Stop doing ‘just any’ AI.

That’s right — I’ve seen too many leadership teams chase AI use cases without a second thought.

Someone in the Executive Team says, “We should do something with AI.” A team is formed. A budget is approved. Months later, a fancy AI-powered dashboard or chatbot exists — but nothing has really changed. No competitive edge, no meaningful impact. And, you have another underutilized tool collecting digital dust.

You could’ve done without it.

The AI Illusion: When Experimentation Masquerades as Strategy

Artificial Intelligence has become the boardroom’s favourite buzzword. From quarterly strategy meetings to investor calls, AI is seen as the ultimate game-changer. But there’s a problem. Most companies are approaching AI in a way that is destined to fail.

In many organizations, AI initiatives begin with excitement — teams spin up pilots, executives announce ambitious plans, and vendors promise transformative outcomes. But after the initial hype, reality sets in. The AI projects don’t scale, business impact remains unclear, and leaders start questioning their investments. A year later, the AI initiative is either quietly shelved or reduced to an isolated experiment with no real impact.

“Sorry, but the ROI on enterprise AI is abysmal” — said The Register. Goldman Sachs said “Too much spend, too little benefit”. Lenovo in it’s report titled ‘It’s time for AI-nomics’ shared — “50% of organizations have adopted AI, but most are still in early stages, grappling with challenges like unclear ROI…”.

Why does this keep happening? Because leaders make three fundamental mistakes when thinking about AI. Let’s break them down and explore how to avoid them.

Mistake #1: Treating AI as a Technology Initiative, Not a Business Strategy

Remember when chatbots were all the rage? Companies rushed to launch AI-powered assistants, yet most just became frustrating FAQ bots that customers ignored. Why? Because they were tech-driven experiments, not strategic solutions to real customer pain points.

The most common pitfall is assuming AI is just another IT-driven innovation, something that can be delegated to the Chief Data Officer or the AI/ML team. But AI is not a technology project — it’s a business transformation lever.

Be honest. You did ask your teams: “What can we do with AI?”. Didn’t you?

You’re not alone.

Executives often ask their teams, “What can we do with AI?” when the real question should be, “What business problems are we trying to solve, and is AI the best solution?”

When AI is led primarily by technology teams with minimal involvement from business leaders, the result is a series of experiments — some clever, some impressive — but without clear alignment to core business objectives. AI should be embedded into business strategy, with clear KPIs tied to revenue growth, operational efficiency, or competitive differentiation.

Technology should follow the problem, not the other way around. Instead of “What can we do with AI?”, ask:

• “Where are our biggest operational bottlenecks?”

• “What’s preventing us from scaling more efficiently?”

• “What gaps exist in our customer experience or competitive positioning?”

Once you answer these, AI becomes a tool for solving the right problems, rather than just a shiny new initiative.

What to Do Instead:

• Start with a clear business problem, not an AI use case. Identify bottlenecks, inefficiencies, or customer needs where AI can provide a meaningful advantage.

• Cross-functional ownership is key — AI should not live in the tech silo. Business leaders, product managers, and data teams should work together from day one.

• Define ROI metrics from the start. What does success look like beyond just deploying a model? How does AI impact cost savings, customer retention, or revenue?

Mistake #2: Pilots Without a Path to Scale

Many companies get stuck in “AI experimentation mode.”

They successfully build a pilot, but it never moves beyond a contained experiment. Why? Because they underestimated what it takes to operationalize AI at scale.

A proof of concept (PoC) in a controlled environment is very different from a full-scale AI deployment handling real-world complexity. Many AI pilots work in a sandbox but break down when exposed to production environments, requiring data pipelines, real-time processing, integration with existing systems, and continuous monitoring.

AI isn’t a project, it’s a capability. If your AI model isn’t continuously improving with better data, better integration, and better decision-making, it will become obsolete. Companies that treat AI as a one-time deployment fail. The winners treat AI like a muscle — constantly training, refining, and evolving it. Ask yourself:

• Do we have a strategy for continuously improving our AI models?

• Are we investing in data quality and feedback loops?

• Are we prepared for AI governance, ethics, and long-term impact?

AI success requires ongoing iteration, investment, and integration into workflows. AI isn’t like traditional IT systems — it needs constant refinement to stay valuable.

What to do instead:

• Before starting an AI pilot, ask: “If this works, what will it take to scale?” Address deployment, data pipelines, security, compliance, and ongoing model monitoring upfront.

• Integration is everything. If AI doesn’t seamlessly fit into existing workflows and business processes, it won’t be adopted. Build with scale in mind from day one.

• Align AI initiatives with long-term budget and resources — many AI projects fail because initial enthusiasm fades, and funding dries up before they can scale.

Mistake #3: Optimizing for Efficiency Instead of Competitive Differentiation

Many companies use AI to improve efficiency — automating tasks, reducing costs, or making processes faster. While this has value, it rarely leads to sustainable competitive advantage.

If AI is only making operations slightly better, you’re running in place while competitors leap ahead. AI shouldn’t just speed up the old way of doing things — it should redefine how you win. Real value comes from AI that creates new capabilities, not just optimizes existing ones.

For example, applying AI to automate customer support is useful, but AI that understands customer intent and predicts their next purchase before they do changes the game. AI should be used not just to reduce operational friction but to unlock new business models, revenue streams, and differentiation.

If AI isn’t driving competitive advantage, it’s being underutilized. Ask yourself:

• Are we using AI to differentiate, or just to optimize?

• How can AI help us deliver something our competitors can’t?

• Can AI help us personalize, predict, or create new business models?

AI should not just be an efficiency tool. The real game is creating new value — new customer experiences, new revenue streams, and new strategic advantages. If you’re only using AI to cut costs, you’re missing out on its biggest potential: making your business uniquely better.

What to Do Instead:

• Ask: “Are we using AI to cut costs, or to build something our competitors can’t easily replicate?” AI should be a strategic differentiator, not just a process optimizer.

• Look at how AI can enhance customer experience, create personalized offerings, or enable new product lines rather than just driving efficiencies.

• Don’t just measure AI’s impact in terms of cost savings — consider how it opens new revenue opportunities or makes your company fundamentally harder to compete with.

To Summarize: AI Success Is a Business Challenge, Not Just a Tech One

AI isn’t failing because the technology isn’t good enough. It’s failing because companies approach it the wrong way. To make AI work, leaders must:

• Treat AI as a business strategy, not just an IT initiative. Start with the problem, not the technology. AI should be an answer to a business challenge, not just an initiative for its own sake.

• Move beyond pilots and build with scale in mind from the start. Invest in AI as an evolving capability. Treat AI as a continuous asset that gets better over time.

• Use AI for competitive differentiation, not just cost-cutting. Think beyond cost-cutting. AI should create strategic differentiation — not just efficiency gains.

The leaders who master AI won’t be the ones who adopt it the fastest. They’ll be the ones who use it most effectively — to solve real problems, create real value, and build sustainable competitive advantages.

So, before your next AI discussion, ask yourself: Are we making one of these mistakes? Because the businesses that get AI right today will define their industries tomorrow.

And, as a Leader — the choice is yours.

What’s Your AI Strategy?

Are you just ‘doing AI,’ or is AI actually transforming your business? If you’re not seeing real impact, it’s time to rethink your AI strategy.

Drop me a note — I’d love to hear how you and your company is approaching AI.