Enterprises worldwide are encountering severe operational bottlenecks as they attempt to transition artificial intelligence initiatives from experimental phases into active production environments. According to a comprehensive joint study released by data management firm Everpure and analyst agency Omdia, published by TechEdt, an overwhelming 97% of organizations currently struggle to advance their AI projects beyond initial pilot stages. Furthermore, 62% of corporate IT leaders report facing moderate to severe obstacles during these deployment attempts, highlighting a systemic friction point in corporate technology adoption.
The Scale and Hazard of “Dark Data”
According to the research, this deployment bottleneck is heavily driven by the presence of “dark data”—information that enterprises systematically collect, process, and store during regular business activities but fail to reuse or analyze for any practical purpose. An astonishing 99% of surveyed organizations admitted to harboring dark data within their digital estates. For many companies, these unmanaged information repositories represent the vast majority of their digital footprint: 37% of respondents reported that dark data constitutes between 51% and 75% of their entire corporate information base.
Much of this dormant material consists of Redundant, Obsolete, and Trivial (ROT) data. This includes duplicate documents, outdated customer records, and low-value administrative files that are intermingled with commercially valuable records. Because these files remain unclassified and difficult to access, they create significant noise, inflating enterprise storage expenses and exposing organizations to severe security, regulatory, and compliance hazards. Consequently, 76% of IT leaders surveyed now categorize dark data as a notable business risk rather than a benign storage oversight.
Infrastructure Fragmentation and the Visibility Gap
The transition to AI requires highly organized, accessible, and clean data pipelines. However, the Everpure and Omdia report reveals that 58% of enterprises still lack basic visibility across their data estates. IT leaders cannot easily determine what information they possess, where it resides, how it is utilized, or whether it remains sufficiently current to train or support modern machine learning models.
This visibility gap is exacerbated by modern architectural complexity. Enterprise records are frequently scattered across multi-environment ecosystems, including software-as-a-service (SaaS) applications, hybrid cloud platforms, on-premises infrastructure, and legacy computing systems. This fragmentation makes centralized data governance exceptionally difficult. In fact, 68% of IT leaders ranked general data management as their principal operational challenge, while 63% specifically cited fragmented storage across disparate systems as a major barrier to AI scaling.
Trustworthy Data as the Foundation for Trustworthy AI
The quality of AI outputs is directly dependent on the quality of the data used to train and prompt these systems. Ashish Gupta, General Manager of Data Management at Everpure, emphasized the dangers of feeding unrefined data into modern enterprise workloads. “Enterprises cannot build trustworthy AI on untrustworthy data,” Gupta stated. “Redundant, Obsolete, and Trivial (ROT) data creates noise at the very layer that should provide context, increasing the risk of inaccurate AI inferences. The path to better AI isn’t simply adding more data, but ensuring the relevant, context-rich data is used, which requires a comprehensive understanding of the data landscape to unlock business value.”
Recognizing this dynamic, 75% of surveyed IT executives now consider the ability to extract useful insights from dormant dark data assets as absolutely essential to achieving long-term AI success. Without addressing the underlying data quality, enterprises risk deploying AI models that generate inaccurate, biased, or hallucinated outputs, which could severely damage corporate reputation and operational efficiency.
Strategic Remediation: The Value-Risk Matrix
To overcome these persistent deployment barriers, Everpure recommends that organizations systematically assess their entire data estate against a standardized business-value and risk-profile matrix before implementing further AI integrations. Under this framework, corporate data is categorized into four distinct quadrants to guide governance decisions:
- High-Value, Low-Risk Data: This information should be prioritized immediately for AI training and production workloads.
- High-Value, High-Risk Data: This valuable data must remain subject to strict governance, tight access controls, and dedicated security measures before being integrated into AI pipelines.
- Low-Value, High-Risk Data: These records present elevated compliance liabilities and should be systematically secured, reduced, or permanently deleted to limit organizational vulnerability.
- Low-Value, Low-Risk Data: This material should be archived or systematically removed to curb unnecessary storage expenses and halt further data sprawl.
By establishing clear visibility over multi-environment ecosystems and eliminating ROT data before feeding inputs into AI workloads, enterprises can transform their unmanaged dark data into structured, context-rich assets that drive measurable business value.