Manufacturers Project Rising Losses, Turn To AI to Stay Competitive – Report

Manufacturers in the consumer-packaged goods (CPG) sector are bracing for higher production losses and rising cost pressures by 2030, as inefficiencies in global supply chains and factory operations continue to grow, according to a new industry survey.

This was revealed in the findings contained in the 2026 Industrial AI in CPG Survey by Schneider Electric.

According to the report, manufacturers expect a sharp increase in operational disruptions, including equipment failures, production downtime, delays, and quality-related issues over the next five years.The report estimated that such inefficiencies already account for about 20.3 per cent of the total cost of manufactured goods today.

It also found that manufacturers are currently losing about 15.2 per cent of their revenue to avoidable production challenges such as downtime, rework, and suboptimal asset use.

These losses are projected to worsen significantly, rising to 21.37 per cent next year and climbing further to 29.14 per cent by 2030 if current conditions persist.

In response, many manufacturers are increasingly turning to industrial artificial intelligence (AI), data analytics, and automation—collectively referred to as industrial intelligence—to strengthen competitiveness and reduce waste across operations.

However, adoption remains at an early stage. The survey shows that only 13 per cent of CPG manufacturers currently have AI fully embedded across their operations and decision-making systems. By 2030, that figure is expected to rise to 37 per cent, indicating a tripling of adoption within four years.

Speaking on the findings, president, CPG at Schneider Electric, Neil Smith, said manufacturers are expecting a significant transformation in AI adoption and performance over the next few years.

“Manufacturers are projecting a tripling of 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,” Smith said.

He added that many organisations are still constrained by legacy infrastructure and fragmented data systems, which limit the full value of AI deployment. According to him, closing this readiness gap has become a critical issue for the sector’s competitiveness.

The report also identifies several barriers slowing AI adoption. These include skills gaps in AI and data science (43 per cent), outdated automation systems (37.5 per cent), lack of usable operational data (36.3 per cent), and workforce resistance (25.7 per cent).

Cybersecurity and compliance concerns were lower on the list at 21.7 per cent.

Country President of Schneider Electric in West Africa, Ajibola Akindele, said achieving the expected returns from industrial AI will require stronger collaboration and improved data practices across the sector.

He said the company is already supporting manufacturers through advisory services and global best practices to help them convert digital investments into measurable operational gains.

“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,” Akindele said.

He added that deploying proven industrial digital transformation strategies would be key to accelerating productivity improvements across manufacturing operations globally.