For more than a decade, manufacturers have invested heavily in “connected worker” technologies. Mobile devices, wearables, digital work instructions, and augmented reality tools promised to give frontline workers real-time access to the information they need to perform complex tasks safely and efficiently. The vision was compelling: connect people to the same digital infrastructure already transforming engineering and production systems.
At its core, the idea made sense. As Canvas Envision CEO Garth Coleman explains, “the connected worker concept was about closing the gap between enterprise systems and the people doing the work.” Workers on the factory floor were meant to become active participants in the digital ecosystem, able to access instructions, collaborate with experts, and capture operational data directly from the point of execution.
But in many factories, the results have been uneven. Companies have deployed devices and applications, yet the underlying operational challenges often remain unchanged. Experienced technicians are still called to troubleshoot complex issues. Knowledge still resides in a handful of experts. And new employees often struggle to navigate the information they are given.
The problem, Coleman argues, is not connectivity. It is knowledge.
Manufacturers already generate vast amounts of operational data. Sensors, connected machines, and digital platforms produce continuous streams of information about production processes. Yet much of that information never becomes actionable insight. During a World Economic Forum discussion on the state of advanced manufacturing, industry leaders estimated that more than 90% of industrial data is never fully captured or utilized.
This disconnect reveals a deeper issue. Many connected worker initiatives focus on delivering data to devices without addressing the knowledge required to interpret and apply that information in real-world situations.
“The connected worker conversation has been dominated by hardware and connectivity,” Coleman says. Companies tend to ask questions such as which devices to deploy or which platforms to adopt. But those discussions often miss the more fundamental issue: “what knowledge does the worker actually need, and how should it be presented so they can act on it with confidence?”
When that knowledge layer is missing, technology becomes little more than a new interface for old problems. “You can hand someone the most advanced device available, but if the knowledge behind the work is still buried in static documents, disconnected databases, or tribal memory, the worker is still left interpreting information rather than executing with clarity.” he adds.
This challenge becomes even more significant as manufacturing grows more complex. The Fourth Industrial Revolution is introducing advanced analytics, artificial intelligence, and increasingly automated production systems that require workers to interact with technology in new ways. According to the World Economic Forum, these digital capabilities are reshaping how factories operate and how value is created across industrial supply chains.
At the same time, the nature of industrial work itself is evolving. One panelist of the already mentioned discussion, described the transition underway in manufacturing as a shift from “labor with our hands to labor with our minds.” In other words, workers are increasingly expected to interpret data, diagnose issues, and adapt processes in real time.
Yet most connected worker programs still treat implementation primarily as a technology rollout. “The first mistake is leading with technology instead of knowledge,” Coleman says. Organizations often select a platform or device first and then try to determine what information to deliver through it. “The starting point should be understanding what knowledge the workforce needs and how it should be structured for the specific tasks they perform.”
This requires rethinking how operational expertise is captured and distributed across the organization. In many factories, critical knowledge exists in the experience of technicians and engineers who understand how systems behave under real conditions. When that expertise is not structured and shared, companies remain dependent on individuals rather than on scalable systems.
Coleman believes the industry needs to move beyond the traditional connected worker framework toward a broader concept he calls connected knowledge. “A connected worker strategy asks how to get data to a person. A connected knowledge strategy asks how to capture, structure, and deliver actionable knowledge across the entire workforce.”
The distinction may sound subtle, but it has major implications for digital transformation. Instead of focusing solely on devices and interfaces, manufacturers must invest in systems that connect engineering data, operational workflows, and frontline experience. Workers should not only receive instructions; they should also be able to capture feedback, insights, and performance data that flow back into engineering and product development.
“Smart factories need connected knowledge, not just connected machines,” Coleman says.
The technology to support that shift already exists. The real challenge is strategic: deciding whether digital transformation will focus on deploying tools—or on building the knowledge systems that allow people to use them effectively.