A practical AI strategy connects mission priorities to a limited set of use cases, clear governance, workforce readiness, and measures for review. It should explain where AI may be appropriate, where it is not, and who remains accountable for decisions and outcomes.
Begin With Mission and Service Outcomes
Do not begin with a list of tools. Identify the service or operational outcome the organization wants to improve, then describe the current process and the people it affects.
For each candidate use case, document the workflow, its owner, the information involved, known exceptions, potential risks, and what evidence would show whether a change is useful. Establish a baseline where practical rather than promising an improvement before testing.
Build the Operating Model
A usable strategy assigns responsibility for:
- Selecting, approving, and prioritizing use cases
- Reviewing vendors, information handling, and access
- Defining human review and escalation
- Preparing and supporting users and managers
- Monitoring quality, risks, and unintended effects
- Updating, pausing, or retiring systems
The U.S. Government Accountability Office’s AI Accountability Framework organizes accountability practices around governance, data, performance, and monitoring. The NIST AI Risk Management Framework is a voluntary resource for organizations seeking to manage AI risks. These are useful references for planning, but each organization must determine its own legal and policy requirements.
Create a Balanced Portfolio
Consider starting with bounded, reviewable tasks such as drafting or summarization, where staff can check outputs against approved sources. Use cases involving eligibility, employment, safety, benefits, or other consequential matters warrant particularly careful assessment and may not be appropriate for automation.
For each use case, consider the possible consequences of error, the quality and permissions of the data, accessibility, how people can question an outcome, and whether a human reviewer has enough information and authority to intervene.
Turn Strategy Into a Roadmap
Sequence initiatives according to expected mission value, organizational readiness, risk, dependencies, and staff capacity. Give each roadmap item an accountable owner, a review date, a success measure, and a decision point for scaling, revising, pausing, or stopping.
Treat the roadmap as something to revisit when priorities, tools, evidence, or applicable requirements change. Do not assume that a pilot should automatically become a permanent service.
Frequently Asked Questions
What is the difference between an AI strategy and an AI policy?
The strategy explains why and where the organization may use AI and how it will prioritize the work. A policy sets the required boundaries, approvals, responsibilities, and safeguards. They should be consistent with one another.
How many AI projects should begin at once?
Choose a portfolio the organization can responsibly supervise, support, measure, and improve. The appropriate number depends on available staff, risk, and implementation capacity.
How often should an AI strategy be reviewed?
Set a regular review schedule and revisit the strategy when organizational priorities, relevant requirements, tools, or risk conditions materially change.