, and potential harm. An internal drafting assistant with review may be a more manageable first project than an automated decision affecting employment, credit, or health. Low visibility does not mean low risk: internal tools can still disclose sensitive information.

Keep the pilot narrow. Use representative test cases, including incomplete inputs and edge cases. Agree in advance on what would cause the team to pause, roll back, or discontinue the pilot. Retain a workable non-AI process.

Scale only after the evidence supports it

At the end of the pilot, compare results with the baseline and include the full cost: licenses, integration, review time, training, and ongoing oversight. Ask whether staff actually use the system and whether errors have become harder to detect.

A positive result is permission to expand deliberately, not to remove oversight. Update the operating procedure, assign monitoring responsibility, and revisit the decision when the model, vendor, data, or business process changes.