FEATURE Industrial AI
FROM PILOT TO PRODUCTION FROM PILOT TO PRODUCTION Automated
Samuel Pasquier, VP, Product Management, Cisco Industrial IoT Networking, presents the human challenge of scaling Industrial AI, and shows how to address it
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ndustrial AI has moved from promise to practice. Across manufacturing, transportation and utilities, organisations are deploying AI to improve productivity, reduce costs and
strengthen operational resilience. Recent global research shows that 61% of industrial organisations are now deploying AI in live environments, rather than experimenting in isolation. Yet for all this momentum, only a minority (20%) have reached truly scaled, mature adoption. The technology is advancing rapidly, but progress often slows once AI moves beyond pilots and into production. The reasons are known: infrastructure readiness, cybersecurity risk, and system complexity. But beneath these technical challenges lies a more fundamental constraint; one rooted in how people work together. Industrial AI sits at the intersection of two are trained to manage networks, data, security and digital platforms. OT teams are experts in industrial processes, safety, reliability and real-time operations. Both bring essential capabilities, but neither can scale AI alone.
AI does not replace this division of expertise; it move decisions closer to operations, and increase reliance on data, the need for coordination grows. When IT and OT operate in silos, organisations regardless of how advanced the technology may be. Survey research based on the responses of 1,000 industry leaders show that while many organisations report some level of IT/OT collaboration (57%), a no meaningful cooperation (43%). Fully converged teams remain rare. This is not because leaders don’t recognise its value, it’s because building combined unrealistic.
Expecting individuals to master both IT and OT disciplines is rarely practical. The combined skill set is unusual by nature. What matters far more is enabling collaboration, creating environments where IT and OT teams can bring their full expertise to the table and work toward shared outcomes. Organisations that enable this kind of ability to scale AI. They also experience greater network stability and place stronger emphasis on
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cybersecurity as a foundational requirement, rather than an afterthought. In contrast, organisations with segregated teams are more likely to experience instability, slower deployment, and elevated risk. This is a human challenge as much as a technical
one. It requires trust, shared language and aligned incentives. It also requires leadership that frames AI not as an IT project or an OT experiment, but as a joint operational capability. cybersecurity concerns rise sharply: 40% of organisations cite cybersecurity as the single biggest obstacle to scaling industrial AI, and 48% identify security and segmentation as their top networking challenge. Organisations with stronger IT/OT collaboration are more likely to recognise these risks early and address them collectively. Where silos persist, risk is often fragmented. OT teams may prioritise availability and safety, while IT teams focus on security controls and compliance. manage, and AI deployments remain constrained to lower-risk environments. By working together, teams can design systems that balance security with operational continuity, a prerequisite for deploying AI in production environments. Organisations that struggle to scale AI often hesitate not because the technology is unproven, but because ownership is unclear. Who is accountable when an Organisations further along in their AI journey tend
to address these questions through shared governance and clearer accountability across IT and OT. This does not require structural overhaul, but it does require agreement on common goals: uptime, safety, resilience, and performance. Industrial AI will continue to advance, but the ability to deploy AI comfortably in production will depend just as much on people as on technology.
Cisco Industrial IoT Networking
www.cisco.com
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