Navigating Industrial Innovation: AI, Automation, and the Future of IT-OT Integration -- Belden
By Lane F. Cooper, Editorial Director, BizTechReports
As industries navigate rapid technological and economic shifts, business leaders are reassessing how they integrate emerging technologies with traditional operations to thrive in the "innovation economy." In a vidcast interview with BizTechReports, Belden CEO Ashish Chand put this conundrum into context by: outlining the key developments shaping industrial automation; describing the convergence of IT and operational technology (OT) in industrial settings; and explaining the evolving role of artificial intelligence (AI) in advancing innovative business strategies.
Lessons Learned Moving Past Pandemic Period
"The post-pandemic period reshaped how companies operate," Chand said. "In addition to enabling the rise of the hybrid office worker, the industrial sector saw new ways to execute remote commissioning of major assets that used to be expensive, time consuming and human resource intensive. For a growing number of operations, this has reduced the need for large teams on-site, fundamentally changing how industrial assets are managed."
Traditionally, industries such as oil and gas, utilities, and manufacturing required dozens of engineers and technicians to be physically present for asset commissioning, maintenance, and troubleshooting. The pandemic forced companies to rethink this approach, leveraging advanced sensors, remote monitoring, and automation to reduce the need for on-site personnel. This shift has increased efficiency and improved worker safety by minimizing exposure to hazardous environments.
Financial constraints are also pushing businesses to rethink their approach to innovation. "When capital was cheap, companies could afford to experiment without clear value propositions. That changed in 2024—innovation had to demonstrate real returns, whether in efficiency, safety, or sustainability," he stated.
Beyond this, he explained, the industrial sector has moved beyond the point where abundant venture capital allows businesses to prioritize rapid expansion over financial viability by "blitzscaling" and focusing on market dominance before profitability.
"The rising cost of capital has forced businesses to prioritize practical, results-driven innovation. Every technological investment must now justify its impact, whether in cost savings, revenue growth, or operational resilience," he said.
While AI represents a driving factor at the forefront of these changes, Chand cautioned against viewing it as a silver bullet. "There's a distinction between precision AI—where there's one correct outcome, like in medical dosing—and generative AI, which can produce multiple possible solutions," he said.
Both applications of AI have industrial applications, but effective adoptio, but integrating IT and OT systems has proven so challenging that actual progress toward integration has been put off.
According to Chand, this convergence is no longer optional—it is increasingly essential for competitiveness today.
"Think of autonomy as the outcome of digital transformation," he explained.
n of either requires complete mastery of the technologies' implications and careful planning before execution.
Precision AI, he observed, has already been widely used in industrial settings for years, if not decades. Predictive maintenance systems, for instance, have long relied on AI to analyze sensor data and predict equipment failures before they occur. It has allowed companies to optimize maintenance schedules, reduce downtime, and extend the lifespan of critical assets.
By contrast, generative AI, which can create simulations, optimize workflows, and assist in design processes, is still in the early stages of industrial adoption. While it has potential, businesses must develop clear use cases to avoid investing in technology without a defined return on investment.
The Road to Autonomy: IT-OT Integration as a Strategic Imperative
Indeed, as leaders focus on meaningful innovation, Chand believes most would be better off obsessing less on AI and spending more on advancing their autonomous objectives. He stated that Industrial digitization has been a long-standing goal
"In an autonomous system, data from multiple sources—whether in a factory or a supply chain—must be processed in real-time to make operational decisions. But to get there, organizations must progress through several stages: digitization, monitoring, diagnostics, control, and finally, autonomy."
Skipping any of these steps, he warned, can lead to ineffective implementations. "One of the challenges with AI adoption is that companies try to jump straight to autonomous systems without first establishing a solid digital foundation," Chand said.
Many industrial businesses still rely on legacy systems that lack connectivity and real-time data analysis capabilities. It is the path to perdition. Instead, before achieving full autonomy, companies should first digitize operations—collecting data from industrial sensors and machinery, implementing centralized monitoring systems, and developing diagnostic tools to interpret data patterns. Only after these foundational steps are taken can businesses implement automated control systems and eventually progress to AI-driven autonomy.
The transition to autonomy is not just about technology—it also requires cultural and operational changes. Employees—from the executive suite through mid-management to the rank-and-file—should be trained to work alongside automated systems, and businesses must establish protocols for integrating AI-driven decision-making with human oversight.
A Competitive Necessity, Not a Luxury
Beyond technology, global shifts and economic shifts are accelerating the need for industrial modernization.
A combination of factors—including supply chain disruptions, rising labor costs, and geopolitical uncertainties—is driving the shift toward regionalized production. Companies can no longer rely on complex, globally distributed supply chains vulnerable to external shocks. Instead, they must invest in digital technologies that enhance local manufacturing capabilities and improve supply chain resilience.
"Manufacturers are under pressure, in a climate of de-globalization, to build closer to points of consumption while also maintaining supply chain flexibility," Chand said. "IT-OT integration plays a critical role in achieving agility. As a result, establishing this competency is rapidly emerging as a competitive necessity rather than a sustained source of strategic advantage."
Executing this shift requires balancing IT's rapid development cycles with OT's focus on stability and reliability. It is an ambition that is easier said than done because IT and OT teams have historically operated in silos, often using different technologies and standards. IT teams have focused on data management, cybersecurity, and enterprise software, while OT teams prioritize equipment reliability and operational efficiency. As businesses integrate these functions, they must navigate priorities, coordinate risk tolerance, and resolve technical expertise differences.
"We're seeing more industries recognizing the need for this balance, particularly in sectors like power transmission, mass transit, and process automation. Others, like discrete goods manufacturing, are beginning to follow suit," he observed.
Adapting Leadership Structures for Digital Transformation
Creating an innovation-driven culture, he added, requires investment in both technology and a shift in how companies approach decision-making. Instead of focusing solely on cost-cutting, businesses must prioritize strategic investments in research, talent development, and long-term technological advancements. The central ingredient for sustained success, however, is focus.
"A common pitfall in the innovation race is spreading investment too thin across multiple projects. This results in underfunded initiatives that fail to deliver impact. Instead, the companies that I see consistently succeeding in the innovation game focus on fewer, high-impact projects and allocate resources accordingly," Chand advised.
Looking ahead, Chand expects digital transformation efforts to accelerate, but he cautioned against an overly aggressive approach.
"AI and automation will continue to play a growing role in industrial operations, but successful implementation requires a structured approach," he said. "Companies need to focus on foundational elements first—digitization, data management, and interoperability—before layering in more advanced technologies."
As organizations prepare for the next decade of industrial automation, the most successful will be those that adopt a pragmatic, step-by-step approach to digital transformation. In this context, businesses that prioritize IT-OT integration, invest in data-driven decision-making and foster a culture of adaptability will be best positioned for long-term success.
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