Practical AI: Cutting Through the Hype

It's Not About the Journey, It's About Getting Work Done

I've said it many times: GenAI and Agentic AI are overhyped.

Saying "we will embark on our AI journey" as if the aim is to "do AI" is like saying "I'm embarking on my Excel journey". Excel is useful, but it's not the strategy.

But ignoring AI? That's just as stupid.

I like to start by calling it the right name Advanced Intelligence not just AI, Calculator was AI. but what we living its birth now is Advanced AI.

Every organisation today should be using AI for efficiency—not as a lab prototype or vanity slide, but as a real operational tool.

Here's the truth most miss: AI adoption is a cultural transformation, not a technology project.

Practical AI in action. When AI is driving real impact, that is what it does:

• Performance optimisation

• Process efficiency

• AI that executes, not just suggests

No theatre. Real results. The kind that unlocks value, not PowerPoint.

Practical Ai makes organisation more effective

Why "AI Use Cases" Are Not a Strategy

Lately I've been puzzled by teams calling a collection of use cases an "AI strategy."

"We'll have AI in customer service, AI in the network, AI in finance..." Great. And?

Let's talk about the brain. My favourite theory: it constantly predicts the future as accurately as possible so we can survive and act better.

GenAI and Agentic AI are like adding extra brain capacity to your organisation. But here's the catch: a brain without good signals becomes dysfunctional. An AI without clean data and clear goals becomes a very fast way to get wrong answers.

Before asking "What's our AI strategy?" try asking:

• What decisions do we need to predict better?

• What clean data do we have or need?

• Which processes work well enough that AI can enhance, not destroy them?

AI is an enabler, not a goal. A catalyst, not a vision. Start with decisions, not demos.

Where Should Practical Advanced AI Live?

Everyone asks: "Who should own AI?" Product? CTO? Strategy?

If your decision is "We'll just create AI products to sell"—fine, put it under CTO + Product. But that's an underplay.

AI done right is like adding 20-30% extra brain capacity to your organisation. That's not a product discussion—it's a corporate-level decision.

The bold version: Create a Chief Capability Officer whose job is redesigning how work gets done with that extra capacity—not "running AI" but changing the operating model to capture the benefit.

AI should sit where strategy is defined, capabilities are built, and work is redesigned. It's either a core capability—or a missed opportunity.

When it is not prtactical AI, It Makes Things Worse

Recently I tried reaching a Tier-1 European mobile operator's call center. It was torture.

They proudly claim they've "activated an AI agent" to improve customer experience. The problem? That AI sits on top of broken processes, using inconsistent data, speaking a language no one understands.

Instead of talking to a tired human, I'm talking to a confused bot—and I'm training it.

If your process is broken, adding AI doesn't fix it. It multiplies the mess.

AI should create clarity, speed, and trust—not become the customer's obstacle course. Maybe it's time to ask: Are we innovating, or just finding smarter ways to frustrate customers?

Stop Calling Everything "AI Strategy"

I'm seeing automation, robotics, and GenAI all thrown into one bucket called "AI Strategy." That's like saying "Our brain strategy is: hands, feet, and coffee."

Let's untangle it:

• Automation: doing known tasks faster, cheaper, more reliably

• Robotics: doing physical work with machines

• GenAI/Agentic AI: prediction, reasoning, decision support

Different tools. Different value. If your "AI strategy" is just a list of automation projects, you have a tool shopping list, not a strategy.

Conclusion

AI isn’t a journey. It’s a capability upgrade.

If you treat it like a slogan, you’ll get demos and disappointment. If you treat it like operational redesign, you’ll get real efficiency and better decisions.

Start with what work must improve, which decisions need sharper prediction, and what data is clean enough to trust. Fix broken processes first, then add AI to accelerate what already works.

Because the point isn’t to “do AI.”

The point is to get work done better, faster, and with less noise.