Consumer packaged goods (CPG) manufacturers expect significant increases in production inefficiencies and cost pressures by 2030. In fact, many are turning to industrial intelligence (the combined power of AI, data and automation) to reinforce competitiveness in a decade of accelerating volatility, according to Schneider Electric’s 2026 Industrial AI in CPG Survey.
“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,” says 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.”
Key takeaways:
· CPG manufacturers expect an accelerating margin crisis, with inefficiencies such as 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.
· 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 one-third (37%) expect AI to be core to their operations, a tripling of adoption in just four years.
- One-third (32.7%) anticipate returns of 50-74% on their AI projects by 2030. And nearly a tenth (7.9%) forecast returns of above 100%, meaning AI investments would pay for themselves in under a year.
- In contrast, 70% of respondents say current AI ROI is under 20%, with nearly one-third (28.4%) seeing ROI of 5% or less, reflecting an industry still extracting limited value from early-stage deployments.
- Despite strong confidence in AI’s potential, survey respondents consistently identify these 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%), and workforce resistance (25.7%). All of these emerge ahead of cybersecurity or compliance concerns (21.7%).
