Industrial AI moving from pilots to operations amid infrastructure and security challenges

Cisco’s latest State of Industrial AI research paints a picture of artificial intelligence moving decisively from pilots into operational industrial settings, while exposing infrastructure and governance shortfalls that could slow its ability to deliver on decarbonisation goals.

According to the report by Cisco, produced with Sapio Research and drawing on a survey of more than 1,000 operational‑technology decision‑makers across 19 countries and 21 industrial sectors, 61% of organisations are now running AI in live industrial operations and 20% describe their deployments as scaled and mature. Respondents point to tangible benefits in process automation, automated quality inspection, predictive maintenance, logistics and energy forecasting , use cases directly relevant to reducing greenhouse‑gas intensity and improving energy efficiency.

Industry drivers for AI adoption are led by productivity gains (63%) and cost reduction (48%) but sustainability is also a material motive, with 30% citing improved environmental performance as a key objective. The report highlights energy optimisation and sustainability among the top use cases cited by industrial firms, signalling that AI is being positioned as a lever for emissions reduction as well as operational improvement.

Yet Cisco’s findings underline that technical readiness will determine which organisations can scale AI to achieve such outcomes. Infrastructure constraints , reliable connectivity, edge compute capacity and adequate bandwidth , top the list of requirements, with 49% flagging connectivity, 44% edge compute and 39% bandwidth as critical for AI at scale. Wireless networking is viewed as essential by 96% of respondents, and 51% expect AI workloads to increase demands for connectivity and reliability across industrial networks.

Security and operational models present further barriers. The survey shows cybersecurity is regarded as foundational for AI‑ready infrastructure by 98% of respondents, while 40% named cybersecurity as the biggest obstacle to scaling AI. Networking challenges singled out include security and segmentation (46%), management of heterogeneous networks (38%) and the cultural and organisational divide between IT and OT teams (34%). Cisco notes that stronger IT/OT collaboration correlates with greater confidence in scaling AI; where collaboration is limited, network instability is more often reported as a top operational challenge.

For industrial decarbonisation practitioners the report offers both opportunity and caution. AI‑driven energy forecasting, predictive maintenance and advanced vision systems can cut waste and extend asset life, accelerating emissions reductions in factories, grids and transport fleets. However, those benefits will be constrained unless organisations address predictable latency and power provisioning for camera and sensor networks, and broaden skills in AL/ML model development, industrial networking and cloud/edge architecture , skills that respondents ranked among the most sought after.

Cisco’s work is complemented by sector and regional variants of the study, including dedicated reports for manufacturing, utilities and EMEA, which reinforce the global trends: 87% of organisations plan to increase AI spending and a similar proportion expect meaningful results within two years. The utilities report, in particular, highlights AI use in machine vision, robotics and safety‑critical operations, areas where reliability and security have direct physical consequences.

The vendor’s broader research agenda also flags the governance and risk dimensions companies must wrestle with. According to Cisco’s separate State of AI Security and Data and Privacy Benchmark studies, the expanding attack surface of AI systems and the escalating demands of data governance are prompting major investments in privacy, supply‑chain assurance and standards adherence. Cisco’s investor communications note rapid expansion of privacy programmes and plans for further investment to keep pace with AI complexity.

Cisco’s messaging at industry events adds emphasis to the infrastructure imperative. The company has positioned its networking platforms and edge compute offerings as the foundational layer for agentic and physical AI workloads, arguing that secure, automated networks are necessary to bridge traditional connectivity and the high‑performance requirements of production AI.

For industrial organisations pursuing decarbonisation, the practical takeaway is clear: AI can accelerate emissions reductions, but real‑world benefits will hinge on solid networking, hardened cybersecurity and integrated IT/OT operating models. Firms that invest in wireless resilience, edge compute and cross‑discipline skills now are likely to be best placed to translate AI pilots into measurable environmental and operational outcomes.