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AI Strategy Explained: Build vs. Buy vs. Partner and Value Alignment
This sixth episode of the ISACA Advanced in AI Audit (AAIA) exam prep series explores how organizations plan their approach to artificial intelligence across multiple levels of governance. It frames the ethical foundations, the people involved, the opportunities that motivate the investment, and the alignment work that keeps an AI program true to the organization’s stated values.
What this episode covers
- The three levels of AI strategy — national, industry, and corporate — and how each one constrains the next.
- Ethical foundations every strategy is built on, including explainability, fairness, transparency, and human centricity.
- The OECD AI Principles and how they split into trustworthy-AI values and government recommendations.
- Writing the strategy — executive sponsorship and the cross-functional team that should draft it.
- The business opportunities that motivate organizations to invest in AI in the first place.
- AI vision and mission statements as a precursor to a usable strategy.
- Value alignment — what it is, the dimensions developers must consider, and the enablers that keep it on track.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the three levels of AI strategy?
AI strategy operates at three levels. National strategies are where governments set ethical boundaries, laws, and national security priorities. Industry strategies are where trade bodies translate those laws into practical sector standards while encouraging innovation. Corporate strategies are where individual companies seek a competitive edge while obeying all industry and national rules.
What are the two categories of the OECD AI Principles?
The OECD AI Principles are divided into two categories. The first is five values-based principles for trustworthy AI: inclusive growth and well-being, respect for the rule of law and human rights, transparency and explainability, robustness and safety, and accountability. The second is five recommendations for governments to build healthy AI ecosystems, covering research investment, an inclusive ecosystem, an interoperable policy environment, building human capacity, and international cooperation.
Who should write an organization’s AI strategy?
An AI strategy requires explicit senior management approval and should never be written solely by the IT department. It needs a cross-functional team with representatives from information technology, risk management, legal, operations, and finance. The drafters do not need to be deeply technical AI programmers, and companies lacking internal expertise should hire outside consultants to guide the team.
What is value alignment in AI?
Value alignment, as defined by the World Economic Forum, is designing AI systems that behave in ways consistent with human values and ethical principles. It is like teaching a child a sport along with sportsmanship and the rules. Developers must consider community, ethical foundations, legal compliance, and operational strategy, supported by four enablers: frameworks, human engagement, organizational change, and audits.
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Reference: This article is based on concepts discussed in AI Strategy Explained: Build vs Buy vs Partner & Value Alignment.