Consumer-packaged goods manufacturers are facing a sharp rise in production inefficiencies over the next decade, even as expectations for artificial intelligence-driven transformation accelerate, according to a new global study by Schneider Electric.

The survey of 1,453 senior executives across food, beverage and life sciences companies highlights a widening gap between the sector’s ambition to deploy industrial AI and its current operational readiness, raising concerns about worsening cost pressures and competitiveness through 2030.

Schneider Electric said manufacturers are increasingly turning to industrial intelligence combining AI, data systems and automation to address structural inefficiencies in production environments. However, it warned that foundational constraints are limiting the pace and impact of adoption.

The study found that inefficiencies such as downtime, production delays, rework and quality deviations already account for 15.2 per cent of manufacturing revenue losses. Respondents also estimated that these issues contribute to 20.3 per cent of the final cost of manufactured products.

These losses are expected to rise further, reaching 21.37 per cent next year and climbing toward 29.14 per cent by 2030 if current trends persist.

Despite these projections, AI integration into core operations remains limited. Only 13 per cent of respondents said AI is currently embedded end-to-end in production and decision-making processes. However, confidence in future adoption is high, with 37 per cent expecting AI to be central to operations by 2030, almost a threefold increase within four years.

President of CPG at Schneider Electric, Neil Smith, said the sector is entering a period of heightened urgency as manufacturers confront both rising inefficiencies and rising expectations for digital transformation.

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

Smith added that many organisations continue to operate with fragmented data systems and legacy infrastructure, limiting the effectiveness of AI tools.

“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.

The report also points to a stark mismatch between expectations and current performance. While 32.7 per cent of executives anticipate AI will deliver returns on investment of 50–74 per cent by 2030, and 7.9 per cent expect returns above 100 per cent, 70 per cent said current AI deployments generate less than 20 per cent ROI. Nearly a third reported returns of five per cent or below.

Schneider Electric said the findings suggest that the primary challenge is not AI capability, but industrial readiness.

Respondents identified several key barriers to scaling AI across operations, led by skills shortages in AI and data science (43 per cent), legacy automation infrastructure (37.5 per cent), lack of contextualised operational data (36.3 per cent), and workforce resistance to change (25.7 per cent). Cybersecurity and regulatory concerns ranked lower at 21.7 per cent.

The Country President of Schneider Electric West Africa, Ajibola Akindele, said addressing the gap would require stronger collaboration across industry players and more standardised approaches to deployment.

“Delivering the transformational ROI expected for industrial AI in just four years requires a step change in collaboration, transparency and shared standards,” he said.

Schneider Electric said it is working with manufacturers through its advisory services to apply best practices from highly automated “lighthouse” factories to broader industrial operations, with a focus on improving data integration, automation and energy efficiency.

The findings come as CPG manufacturers contend with volatile input costs, supply chain disruptions and growing operational complexity, all of which are intensifying pressure on margins and accelerating interest in digital transformation strategies.

The company warned that without significant improvements in data infrastructure, workforce capability and systems integration, production losses are likely to rise further even as AI investment increases across the sector.