Every company has data it guards more closely than the rest. For a bank, it might be millions of customer transactions. For a pharmaceutical company, it could be years of proprietary research. For a cybersecurity business, detailed information about threats and vulnerabilities across its networks.

This is often the data that makes a company different from its competitors. It’s also exactly the kind of information businesses are wary of putting anywhere near an external AI model — and for good reason.

Inputting sensitive customer information, intellectual property or regulated data into an AI service can create questions about who can access it, where it's processed and what happens to it afterwards. For companies dealing with GDPR, AI and industry regulation or national security requirements, getting those questions wrong can carry serious consequences.

The result is something of a contradiction. Businesses are spending heavily on AI while some of the data most likely to generate real commercial value from it remains locked away.

For much of the past few years, discussion around enterprise AI has focused on the models: which ones are the smartest, fastest or cheapest. But the model is only part of the equation.

Take a bank trying to spot sophisticated fraud. The more context an AI system has across transaction histories, customer behaviour and previous incidents, the more useful its analysis could become.

Or a pharmaceutical company sitting on years of experimental data. Combining that information with powerful AI models could help researchers uncover solutions that would otherwise take months to find.

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