Many organisations treat AI governance as a compliance task that starts only after a regulation lands. In practice, strong AI governance is a strategy capability: it determines which AI bets you can safely place; how fast you can scale them; and how confidently you can partner with customers, regulators, and vendors.
The OECD AI Principles—produced by the Organisation for Economic Co-operation and Development (OECD)—offer a practical, globally recognised foundation for doing this at the strategy level. They are values-based, technology-neutral, and flexible enough to work across sectors, which makes them ideal for executives who need a stable “north star” while AI technologies and laws keep moving.
Why Strategy Leaders Should Start With the OECD AI Principles
As a foundational document, the OECD AI Principles are a great place to start laying the groundwork for organization-wide AI governance. This is because:
- They translate quickly into governance design decisions (who owns what, what evidence is required, what gets escalated).
- They align with the direction of travel in global policy (including the OECD Recommendation on AI and related initiatives).
- They help unify privacy, security, risk, product, and business stakeholders under one shared language.
- They support portfolio-level decisions instead of only system-level controls.
The OECD AI Principles as Five Strategic Commitments
There are five principles set forth in the OECD AI Principles, which we identify below. At an executive level, these five principles can be reframed as strategic commitments. This framing makes it easier to embed them into your AI strategy, operating model, and Key Performance Indicators (KPIs).
(1) Inclusive growth, sustainable development, and well-being
Strategic commitment: We invest in AI that creates measurable value for people and the organization — without externalizing harm.
Strategy implications:
- Portfolio criteria include stakeholder benefit, sustainability, and measurable outcomes (not only Return on Investment (ROI)).
- Leadership defines what “good outcomes” look like and monitors real-world impact post-launch.
(2) Human-centered values, fairness, human rights, and rule of law
Strategic commitment: We set ethical boundaries, protect rights, and design human oversight that works in real operations.
Strategy implications:
- Risk appetite and “unacceptable use cases” are defined at leadership/policy level (what we will not do).
- Human oversight is specified by risk tier, including override/stop controls and accountability for exceptions.
(3) Transparency and explainability
Strategic commitment: We communicate clearly about AI use, limitations, and decision logic — appropriate to each audience.
Strategy implications:
- A transparency position is set for products and internal use (what we disclose to users, regulators, partners).
- Documentation standards (system cards / model cards) are mandatory for material AI systems.
(4) Robustness, security, and safety
Strategic commitment: We fund and operate AI systems safely across the lifecycle, including modern AI-specific threats.
Strategy implications:
- Security-by-design is embedded into the AI reference architecture (data pipelines, MLOps/LLMOps, access control).
- Continuous monitoring is funded and owned (drift, abuse, safety incidents, security events).
(5) Accountability
Strategic commitment: We define who is accountable for AI outcomes, and we produce audit-ready evidence by default.
Strategy implications:
- An AI governance operating model exists (council/committee, decision rights, escalation).
- RACI is clear for product owners, model owners, risk, privacy, security, and legal.
- Assurance is planned (2nd line review and/or internal audit model).
A Simple Executive Operating Model (What to Decide at the Strategy Level)
If you want to keep it lightweight, executives typically need to decide five things:
- 1. Strategy scope: Which business lines, products, and internal functions are in scope for AI governance?
- 2. Risk appetite: What risk tiers exist and what evidence/approval gates apply to each tier?
- 3. Ownership: Who is accountable for AI outcomes at the executive level, and who owns systems day-to-day?
- 4. Assurance: How do we verify controls and measure effectiveness over time?
- 5. Measurement: Which portfolio KPIs must be reported quarterly (outcomes, fairness, transparency, safety, incidents)?
The OECD AI Principles Strategy Scorecard Explained
To support strategy teams, Privacy Bootcamp has published a companion Excel template. It is designed for leadership workshops and steering committees, and it produces an executive dashboard and prioritized roadmap.
Template highlights:
- Executive-level assessment questions mapped to OECD AI principles and strategy domains.
- Maturity scoring with target setting and gap calculation.
- Auto-prioritized roadmap based on Gap × Impact × Weight.
- Dashboard view by principle and by strategy domain.
Also check out the FREE OECD AI Principles Assessment Template