AI is now delivering measurable operational benefits in use cases such as process automation, automated quality inspection, predictive maintenance, logistics, and energy forecasting says the latest Cisco State of Industrial AI Report.
However, many organisations are increasingly constrained by readiness gaps in networking infrastructure, cybersecurity, and IT/OT operating models as AI shifts into realtime, production‑grade use in physical environments.
The report exams how critical infrastructure like factories, utilities, and transportation systems are accelerating their direct deployments of AI. It provides a data‑driven view into how industrial organisations are adopting AI, the challenges they face as AI moves into live operations, and the opportunities created as AI becomes embedded in physical systems, infrastructure, and workflows.
The double-blind global study surveyed more than 1 000 operational technology (OT) decision‑makers across 19 countries and 21 industrial sectors.
“Industrial AI is moving from experimentation into production where AI systems sense, reason, and act in the real world,” says Vikas Butaney, senior vice-president and GM of secure routing and industrial IoT at Cisco. “At this stage, success is no longer determined by models alone, but by whether networks, security, and teams are ready to support AI at the edge, in motion, and at scale.
“The research shows that organisations confident in scaling AI are those treating infrastructure, cybersecurity, and IT/OT collaboration as foundational, not optional.”
The survey shows industrial AI has moved from a future consideration to active deployment, with 61% of organisations now using AI in live industrial operations where performance, reliability, and security have direct physical consequences – and 20% reporting scaled, mature deployments.
Across manufacturing, transportation, and utilities AI is powering machine vision, robotics, mobility, and safety‑critical operations.
Most organisations plan to increase AI spending (83%), and nearly nine in 10 expect meaningful outcomes within the next two years (87%). Yet, as adoption accelerates, many are struggling to sustain and expand deployments, with readiness across network infrastructure, security, and skills increasingly determining whether AI can scale consistently across core physical environments.
Infrastructure readiness is emerging as a primary determinant of scale
As AI becomes embedded in machines, sensors, vision systems, and autonomous operations, organisations face rising demands for reliable connectivity, wireless mobility, predictable latency, edge compute, and power making network readiness a gating factor for physical AI deployments.
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- 97% expect AI workloads to impact their industrial network requirements.
- 51% of organisations expect AI workloads to increase connectivity and reliability requirements in their industrial networks.
- 96% say wireless networking is essential to enabling AI.
Cybersecurity is shaping both the pace and confidence of AI adoption
As AI expands connectivity and data flows across industrial environments, security remains the top barrier to scale.
At the same time, organisations increasingly view AI as part of the solution – with a majority expecting AI to strengthen monitoring, detection, and operational resilience.
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- 98% say cybersecurity is foundational for AI-ready infrastructure.
- 40% cite cybersecurity as the biggest obstacle to scaling AI.
- 85% expect AI to improve their cybersecurity posture.
IT/OT collaboration is proving critical to operationalising AI at scale
Organisations with closer collaboration between IT and operational teams report greater confidence in expanding AI, more stable networks supporting physical operations, and a stronger emphasis on cybersecurity as a baseline requirement underscoring the need to build the skills required for scalable AI adoption.
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- 57% report some level of IT/OT collaboration.
- 43% report limited or no collaboration.
- 47% of organisations with limited IT/OT collaboration cite network instability as a top operational challenge to scale AI.