You Bought the AI Dream. You Got a Nightmare Instead.

Yes, let’s be honest this morning. Many companies have spent millions on data and now AI, and what do they have to show for it? A bunch of dashboards no one looks at. A data lake that’s more of a swamp. AI pilots that never made it past the “cool demo” stage.

Yet there are others who are doing it well and those are probably your competitors using AI to drive revenue, automate operations, and scale faster.

What do they know that you don’t?

From my experience they know this: Data and AI aren’t the product. They’re the byproduct of having the right operating model and knowing how to execute. If your AI investments aren’t delivering, your problem isn’t AI, it’s how your company is set up to use it.

Let’s cut through the noise and work on how to fix it.

Lie #1: “If You Collect Enough Data, Value Will Follow”

No, it won’t. Data sitting in a warehouse doesn’t create value, it’s just expensive storage. You don’t generate ROI from “having data.” You generate ROI by using data to make better decisions that drive revenue, reduce costs, or cut risks.

Fix it: Flip your approach and start with high-value business use cases, then pull in the data you need.

Reality check: If your data strategy doesn’t have a direct link to business strategy and what matter for growth etc., then you don’t have a strategy, you have a hobby.

Lie #2: “AI Is a Technology Problem”

Your CIO isn’t the answer to your AI struggles. AI isn’t a tech issue; it’s a business execution issue. If AI isn’t embedded into your core business processes, no one will use it.

Your teams don’t trust AI-generated insights? Your leadership doesn’t act on AI recommendations? That’s not a tech problem, that’s a leadership and operating model failure.

Fix it: Treat AI as a business transformation, not an IT project. Align it with decision-making structures, incentives, and workflows. AI that isn’t used is worse than useless, it’s a drain on resources.

Reality check: If your AI models aren’t embedded into the way your company actually runs, they’re just expensive experiments.

Lie #3: “If We Prove AI Works in a Pilot, It’ll Scale”

Wrong. Most AI pilots never scale. Why? Because a PoC is a controlled test, it doesn’t deal with legacy systems, cultural resistance, or operational complexity.

Scaling AI requires:

• Executive alignment (AI needs business buy-in, not just IT enthusiasm.)
• A clear operating model (Who owns it? How does it integrate into business processes?)
• A strong data foundation (Not perfect data, just good enough data that works in real-time.)

Fix it: Build your AI roadmap before you build your AI models. If you can’t answer how an AI pilot will integrate into the business from Day 1, you’re setting yourself up to fail.

Reality check: If your AI strategy relies on “proving it works” first before thinking about scale, you’ll never get past the pilot phase.

1. Define the Business Problem First, Not the AI Model

Instead of asking “How do we use AI?” start with:

• Where are we losing revenue?
• Where are we wasting time and resources?
• What high-value decisions could AI improve?

If your AI strategy doesn’t answer these questions, stop spending money.

2. Align AI to Revenue, Cost, or Risk and Nothing Else

Every AI investment must be tied to one of three things:

• Driving revenue growth (personalisation, demand forecasting, cross-sell strategies)
• Cutting operational costs (process automation, fraud detection, predictive maintenance)
• Reducing risk exposure (supply chain risk, compliance monitoring)

If you can’t connect your AI project to a financial outcome, kill it.

3. Build the Right AI Operating Model Or Watch AI Fail

AI isn’t magic it needs structure. You need:

• Clear ownership (Who makes AI-driven decisions? Who is accountable?)
• Integrated workflows (How does AI get used in business processes?)
• Change management (Are teams incentivised to trust and use AI?)

If AI doesn’t fit into how your company runs, it won’t deliver value.

4. Stop Fixating on “Perfect Data” and Focus on “Good Enough” Data

Your data doesn’t need to be flawless; it just needs to be fit for purpose. If you wait for perfect data, you’ll never get AI off the ground. Instead, focus on iterative improvements:

• Start with good enough data
• Use AI to fill in gaps (synthetic data)
• Improve data quality as you scale AI adoption (but only from a use case perspective)

If your AI project is stalled because of data quality concerns, your real problem is leadership indecision.

There are two types of CEOs when it comes to AI:

The ones who stop treating AI as an expensive science experiment and start embedding it into real business execution. The ones who keep setting up AI task forces, approving endless pilot projects, and waiting for magic to happen.

The first group wins. The second group? They watch their competitors eat their market share.

So, here’s my question to you:

Are You Leading AI? Or Is AI Leading You in Circles?

Because right now, your AI investments are either:

• Driving revenue, cutting costs, and reducing risk, or
• Burning cash while you wonder why nothing’s changing.

There’s no middle ground.

The companies that get this right will outpace and outlast those that don’t, and in five years, when AI has reshaped your industry, you’ll either be:

• A leader who made the right calls
• Or the CEO who funded AI for years, only to realise that it never moved the needle.

So, ask yourself: Are you making AI real in your business, or just funding the illusion of progress?

If you’re ready to stop wasting money and start making AI work, let’s talk. Because the companies that hesitate now won’t be here in five years.