AI is the Solution. But What is the Problem?

Walk into any boardroom meeting today, and you’ll hear a familiar rhythm.

“Can we do this faster with AI?”
“Can we reduce costs using AI?”
“Can we automate this entirely?”

Artificial intelligence has moved from being a capability to becoming a reflex. It is no longer just a tool; Instead, it is increasingly being treated as the starting point of every conversation. The scale of this shift is hard to ignore.

In just three months of 2025, 306 companies in the S&P 500 mentioned AI in their earnings calls—over 60% of the world’s largest companies. More telling is how widely this conversation has spread. It is no longer limited to technology firms. AI is now being discussed across real estate, energy, manufacturing, and finance.

AI is everywhere. And when something becomes omnipresent, it creates pressure.

When everyone is talking about AI, not talking about it begins to feel like falling behind. What starts as curiosity quickly turns into urgency, and eventually that same urgency unknowingly turns into fear. Organizations begin to feel that if they are not using AI, they are making a mistake. Leaders feel the need to show adoption. Teams feel the need to experiment. Roadmaps start shifting—not around clear problems, but around the need to include AI.

At this point, something changes.
The question is no longer: What problem are we trying to solve?
It becomes: Where can we use AI?
That shift is small, but it changes everything.

We now see AI everywhere—even in places where it feels unnecessary. Toothbrushes, rice cookers, and everyday products now claim to be “AI-powered.” It may seem harmless, but it reflects a broader pattern: AI is being used because it can be, not because it needs to be.

Within organizations, this shows up differently.
Simple problems are becoming complicated. Tasks that once required clear logic are now routed through layers of AI systems. What used to be straightforward is now wrapped in prompts, checks, and multiple model calls.

At first, this feels like progress. Over time, it starts to feel excessive.
And the data reflects this: while over 60% of companies are talking about AI, only about 13% have shown measurable results. AI has become a strong narrative, but not yet a consistent source of value.

Everyone is talking about AI. Very few are proving their impact.

When adoption is driven by pressure, the goal changes. The focus shifts from solving problems to simply using AI. Visibility begins to matter more than value. This has consequences. AI systems are not free. Each layer adds cost. What starts as an optimization effort can become something harder to maintain and more expensive to scale.
A recent example highlights this. Uber Technologies, Inc exhausted its planned AI budget just months into 2026, despite allocating $3.4 billion to R&D. According to The Information, increased use of AI coding tools—particularly from Anthropic—drove costs far beyond expectations.

Using more AI does not automatically mean doing things better. There is also a quieter risk. As AI becomes part of everyday work, we may begin to rely on it not just for execution, but for thinking. Over time, this can reduce our ability to question and simplify.

This is not an argument against AI. It is an argument for using it with intent.

AI works best in situations that involve ambiguity, judgment, or unstructured inputs. But the real advantage does not come from using the same models as everyone else. It comes from what organizations build on top of them—their workflows, their context, and most importantly, their data.

Because one thing has remained constant across Traditional ML Models & Modern-Day Cutting Edge LLMs:

Data is the real differentiator.

If the data is weak, even the best models will fail. If it is strong, simpler approaches often work just as well.
The real challenge is not adopting AI everywhere, but using it thoughtfully. It means asking better questions before reaching for better tools. Your MOAT cannot just be a one-prompt change away from being replicated

Because if we don’t define the problem, we risk building solutions that look impressive, but solve very little. And in that world, AI will still be the solution. We just won’t be sure what the problem was.

Aneesh Chopra is a PGDBM student at XLRI Jamshedpur