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A new report from Cisco reveals that most industrial organizations have moved AI into live operations
In sum – what we know:
- Rapid operational adoption – 61% of industrial organizations are running AI in live operations, though only 20% consider their deployments to be mature and fully scaled.
- The cybersecurity paradox – Security is cited as the biggest barrier to AI adoption, yet 85% of respondents believe AI is their best tool for improving their overall security posture.
- Critical infrastructure gaps – Success depends on high-reliability wireless and edge compute, with 97% of leaders expecting AI workloads to significantly increase their connectivity requirements.
Cisco and Sapio Research have released the 2026 State of Industrial AI Report, pulling together survey responses from over 1,000 operational technology decision-makers spanning 19 countries and 21 industrial sectors. The report shows a snapshot of an industrial world that’s rapidly reorienting itself around AI, and struggling to keep up with the fallout. AI has overtaken general networking as the number one topic on the minds of industrial teams, a pretty dramatic shift from the 2024 edition, which was much more focused on industrial networking challenges broadly. The global AI in manufacturing market is projected to balloon from $34 billion in 2025 to $155 billion by 2030, and organizations across the board are scrambling to position themselves.
AI adoption status
According to the Cisco report, 61% of organizations are now running AI in live industrial operations — not just in sandboxes or pilot programs. These deployments include factory floors, across logistics networks, and inside energy grids. These are environments where performance, reliability, and security carry direct physical consequences. That said, only 20% of respondents describe their AI deployments as mature and scaled, so most organizations have moved past proof-of-concept but are still somewhere in the messy middle of their rollout.
Manufacturing is leading the charge. 61% of organizations say they’re actively deploying AI, with 20% having deployed at scale, and the investment pipeline backs that up — 83% of surveyed organizations plan to bump their AI spending. 87% of organizations expect AI outcomes within the the next two years. That kind of compressed return window is fueling urgency everywhere, though it’s worth remembering that expectations and reality have a way of diverging. Early wins in controlled environments don’t always survive contact with full-scale operations.
Key drivers
On the application side, process automation tops Cisco’s list as the leading AI use case, followed by supply chain and logistics, then automated quality inspection. Energy optimization and sustainability, and predictive maintenance also rank highly, reflecting the twin pressures of operational efficiency and regulatory or environmental mandates.
The motivations are about what you’d expect. Improving productivity comes in first at 63%, with cost reduction following at 42%. Beyond the core economic calculus, organizations also point to improving security (36%), competitive advantage (33%), and sustainability (29%) as key motivators.
The cybersecurity paradox in AI
Probably the most interesting finding in the report is the tension between AI and cybersecurity. Forty percent of organizations say cybersecurity concerns are the single biggest obstacle to AI adoption, and 48% flag security as their top networking challenge overall. In industrial environments, where a compromised system can mean physical danger, those concerns aren’t abstract.
85% of respondents to Cisco also expect AI to improve their overall cybersecurity posture. So you end up with a paradox — AI cybersecurity ranks as both the biggest barrier to entry and the most anticipated asset for industrial networking teams. Organizations are essentially saying, they’re worried about the security risks AI brings, but they also think AI is the best tool they have to solve their security problems.
This isn’t inherently contradictory. The risks of deploying AI are different from what AI offers on the defensive side. You need robust security infrastructure before you can deploy AI at scale, yet many organizations are banking on AI itself to deliver that security. Treating cybersecurity as a baseline requirement for AI-ready environments rather than something you bolt on downstream seems obvious, but only 20% of organizations report the kind of fully collaborative IT/OT security posture that would actually support it.
Infrastructure and networking requirements for AI
Getting AI running in industrial environments demands serious infrastructure investment. When asked what their networks need to support AI at scale, respondents highlighted reliable connectivity (51%), edge compute capacity (44%), bandwidth (42%), and mobility (40%) as the most critical gaps. 96% of respondents call wireless reliability critical for enabling industrial AI, and 97% expect AI workloads to significantly increase their connectivity requirements.
The technologies powering industrial AI only reinforce this dependency. Robotics and autonomous systems (50%), AI vision systems (47%), and edge computing platforms (42%) all demand low-latency, high-reliability networking to work properly. Organizations are shifting from human-in-the-loop workflows toward machine-to-machine decision-making and increasingly autonomous operations. In that world, AI handles process optimization while humans move into monitoring OT safety and reliability. But that transition requires a level of connectivity, edge computing, and data infrastructure that most organizations simply haven’t built yet.
IT/OT collaboration and integration challenges
The organizational side of industrial AI might be every bit as hard as the technical side. Only 20% of organizations report fully collaborative IT/OT interworking on cybersecurity, which is a real problem when the convergence of those two domains is essentially a prerequisite for scaling AI in industrial settings. The report identifies IT/OT collaboration as a mandatory requirement for driving AI impact, scaling environments, and surfacing cyber risks. As these teams work more closely together, previously hidden risks become visible.
The challenge landscape is also shifting in telling ways. AI technology integration has climbed to become the second biggest challenge for industrial operations, a reflection of just how hard it is to weave AI into existing workflows, legacy systems, and established processes. Meanwhile, the shortage of skilled workers has slipped to third place. That’s because integration and infrastructure concerns have grown more urgent as organizations move from planning into actual deployment. The problems have evolved because the ambitions have evolved, and now the difficult work of making AI function at industrial scale is front and center.
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