Schneider Flags Widening Gap Between AI Ambition and Reality in CPG Sector

Fadekemi Ajakaiye

Consumer-packaged goods manufacturers are accelerating their expectations for artificial intelligence in operations but remain significantly constrained by legacy systems, poor data quality and skills shortages, according to a new global study by Schneider Electric.

The findings, drawn from a survey of 1,453 senior executives across the food, beverage and life sciences segments, highlight a growing disconnect between the sector’s AI ambitions and its operational readiness as firms look toward 2030.

Schneider Electric said manufacturers are increasingly turning to industrial AI combining automation, data systems and machine learning to address rising inefficiencies and protect margins in an environment of persistent cost pressure.

The survey shows the industry is already experiencing substantial production-related losses. Respondents estimated that inefficiencies such as downtime, delays, rework and quality deviations account for 15.2 per cent of manufacturing revenue today, while the cost impact embedded in final production is estimated at 20.3 per cent.

These pressures are expected to intensify sharply over the decade. Manufacturers project preventable losses will rise to 21.37 per cent next year and approach 29.14 per cent by 2030 if current conditions persist.

Despite this outlook, adoption of AI across core operations remains limited. Only 13 per cent of respondents said AI is currently embedded end-to-end in decision-making and production processes. However, optimism about future deployment is strong, with 37 per cent expecting AI to become central to operations by 2030 nearly a threefold increase.

The study also highlights ambitious expectations around financial returns. About 32.7 per cent of executives expect AI-driven returns on investment of between 50 and 74 per cent by 2030, while 7.9 per cent anticipate returns exceeding 100 per cent. By contrast, 70 per cent said current AI initiatives deliver returns below 20 per cent, with nearly a third reporting returns of 5 per cent or less.

The gap between expectations and delivery reflects what Schneider Electric describes as an “industrial readiness problem” rather than a technology constraint.

Manufacturers identified several structural barriers to scaling AI, led by shortages in AI and data science skills (43 per cent), legacy automation systems (37.5 per cent), lack of contextualised operational data (36.3 per cent), and workforce resistance to change (25.7 per cent). Cybersecurity and compliance concerns ranked lower at 21.7 per cent.

Neil Smith, president of CPG at Schneider Electric, said the sector’s expectations represented a significant shift but warned that operational foundations were not yet aligned with its ambitions.

“Manufacturers are projecting a tripling of end-to-end AI adoption by 2030, alongside a step change in the returns they expect to see,” Smith said. “This expectation gap is the strongest signal of urgency we’ve seen in years.”

He added that many companies were still operating with fragmented data and legacy infrastructure, limiting the effectiveness of AI deployments.

“AI can only be transformative when it delivers true industrial intelligence: the ability to turn real-time operational data, modern automation and AI into synchronised decisions that improve efficiency at scale,” he said.

Ajibola Akindele, country president for Schneider Electric West Africa, said closing the gap would require deeper collaboration across the industry and stronger deployment of shared standards.
“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,” he said.

The company said its advisory services are already working with manufacturers to apply lessons from “Lighthouse” factories highly advanced digital operations recognised for automation and productivity gains to broader industrial settings.

The findings come as CPG firms face intensifying pressure on margins from volatile input costs, supply chain disruptions and rising operational complexity, prompting increased investment in digital transformation initiatives.

Schneider Electric said industrial AI would be central to competitiveness in the sector over the next decade, but cautioned that progress would depend on overcoming foundational constraints in data infrastructure, workforce capability and legacy systems.