Connected, Data-driven, and Human-led Industrial Automation
September 15, 2026
Manufacturers have always looked to automation to improve productivity. Today, however, automation is taking on a much larger role. Persistent workforce shortages, rising production costs, and supply chain uncertainty are pushing manufacturers to do more with the people, equipment, and resources they already have. At the same time, advances in artificial intelligence, industrial IoT devices, machine learning, and robotics are creating new opportunities to improve operations.
The result is a fundamental shift in industrial automation from stand-alone systems to connected operations powered by data.
“Manufacturers are no longer looking at automation simply as a way to automate a task,” said Kevin Zak, Vice President, Industrial Automation Business at Graybar. “They’re asking how technology can help them make better decisions, reduce risk, and create measurable value for the business.”
From Isolated Systems to Connected Operations
For decades, many industrial automation systems operated independently. That is changing quickly. Manufacturers are connecting operational technology on the plant floor with information technology and enterprise systems. Instead of simply controlling an individual process, connected systems can provide a broader view of production, equipment performance, quality, and energy use.
Technology, such as industrial IoT devices, cloud and edge computing, digital twins, and advanced analytics are helping manufacturers capture and use information that was previously unavailable or difficult to access. This connectivity can give manufacturers the visibility to identify problems earlier and make decisions faster. It also creates the digital foundation required for more advanced capabilities, including predictive maintenance, process optimization, and, increasingly, autonomous operations.
“Every decision is only as good as the data behind it,” Zak said. “Manufacturers that can capture, connect, and act on reliable operational data will be in the best position to take advantage of AI and other emerging technologies.”
Addressing the Manufacturing Workforce Challenge
Workforce availability remains one of the most significant challenges facing manufacturers. Companies across the country are struggling to recruit and retain skilled workers while experienced employees retire and take decades of institutional knowledge with them. The challenge is expected to intensify. Deloitte and The Manufacturing Institute estimate that U.S. manufacturers may need 3.8 million new employees between 2024 and 2033, with approximately 1.9 million positions potentially going unfilled.
“The goal of automation shouldn’t be to replace people,” Zak said. “The greater opportunity is to use technology to increase the impact of the people you already have and allow them to focus on work that requires experience, judgment, and problem-solving.”
Asset reliability and management tools are particularly important. When manufacturers understand how their equipment operates, they can identify potential issues before a failure, schedule maintenance more effectively, and possibly extend the useful life of critical assets. This reduces the burden on already-stretched teams while helping the organization avoid costly downtime.
For small and medium manufacturers, this can be particularly useful because they may not have dedicated automation, data science, or information technology teams. They need specialists who can help them identify practical opportunities, select appropriate technologies, and build a realistic path toward smart manufacturing.
Where AI Is Delivering Value
AI is only as good as the quality of the data it analyzes. It delivers the best results when it is built on accurate, relevant, and accessible operational data. If the underlying information is incomplete, disconnected, or unreliable, AI may simply produce a faster version of the wrong answer.
Human experience also remains essential. An AI tool might identify an unusual pattern or recommend an action, but an experienced operator, engineer, or maintenance professional understands the production environment and the practical consequences of that decision.
“The strongest model is human-guided AI,” Zak said. “Technology can analyze the data and identify opportunities, but experienced people still need to apply context, judgment, and an understanding of the production environment.”
“Start With the Problem, Not Technology”
One of the most common mistakes is beginning with a specific technology instead of a clearly defined business problem.
“Start with the problem, not the technology,” Zak said. “Before deciding you need a robot, digital twin, or AI platform, clearly define the outcome you’re trying to achieve. The right technology decision becomes much clearer when it is tied to a real business need.”
Once the objective is clear, Graybar can help identify the process changes needed to achieve it, evaluate available options, and select the right technology to support the organization’s goals.
Manufacturers should also resist the pressure to transform everything at once. Successful organizations often begin with a focused pilot project tied to a measurable outcome. They implement the solution, evaluate the results, learn from the experience and then apply those lessons to the next opportunity.
“Starting small doesn’t mean thinking small,” Zak said. “A focused pilot can demonstrate value and reduce risk, but it should also fit within a larger roadmap so the technology, data, and infrastructure can scale.”
Bridging the Divide Between OT and IT
As industrial systems become more connected, collaboration between operational technology and information technology teams becomes increasingly important.
Traditionally, OT teams focused on operating equipment safely and reliably, while IT teams concentrated on networks, data, enterprise applications, and cybersecurity. Connected manufacturing brings those responsibilities together. A plant floor cannot become truly data-driven without collaboration between the people who understand the production processes and those who manage the infrastructure that connects and protects information.
That convergence can be difficult. OT and IT teams may have different priorities, technologies, and approaches to risk. Manufacturers need specialists who understand both environments and can help align them with shared business outcomes.
“Graybar sits at the intersection of industrial automation, data communications, and infrastructure,” Zak said. “We can bring together the people who understand the plant-floor process with the experience needed to connect and protect the data, helping customers build solutions that are practical today and scalable for the future.”
This support is becoming increasingly important as customers look for companies who can help them navigate complexity, reduce implementation risk, and make the right technology investments.
Graybar continues to expand its industrial automation capabilities across the country. By building robust regional teams and connecting local customer knowledge with national capabilities, Graybar can help manufacturers move from isolated automation efforts to coordinated, scalable strategies.
Preparing for a More Automated Future
Over the next three to five years, manufacturing will become more connected, more data-driven, and increasingly automated. AI will support more operational decisions, machines will provide deeper insight into their condition and performance, and automation systems will become more integrated with the rest of the business.
Manufacturers do not need to achieve that future all at once. But they do need to begin building the foundation for it now. That foundation starts with digital infrastructure and sound operational data. Companies must be able to capture information from their assets, connect it across systems, and turn it into meaningful action. They also need the right people, relationships, and processes to apply technology to operational challenges.
“The manufacturers that thrive won’t necessarily be the ones that adopt every new technology first,” Zak said. “They’ll be the ones that define the right problems, build a reliable data foundation, and scale successful solutions with purpose.”
The future of industrial automation will be connected and intelligent. But its success will continue to depend on human experience, sound strategy, and the ability to turn information into action.
https://www.graybar.com/connected-data-driven-and-human-led-industrial-automation
