Q&A: Cisco IoT Networking VP Samuel Pasquier Says as Industrial AI Moves to Production, Network Readiness, IT/OT Alignment and Security Are Key to Scalability

Industrial organizations are moving deploying AI in live operational environments with a priority on security and network readiness. Cisco’s recently released State of Industrial AI Report explores how critical infrastructure is accelerated AI deployments.  

Tech-Channels went through the new research with Samuel Pasquier, Vice President, Product Management, Cisco Industrial IoT Networking. He explains that industrial AI has moved well past experimentation to how effectively organizations can scale and harness the expected returns. This is where having AI-ready network infrastructure plays an important role.

Q. There’s a wide agreement that AI spending will increase and nearly nine in 10 expect returns. Does that surprise you? Why or why not? What are the investment priorities?

A. It doesn’t surprise us at all. What stood out in the research is how pragmatic organizations are being about industrial AI. This isn’t speculative spending, it’s highly outcome‑driven. Nearly 90% expect returns, and 87% expect to see those outcomes within two years, which tells us leaders see AI as an operational lever, not a long‑term science project.  The investment priorities reflect that mindset. Early spending is focused on efficiency and reliability, things like process automation, cybersecurity, and supply chain optimization. As maturity increases, we see a shift toward enabling technologies, including edge compute, bandwidth, wireless mobility, and industrial‑grade connectivity.

Q. Where are organizations largely today on the AI adoption maturity scale? Is there a need to speed up adoption and implementation?

A. Most industrial organizations have moved beyond experimentation. The report shows that 61% are actively deploying industrial AI, and 20% are already doing so at scale, across multiple sites. That’s a significant shift from pilots to production. That said, only about a quarter of leaders say AI is truly transformative today. So, the issue isn’t whether industrial organizations are adopting AI, it’s how effectively they can scale and harness the expected returns. This is where having AI-ready network infrastructure comes into play. Speed matters, but network readiness matters more. Organizations that rush AI adoption without modern networking infrastructure, with cybersecurity readiness built-in, will likely have projects that stall. In fact, network readiness, such as throughput, connectivity, and cybersecurity, were identified as the leading factors that will constrain industrial AI adoption. The real opportunity is accelerating scalable adoption, moving from isolated wins to repeatable, enterprise‑wide impact.

Q. With infrastructure perceived as limiting, what do organizations need to do to modernize? Can you speak to connectivity, reliability requirements, and the importance of wireless networks? Why are they important to enabling AI?

A. AI fundamentally changes what industrial networks are expected to do. In the report, 97% of respondents say AI workloads will impact their network requirements, and 51% expect major increases in connectivity and reliability demands. It’s critical to move quickly to avoid these bottlenecks.  Reliable connectivity is the foundation for industrial AI. AI depends on consistent data flows from sensors, cameras, machines, and systems, many of which are mobile. That’s why 96% of decision‑makers say reliable wireless networks are critical to enabling AI, and why wireless instability shows up so clearly as a barrier when networks are siloed. Modernization means building networks that deliver predictable latency, sufficient bandwidth, network segmentation, and edge compute – across both wired and wireless environments. Without that foundation, AI can’t move from localized use cases into real‑time, mission‑critical operations.

Q. Please speak to the duality of AI and security — both a barrier and an asset. How do organizations leverage it while at the same time tamping down on risk?

A. This duality is one of the clearest messages in the report. Concerns over cybersecurity is the number one barrier to AI adoption, cited by 40% of respondents, yet 85% expect AI to improve their cybersecurity posture. What’s happening is that AI increases connectivity and visibility across industrial environments, which naturally expands the attack surface. That creates understandable concern. At the same time, AI is uniquely suited to help manage the scale and complexity of industrial environments, detecting anomalies, monitoring behavior, and responding faster than humans ever could. The industrial organizations making progress on AI treat cybersecurity as a foundational requirement, not an afterthought. They invest in secure‑by‑design architectures, segmentation, and visibility across IT and OT. When those foundations are in place, AI becomes a force multiplier for resilience rather than a source of unmanaged risk.

Q. Why is collaboration between IT and OT so important?

A. IT/OT collaboration is critical because AI doesn’t adhere to organizational boundaries, it runs across them. The report shows that 43% of industrial organizations still operate with limited or no IT/OT collaboration, and those organizations consistently report lower confidence, slower deployments, and more wireless instability.  In contrast, industrial organizations with collaborative IT/OT models have greater confidence in scaling AI, stronger cybersecurity alignment, and more reliable operations. AI brings IT and OT together by necessity: data, networks, security, and compute all converge at the edge. Without shared ownership, AI initiatives become fragmented and fragile. Simply put, AI scale is as much an organizational challenge as a technical one, and collaboration is the difference between isolated pilots and sustainable impact.

Q. What surprised you most about the findings?

A. What really stood out to us is how persistent the IT/OT role divide remains. Despite broad agreement that collaboration is critical to scaling AI, 43% of industrial organizations still operate with little to no IT/OT collaboration, and that figure hasn’t meaningfully changed from previous years. The data shows that industrial organizations with limited collaboration between these teams have lower confidence in scaling AI, greater wireless instability, and slower deployment timelines. In contrast, more collaborative organizations report stronger network reliability, better cybersecurity alignment, and greater confidence in moving AI from pilots into production. What’s striking is that the technology is moving faster than the operating model. AI is inherently cross‑domain, as it depends on shared networks, shared data, and shared security. Until organizational structures catch up or organizations find ways to close the collaboration and skills gap, that lack of alignment will risk being a major constraint on AI scale and reliability in industrial environments.

Q. What steps do organizations need to take to leverage AI and better position themselves for growth and success? What’s the risk if they don’t?

A. The report points to three clear priorities. First, foundational network readiness. AI can’t scale without modern networks, edge compute, and reliable connectivity, especially wireless. Second, investing in cybersecurity readiness is critical to AI adoption. Secure‑by‑design architectures and visibility across IT and OT are essential for confidence and scale. Third, developing mechanisms to address IT/OT convergence. Organizations need to explore new ways of bridging the skills divide and closing the IT and OT gap. Investing in personnel, creating shared governance initiatives, with shared accountability are essential. The risk of not taking these steps is that industrial AI remains stuck in isolated use cases and pilot projects while competitors move ahead and harness real results. Organizations that don’t invest in network readiness, risk higher operational complexity, greater cyber risk exposure, and missed opportunities to turn AI into a real competitive advantage.

Industrial organizations are deploying AI with clear expectations for near-term returns. However, as Cisco’s research highlights, the real challenge lies in scaling those deployments, which depends heavily on modern, secure, and reliable network infrastructure. Without strong connectivity, cybersecurity, and IT/OT alignment, AI initiatives risk stalling before delivering meaningful impact. Those that invest in these foundations will be best positioned to turn AI into a sustained competitive advantage.