Manufacturers may know where their data is, but many are still struggling to govern it, unify it, and put it to work in AI applications, according to new research from data and AI platform company Cloudera.

The findings from its Data Readiness Index 2026, released on Sept. 8, show gaps in data access, governance, and infrastructure that are creating barriers to scaling AI across manufacturing organizations.

The research, part of Cloudera’s broader data readiness study, indicates that while 82% of manufacturers have visibility into where their data resides, only 58% report that all or nearly all of their data is fully governed.

A gap between access and trust

The core finding is the gap between knowing where data lives and trusting how it is managed. The research identifies the ability to integrate, govern, unify, and operationalize data as a continuing challenge for manufacturers.

The study shows that 20% of manufacturing organizations cite weak integration of AI and analytics into operational workflows as the leading reason their AI initiatives fail to deliver expected returns. This suggests that even when models work, connecting them to the factory floor remains a challenge.

"Realizing those opportunities requires more than access to data. It requires confidence in the quality, governance, and availability of that data across the business."

That's Morgan Bowling, Cloudera’s director of global industry AI solutions for industrial and manufacturing.

Challenges in translating insights into action

The challenge becomes particularly apparent when manufacturers attempt to translate AI-generated insights into operational decisions. Manufacturing data and processes can be distributed across factories, supply chains, enterprise applications, and edge environments, which complicates consistent data use across the organization.

Cloudera emphasizes the need for manufacturers to operationalize AI across these distributed environments so that trusted data can support decisions and measurable business outcomes.

The importance of data readiness

The report highlights infrastructure performance as one of the barriers manufacturers face as they attempt to scale AI across increasingly distributed environments.

For manufacturers, the research identifies two related challenges: making data trustworthy and making AI useful within the processes where manufacturing decisions are actually made.

The findings are part of Cloudera’s Data Readiness Index 2026, which examines data-readiness issues across industries, positioning manufacturing's governance gap as a fixable — but urgent — problem.

Manufacturers are pursuing AI for applications including production-process optimization, quality control, predictive maintenance, and supply-chain resilience. However, the research indicates that having access to data does not necessarily mean organizations can use it effectively across these applications.

Bowling's advice aligns with the report's broader purpose, underscoring the importance of investing in data readiness to strengthen AI initiatives.