Schneider Electric has published new survey data suggesting consumer-packaged goods manufacturers are entering the next phase of industrial AI adoption with ambitions that are running well ahead of plant-level readiness.
The company’s 2026 Industrial AI in CPG survey, based on 1,453 senior decision-makers across food and beverage and life sciences manufacturing in 14 countries, found that respondents currently attribute 15.2% of mean manufacturing revenue loss to delays, downtime, rework, quality deviations, and suboptimal asset use. Those same respondents said inefficiencies already account for 20.3% of final manufactured product cost, and expect preventable losses to rise to 29.14% by 2030 if current conditions persist.
Yet the deployment base remains thin. Only 13% of respondents said AI is embedded end-to-end in core operations today, even though 37% expect that to be the case by 2030. At the same time, 70% reported current AI return on investment of under 20%, which leaves a sizeable distance between the sector’s expectations and what most brownfield manufacturing environments are delivering now.
The barriers are familiar, and that is the point. Skills shortages in AI or data science were cited by 43% of respondents, followed by legacy automation systems and infrastructure at 37.5%, and a lack of contextualised operational data at 36.3%. Workforce resistance, at 25.7%, ranked ahead of cybersecurity and compliance concerns, which came in at 21.7%. In other words, the technology itself is not what most respondents see as the main obstacle.
Schneider also broke out a steeper picture for the UK and Ireland, where respondents said manufacturing inefficiencies currently cost an average 17.8% of revenue and could rise to 34% by 2030. The implication is straightforward enough: industrial AI is being treated as a competitiveness lever, but the route to value still runs through controls modernisation, data architecture, and operational change rather than pilot-stage enthusiasm.
Schneider and AVEVA have published the accompanying paper, Beyond the Hype: Practical AI for Competitive Consumer Goods Manufacturing, alongside the survey findings.
