Beyond the Hype: A CEO's Guide to Building a Winning AI Strategy

I think about AI strategy in three parts: your competitive landscape, your internal readiness, and your customers' unmet needs. Get all three right and AI becomes a real advantage. Miss any one and you're just buying expensive tools.

Your competitive landscape

AI isn't happening in isolation. Your competitors are thinking about this too—or they should be. The strategic value of any AI investment depends entirely on your competitive context.

Where can you take market share?

Even fractional gains in market share can move EBITDA significantly. I've seen AI-powered pricing models help companies win deals by thin margins. Intelligent lead scoring can focus sales teams on prospects five times more likely to close.

These aren't moonshot projects. They're targeted applications that give you a sharper edge where you're already competing.

Where are you vulnerable?

Here's an uncomfortable exercise: imagine an AI-native startup entering your market tomorrow. They have no legacy systems, no technical debt, no "that's how we've always done it." What could they offer that's dramatically cheaper, faster, or more personalized than what you do?

That answer tells you where to invest defensively. Better to self-disrupt than wait for someone else to do it.

Can you change the game entirely?

AI can do more than help you compete better. It can change what you're competing on.

A manufacturing company I know pivoted from selling equipment to selling guaranteed uptime. They instrumented their products with sensors and used AI to predict failures before they happened. Now they don't have competitors in the equipment business—they have competitors in the uptime business. Totally different game.

That's not incremental improvement. That's creating a new category where you're the incumbent from day one.

Your internal readiness

Even brilliant strategy fails if your organization can't execute it. AI implementation is different from most technology rollouts. It requires shifts in culture, data practices, and operational thinking that catch people off guard.

Do you have the right people?

An AI-ready culture tolerates intelligent failure, runs experiments, and uses data to make decisions at every level. Before investing in technology, I'd ask: do you have internal champions who can translate business problems into technical requirements? How will you upskill existing talent? How will you address fears about automation?

The biggest barrier to AI adoption isn't technical. It's organizational inertia. Your change management plan matters as much as your technology roadmap.

Is your data ready?

Many leaders are stuck in what I call the "data paradox": they feel their data isn't good enough for AI, but they need AI to prove the ROI for improving their data.

The way out is running two tracks simultaneously. Track one: find projects that work with your current data, imperfect as it is. This builds momentum and demonstrates value. Track two: start the long-term work of building proper data governance and infrastructure. This foundation supports more ambitious applications later.

Don't wait for perfect data. Start with what you have while improving what you need.

Are you thinking about workflows, not tasks?

The trap I see most often: companies automate isolated tasks and wonder why the ROI is disappointing. They speed up one step in a ten-step process and call it a win.

Better approach: map your end-to-end value streams. Lead-to-cash. Order-to-fulfillment. Candidate-to-hire. Then ask how AI can reinvent the entire flow—eliminating steps, removing handoffs, compressing cycle times.

That's the difference between making the cow path smoother and building a highway.

Your customers' unmet needs

Your AI strategy should ultimately deliver value to customers. Their expectations are evolving fast. What felt impressive six months ago is table stakes today.

What job are they hiring you for?

Your customers don't buy your product. They hire it to accomplish something. Go beyond your features and map the entire journey around that job.

If you sell project management software, the job isn't "use project management software." It's "complete projects successfully." Where do customers struggle with that job outside your product? What other tools do they cobble together?

AI can smooth those gaps. Predict timeline risks. Suggest resource allocation. Summarize progress for stakeholders. These services live outside your core product but are central to the customer's success.

What can you do now that was impossible before?

Frame your brainstorming around AI's unique strengths:

Anticipation over reaction. Instead of waiting for customers to report problems, predict and solve them before they happen. Move support from reactive to proactive.

Remove friction. What tedious, frustrating steps do your customers endure? Use AI to make those steps invisible.

Enable the impossible. What service could you offer that was unthinkable before? Could every customer have a personal advisor powered by an LLM? Could you generate customized deliverables instantly instead of manually?

What to do next

Don't hire a team of data scientists first. That's the wrong starting point.

Start by assembling a small cross-functional group: someone from strategy, someone from technology, someone from operations, someone from product. Task them with honestly auditing these three pillars—competitive landscape, internal readiness, customer needs.

That assessment grounds your ambitions in reality. It reveals which AI initiatives will actually move your business versus which ones just sound exciting.

AI is the most powerful tool for creating value that I've seen in my career. But it's just that—a tool. The strategy ensures you're pointing it at problems that matter and that you have the organizational muscle to execute.