How do you develop an AI strategy? Start with the business outcomes that matter, establish a clear performance baseline, assess AI readiness, build and rank a portfolio of use cases, design governance and ownership, prepare people and workflows, and turn the choices into a funded roadmap with decision gates.
The process is not owned by technology alone. Business, data, technology, risk, legal and people leaders must make the main choices together. Otherwise, the result may be technically sound but difficult to adopt, govern or scale.
In this article
- What to settle before starting
- The seven-step AI strategy process
- What the final strategy should produce
- Common development mistakes
- Frequently asked questions
“The quality of an AI strategy is revealed by the choices it forces. If every idea remains a priority, the organization has collected ambitions, not built a strategy.”
What should leaders settle before starting?
Agree on the sponsor, scope and decision process. The sponsor needs enough authority to settle trade-offs across departments. The scope should state whether the work covers the whole organization, one business unit or a specific value chain. Set a time limit too. A focused strategy can often be developed in weeks, but evidence gathering and executive decisions should not be rushed.
Bring in people who understand customers, operations, data, technology, risk and workforce realities. NIST’s AI Risk Management Framework stresses the value of varied perspectives when defining context, intended use, benefits and possible harm.
How do you develop an AI strategy? Seven proven steps
Step 1: Clarify the strategic outcomes
Identify three to five outcomes that AI could influence, such as revenue growth, service speed, quality, cost, resilience or workforce capacity. Link each outcome to the wider business strategy and an accountable executive. This prevents the process from starting with tools.
Be specific. “Improve customer service” is vague. “Reduce average response time while maintaining satisfaction and complaint quality” gives the team something it can investigate and measure.
Step 2: Establish the business baseline
Document current performance, process pain points, costs, delays, quality issues and existing AI activity. A baseline makes later value claims testable. It also exposes shadow AI, duplicate tools and experiments that leaders may not know about.
Step 3: Assess AI readiness
Review data quality and ownership, technology, skills, leadership, governance, culture and delivery capacity. Do not collapse the result into one headline score. The point is to find the few gaps that could block priority use cases.
For example, a company may have solid cloud infrastructure but weak process data. Another may have capable analysts but no clear approval route for external AI vendors. See our guide to how to assess AI readiness.
Step 4: Build and rank the use-case portfolio
Invite ideas from real business problems, then describe the user, process, decision, data and expected benefit for each. Score proposals using strategic fit, expected value, feasibility, time to benefit, adoption effort and risk.
Ranking matters. Keep a small group for early validation, place promising but dependent ideas later, and reject weak cases. If every proposal survives, the scoring process has not done its job.
Step 5: Design governance and ownership
Assign a business owner for each outcome and define who approves data, vendors, deployment and risk acceptance. Classify uses by possible impact so controls remain proportionate. Also cover human oversight, monitoring, incidents, documentation and retirement.
The NIST AI RMF organizes risk work through Govern, Map, Measure and Manage. The OECD AI Principles add expectations for transparency, robustness, safety and accountability. Use these sources as a base, then tailor controls to sector and context.
Step 6: Plan workforce adoption and capability
Identify how work, roles, decisions and measures will change. Build training around those tasks, not generic AI awareness alone. Managers need guidance on review, quality and accountability. Employees need safe ways to raise concerns and report poor outputs.
This is practical, not soft. Statistics Canada found that among Canadian businesses using AI in its second-quarter 2025 survey, 40.1% developed new workflows and 38.9% trained current staff. AI implementation changes work.
Step 7: Build the investment roadmap and measures
Sequence work by value and dependency. Fund discovery and validation before full scale. Every stage should have an owner, budget, evidence requirement and go, adjust or stop decision.
Measure business outcomes, adoption, quality, risk and total cost. Model accuracy alone does not prove value. Link the roadmap to a regular executive review and update it as evidence changes. Our article on building an AI transformation roadmap explains the delivery sequence.
What should the final AI strategy produce?
- A short executive statement of ambition, priorities and boundaries.
- A ranked portfolio of use cases with value, feasibility and risk evidence.
- An AI readiness assessment focused on material gaps.
- Governance, decision rights and an operating model.
- A workforce and adoption plan tied to real processes.
- A funded roadmap with dependencies and decision gates.
- A measurement plan with baselines, targets and benefit owners.
These outputs should connect. A use case without an owner, readiness requirement, control or measure is still only an idea.
Common AI strategy development mistakes
The first mistake is starting with a vendor. The second is treating a workshop list as a strategy. Others include scoring value without full cost, separating governance from use-case design, ignoring employees until launch and funding scale before a pilot has produced credible evidence.
Another trap is excessive documentation. A long report can hide unresolved choices. Keep the executive strategy concise and put detailed analysis in the portfolio, roadmap and governance appendices. Review our article on the biggest AI strategy mistakes before final approval.
Frequently asked questions
Who should lead AI strategy development?
An executive sponsor should lead the business choices. A cross-functional team should support the work, with strong involvement from operations, technology, data, risk and human resources.
How long should the strategy last?
Set a direction for roughly two to three years, but review priorities and risks at least quarterly during active delivery. The roadmap should change when evidence, regulation or technology changes.
Should small firms use the same seven steps?
Yes. The work can be lighter, but the decisions remain necessary. A small company still needs outcomes, priorities, ownership, controls, adoption and measures.
Executive takeaway
How do you develop an AI strategy? Make seven linked decisions, test them with evidence and give each result an owner. The process should narrow choices, not preserve every idea. Strategy becomes useful when leaders know what to fund, what to stop and what must change around the technology.
Praevion Consulting Inc.’s management consulting services help executive teams assess readiness, rank opportunities and create practical AI roadmaps.
To discuss your strategy, contact Praevion Consulting Inc.
References
- National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework 1.0. NIST AI 100-1, January 2023.
- OECD. OECD AI Principles. Adopted 2019 and updated 2024.
- Statistics Canada. Analysis on artificial intelligence use by businesses in Canada, second quarter of 2025. Released June 16, 2025.
- International Organization for Standardization. ISO/IEC 42001:2023, Artificial intelligence management systems. December 2023.
- PwC. The blueprint for an AI-powered enterprise. August 17, 2026.
