By Edgar Randall, UK&I Managing Director, Dun & Bradstreet.
As AI becomes the engine of modern business, the question of verifiable data ownership is no longer theoretical, it’s central to how organisations build trust in AI-driven decisions. The rise of AI means models depend entirely on the quality and integrity of the data they consume. If that data cannot be traced, validated and governed, the risks multiply: regulatory exposure, compliance failures and unreliable outputs.
AI adoption is accelerating, with 73 per cent of organisations piloting or exploring AI use cases, according to our latest Global Business Optimism Insights Report. This makes clear ownership and provenance a business imperative. The rise of AI is forcing every organisation to answer three fundamental questions: Who owns the data, where did it originate and who is accountable for its deployment?
The threat of unreliable data supply chains
The rapid proliferation of AI has fundamentally changed the risk landscape, heightening concerns around regulation, data misuse, IP concerns and compliance failures. In this new era, knowing who owns the data and how reliably it has been collected and maintained is critical. There is an urgent focus on the stability of the data supply chain and, importantly, what the owner deems a permissible use case.
Opaque, extended data supply chains where ownership is unclear create a liability black hole, making it nearly impossible to trace errors or breaches. This opacity isn’t just a technical hurdle; it erodes trust. When leaders can’t validate the logic behind a high-stakes prediction, they are left with a choice between blind acceptance of an unverified output or setting back operations with a costly rejection.
AI models built on inconsistent data will not correct errors; they will magnify them, leading to hallucinated outputs. Since AI is entirely dependent on input quality, data accuracy and transparency are now the essential antidotes to these systemic failures. In fact, more than half (52 per cent) of financial services and insurance companies for example have seen AI projects fail because of poor data quality2. The ability to verify the provenance of every data point is the only way to establish the necessary trust and defensibility for successful high-stakes AI applications.
Accountability as a regulated requirement
The escalating risk and need for reliability in AI deployment mean data accountability will inevitably become a regulated requirement. Regulatory bodies will demand verifiable proof of data integrity and provenance, forcing leaders to recognise that ownership is inseparable from responsibility. This mandates proactive preparation: establishing governance frameworks that treat data quality as a top-tier issue, not just a technical task. True modernisation goes beyond replacing hardware and addresses behavioural challenges. Many firms believe they can sustain their current operations without fundamentally changing their data infrastructure, underestimating the hidden costs of maintaining legacy systems.
How organisations can prepare
To maintain regulatory compliance, organisations need to adopt robust standards and governance practices now. This means moving beyond basic data management to implement systems for rigorous permissioning, protection and provenance tracking. The strategic shift is from simply aggregating volume to building a secure, verifiable data foundation.
Organisations need transparent, C-level-sponsored frameworks that outlines how data is collected, validated, linked and audited. By embedding metadata and source transparency into their data infrastructure today, leaders can ensure that their AI initiatives are built on integrity, transforming data ownership from a mere technical concern into a strategic, competitive differentiator.
Data governance as a competitive advantage
As data grows in value, ownership and protection have become matters of competitive survival. With AI becoming deeply embedded in every business function, owning data, rather than simply accessing it, is essential for ensuring trust and integrity. We are entering a defining phase in which mastery of provenance and permissioning will determine market leaders, making accountability inseparable from innovation. In the decade ahead, advantage won’t belong to those with the most data, but to those who can prove their data is clean, transparent and responsibly governed.
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