Poor data visibility slows enterprise AI adoption

July 15, 2026 2 min read

Experts say AI initiatives depend on trusted, well-governed data, warning that poor data quality can reduce accuracy, delay projects and increase security risks.

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Artificial intelligence is only as reliable as the data it uses to make decisions.

Experts warn that limited visibility into enterprise data is slowing the development of AI initiatives.

Data quality is becoming one of the key factors that will determine the success of artificial intelligence in organizations. As companies adopt AI to optimize processes and support decision-making, they also face the challenge of ensuring that the data powering their models is reliable, up to date and governed by appropriate data governance practices.

According to Veeam’s 2026 Data Trust and Resilience Report, 42% of organizations report having limited visibility into all the artificial intelligence tools and models used across their businesses.

This lack of visibility makes it more difficult to monitor AI deployments, manage risk and comply with regulatory requirements.

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In practice, artificial intelligence depends on the quality of the data it receives. Redundant, outdated or unreliable information can reduce the accuracy of AI outputs, limit the development of new initiatives and increase security risks.

Javier Castrillón, Regional Sales Manager at Veeam: “AI does not generate knowledge on its own; it depends entirely on the quality of the data it consumes. Without a trusted foundation, its outputs can be inaccurate and, in some cases, create risks for the organization.”

When data determines AI’s value.

As organizations adopt large language models and develop customized AI solutions, they also face the growing challenge of managing vast volumes of information, including redundant, obsolete and trivial (ROT) data.

In many cases, this type of information remains hidden within enterprise systems, affecting AI performance.

The result can be inaccurate responses, lower operational efficiency, difficulties scaling pilot projects and increased security risks resulting from uncontrolled access to large volumes of data.

Visibility is the first step.

To strengthen AI initiatives, organizations need a comprehensive data management and governance strategy that enables them to understand what data they have, assess its quality and determine how it is being used.

Javier Castrillón: “Before scaling AI initiatives, organizations need to understand what data they have, assess its quality and determine how it is being used. Visibility is the first step toward trustworthy AI.”

Among the priorities experts recommend are:

Identifying and reducing redundant, obsolete and irrelevant data.

Establishing clear controls over the data sources used by AI systems.

Aligning data management with risk management and regulatory compliance strategies.

Governance also requires visibility.

Data observability and analytics platforms enable organizations to gain a comprehensive view of their information, identify anomalies, uncover blind spots and establish controls that strengthen governance over the data used by artificial intelligence.

Greater visibility into enterprise data helps AI models operate with more accurate and reliable information while enabling organizations to anticipate operational risks and respond to future regulatory requirements.

A strong foundation for scaling AI.

As artificial intelligence becomes increasingly central to organizations, experts agree that its success will depend as much on data quality, governance and control as on the technology itself. Without a clean and trusted data foundation, AI can become a source of risk rather than a competitive advantage.