Rockwell says AI adoption outpaces data use in factories

Rockwell Automation has published research on AI adoption in the food and beverage manufacturing sector, highlighting a gap between uptake and the effective use of data.

Its 2026 State of Smart Manufacturing Report found that nearly 90% of food and beverage manufacturers already use artificial intelligence and machine learning or plan to do so soon. Yet respondents said they effectively use only about 45% of the data they collect.

That mismatch is emerging as a central obstacle for manufacturers trying to move AI projects beyond trial phases into broader use across plants and business functions. The report says companies need stronger links between operational technology and business systems, wider access to data, and more preparation for changes in how staff work.

Manufacturers are also tying AI projects more closely to specific business goals, including improving quality, increasing productivity, reducing costs and lowering operational risk.

Greg Schmidt, Vice President, CPG Global Industry, Rockwell Automation, said the conversation in the sector has shifted from whether AI is useful to how companies can extend early gains across the wider business.

"Many manufacturers have moved beyond asking whether AI can deliver value," said Greg Schmidt, Vice President, CPG Global Industry, Rockwell Automation.

"The focus now is on how to scale successful pilots across the enterprise. That requires more than new technology. It requires connected data, integrated operations and a clear link between AI initiatives and business outcomes," Schmidt said.

Data bottleneck

The report suggests the main constraint is not enthusiasm for AI, but the condition of the data environment around it. If less than half of collected data is being used effectively, companies face a basic problem when trying to apply AI tools more broadly to production, planning and quality management.

That raises questions about data quality, access and structure. AI systems depend on connected, contextualised information, and the findings indicate many food and beverage companies still operate across fragmented systems that make that difficult.

The study places particular emphasis on integrating information from factory operations with data from wider business systems. The link between IT and OT has become more pressing as companies seek to apply AI across more of the production process rather than in isolated tasks.

Industry respondents expect AI-supported manufacturing processes to rise from about 37% today to more than 60% by 2030, according to the report. That projected increase suggests manufacturers will need more consistent flows of information between plant equipment, production systems and management platforms if broader deployment is to deliver measurable results.

Workforce shift

The findings also point to workforce readiness as a major part of the challenge. While AI investment often centres on software, analytics and automation tools, the report says staff preparation remains a deciding factor in whether programmes can be sustained.

Rockwell Automation found that 93% of manufacturers expect smart manufacturing initiatives to reshape their workforce, while 40% said they had reskilled employees during the past year.

That reflects a wider shift in industrial businesses as digital systems take on a larger role in day-to-day operations. Companies introducing AI at scale may need operators, engineers and managers to work with new processes, use different information sources and make decisions in changed workflows.

The report argues that organisations are more likely to succeed when they treat AI as part of a broader transformation effort rather than as a series of separate pilot projects. In practical terms, that means combining technology changes with repeatable processes and staff development.

Business test

The pressure on manufacturers is not only to deploy AI, but to show that the technology improves business performance. In the food and beverage sector, where margins, quality control and compliance demands can be tight, projects that fail to move beyond experimentation may struggle to secure ongoing backing.

The report indicates companies are placing greater weight on measurable outcomes. Rather than adopting AI for its own sake, manufacturers are linking initiatives to production efficiency, product quality, cost control and risk management.

That focus may help explain why scaling remains difficult. Broader roll-outs require systems that can support repeatable use across facilities, along with internal agreement on what success looks like and how results will be measured.

For suppliers of industrial automation, the findings underline a market where adoption is advancing but practical implementation remains uneven. For food and beverage manufacturers, the message is that widespread interest in AI has yet to solve the harder problem of turning data, systems and workforce changes into consistent business outcomes.