Rockwell Automation is highlighting data integration, IT/OT connectivity and workforce readiness as key requirements for scaling artificial intelligence across food and beverage manufacturing. Drawing on its 2026 State of Smart Manufacturing Report, the industrial automation company says nearly 90% of food and beverage manufacturers already use AI and machine learning or plan to adopt them soon. Yet manufacturers report effectively using only about 45% of the data they collect, underscoring a gap between AI adoption and enterprise-wide business impact.
Rockwell Automation Says Data Gaps Are Holding Back Industrial AI
Artificial intelligence is becoming a standard part of manufacturing technology strategies, but widespread adoption has not necessarily translated into enterprise-wide results. For food and beverage producers, the challenge is increasingly about connecting AI systems to operational data, integrating factory and business software, and preparing employees to work with more automated processes.
That is the central message of a new analysis from Rockwell Automation, based on its 2026 State of Smart Manufacturing Report. The company argues that manufacturers must move beyond isolated AI pilots and build the data foundations and operational processes needed to deploy successful applications across their businesses. PR Newswire+1
The findings highlight a broader issue for enterprise AI: deploying a model is only one step in putting artificial intelligence to work. Manufacturers also need reliable information, connections between systems and clearly defined business objectives against which to measure performance.
AI Adoption Is Outpacing Effective Data Use
According to Rockwell Automation’s research, nearly 90% of food and beverage manufacturers already use AI and machine learning or plan to adopt the technologies in the near future. However, respondents report effectively using only about 45% of the data they collect.
That gap matters because industrial AI depends on more than model capability. Applications supporting quality control, production planning, predictive maintenance and process optimization need timely, contextualized information from equipment, production systems and enterprise applications.
When operational technology (OT) and information technology (IT) environments remain disconnected, AI applications may lack the context needed to produce useful recommendations. Data silos can also make it harder to compare performance across production lines, facilities or business units.
Rockwell’s analysis recommends connecting operational and business systems, improving data accessibility and tying AI initiatives to measurable goals. Those goals include improving product quality, increasing productivity, reducing operating costs and managing operational risk.
The findings do not establish that data connectivity alone guarantees better AI performance. They do, however, identify data availability and integration as practical considerations for manufacturers trying to extend successful experiments into production environments.
IT/OT Integration Becomes More Important
The report also points to an expected increase in AI-supported manufacturing processes. Industry respondents anticipate that the share of manufacturing processes supported by AI will rise from approximately 37% today to more than 60% by 2030.
This anticipated expansion places greater emphasis on industrial data infrastructure. Factory equipment, control systems and manufacturing execution software generate information that can inform AI applications, while enterprise resource planning and supply chain systems provide additional business context.
Connecting these environments can help manufacturers build a more complete view of production conditions. It can also support the deployment of AI capabilities across multiple facilities rather than requiring each plant to develop separate processes.
The trend has implications for industrial automation providers and enterprise software companies alike. Vendors such as Microsoft, Amazon Web Services and NVIDIA participate in the wider ecosystem through cloud infrastructure, industrial AI services and accelerated computing. Rockwell’s analysis, however, focuses on the operational integration required to make AI useful in food and beverage production.
Workforce Readiness Is Part of the AI Infrastructure
Technology integration is only one part of the challenge. Rockwell’s report found that 93% of manufacturers expect smart manufacturing initiatives to reshape their workforce, while 40% said they had reskilled employees during the previous year. US+1
As AI tools become more involved in industrial workflows, employees may need to interpret automated recommendations, investigate anomalies and collaborate with teams responsible for data, software and production operations. Organizations must also define who is accountable when AI-supported decisions affect product quality, equipment performance or safety-related processes.
Reskilling can help address those needs, but training alone will not resolve every implementation barrier. Manufacturers also need clear operating procedures, suitable governance and a way to evaluate whether AI-supported changes produce the intended results.
For food and beverage companies operating complex production environments, these considerations are particularly relevant when scaling AI across facilities with different equipment, processes and data standards.
From AI Pilots to Measurable Outcomes
Rockwell’s recommendations align with a wider manufacturing technology shift from experimentation toward operational deployment. Its 2026 global report surveyed more than 1,500 manufacturers across 17 countries, although the food and beverage findings represent a specific segment of that broader research. IN+1
The company is not announcing a new AI model or manufacturing platform in this release. Instead, it is outlining an implementation framework centered on data connectivity, IT/OT integration, workforce development and measurable business outcomes.
For technology leaders, that distinction is important. A successful pilot may demonstrate that an AI application can solve a specific problem under controlled conditions. Scaling it requires repeatable deployment practices, consistent data access, integration with existing systems and ongoing performance evaluation.
The next phase of industrial AI adoption may therefore depend less on the number of pilots launched and more on how effectively manufacturers incorporate AI into everyday operations. Rockwell’s research suggests that food and beverage producers will need to address these foundational challenges if they want broader deployments to deliver sustained business value.
Market Landscape
Industrial AI is moving from experimentation toward integration with manufacturing execution, quality management, supply chain planning and production control. Rockwell Automation’s findings highlight three interconnected priorities:
- Industrial AI infrastructure: Contextualized data from equipment, sensors and business systems is essential for production-grade AI applications.
- IT/OT convergence: Connecting factory-floor operational technology with enterprise IT systems can support AI deployment across facilities.
- Workforce transformation: Manufacturers need reskilling, governance and operational processes alongside new AI capabilities.
- Business outcome measurement: Quality, productivity, cost reduction and risk management provide practical measures for evaluating AI investments.
The competitive landscape includes industrial automation companies, cloud AI providers and enterprise software vendors. The differentiator is increasingly how effectively these technologies integrate with existing operations rather than AI capability alone.
Top Insights
- Nearly 90% of food and beverage manufacturers use AI and machine learning or plan to adopt them soon, according to Rockwell Automation’s 2026 research.
- Manufacturers report effectively using only 45% of collected data, highlighting a foundational challenge for scaling AI across production environments.
- Respondents expect AI-supported manufacturing processes to grow from approximately 37% today to more than 60% by 2030.
- Workforce readiness remains important, with 93% of manufacturers expecting smart manufacturing to reshape their workforce and 40% reporting recent employee reskilling.
- Rockwell recommends connecting IT and OT systems, improving data accessibility and linking AI investments to measurable operational outcomes.
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