How to build an AI strategy: Steps and checklist

An AI strategy is a plan for applying artificial intelligence to business objectives, including the capabilities and organizational preparation required, as outlined in this AI strategy guide. To make that plan executable, specify which work to improve, who owns the result, what the system may do, and what evidence permits release. Start with a bounded business process, then decide whether AI belongs in it.

Key takeaways

• Choose an initial process only when its business result, authorized data, operating owner, and fallback can all be defined.
• Assign separate responsibility for business acceptance, technical operation, and consequential approvals.
• Test permission boundaries and failure handling before allowing recurring production actions.
• Measure the complete process, including review and exception handling, before approving expansion.

Define what your enterprise AI strategy must decide

Use the strategy to make investment and operating decisions. Require it to identify which business priorities deserve attention, which work should remain unchanged, and which evidence would justify further investment. Keep model selection subordinate to those decisions.

The published strategy framework includes assessments of technology, data infrastructure, workforce skills, and organizational readiness. Translate those assessment categories into specific questions about the proposed process. Can the team authorize the required data access? Who can judge whether the output is usable? Who will respond when the system cannot complete the work?

Keep three planning documents distinct. The enterprise AI strategy should set priorities and shared boundaries. An AI implementation strategy should specify how a selected system will be built, tested, and introduced. An operating plan should name the people, procedures, and limits that apply after release.

For a generative AI strategy, add explicit decisions about which generated outputs require supporting evidence and which require approval before use. Generative AI is one of several technical approaches covered in this research survey, alongside approaches such as causal AI and federated learning. Do not require every candidate process to use the same technique.

Select the first process with a stop rule

Use a selection rule that can reject an attractive proposal before development. Our recommendation is to require a defined result, authorized access, an accountable operating owner, and an acceptable fallback. If any element is missing, fund the work needed to resolve that gap before funding production deployment.

Write the result in operational language. Prefer “prepare a service case for reviewer approval” to “deploy a document assistant.” Specify the start and end of the process, the people involved, the records it may read, and the records it may change. This is the operational perimeter: the agreed boundary of the work.

Compare each candidate with a simpler alternative. Ask whether an existing software feature, a deterministic rule, or a clearer handoff would address the same problem. Choose AI only where its contribution warrants the additional testing and operating obligations.

Use these parallel questions in the selection review:

• Which business measure should improve, and how will the team establish its baseline?
• Which inputs can the team access with documented authorization?
• Which outputs can a qualified reviewer assess against explicit criteria?
• Which actions must remain prohibited or approval-gated?
• Which owner can restore manual operation when the system stops?
• Which evidence would cause leadership to decline further investment?

When scoring a proposal, check each prerequisite separately so that a high opportunity score cannot compensate for a missing requirement. Even with a strong business case and an enthusiastic sponsor, the proposal still needs authorization for the required access and a named operating owner before it can proceed. Record a decision to proceed, resolve a prerequisite, narrow the scope, or stop.

Turn the selected process into an implementation and ownership plan

Create a short decision record before selecting components. Include the business result, process boundary, accountable roles, permitted actions, acceptance criteria, and operating costs. Use the following sequence to turn that record into a release decision.

Establish a baseline that includes review work

Measure the existing process using representative records and a stated measurement period. Include completion quality, elapsed processing time, manual effort, rework, and exceptions where relevant. Define what counts as a completed item so the team compares equivalent work before and after implementation.

Include the proposed review burden in the evaluation. Require reviewers to record corrections, escalations, and items returned to manual handling. Reject an evaluation that counts generated drafts as completed business work when approval is still pending.

Name the owners before assigning the build

Make the executive sponsor responsible for deciding whether to invest, and put acceptance and adoption of the system under the business process owner. The technical operator should be responsible for service health, access changes, incident response, and recovery. Record these responsibilities before assigning the build.

Name the approver for consequential actions separately, even when one person holds several roles. Give security, privacy, and risk reviewers explicit decisions to make about the proposed data and action paths. Avoid assigning a shared AI owner label without specifying each participant's responsibilities; otherwise, each person may expect someone else to act.

Separate data admission from action permission

Use two release decisions. First, decide whether the exact data path may receive authorized real data. Then decide whether the tested system may take its specified recurring actions.

At Compsia, our release standard separates review of data access, providers, retention, and incident ownership from the additional controls required for action-capable recurring production. Those additional controls include permitted-action boundaries, attributable approval for consequential actions, failure tests, and manual fallback. Use our explanation of how we scope trust and claims per system to examine the distinction without treating it as a certification claim.

Test failures and acceptance together

Require tests for missing information, conflicting records, unavailable target systems, repeated requests, and unauthorized access. Specify the expected response for each test. “Ask a reviewer” needs a named recipient, sufficient context, and a defined way to resume or close the item.

