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A new study from Schneider Electric has found that consumer-packaged goods (CPG) manufacturers are increasingly turning to industrial AI as they brace for rising production inefficiencies and cost pressures.
The company’s 2026 Industrial AI in CPG Survey, which polled 1,453 global executives, found that CPG manufacturers expect preventable production losses to increase significantly by 2030, driven by manufacturing delays, downtime, equipment failure and suboptimal asset use.
Currently, these inefficiencies account for an estimated 20% of final product costs, with 15% of mean manufacturing revenue lost due to operational issues. Respondents expect these losses to rise to 21% next year and reach 29% by 2030.
In response, many CPG manufacturers are planning to adopt industrial AI, which combines the use of AI, data and automation, to improve efficiency and competitiveness.
The survey found that while only 13% of CPG manufacturers currently have AI embedded end-to-end in core operations, 37% expect this to be the case by 2030.
“Manufacturers are projecting a tripling of the end-to-end AI adoption by 2030, alongside a step change in the returns they expect to see, matching the levels only the most advanced Lighthouse and autonomous factories achieve today,” said Schneider Electric CPG President Neil Smith.
“This expectation gap is the strongest signal of urgency we’ve seen in years.”
The study also found that respondents also expected AI-driven return on investment to rise sharply. About one-third of respondents anticipate AI-driven returns on investment between 50% and 74% by 2030, while 8% expect returns exceeding 100%.
However, current returns remain low. 70% of respondents reported AI ROI is under 20%, with about a third seeing ROI of 5% or less.
“AI can only be transformative when it delivers true industrial intelligence: the ability to turn real-time operational data, modern automation and AI into synchronized decisions that improve efficiency at scale,” Smith said.
The survey also identified several barriers to scaling AI adoption, including skills gaps in AI and data science, legacy automation systems, lack of contextualised operational data (36%), and workforce resistance.
“The results are clear: delivering the transformational ROI expected for industrial AI in just four years requires a step change in collaboration, transparency and shared standards,” said Cecile Vercellino, Schneider Electric SVP Services of Industrial Automation .
