Everyone says AI is a priority. The surveys keep coming, the numbers pile up in board decks, the leadership team nods along. Investment plans go up every year. Satisfaction with the results does not.
This is not a technology problem. It is a leadership problem.
Most executives have never sat down with their leadership team and defined what they actually want AI to do. They buy licences. They launch pilots. They create a role. Nobody asks the only question that matters: where exactly are we going with this?
Innovating and deploying are two different jobs
Plenty of countries sit near the top of the world for AI research and near the bottom for AI adoption inside their own companies. Great labs, great researchers, great patents. And then almost nothing that reaches the people doing the work.
How is that possible?
Because innovating and deploying are two different jobs. Innovation comes out of labs. Deployment comes out of leadership teams. And most leadership teams have not done the strategic framing the second job requires.
Daily AI use at work is still a minority sport in most organisations. Not because employees resist. Because nobody told them what to use it for, or why.
A CIO I work with summed it up in one line: "We have had the tools for eight months. We still do not have the roadmap."
Why so many companies see zero results
Ask companies what AI has done to their revenue and the large majority answer the same way. Nothing measurable.
Zero.
That should make everyone humble. Not because AI does not work. Because the way it gets deployed does not work.
What I see inside companies is always the same pattern. The CEO hears about AI. She asks the CIO to "look into what we could do". The CIO launches a pilot on a technical topic. The pilot runs four months, costs 80,000 euros, and nobody on the leadership team can explain what it produced.
Buying tools is not a strategy. Running a pilot is not a strategy. Naming a head of AI is not a strategy.
A strategy answers two questions. Where do we concentrate our effort? How do we know if it worked? Until the leadership team has answered those two, everything else is noise.
Scaled deployment is still rare. Most large companies are stuck in experiments, demos and proofs of concept that never leave the slide deck.
Fewer use cases, better results
BCG published a number that belongs on the wall of every boardroom. Companies that succeed with AI pursue 3.5 use cases on average. Companies that fail pursue 6.1.
And the outcome: the first group gets 2.1 times more ROI than the second.
Focus beats coverage. Every time.
A CEO told me last week: "We had launched seven AI projects in parallel. Six months in, not one had a measurable result. We stopped everything and kept two. Within three months we had cut customer complaint handling time in half."
This is counter-intuitive when you run a company. You want to cover every department, show that things are moving everywhere. But spreading the effort is the surest way to guarantee that none of it lands. Two use cases taken all the way beat ten started and abandoned.
What a leadership team with a real AI strategy does
Let me be concrete. A leadership team with a real AI strategy does four things.
It picks two specific business irritants. Not "improve productivity". Not "digitise our processes". Two problems everyone already knows about, that cost real money, and that AI can attack. A CFO I work with picked his two: the time it takes to close the books, and the accuracy of the cash forecast. Nothing else.
It sets a measurable success metric. Not a feeling, a number. "We take the close from 12 days to 7." "We improve cash forecast accuracy by 15 points." If you cannot measure it, you cannot steer it.
It iterates in short cycles. Not a six-month project with a final deliverable. A 30-day sprint, a measurement point, an adjustment. Then the next sprint. AI is not an IT rollout. It is collective learning.
It puts the results on the agenda of every leadership meeting. No dedicated reporting, no parallel AI committee. AI is a line in every performance review. The CFO shows his gains. The HR director shows hers. It becomes a reflex, not a special topic.
The real problem: the CEO is doing it alone
In most companies, one person is pushing AI. Not the leadership team. Not a squad. The CEO, solo.
That does not hold.
One person cannot define the use cases for every department. Cannot train the teams. Cannot measure results on ten fronts at once. And while they try, shadow AI settles in. Teams use ChatGPT in their corner, with no frame, no governance, no connection to the company strategy.
The leadership team has to become the steering committee for AI. Not a separate technical committee sitting next to it. The leadership team itself.
That means every member owns AI inside their perimeter. The CFO drives AI in finance. The HR director drives AI in HR. The sales director drives AI across the sales cycle. The CEO arbitrates, coordinates, and above all uses AI personally, so nobody can treat it as optional.
One leadership team I work with set a simple rule: at every monthly meeting, each director presents one AI use case they tested that month. Not an idea. A test. With a result. In four months, adoption went from 2 users to 35 in a 120-person company.
When the leadership team owns it, the message to the whole company is unambiguous. This is not a gadget. This is not the CEO's pet project. This is how we work now.
Frequently asked questions
How do you build an AI strategy when you run a mid-sized company?
Do not start with a technology audit. Start with two concrete business irritants your leadership team already knows by heart. Define a measurable outcome for each one, test it with AI over 30 days, then measure the gap. That is an AI strategy. Not a 40-page document.
Why do so many companies fail to scale AI?
Because they confuse buying tools with having a strategy. Most large companies are still stuck in pilots and proofs of concept that never reach the whole organisation. The blocker is rarely technical. It is the absence of strategic framing at leadership level.
How many AI use cases should you run at the same time?
Fewer than you think. BCG found that companies succeeding with AI pursue 3.5 use cases on average, against 6.1 for the ones that fail. Focus generates 2.1 times more ROI than spreading yourself thin.
Does the whole leadership team need to own the AI strategy?
Yes. In most companies today it is the CEO alone pushing AI. That does not hold. If the leadership team is not the steering body for AI, nobody coordinates, nobody measures, and shadow AI takes over.
Want to build your AI strategy with your leadership team? Our services help executives move from scattered pilots to measurable impact.
Explore our services →
