Every company today seems to be asking: How do we get more from AI? Perhaps there is an important question that should come before that: Is our data ready for AI? Most organizations have enormous amounts of data. But it rarely sits neatly in one place.

Sales has prescription and customer data. Marketing has market research, campaigns and customer insights. Finance has profitability and investment data. Supply Chain has inventory and demand information. Medical has scientific knowledge. HR has people and capability data. Management has years of presentations, reviews and decisions. And different levels of the organization may hold different pieces of the same story.

Having more data is therefore not the same as having better data.

As AI gets embedded into decision-making, the ability to bring together the right data, in the right context and with the right quality could become a major competitive advantage.

Recent research reinforces this. Gartner reports that organizations achieving successful AI outcomes invest up to four times more in foundations such as data quality, governance, people and change management than organizations experiencing poor outcomes. Accenture's 2026 research found that only 7% of surveyed organizations had advanced sufficiently in building the data capabilities required to scale advanced AI.

For Indian pharmaceutical companies, imagine bringing together:
Sales performance + customer campaigns + prescription trends + patient insights + digital interactions + market research + competitor activity + supply availability. Individually, each dataset tells us something. Together, they could tell us much more.

But this should not become a massive IT exercise to put everything together.
The starting point should be the business opportunities we want AI to help with. McKinsey similarly recommends connecting data-quality efforts to specific AI applications rather than trying to perfect data across every corner of the enterprise.

And this cannot be the responsibility of the IT or Data team alone. Commercial, Medical, Finance, HR, Operations and Technology leaders need to come together and decide:

Where do we want AI to create value?
 What data will make that possible?
 How do we make that data reliable, accessible and appropriately governed?

As AI models become increasingly available to everyone, perhaps the model itself will provide less differentiation. The real competitive advantage may increasingly lie in the high-quality, integrated and proprietary datasets that only your organization possesses.

Before building more AI, perhaps we need to build the data foundation that makes AI valuable.