CPG manufacturers brace for mounting production losses and see industrial AI as a critical competitiveness lever by 2030, Schneider Electric study finds

Schneider Electric, the global leader in energy technology, today released new findings from its global 2026 Industrial AI in CPG Survey,[1] revealing that consumer-packaged goods (CPG) manufacturers expect significant increases in production inefficiencies and cost pressures by 2030. Many are turning to industrial intelligence - the combined power of AI, data and automation - to reinforce competitiveness in a decade of accelerating volatility.

Structural manufacturing costs expected to rise by 2030

The survey reveals CPG manufacturers expect an accelerating margin crisis, with inefficiencies including manufacturing delays, downtime, and equipment failure already amounting to an estimated 20.3% of the final manufactured product cost today. Respondents report 15.2% of mean manufacturing revenue lost today due to delays, downtime, rework, quality deviations or suboptimal asset use.

These preventable losses are expected to worsen sharply, reaching 21.37% next year and rising toward 29.14% by 2030.

Many CPG manufacturers are betting on industrial AI to cut the projected rise in preventable production losses.

AI expectations surge while readiness lags

Today, just one in eight (13%) CPG manufacturers say AI is embedded end-to-end in core operations and decision-making. By 2030, more than a third (37%) expect AI to be core to their operations, a tripling of adoption in just four years.

Respondents also expect AI-driven Return on Investment (ROI) to rise sharply:

a third (32.7%) anticipate returns of 50-74% on their AI projects by 2030

nearly a tenth (7.9%) forecast returns of above 100%, meaning AI investments would pay for themselves in under a year

This level of performance today is seen only in WEF Lighthouses[2] or autonomous factories.

In contrast, seventy percent of respondents say current AI ROI is under 20%, with nearly a third (28.4%) seeing ROI of 5% or less, reflecting an industry still extracting limited value from early-stage deployments.

“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 Neil Smith, President, CPG, Schneider Electric. “This expectation gap is the strongest signal of urgency we’ve seen in years. 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. Many organizations are still operating brownfield sites with fragmented data and legacy systems that limit AI’s value and adoption. Closing this readiness gap is now one of the most important competitiveness priorities for the CPG sector.”

Embedding AI in CPG to boost productivity is not an abstract concept. For instance, for manufacturers across the GCC region, where food security, industrial resilience, and regulatory compliance have become strategic imperatives, this research is timely. The region imports around 85% of its food, leaving local supply chains exposed to geopolitical shocks and transport disruption, even as governments push to accelerate domestic food processing and life sciences manufacturing. At the same time, Gulf manufacturers operate in some of the world’s most resource‑intensive environments, intensifying pressure to reduce energy and water consumption while maintaining consistent quality and compliance. The new paper outlines how manufacturers can progress toward autonomous, compliant operations by combining industrial data, modular automation, electrification, and Industrial AI - helping lift productivity, improve resource efficiency, and shift from reactive maintenance to AI‑enabled predictive maintenance, which regional studies show can reduce unplanned downtime by 30-50% - significantly strengthening operational resilience in industrial environments across the Middle East.

Amel Chadli, President, Gulf Cluster, Schneider Electric, said: “Food security and industrial resilience are immediate operational priorities for the Gulf region, rather than future ambitions. Practical, deployable AI is central to our operations. By integrating industrial data, electrification, and AI‑driven automation, manufacturers here can increase productivity, reduce energy and water intensity- and move from reactive maintenance to predictive, resilient operations that boost local manufacturing growth while meeting stringent regulatory standards.”

The barrier is not AI technology, it’s foundational readiness for industrial intelligence

Despite strong confidence in AI’s potential, survey respondents consistently identify structural, not technological, hurdles as the primary obstacles to scaling:

skills gaps in AI or data science (43.0%),
legacy automation systems and infrastructure (37.5%),
lack of contextualized operational data (36.3%),
workforce resistance (25.7%),

All of these emerge ahead of cybersecurity or compliance concerns (21.7%).

“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, SVP Services, Industrial Automation at Schneider Electric. “Through SE Advisory Services, we are already applying our own Lighthouse manufacturing know-how with customers around the world, helping them turn digital ambition into measurable impact. We believe that sharing and deploying best practices and sector-specific expertise will catalyze the next wave of industrial digital transformation.”

The new paper: “Beyond the Hype: Practical AI for Competitive Consumer Goods Manufacturing” published today by Schneider Electric in collaboration with AVEVA, provides guidance on successful AI implementation across the food and beverage and life sciences sectors. It outlines the pathway to autonomous operations through industrial data, modular automation, electrification and Industrial AI implementation steps.