How should executives approach AI strategy? They should treat it as a business strategy, investment, risk, and workforce issue, not as a technical programme owned by IT. Executives must set the outcomes, choose the priority portfolio, define acceptable risk, assign business owners, fund shared capabilities, sponsor work redesign, and verify whether value is delivered.
Senior leaders do not need to become model engineers. They do need enough knowledge to challenge optimistic claims, understand where human judgement remains necessary, and decide which opportunities deserve capital and attention.
Table of contents
- Seven priorities for executive leadership
- Questions executives should ask
- Executive management and board oversight
- Frequently asked questions
“Executives do not need to become AI engineers. They do need to become informed owners of the choices AI creates about capital, risk, work, and customer value.”
How should executives approach AI strategy? Seven priorities
1. Set a clear strategic purpose
Begin with the company’s approved goals. State what AI is expected to improve, such as service speed, revenue, operating cost, decision quality, or risk control. Also state where AI use would conflict with legal duties, company values, or the agreed risk level.
Do not use “become AI-first” as a substitute for strategy. It tells teams to prefer a technology without explaining the customer or business problem. A useful purpose connects AI to a small number of measurable outcomes and gives leaders a basis for saying no.
2. Choose a portfolio, not a collection of requests
Departments will bring attractive ideas, often based on tools they have already seen. Executives should compare proposals using common criteria: business value, strategic fit, user need, data readiness, technical feasibility, risk, cost, and time to benefit.
Limit the first portfolio. A few properly governed use cases will teach the organization more than dozens of lightly supported pilots. Keep expected value separate from proven value, and require a decision gate before any experiment moves to wider use.
3. Put business leaders in charge of outcomes
Technology leaders should own platforms, architecture, integration, and technical delivery. The executive who owns the affected business process should own the result. A customer-service use case, for example, needs a service leader accountable for customer outcomes, workflow changes, staffing choices, and benefits.
Name one benefit owner for every major initiative. Shared delivery makes sense; blurred accountability does not. The owner should have the authority to change the process and should report results against an agreed baseline.
4. Define decision rights and acceptable risk
Executives must decide who can approve AI use, data access, testing, deployment, material changes, and retirement. Risk controls should match the potential harm. A low-risk internal drafting aid should not face the same process as a system that affects credit, employment, health, or access to a service.
The NIST AI Risk Management Framework helps organizations structure this work through Govern, Map, Measure, and Manage. The OECD AI Principles address transparency, robustness, safety, and accountability. Leaders can use these sources as practical reference points while adapting controls to their sector and obligations.
5. Fund capabilities as well as use cases
AI initiatives depend on data quality, security, integration, testing, monitoring, supplier management, skills, and support. If every use case builds these separately, cost rises and standards drift. If leaders fund only a central platform, business teams may struggle to turn it into value.
Executives need a balanced funding model. Pay for a small set of shared capabilities, then require business units to co-own use-case delivery and outcomes. Make dependencies visible in the AI transformation roadmap.
6. Sponsor workforce and workflow change
AI changes how decisions and tasks are performed. Leaders must define what the system will do, what employees will decide, when human review is required, how exceptions move, and how performance measures or incentives should change.
Statistics Canada reported that among businesses using AI in the second quarter of 2025, 40.1% developed new workflows and 38.9% trained current staff. This is why an executive agenda cannot stop at licences and models. People need clear rules, useful training, time to adapt, and a safe way to report problems.
7. Review evidence and redirect capital
Executives should review business outcomes, adoption, quality, risk, cost, incidents, and capability growth. Pilot counts and user licences are activity measures. They do not prove that customers, employees, or financial performance improved.
Use the original baseline. Release more funding only when the evidence supports it. Revise, pause, or stop work when value is weak, risk cannot be controlled, operating cost is too high, or the process no longer fits the strategy.
Questions executives should ask about AI strategy
- Which approved business outcome will this use case change?
- What reliable baseline supports the value claim?
- Who owns the process, benefit, and final scale decision?
- Which employees, customers, or other groups may be affected?
- What data enters the system, and is its use permitted and suitable?
- Where must human judgement remain, and what happens when the output is wrong?
- Which supplier, platform, security, or integration dependencies are created?
- What evidence is required to scale, revise, pause, or retire the system?
Executive management and board oversight
Executive management develops the strategy, runs the portfolio, assigns resources, manages delivery, and reports performance and risk. The board provides oversight. It should challenge whether the strategy supports company goals, whether major risks are understood, and whether management has suitable ownership and controls.
The board should not approve individual low-risk experiments. Nor should it accept vague assurance that AI is “under control.” Reporting should show material use cases, value against baseline, major incidents, risk exposure, workforce effects, and decisions required from directors.
The depth of oversight should fit the organization. A smaller company may use its existing executive and risk meetings. A regulated or high-risk business may need a dedicated AI committee or formal management system. Structure should follow risk and scale, not fashion.
Frequently asked questions
What AI knowledge do executives need?
They should understand common AI capabilities and limits, data dependence, evaluation, human oversight, security and privacy risks, supplier dependence, and the economics of running systems. They do not need to code models.
Who should lead the AI strategy?
The CEO or a named executive sponsor should lead the enterprise choices. A cross-functional group can support the work, but business leaders must own outcomes and technology, data, risk, legal, finance, and human resources must have clear decision rights.
How often should executives review the AI portfolio?
Quarterly review is a practical minimum for the full portfolio. Higher-risk or fast-moving initiatives need more frequent delivery and risk gates. The full strategy should be reconsidered at least annually or after a material change.
Executive takeaway
Executives should approach AI strategy as owners of enterprise choices, not spectators to a technical programme. Their job is to connect investment with business outcomes, set boundaries, create accountability, prepare the workforce, and let evidence determine what grows.
For the wider foundation, review the key components of an AI strategy and the seven biggest AI strategy mistakes. Praevion Consulting Inc. helps executive teams set AI direction, govern portfolios, define operating ownership, and build a practical roadmap.
To discuss your priorities, contact Praevion Consulting Inc.
References
- National Institute of Standards and Technology. AI Risk Management Framework.
- OECD. OECD AI Principles.
- Statistics Canada. Artificial intelligence use by businesses in Canada, second quarter of 2025.
- Deloitte. A C-suite guide to capturing the potential value of AI.
- McKinsey & Company. The operating model advantage, 2026.
