Artificial Intelligence is no longer confined to lab tests inside factories, utilities, or logistics networks. According to the new State of Industrial AI Report from Cisco, 61% of industrial organizations are already using AI in real operations, and 20% report having achieved mature and scaled deployments. The study, developed in partnership with Sapio Research, surveyed over 1,000 operational technology leaders across 19 countries and 21 sectors, and draws a clear conclusion: industrial AI has moved from the experimental phase to production, but scaling it safely remains much more challenging than headlines suggest.
Cisco’s insights are especially relevant because they shift the focus from models to infrastructure. In physical environments where AI impacts machines, sensors, vision systems, robots, or energy processes, the issue isn’t just whether the algorithm works, but whether the network, security, and IT and OT equipment are prepared to support it. This change in perspective is crucial: in industrial AI, success isn’t measured solely by reasoning ability but by how reliably it can operate under real-world conditions.
The network stops being just support and becomes a key factor
One of the most impactful messages from the report is that network readiness is becoming a bottleneck. 97% of respondents believe AI workloads will affect their industrial network requirements, and 51% anticipate a direct increase in connectivity and reliability demands. Furthermore, 96% consider wireless connectivity essential for enabling these deployments. In other words, industrial AI cannot be scaled on infrastructures designed for much more predictable, less demanding traffic.
This fact aligns with Cisco’s described use cases currently active in the market: process automation, automated quality inspections, predictive maintenance, logistics, and energy forecasting. These applications demand low latency, mobility, link stability, and the ability to bring intelligence to the edge — features that are no longer just enhancements but fundamental requirements. When AI connects to physical assets rather than just data streams, an unstable network shifts from a nuisance to an operational risk.
Cybersecurity remains the major barrier to industrial AI
The second significant obstacle is security. Cisco states that 98% of organizations see cybersecurity as a foundational element for an AI-ready infrastructure, and 40% identify it as the main challenge for scaling. At the same time, 85% expect AI itself to help improve their cybersecurity posture. This dual perspective reflects the current market situation: industrial AI promises greater automation and visibility but also increases the attack surface and requires hardened systems that are often already complex before adding distributed intelligence.
Here lies one of the key tensions in the report. Most organizations see value in AI and expect meaningful results soon — 87% foresee significant impacts within the next two years, and 83% plan to increase their AI investments. However, not all are equally prepared to turn that ambition into operational reality. Cisco describes a scenario where deployment pressure is high, but many companies still lack network readiness, cybersecurity measures, and an operational model that integrates IT and OT seamlessly.
IT and OT collaboration makes a difference
The third major finding concerns the relationship between IT and OT teams. 57% of organizations report some level of collaboration between these groups, but 43% acknowledge limited or no collaboration. Notably, among companies with poor cooperation, 47% cite network instability as a major operational challenge for scaling AI. Cisco interprets this correlation as a clear sign that industrial AI cannot be well governed if a strict divide persists between the corporate IT side and the plant, field, or critical infrastructure side.
This makes sense because for years many organizations treated IT and OT as separate domains with different priorities, tools, and metrics. Industrial AI, however, compels integration. Data comes from sensors, machines, and physical systems; processing can depend on edge, cloud, or data centers; and security must be end-to-end. Failure in collaboration hampers scalability. Conversely, effective cooperation increases confidence to expand AI without compromising operational continuity or cybersecurity.
Ultimately, Cisco’s report leaves a clear message for 2026: the question isn’t whether industrial AI will be deployed but which companies are truly prepared to adopt it. Those with ready networks, better security practices, and stronger IT-OT coordination will advance faster. Others will continue to see AI as a valuable promise but find it hard to turn into a stable operational capability. In physical infrastructures, this difference is far more substantial than just a good demo.
Frequently Asked Questions
What percentage of industrial companies are already using AI in real operations?
According to Cisco, 61% of surveyed industrial organizations are already deploying AI in live operations, with 20% having achieved mature and scaled deployments.
What is the main barrier to scaling industrial AI?
The report highlights two key factors: network readiness and cybersecurity. Specifically, 40% cite security as the biggest obstacle, while nearly all organizations anticipate increased demands on their industrial networks.
What role does IT-OT collaboration play in industrial AI?
Cisco concludes that it’s a critical factor. Organizations with better collaboration between IT and OT exhibit more confidence to expand AI, have more stable networks, and prioritize cybersecurity as a deployment foundation.
Do companies plan to continue investing in industrial AI through 2026?
Yes. 83% of surveyed organizations intend to increase their AI spending, and 87% expect to see significant results within the next two years.
via: investor.cisco