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AI Strategy: Vision, Value Alignment, Build vs. Buy, and Vendors
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series explores why a clear AI strategy is what stops organizations from buying tools at random and hoping for the best. It defines what an AI strategy actually is, who should own it, the opportunities it should unlock, and the practical decisions that flow from it — value alignment, the build-versus-buy choice, vendor management, and the shared responsibility model for bought-in tools.
What this episode covers
- What an AI strategy is and how it operates at national, industry, and corporate levels.
- The OECD AI Principles — two groups of guidance for trustworthy AI and government action.
- Who owns the strategy — senior management, cross-functional teams, and the role of outside expertise.
- Five opportunities an AI strategy targets, from innovation to continuous risk monitoring.
- Vision and value alignment through community, ethical foundations, legal compliance, and operational strategy.
- The build-versus-buy decision — on-premises versus cloud, weighed across six core questions.
- Vendor management — risk assessment, SLAs, and ongoing oversight of AI suppliers.
- The shared responsibility model for off-the-shelf AI and why clear ownership is the buyer’s job.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What is an AI strategy?
An AI strategy is an organization’s overall approach to adopting, developing, and governing AI, designed to capture the benefits while keeping risk under control. Strategies exist at three levels: national strategies align AI with a country’s values, laws, and security; industry strategies promote innovation and shared standards within a sector; and corporate strategies hunt for competitive advantage while staying compliant.
What are the OECD AI Principles?
The OECD provides two groups of principles. The first is five values-based principles for trustworthy, human-centric AI: inclusive growth, respect for human rights and the rule of law, transparency and explainability, robustness and safety, and accountability. The second is five recommendations to governments: investment in research, an inclusive AI ecosystem, sound governance, building human capacity, and international cooperation.
What is value alignment in AI?
Value alignment means designing AI to behave in ways consistent with human values and ethics. It differs from one organization and one country to the next, so developers must consider their specific users. Four elements shape it: community feedback over time, ethical foundations respecting human values and cultural traditions, legal compliance with regional laws, and operational strategy through ongoing stakeholder engagement.
How should an organization decide between build and buy for AI?
Weigh six questions: how total costs compare, whether you have the data, skill, and time to build something as good as you could buy, the regulatory risk of each path, which option fits your current operating model, the data-privacy challenges of each, and the danger of vendor lock-in. On-premises gives more control but costs more. Cloud scales easily but reduces control and risks lock-in.
What is the shared responsibility model for AI?
When using an off-the-shelf AI tool from a third party, the buying organization must clearly define who owns what, what the provider owns, and what is shared. Three risks stand out: accountability and ownership where unclear maintenance duties hurt performance, ethical concerns over who is to blame for biased results, and data security since sharing data widens the attack surface.
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Reference: This article is based on concepts discussed in AI Strategy: Vision, Value Alignment, Build vs. Buy & Vendors.