For a long time, data quality problems were perceived as technical irritants. An erroneous value could degrade an indicator, distort a report or slow down an analysis. The consequences existed, but they were generally confined to restricted circles, known only to the teams who handled the data on a daily basis.
This implicit model is breaking down with generative AI.
AI no longer simply presents information. It produces an answer, constructs a reasoning and, more and more often, directly influences business decisions. At this stage, an inconsistency in the data is no longer a simple quality flaw. It becomes an active element of automated reasoning, integrated into a confidently formulated response.
In most organizations, the first uses of generative AI involve querying internal databases: customer repositories, contracts, business rules, operational histories. As long as this information is consulted manually, its limitations are generally well known. Users know that a field is approximate, that a repository is not completely up to date, or that a rule is not applied consistently.
When the same data is used by a model, this context disappears.
The user no longer sees the grey areas, only a seemingly consistent response. It is precisely this shift that makes generative AI riskier than traditional analytical tools: it replaces visible uncertainty with apparent certainty.
This shift is taking place at a time when adoption of the tools is extremely rapid. According to the GenAI Data Exposure Report published by Cyberhaven (2024), the share of sensitive data transmitted by employees to generative AI tools has more than tripled in one year. At the same time, Gartner points out that the majority of data held by companies is of a personal, financial or strategic nature(Gartner, The State of Data Management, 2023).
These two dynamics intersect without really meeting.
Uses progress rapidly, often out of opportunity or pragmatism, while the structuring of governance advances more slowly. AI does not create this imbalance: it acts as a brutal revelation of fragilities already present in information systems.
This is where the question becomes concrete.
Hallucinations are often presented as a flaw in the model. In reality, they occur mainly when data lacks clarity, consistency or context. When a business rule is implicit, when a field is interpreted differently by different teams, or when a repository is only partially synchronized, the model must arbitrate. It does so statistically, not logically.
Studies confirm this observation. Work by the Stanford Human-Centered AI Institute shows that, in legal use cases, models frequently produce incorrect answers when they have to interpret rules from ambiguous data(Stanford HAI, Legal Benchmarks for LLMs, 2024). Accenture, for its part, observes that the majority of hallucinations analyzed in companies originate in data inconsistencies rather than in the model itself(Accenture, Responsible AI Report, 2024).
This phenomenon is amplified by the reality of data environments. Varonis reports that almost all organizations have sensitive databases accessible beyond what is strictly necessary(Varonis, Data Risk Report, 2023). In many cases, teams discover the existence of these exposures after deploying analytical or AI tools.
This situation can be compared to a warehouse where some doors have been left open out of habit: as long as no one enters, the risk remains theoretical. As soon as automated processes start to circulate freely, every opening becomes a point of fragility.
At this point, the question is no longer just one of model performance. It becomes a question of legal and organizational responsibility.
The RGPD already requires organizations to demonstrate that they have control over the processing of personal data: what data is used, for what purposes, according to what rules and with what controls. As long as systems remain deterministic and compartmentalized, this demonstration is complex, but still achievable.
Generative AI changes the nature of this requirement. When a model formulates an answer from multiple sources, sometimes transformed, enriched or aggregated, responsibility no longer rests solely on the raw data, but on the entire path that led to the answer.
The future European AI Act explicitly reinforces this point. It requires organizations using risky AI systems to be able to document, explain and justify the use of data throughout the system's lifecycle.
In concrete terms, during an audit or control, the same questions always come up:
The central question then becomes very simple, and profoundly structuring: would you be able to explain, with supporting evidence, why an answer generated by a model conforms to your organization's data and rules?
If the answer is uncertain, the risk is not theoretical. It's already there.
When an organization becomes aware of the risks associated with generative AI, the most common reaction is to act at the visible level: the model. We restrict usage, add application safeguards or a posteriori validations. These measures are necessary, but they come after the data has already been exposed, transformed and interpreted.
The most advanced organizations have understood a fundamental point: you can't secure AI in the long term without securing what feeds it. Governance cannot be an afterthought. It must precede automation.
This is precisely where data quality and governance platforms like Tale of Data come in. Not as an additional layer of control, but as a foundation of reliability between existing systems and the advanced uses of AI. Even before data is exposed to a model, it is audited, qualified, documented and made traceable. Business rules are no longer implicit or scattered in scripts, but explicit, shared and historized.
Feedback from experience converges on one point. Gartner observes that the majority of data governance initiatives fail when they remain disconnected from actual business uses(Gartner, Data Governance Failure Patterns, 2023). Problems rarely arise during pilot phases. They emerge when models go into production and data begins to flow between systems without an explicit framework.
Organizations that succeed in industrializing AI adopt the opposite, more pragmatic approach. Even before talking about models, they ensure that their data is understandable and controllable over the long term:
business rules are spelled out and shared, critical repositories stabilized, transformations traceable and content drift observed over time.
This approach lies at the heart of AI Readiness, and is described in greater detail in this article:
As generative AI becomes integrated into business processes, it ceases to be a simple innovation tool. It becomes a fully-fledged player in decision-making, with direct legal, economic and organizational implications.
In this context, data quality and governance condition an organization's ability to deploy AI reliably, explain its results and meet current and future regulatory requirements. The companies that will succeed in the long term will not be those that have adopted the most sophisticated models, but those that have taken the time to master what feeds them.
This is precisely the logic behind Tale of Data. The platform makes it possible to audit data, identify inconsistencies and sensitive data, document business rules and trace each transformation. It acts as a layer of reliability between existing systems and the advanced uses of AI, without imposing a major overhaul or technical dependency.
To kick-start this approach simply, a Flash Audit quickly provides an objective view of data quality and governance, before embarking on a more structured trajectory.
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