The Wide Scope Of AI: Everything Everywhere!

What exactly should AI do for a business? Where does it stop? How do we define
the scope? This lack of definition is the problem.

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

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I think I can share a personal experience which might help you relate to the effect this kind of speed has on all of us. Back in 2003, let’s say someone buys a bike, registers it, and fixes the number plate with a white background and yellow font. A week later, the government changes the rule: the number plate must now be white with black lettering. Two weeks later, it changes again. At some point, the person simply decides to accept the situation and, no matter what rule comes next, they are ready to change. This was a common problem for all who got their driving licenses during 2003-2005.

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

Problem

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 You return to your team after a brief holiday, and within minutes, someone says, “You are out of date. Something has changed.” That is how fast the stacks, algorithms, and platforms evolve.

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 exact 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.

To solve this, I work with four principles that help create clarity: Define, Target, Scale, and Grow.

Define

The first principle is to define the level of intelligence actually required in a solution. We must be deliberate here because not every problem needs an LLM or a deep learning model. The principle can be handled on three levels.

Good old-fashioned AI (GOFAI)

At its core, GoFAI is a rule-based system. The logic sits in the code. If the business rule changes, we simply edit the rule. GOFAI remains extremely useful, practical, and the right answer for many use cases. There is no need to complicate everything.

Machine learning

We use ML when the system needs to learn from patterns rather than rules. This is where training data, predictions, supervised and unsupervised learning come in.

Complex AI

This includes deep learning, dynamic models, advanced architectures, and LLMs. While AI needs to evolve, setting limits is just as important. At each stage, simply keep evaluating the question: ‘Is the current level of complexity strictly necessary to resolve the customer’s problem? Often, the honest answer is no.

Developers must therefore pause and choose the minimum intelligence level that meets the requirement. Nothing more. Nothing less.

Target