Bringing Clarity to the Chaos in AI

AI feels powerful, yet most teams struggle because they cannot define what intelligence they really need. But there are ways to address this challenge.

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For anyone working with AI, the blinding speed of growth is a constant struggle. The field never slows down, and staying updated becomes a daily task.

I think I can share a personal experience which may help you relate to the effect this kind of speed has on all of us. Back in 2003, let's say someone bought a bike, registered it, and fixed the number plate with a white background and yellow font. A week later, the government would change the rule the number plate must now be white with black lettering. Two weeks later, the rule would change again.

With this constant change, at some point, some people decided to put every possible format on one number plate so that, no matter what rule came next, they were technically covered. This was a common problem for anyone who got their driving licences during 2003-2005.

I, too, went through this confusion when I got my licence in 2005. And this is precisely what developing AI applications feels like today.

If you are an AI developer, you already know the biggest pain point.

The field is changing so fast that even a small break can set you behind exponentially. You return to your team after a small holiday, and within minutes, someone says, "You are out of date; something has changed." This is how fast the stacks, algorithms and platforms are evolving.

Transformation itself is not new. The industry has seen the birth of the internet, cloud computing, mobility, and even earlier phases of AI. But what makes this phase different is that AI's scope is undefined. Leaders across industries use the phrase: "everything everywhere." Unlike cloud or internet, both of which have clear functional boundaries, AI has no obvious box. What exactly should AI do for a business? Where does it stop? How do we define the scope?

This lack of definition is the root problem. AI is everywhere, but organisations do not know what "everywhere" should mean for them.