Keep business acceptance separate from a successful technical connection. Require the process owner to assess output quality and exception handling against the agreed criteria. Use the AI implementation steps and checklist when translating these decisions into a delivery plan.

Train people for their actual decisions

Build role-specific practice around the accepted process. Reviewers should practice rejecting unsupported output, approvers should inspect the proposed action, and operators should rehearse fallback. Microsoft Learn provides AI learning resources and training, which you can use for foundational knowledge alongside process-specific instruction.

Require the people who will use and supervise the system to participate in acceptance testing as part of preparing them to adopt the revised process. Ask them to work through normal cases and exceptions before release. Record unresolved operating questions as release conditions rather than leaving them for informal follow-up.

Work through a service-case preparation pattern

Use service-case preparation as a generic planning pattern. Set the proposed boundary at receiving an authorized request and preparing a case for review. Keep sending external messages, changing service entitlements, and closing cases outside the initial permitted actions.

Specify the inputs as the request, authorized account records, and approved service guidance. Require the proposed output to identify the requested action, relevant supporting records, missing information, and a draft response. Direct unresolved conflicts to a reviewer instead of instructing the system to select whichever record appears most plausible.

Define acceptance through observable behavior

Ask the process owner to assemble a test set covering complete requests, incomplete requests, conflicting records, and records outside the user's permission scope. Require each prepared case to distinguish supported information from unresolved questions. Test that the system refuses prohibited access and leaves prohibited actions untouched.

For the business review, compare accepted cases with the baseline. Count reviewer corrections and manual exceptions as part of the process. Do not approve expansion solely because the draft reads well.

Expand only the action that has earned approval

If the first release meets its criteria, evaluate a specific extension, such as creating a draft case record in the target system. Define exactly which fields may be written and who approves them. Test duplicate requests and interrupted writes before enabling the extension.

Keep the existing preparation function available when the write path is disabled. Require the release record to state separately whether preparation is available and whether record creation is available, so the operator can check each function's status. That gives the operator a narrower recovery choice than stopping every function or leaving every action enabled.

Decide whether review and exceptions make the system worth operating

Treat human review as work with an owner and a capacity limit. Require the business case to include the effort needed to inspect outputs, resolve ambiguity, maintain guidance, and recover failed items. Set an exception threshold before launch, then specify who decides whether crossing it requires investigation, scope reduction, or suspension.

Make the trade-off explicit. Broad access and greater action freedom may support a wider proposed use, but require evidence for each added path. A narrower release may leave more work with people while providing a clearer acceptance decision. Choose the boundary that the team can evaluate and operate responsibly.

Use an exception budget as an operating rule. Define the categories of work that may reach manual handling, the capacity available to handle them, and the response when demand exceeds that capacity. Do not hide unresolved work in a queue with no accountable owner.

Also require a retirement decision. Ask what would justify replacing the system with a native feature, returning to manual handling, or removing a component. At each review, compare the accepted result with the full operating obligation, including maintenance and supervision.

Compsia designs, builds, launches, and operates custom AI production systems for a defined business result and operational perimeter. Our managed AI operations approach includes monitoring, maintenance, support, performance review, and controlled improvements. Whether you operate internally or use an external operator, name those responsibilities before approving the build.

Frequently asked questions

What is the 30% rule of AI?

Do not use a “30% rule” as a planning or release criterion without defining what it measures and identifying evidence for that interpretation. Ask whether the proposed rule concerns task coverage, review effort, quality, or another measure. For enterprise decisions, use process-specific acceptance criteria instead of adopting an unexplained percentage.

How to make $1000 a day using AI?

Treat $1,000 a day as a business target to validate, not an AI strategy or a promised result. Define the offer, the buyer, the delivery obligations, and the costs before selecting technology. For an enterprise initiative, require finance to distinguish income, avoided expense, and released capacity when evaluating the proposal.

What three jobs will not be replaced by AI?

Do not base workforce decisions on a list of three supposedly protected jobs. Assess the work within each role, then decide which tasks may be assisted, which require accountable human judgment, and which should remain unchanged. Specify who will supervise the revised process and how their responsibilities will change.

What are the big 5 AI ideas?

For business planning, use five questions: What result matters? What information is authorized? What may the system do? What evidence permits release? Who operates it? Treat this as a planning checklist rather than a technical taxonomy or a claim that every AI discipline fits five categories.

Choose the process to assess before authorizing a build

Select a process owner and ask them to bring a process map, representative records they are authorized to share, the current exception procedure, and a proposed acceptance measure. Use that material to decide whether to proceed, resolve a prerequisite, narrow the boundary, or stop. For a review with Compsia, start with how we design, launch, and operate a production system.

We look at how your team runs its core processes and show you what we would make more efficient.