AI isn’t just another tech trend — it’s a strategic imperative.
The CEOs I’ve spoken with recently are still at the beginning of their AI journey. They’re not yet asking, “How do I use AI to grow revenue or reduce cost?” They’re asking, “What should I even be doing here?” And that’s completely fair — the landscape is noisy, the tools are evolving fast, and the stakes feel high.
But it’s the next set of questions that will define market leaders:
Where can AI create real business leverage? What problems are we uniquely positioned to solve better or faster with AI? How do we move with clarity instead of chasing hype?
In my work leading AI strategy and product across companies in InsureTech, HRTech, and enterprise SaaS, I’ve helped leadership teams move past the noise and focus on what matters: creating measurable value through practical AI adoption.
This guide is for CEOs who want to lead from the front — not by becoming AI experts, but by asking the right questions, choosing the right bets, and building an organization ready to win in the age of AI.
What Most CEOs Really Want from AI
Most of the CEOs I’ve spoken with aren’t chasing the next viral AI tool. They’re not trying to build their own ChatGPT or spin up an in-house research lab. What they really want is clarity.
They want to understand how AI can help them:
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Serve customers better
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Improve operational efficiency
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Stay competitive — without chasing hype or burning out the team
There’s often a healthy skepticism in the room. They’ve seen the flashy demos. They’ve heard the big promises. But what they’re looking for is something more grounded:
Can AI actually move the needle on growth, margins, or retention — in our business, with our team, and within our constraints?
That’s the right question to ask.
Because while AI is powerful, it’s not magic. The companies that benefit most aren’t the ones who throw money at the trend — they’re the ones who identify a few high-leverage areas, run focused experiments, and build from there.
You don’t need a massive budget to get started. You need a clear problem to solve, a thoughtful way to test it, and a willingness to learn fast.
Common Pitfalls to Avoid
Over the past year, I’ve seen a lot of smart companies stumble with AI. Not because they lacked ambition — but because they either overcomplicated it or missed the point. Here are a few patterns I’d steer any leadership team away from:
1. Chasing shiny demos instead of solving real problems
It’s easy to get caught up in what AI can do and forget to ask what your business needs. I’ve seen teams pour months into building flashy copilots that looked impressive, but didn’t move any metrics. If you can’t tie an AI project to a specific KPI — revenue lift, cost savings, margin improvement — it’s probably not worth doing.
2. Starting with the tech, not the outcome
Too many teams begin with “Let’s use ChatGPT” instead of “Let’s prioritize leads.” The tech should serve the goal — not the other way around. I’ve had the most success when we picked a pain point, then figured out whether AI could solve it better, faster, or cheaper than our current approach.
3. Thinking this is an IT or data science problem
It’s not. This is a cross-functional opportunity. Your product, operations, customer success, finance — all of them can benefit from AI. If you leave it entirely to your data team, you’ll get technically sound experiments that don’t land with the business.
4. Waiting for perfect data
Yes, your data matters. But if you wait for it to be clean, centralized, and labeled, you’ll be waiting a long time. The beauty of modern AI — especially large language models — is that you can often do something useful even with messy, unstructured inputs. Start where you are.
5. Treating AI as a one-and-done initiative
AI isn’t a project with a start and end date. It’s a capability you build over time. The teams that win treat it like a product function — small experiments, fast feedback loops, continuous improvement. It’s not about hitting a home run right away. It’s about learning quickly and scaling what works.
A Simple Framework to Get Started (Without Burning Millions)
You don’t need a moonshot. You need momentum.
Here’s the approach I’ve seen work — not just in theory, but in the trenches across companies. It’s a simple three-phase playbook to get going without getting lost.
Phase 1: Identify High-Impact, Low-Risk Use Cases
Start small, but strategic. Look for internal bottlenecks where AI can create immediate leverage — things like:
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Automating email summaries or internal documentation
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Drafting responses in customer support or sales
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Prioritizing leads with existing data
These aren’t headline-grabbers, but they save time and free up your team for higher-value work. Most importantly, they build trust. Early wins matter.
What you need:
A cross-functional team — product, ops, a couple engineers — and a clear KPI to track impact. Not perfection, just momentum.
Phase 2: Prove Value in One or Two Customer-Facing Areas
Once your team sees what’s possible, shift focus outward. Where can AI help your customers? Maybe it’s smarter onboarding, self-service support, or tailored recommendations.
These use cases start to move the needle on NPS, retention, and revenue. They also begin to differentiate your product or service — this is where AI stops being a cost-saver and starts becoming a growth lever.
What you need:
Someone who deeply understands your customer journey, a lightweight experiment (no massive rebuilds), and a tight feedback loop.
Phase 3: Make AI Part of Your Company’s DNA
This is the longer game. You’re building internal capability — not just in engineering, but across your org. That means:
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Training teams to use AI tools responsibly
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Hiring or upskilling product managers and operators who can spot opportunities
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Putting in place light governance to avoid risk without slowing things down
AI should become like design thinking or agile — something baked into how you build, not a special project.
What you need:
Executive alignment, a few internal champions, and enough success stories to get buy-in across the org.
The CEO’s Role in AI Adoption
If there’s one thing I’ve learned: AI adoption doesn’t succeed because the tech is good. It succeeds because the CEO makes it a priority.
You don’t need to write Python or know how transformers work. But you do need to set the tone — and that starts with asking the right questions in the boardroom and with your exec team:
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Where can we apply AI to move the needle on revenue or cost?
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What problems are we uniquely positioned to solve faster or better with AI?
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Are we empowering the right teams to run quick, scrappy experiments?
The companies that win with AI aren’t the ones with the biggest models — they’re the ones with the clearest conviction and the sharpest focus.
As CEO, your job is to:
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Frame AI as a business capability, not a tech initiative.
Just like mobile or cloud before it, AI is infrastructure for the next decade. Make it part of your product and operations conversations — not just IT. -
Push for measurable value early.
You don’t need a “Chief AI Officer” to get started. You need cross-functional teams, a few focused pilots, and a clear expectation: this should either grow revenue, reduce cost, or improve experience — or we’re not doing it. -
Model curiosity, not fear.
Your team takes their cues from you. If you treat AI as a risk to manage or a buzzword to ignore, they will too. If you ask smart questions, stay open to learning, and reward initiative, you’ll create the right kind of momentum. -
Invest for the long-term — with eyes wide open.
AI is not magic. It’s messy, it’s evolving fast, and it doesn’t replace critical thinking. But the companies that develop the muscle now will outpace those that wait for “perfect timing.”
You don’t have to bet the company on AI.
But you do have to bet on your team’s ability to learn fast, adapt, and lead — just like you always have.
That’s your edge.
Final Thoughts: It’s a Journey, Not a Magic Bullet
There’s no AI “silver bullet.” No tool that instantly transforms your company. But there is a path — and it starts with small, smart steps that build momentum.
The most successful CEOs I’ve seen treat AI like any other strategic initiative:
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They look for leverage, not hype.
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They back teams that move fast and learn.
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And they don’t wait around for a playbook — they write their own.
If you’re feeling behind, don’t worry — most companies are still early in the game. But this is one of those shifts where being early and deliberate can create real compounding advantage. Not just in tech, but in talent, culture, and customer experience.
I’m convinced:
The CEOs who lean in now — thoughtfully, without panic — will be the ones shaping the next generation of category leaders.
And if you’re a CEO ready to start that journey? You don’t need to go it alone. But you do need to start.