Cloudera released manufacturing findings from its latest research report, The Data Readiness Index 2026. The report reveals that manufacturing companies face significant barriers to scaling AI due to persistent gaps in data access, governance, and infrastructure performance.
As manufacturing organizations pursue digital transformation, they are managing rapidly growing volumes of operational, production, supply chain, customer, and IoT data across distributed environments. AI is essential for optimizing production processes, improving quality control, enabling predictive maintenance, and strengthening supply chain resilience. However, many manufacturers still lack the governed, unified data foundation required to operationalize AI at scale.
Although 82% of manufacturing respondents report having visibility into where their data resides, many organizations still struggle to integrate, govern, unify and operationalize trusted data across increasingly complex environments. While manufacturers outperform many other industries surveyed, significant governance gaps remain, with only 58% reporting that all or nearly all of their data is fully governed. These challenges have direct business consequences: 20% of manufacturing organizations cite weak integration of AI and analytics into operational workflows as the leading reason their initiatives fail to deliver expected ROI.
Translating AI insights into operational action remains difficult when data and processes are spread across factories, supply chains, enterprise applications, and edge environments. Success increasingly depends on the ability to operationalize AI across distributed environments, ensuring trusted data can drive faster decisions and measurable business outcomes wherever work happens.
“AI is creating new opportunities for manufacturers to optimize operations, improve quality, and build more resilient supply chains,” Morgan Bowling, Director, Global Industry AI Solutions – Industrial and Manufacturing, at Cloudera. “Realizing those opportunities requires more than access to data. It requires confidence in the quality, governance, and availability of that data across the business. Manufacturers that invest in data readiness today will be in a stronger position to scale AI initiatives tomorrow.”
