42% of Enterprises Still Don’t Trust AI Models, Ataccama-BARC Report Finds
42% of Enterprises Still Don’t Trust AI Models, Ataccama-BARC Report Finds
A new report from the Business Application Research Center (BARC), released in partnership with data trust platform Ataccama, has spotlighted a major gap in enterprise AI readiness: 42% of organizations still don’t trust the outputs of their AI/ML models, despite 58% having implemented or optimized data observability programs.
Titled The Rising Imperative for Data Observability, the June 2025 report surveyed over 220 data and analytics leaders across North America and Europe. The results indicate that while adoption of observability tools is high, confidence in AI remains significantly lower than trust in BI dashboards, which 85% of organizations said they trust. The growing reliance on unstructured data – such as PDFs, images, and long-form documents – is partly responsible, pushing observability beyond what traditional tools were designed to handle.
According to the findings, many observability programs are reactive, fragmented, and poorly governed, reflecting wider issues like siloed teams, skills gaps (51%), budget limits, and lack of cross-functional alignment. The report emphasizes that leading enterprises are now embedding observability into the full data lifecycle – from ingestion and pipeline execution to AI model consumption – supported by automated quality checks and remediation workflows that reduce manual triage and increase trust.
“Data observability has become a business-critical discipline, but too many organizations are stuck in pilot purgatory,” said Jay Limburn, Chief Product Officer at Ataccama. “They’ve invested in tools, but they haven’t operationalized trust.” He cited a real-world case in which a global manufacturer used observability to eliminate false sensor alerts that had been triggering unnecessary production shutdowns, demonstrating how upstream resolution directly builds trust.
BARC Vice President Kevin Petrie added that unstructured data is increasingly driving observability strategy evolution, especially with the rise of GenAI and retrieval-augmented generation (RAG). However, less than one-third of organizations currently feed unstructured data into their models, and only a small fraction apply automated observability to these inputs. As a result, new risks are introduced when such data lacks real-time monitoring or validation.
Leading enterprises are adapting by integrating observability into their DataOps, MDM systems, and data governance frameworks. In these mature setups, observability is no longer siloed – it functions alongside data catalogs and automation to deliver greater reliability, quicker decision-making, and lower operational risk.
Through its unified platform Ataccama ONE, the company offers anomaly detection, lineage tracking, and automated remediation for both structured and unstructured data. Observability in this context becomes part of a broader data trust architecture, enhancing governance and reducing the workload on technical teams managing AI infrastructure.
The full report is available for download from Ataccama here.
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About Ataccama
Ataccama is the global leader in data trust. Its unified platform, Ataccama ONE, helps enterprises ensure data is accurate, accessible, and actionable by combining observability, data quality, governance, lineage, and master data management. Recognized as a Leader in the 2025 Gartner Magic Quadrants for Augmented Data Quality and Data & Analytics Governance, Ataccama enables organizations to reduce costs, mitigate risk, and scale their AI strategies with confidence.
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