In recent months, I've spoken to dozens of leaders wrestling with what AI means for their business. Many have detailed decks. Some are mid-pilot on a range of use cases. A few have committed to specific vendors or platforms. And yet, even among the most sophisticated, there’s often a lingering sense of uncertainty - not about the importance of AI, but about how to bring it into focus.

This is understandable in a moment of rapid change, where experimentation is essential. But as the pace of activity accelerates, the challenge is no longer whether to engage with AI, but how to do so in a way that actually creates value.

That's where 'AI strategy' comes in, and the best ones don’t start with technology. They start with the business, and use AI as a lever, not a destination.

When Activity Outpaces Alignment

In the early stages of AI adoption, it's natural for teams to explore AI's potential on many fronts, often simultaneously. For example:

• A chatbot for customer service
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• A training assistant powered by generative AI
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• A predictive maintenance model in operations
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Each of these may be well-justified and thoughtfully built. But without a unifying strategy, they tend to remain siloed—each moving in its own lane, without reinforcing a broader direction.

That doesn’t mean experimentation is the problem. Far from it; it's critical in fast-moving domains like AI. But without strategic alignment, it rarely scales.

So What Is an AI Strategy?

Here’s a definition we've found helpful:

An AI strategy is a focused set of decisions about where AI will drive business value—and how the organization will turn that value into lasting advantage.

It's not a list of tools or set of disconnected pilots. It's a business strategy: one that uses AI as a lever for impact.

Microsoft CEO Satya Nadella has emphasized that AI’s potential lies in empowering individuals and organizations—not in showcasing technical novelty. Technology serves the mission, not the other way around.

In practice, the best AI strategies I’ve seen come down to three (seemingly) simple questions:
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1. Where will AI create disproportionate value for us?
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2. What will it take to realize that value?
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3. How will we learn, adapt, and scale what works?
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Getting these answers right requires leadership focus, in addition to technical analysis.

A Framework for Turning Insight into Alignment

You don’t need deep technical expertise to lead a strong AI strategy. But you do need a structured way to move from a broad set of ideas to focused, business-aligned bets. Here is one approach we’ve seen drive real progress.

Note: This doesn’t replace technical roadmaps or delivery plans. Think of it as scaffolding that helps leaders create strategic clarity so downstream execution actually drives value.

Step 1: Ask the Right Questions

Begin with fast, structured exploration. You’re not looking to pick projects yet, but to uncover where AI could create the most value. The goal is insight, not answers. Start with questions like:

• What business outcomes matter most right now?
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• Where are we facing constraints - in time, trust talent, or margin?
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• What are customers telling us they need more (or less) of?
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• Which types of AI (automation, prediction, generation, etc.) fit these needs?
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• What data, talent, or tools do we need to build and scale solutions?
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This step is about surfacing areas of opportunity and sharpening your lens. It's the foundation for everything that follows.

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Step 2: Make Strategic Choices
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Once you’ve surfaced promising areas through exploration, the next step is to turn that insight into a focused strategy. This means identifying where AI can create the greatest value, aligning on what it will take to realize it, and deciding where to place early bets.
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The goal here is to commit to a few high-impact priorities that reflect both opportunity and readiness. To do this well, we encourage leadership teams to follow a few key principles:
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• Favor clarity over coverage. You don’t need a long list of ideas; you need one or two that clearly tie to business outcomes and competitive advantage.
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• Test your theory of value. Ask: Why this domain? Why now? Why AI? If you can’t answer all three, it’s probably not a strategic bet.
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• Prioritize impact, not just ease. Just because something is technically easy doesn’t mean it’s worth doing. Focus on areas where success would truly move the needle.
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• Favor coherence across bets. Look for opportunities that reinforce each other, through shared data, shared capabilities, shared customer outcomes, rather than standalone experiments.
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Of course, some companies like Amazon are able to run thousands of AI experiments at once. But they do so within a mature ecosystem of strong data infrastructure, a culture of experimentation, and deep alignment around customer and operational priorities. For many companies earlier in the journey, focus isn’t a limitation, but the thing that makes progress possible.

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Step 3: Validate and Operationalize
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Once priorities are clear, it's time to move from theory to traction. This means:
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• Testing quickly – What’s the smallest way we can validate this opportunity in the real world?
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• Learning systematically – What worked? What didn’t? What do we need to refine?
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• Scaling selectively – Double down on what delivers. Let go of what doesn’t.
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This is also where strategy becomes visible to the full organization. For communicating it, shorter is better. We often use a one-page 'AI strategy canvas' as a tool for alignment, communication, and ongoing refinement.
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From Pilots to Progress: What Better Can Look Like
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Imagine a global consumer brand running 14 different AI pilots—customer service, logistics, marketing, forecasting. Each has merit, but together they feel scattered.
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• The CFO struggles to identify which ones matter most.
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• The CTO faces mounting integration complexity.
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• Business leaders are asking: “Are we making progress or just staying busy?”
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Eventually, the leadership team reframes the conversation by asking 'where can AI drive near-term results that build toward our longer-term strategy?'
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The answer? Retail execution in Latin America—a tangible area with a direct line to value. That focus leads to:
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• Three high-impact use cases with a shared data foundation
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• A 6-week test-and-learn sprint
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• Cross-functional alignment on goals, metrics, and governance
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In this example, the breakthrough isn't about better AI models. It's about better clarity.

The First Challenge Isn’t AI. It’s Alignment.
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Andrew Ng has observed that successful AI initiatives aren’t just defined by technical excellence, they’re driven by shared intent. When leadership is aligned on the problem being solved, the system can move with purpose.
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A good test is to ask: can explain your AI strategy without saying 'AI?'
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If you can describe your strategy in terms of what you’re solving for and why it matters, you’re on solid ground. If you can’t, you may have a list of initiatives. But not a strategy.
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AI should be a lever, not a distraction. Like any lever, it works best when it's anchored to something that matters.
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How are you channeling AI's potential in your organization? I’d love to hear what’s working, and what’s proving harder than expected.

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