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A.I. Adoption Is Surging. Data Governance Is Not Keeping Up.

Companies are investing heavily in A.I. oversight while neglecting the quality, governance and accountability of the data powering those systems.

A few years after the launch of consumer A.I., companies are racing to build governance structures, appointing chief A.I. officers, drafting policies and formalizing oversight processes. The goal is to ensure that A.I. adoption delivers measurable value while minimizing operational, legal and reputational risks. But in the scramble to establish new frameworks, organizations are overlooking something fundamental: the state of their own data.

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Most companies have been collecting transactional, operational and customer data for decades. In the A.I. era, how that existing data is managed will determine whether A.I. systems deliver meaningful returns or amplify existing weaknesses. 

The data foundations of A.I. governance

Public debate around A.I.’s data use has largely centered on model developers scraping the open internet, including social media, books and journalism, to train generative A.I. systems. These practices have provoked backlash on privacy and copyright grounds, exposing unresolved questions about what constitutes fair use in the digital age.

Less attention has been paid to how enterprises themselves are using data. Harvard’s 2025 A.I. Index Report found that 88 percent of organizations are adopting A.I. in some capacity. These companies feed internal data into A.I. models to streamline operations and generate insights. But much of that data was........

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