AI Strategy vs AI Policy: Why You Need Both

What Is an AI Strategy?

An AI strategy is a high-level plan for how an organization will use artificial intelligence to achieve its goals. In simple terms, it’s like drawing a roadmap for your AI journey. According to IBM, “an artificial intelligence strategy is a plan for integrating AI into an organization so that it aligns with and supports the broader goals of the business”. A good AI strategy outlines which business problems AI will tackle (for example, improving customer service or automating internal processes). It explains where AI can add value and sets targets or KPIs for success (such as reducing costs or boosting revenue). In practice, a clear AI strategy becomes the compass that guides technology investments, data initiatives and talent development toward concrete business outcomes.

What Is an AI Policy?

An AI policy, on the other hand, is the rulebook for AI use. It’s not about what you want AI to achieve but about how AI should be used responsibly. Experts define an AI policy as “a dynamic, documented framework for AI governance that helps organizations set clear guidelines, rules, and principles for how AI technology should be used and developed”. In other words, the policy tells your team what they should or shouldn’t do with AI. For example, it might require that customer data be anonymized or specify which AI tools need formal approval. A solid AI policy covers data privacy, security standards, ethical considerations (like bias prevention), and roles/responsibilities. It acts like guardrails and speed limits on your AI road, ensuring projects run safely and in compliance with laws and corporate values.

Key Differences Between Strategy and Policy

Think of strategy and policy as two sides of the AI coin: strategy decides where you’re headed and why, while policy defines how you travel safely. Key distinctions include:

Goals vs Guardrails: Strategy focuses on vision and objectives (the “what”, "where" and “why”). Think of it as a plan that aligns AI with business objectives. Policy focuses on rules and safeguards (the “how”). It sets the guardrails to ensure AI use is ethical and legal. In short, strategy says “Here’s the destination,” while policy says “Here’s how to get there without going off course.”

Outcomes vs Process: Strategy is about desired outcomes (e.g. “increase sales 20% with AI”). Policy is about processes (e.g. “customer data must follow privacy rules”). Strategy lays out the destination; policy lays out the route and conditions.

Leadership vs Governance: Strategies are driven by business leadership for competitive advantage, whereas policies are enforced by governance or compliance teams to manage risk. The executives set the strategic vision, and the ethics/compliance team translates it into specific standards.

These differences show that strategy and policy address different questions but must work together. In fact, when drafting policies experts advise considering your strategy. As one compliance author notes, “when drafting your policy… your organization’s AI strategy will help you understand what types of guidance your employees need.”

Why You Need Both Strategy and Policy

Without a strategy, AI projects can be aimless; without a policy, they can create risk. Here’s why both are critical:

Maximize value while managing risk: A strategy makes sure you pick high-impact AI projects. A strong strategy allows organizations to purposefully harness AI capabilities and align AI initiatives with overall business objectives. Once projects are chosen, a policy ensures they follow laws and ethical norms. In effect, strategy is the accelerator, and policy is the steering wheel and brakes.

Clear guidance for teams: A formal AI policy provides a “roadmap for proper AI use in the workplace”. This means employees know exactly what they can do and what needs approval. With these guardrails in place, teams can innovate confidently rather than guessing what’s allowed. (Think: clear speed limits let drivers focus on the journey without worrying they might be speeding.)

Build trust and accountability: Combining strategy and policy shows customers, regulators and employees that you’re pursuing AI innovation responsibly. It also clarifies who is accountable for each part of the process (data stewardship, model monitoring, etc.), which boosts credibility and helps avoid scandals.

Organizations that develop strategy and policy together tend to move faster and safer. As one expert puts it, an AI policy is “paramount” for providing guidance on usage, freeing teams to focus on innovation.

A real-world illustration comes from Telstra, a major telecom. Telstra’s technology leader said that to become “an AI-fueled organization,” they had to make AI adoption “a whole-of-business strategy”. In practice, this meant AI projects and data initiatives were woven into every part of the company under one unified plan. (Behind that broad strategy, Telstra naturally put governance and policies in place so all teams could move forward safely.) This example shows that top companies treat AI as a strategic priority, not an isolated pilot, and back it with policies to ensure success.

Steps to Formulate AI Strategy and Draft Policy

Putting this into action typically involves these steps:

1. Define what business goals AI should achieve (e.g. reducing costs, improving customer experience) and how you will measure success.

2. Inventory your data, tools, and team skills. Identify any gaps in data quality, technology, or talent that your strategy or policy must address.

3. Outline the key AI initiatives, timelines, and resources needed to meet your objectives. Prioritize projects that deliver quick wins and align with strategic goals.

4. Draft an AI policy covering data privacy, security, ethical guidelines, and approval processes. Clearly assign roles (who owns each model, who audits outcomes) as part of this framework.

5. Share the strategy and policy across the company. Train teams on approved AI tools and workflows. Monitor progress and compliance, and then refine the strategy and policies as needed.

Many companies find it helpful to partner with AI consultants for this. Digital Bricks specialises in AI strategy formulation and AI policy drafting, guiding businesses to harness AI effectively while managing risk.

At Digital Bricks, we approach AI strategy consultancy with a human-centric lens. We don’t just drop in a templated roadmap and disappear, we work with your leadership team to understand your business goals, operational realities, and competitive pressures. From there, we translate complex AI capabilities into practical, actionable strategies. Whether it’s identifying high-value AI use cases, shaping scalable data foundations, or integrating tools like Microsoft Copilot and custom AI agents, we ensure every recommendation is tailored to your organisation’s context.

But strategy alone isn’t enough. That’s why we also draft robust AI policies designed to help your teams innovate confidently while staying compliant with emerging regulations like the EU AI Act. We build frameworks that balance creativity with control: defining guardrails, establishing governance processes, and embedding ethical guidelines at every stage of your AI adoption journey. This holistic approach means leaders don’t have to choose between moving fast and staying safe.

If you’re looking for an AI strategy consultant who can help your organisation make sense of the noise, chart a clear course, and create the governance structures to back it up, Digital Bricks is your partner.

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