AI is moving from something companies experiment with at the edges to something that runs at the core of how they operate. The leaders I speak with can feel that shift, and most want to move faster rather than miss it.
Access to the best AI model is not what will give you a genuine AI advantage. That is quickly becoming available to everyone. It is something quieter, and something most are not yet looking at.
A conversation that stayed with me
Not long ago I was speaking with the CEO of one of the largest enterprises in Pakistan. He had connected one of the most capable general models available directly to his ERP, expecting to ask questions about his business in plain language and get reliable answers back. The setup seemed logical: the data existed, the processes existed, and the model itself was excellent. Yet the outputs kept coming back wrong, and he could not understand why such a powerful model was missing things his teams considered obvious.
The problem was never the model. General models understand language, but they do not understand how a specific business defines an open invoice, which data sources people trust, or where exceptions exist in real workflows. Connected directly to raw enterprise data, the model was answering confidently without meaningful context. Once we rethought how the data was structured, integrated, and delivered to the model, the outputs changed completely. That was the moment the real insight clicked: the decisive work in AI rarely sits inside the model. It sits underneath it.
CEOs are focusing on the wrong layer
The pattern is becoming surprisingly common.
Executive teams spend months discussing which model to use, whether it should be OpenAI, Claude, Gemini or a local alternative. They debate licensing costs, security concerns and model performance. Yet very little time is spent discussing the quality of the underlying data, how information moves across systems, or whether the business processes themselves are ready for AI.
In reality, most AI projects do not fail because the model is weak. They fail because the model is connected to fragmented systems, inconsistent data definitions and processes that were never designed to be interpreted by machines.
A model can only reason with the information it is given. If customer records sit in one system, invoices in another, contracts in a third, and every department defines key metrics differently, even the most advanced AI will struggle to produce reliable answers.
The uncomfortable truth is that many organizations are trying to build intelligence on top of operational complexity they have not yet resolved.
Where should CEOs be looking instead?
When AI investments fail to generate returns, the instinct is often to blame the technology. In most cases, the technology is not the bottleneck; its poor data quality, multiple versions of the truth, disconnected enterprise systems, manual workflows hidden between applications, lack of governance over business definitions
Consider a simple executive question:
“Which customers generated the highest margin last quarter?”
To a CEO, that sounds straightforward. To an AI model, it may require combining ERP data, CRM data, project delivery costs, support costs, and customer master records across multiple systems. If those systems are not integrated, there is no single answer for the model to find.
This is why some organizations spend millions on AI and see little return, while others achieve significant gains with relatively modest technology investments. The difference is rarely the sophistication of the model. It is the maturity of the environment around it.
At JBS, we increasingly see successful AI initiatives begin here, not with model selection, but with understanding the business objective, preparing the data foundation, and designing how intelligence fits into the operating model. Get that layer right and value compounds. Get it wrong and you have paid for a system that produces answers nobody can rely on, which is more expensive than having no system at all. The difference between those two outcomes has very little to do with the model and almost everything to do with the work most people skip.
Start asking the right question.
AI will keep becoming more capable and more accessible. But access alone has never created advantage. If it did, every company with a cloud account would already have one.
CEOs often ask, “Which AI model should we choose?”
Increasingly, that is the wrong question.
The better question is:
“Do we have the data, processes and integration required for any model to succeed?”
The organizations creating the greatest value from AI are not necessarily buying better models. They are building better systems around them. The model was never the competitive advantage. The operating model behind it is.
This article was originally published on ProPakistani.
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