Research shows more than 90% of industrial organisations report improved reliability and performance, but critical AI safety and oversight controls remain limited.
Industrial frontline teams are moving faster than executive leadership in adopting artificial intelligence, delivering measurable operational improvements while governance and safety controls struggle to keep pace, according to the IFS Industrial Index 2026.
The study surveyed 2,275 executives and frontline workers across 13 countries, including the UAE and Saudi Arabia, spanning manufacturing, energy, aerospace and defence, construction, transportation and telecommunications. It found that more than 90% of organisations reported gains in reliability and performance, with 93% reporting improved first-time-right performance, 90% higher asset availability and 88% better on-time delivery adherence.
AI is also freeing up employee capacity. More than 50% of employee time was reportedly saved across key workflow areas, including production throughput and quality assurance. Organisations are redirecting that capacity towards workforce safety and training, asset reliability, process improvement and market growth.
“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.” — Cathie Hall, Chief Product & Customer Officer, IFS
Frontline-led adoption is becoming a significant driver of this change. The research found that 43% of respondents had trained and introduced agentic digital workers into daily operations over the previous 12 months, while 37% had upskilled colleagues to use industrial AI tools. By comparison, 41% identified improving management’s AI literacy as leadership’s primary AI-related effort.
However, the adoption gap exposes a critical weakness in industrial AI readiness. Although more than 70% of organisations expressed confidence in their safety and data policies, only 11% had validated AI outputs for safety-, quality- or mission-critical processes. The same proportion had established controls for AI use involving sensitive or export-controlled data, while just 4% had mandatory human oversight or approval requirements for AI-supported decisions.
The findings highlight the risk of scaling AI without corresponding safeguards. In industrial environments, unvalidated outputs or autonomous decisions can have direct consequences for equipment, workforce safety and supply-chain continuity.
IFS argues that organisations must align frontline experimentation with enterprise-wide governance, secure data access and human accountability. As agentic AI expands into operational workflows, the ability to scale safely—not adoption alone—will determine whether early productivity gains translate into sustained business value.
