IFS has released new research showing a divide between frontline execution and C-suite strategy in Industrial AI usage. The IFS Industrial Index 2026 surveyed over 2,200 senior decision makers across 14 countries.

According to the findings, operational workers are driving real-world AI implementation. They are also delivering tangible commercial value. However, formal AI governance frameworks and data access structures are not keeping pace.

Frontline teams include engineers, plant managers, service leads and logistics managers. When they connect their domain expertise and operational data with Industrial AI, the business impact is immediate. Direct savings are appearing across key operational areas, even though solutions are not yet being used to the fullest.

Over 90% of global organizations report significant gains in reliability and performance. Specifically, 93% report an improvement in first-time-right performance. Meanwhile, 90% report an increase in asset availability. In addition, 88% report better on-time delivery schedule adherence.

Removing operational friction also frees employee capacity. Over 50% of employee time is saved across key workflow areas such as production throughput and quality assurance. As a result, companies are repurposing workforce capacity toward higher-value initiatives.

Among these, 36% are prioritizing workforce safety, training and operational compliance. Another 35% are optimizing asset performance and reliability. Then, 26% are accelerating continuous process improvement. Finally, 25% are focused on growing overall market share.

The research also points to a knowledge impact. Factory floor teams know the operational problems that need to be solved. They recognize that the answers already lie in their institutional knowledge and operational data.

Over the past 12 months, 43% of frontline workers have trained and introduced agentic digital workers into their day-to-day operations. Additionally, 37% have upskilled peers to use industrial AI tools in everyday operations. Another 36% have directly trained staff to collaborate with digital workers.

Executive leadership focused elsewhere during the same period. Its primary AI effort was upskilling management to improve their own AI literacy, at 41%. This directed attention inward rather than toward enabling the floor.

Still, 27% of respondents view AI literacy as the first vital step for organizational readiness. Ground-level execution is moving regardless. Leadership confidence, at 30%, remains one of the biggest operational barriers to scaling success.

Cathie Hall, Chief Product & Customer Officer at IFS, commented on the findings. “The factory floor is moving at full speed. Workers see the operational insights, know their data better than anyone, and they know they need industrial-grade AI solutions to turn that expertise into action. The real challenge for leadership isn’t driving adoption, it’s having the insights to clear legacy roadblocks and delivering the secure industrial harness, data pipelines, and governance required to scale these wins safely and globally.”

The research also highlights a governance gap. Over 70% of organizations claim confidence in their safety and data policies. Yet critical controls are missing where they matter most.

Only 11% have validated AI outputs for safety-, quality-, or mission-critical processes. Likewise, only 11% have established control mechanisms for AI use with sensitive or export-controlled data. Only 4% have implemented mandatory human oversight or approval requirements for AI-supported decisions.

In addition, only 20% have embedded industrial AI into supply chain management or maintenance planning (MRO). These are the functions where operational failures carry the highest consequences.

The report describes this gap between confidence and control as an operational liability. Agentic AI at scale without robust safety frameworks and decision oversight creates compounding risk. That includes unvalidated outputs in mission-critical processes, uncontrolled data exposure and decisions without human accountability.

Cathie Hall concluded with a call for leadership to match the frontline’s pace. “Industrial operations are defined by speed and consequences. Every decision, every asset, every second compound. To compete, leadership must match the frontline’s pace. That means having Industrial AI at the heart of operations to support rapidly scaling agentic AI, enabling the safety, governance, and data controls that make rapid decision-making trustworthy and operationally sound. Companies that move now will compress decision cycles and extend competitive advantages. Leadership cannot afford to lag.”

Learn more about IFS Intelligence here.

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