Industrial AI has moved from promise to practice. Across manufacturing, transportation and utilities, organizations are deploying AI to improve productivity, reduce costs and strengthen operational resilience. Recent global research shows that a majority (61%) of industrial organizations 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 Is a Team Sport

Industrial AI sits at the intersection of two disciplines with very different histories. IT teams 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 amplifies it. As AI systems connect more assets, move decisions closer to operations, and increase reliance on data, the need for coordination grows. When IT and OT operate in silos, organizations struggle to deploy AI confidently in production, regardless of how advanced the technology may be.

Survey research based on the responses of 1,000 industry leaders show that while many organizations report some level of IT/OT collaboration (57%), a significant proportion still operate with limited or no meaningful cooperation (43%). Fully converged teams remain rare. This is not because leaders don’t recognize its value, it’s because building combined IT/OT skill sets in individuals is difficult, and often unrealistic.

Collaboration, Not Convergence of Roles, Is the Unlock

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.

Organizations that enable this kind of collaboration report higher confidence in their ability to scale AI. They also experience greater network stability and place stronger emphasis on cybersecurity as a foundational requirement, rather than an afterthought. In contrast, organizations 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.

Breaking Silos Changes How Risk Is Managed

As AI expands connectivity and data flows, cybersecurity concerns rise sharply: 40% of organizations cite cybersecurity as the single biggest obstacle to scaling industrial AI, and 48% identify security and segmentation as their top networking challenge. Organizations with stronger IT/OT collaboration are more likely to recognize these risks early and address them collectively.

Where silos persist, risk is often fragmented. OT teams may prioritize availability and safety, while IT teams focus on security controls and compliance. Without collaboration, trade‑offs are harder to 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 where failure is not an option.

Building Confidence to Move into Production

Organizations that struggle to scale AI often hesitate not because the technology is unproven, but because ownership is unclear. Who is accountable when an AI‑driven system affects operations? Who responds when performance degrades or security alerts appear?

Organizations 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.

Over time, this collaboration also supports workforce readiness. Skills shortages remain a barrier, cited by 34% of organizations overall, but this drops to 27% among more mature AI adopters, suggesting that experience and collaboration help close the skills gap over time.

The Human Foundation of Industrial AI

Industrial AI will continue to advance. Models will improve, and platforms will evolve. But the ability to deploy AI comfortably in production will depend just as much on people as on technology.

Ultimately, realizing the full potential of Industrial AI requires dismantling silos so that IT and OT teams can bring their distinct competencies to a shared table. The goal is not to engineer a rare breed of hybrid super-worker, but to forge truly connected teams. By seamlessly combining digital agility with operational rigor, these unified teams are the true engine for turning AI’s promise into sustained, everyday impact.

Samuel Pasquier, VP, Product Management, Cisco Industrial IoT Networking