The next chapter of advanced manufacturing is no longer about isolated pilots or incremental upgrades. The planned construction of Rockwell Automation’s new Wisconsin plant signals a shift toward fully integrated industrial systems shaped by software, data fluency, and adaptive automation.
The announcement at Automation Fair 2025 in Chicago landed with a clarity that is rare in a market crowded with competing interpretations of industrial innovation. A million-square-foot greenfield factory, built from the ground up, with the explicit purpose of demonstrating how software-defined automation, robotics, and distributed intelligence can operate as a single, orchestrated environment. That ambition shifts the debate beyond the familiar claims about productivity or efficiency. It points to an emerging truth that industrial organisations can no longer avoid. The hard part of transformation is no longer technology adoption. It is system design.
Rockwell Automation has spent the past decade positioning itself at the crossroads of industrial control, enterprise software, analytics, and robotics. Yet the Wisconsin project is a departure from the incremental pattern familiar to most manufacturers. It is an attempt to rethink what a factory should be in an era where data moves faster than materials, and where engineering decisions depend as much on model behaviour as mechanical layout.
Blake Moret, chairman and CEO, Rockwell Automation, frames the moment with the same steady tone heard throughout Automation Fair. “We are building a greenfield plant in southeastern Wisconsin, a million square feet from the ground up, to orchestrate the technology and the capabilities of our people,” he says. “This is the next level of customer service and performance.”
Moret sees this as more than a capital investment. “We are placing a bet that even in a region with relatively high labour costs, we can compete and win with the thoughtful application of technology,” he adds. “The combination of autonomous systems, AI, robotics, and workforce expertise can take us to a new level.”
The rationale behind a new operational model
Senior engineers and transformation leaders will recognise the deeper theme running beneath the announcement. The traditional production environment is shaped by heterogeneous systems. A factory may rely on dozens of PLC families, incompatible control layers, legacy HMI architectures, patches of robotics, and manual workarounds that become permanent. The inefficiency is not simply a technical problem. It is a structural constraint that limits how fast an organisation can adapt.
The Wisconsin site provides an opportunity to redesign the digital core rather than repair it. This is significant. Manufacturers have grown accustomed to layering analytics, AI, or cloud connectivity on top of entrenched architectures. The result has been uneven. Some plants achieve real-time visibility, predictive insights, and adaptive control. Others remain bound by the friction of ageing systems that cannot support modern workloads.
The purpose of a ground-up facility is to eliminate that drag. A coherent systems architecture offers the possibility of synchronised robotics, context-aware control, automated workflows, and a digital thread that covers every layer, from design and commissioning to optimisation and maintenance. It allows for continuous improvement without the burden of compatibility issues.
Moret articulates the shift in philosophy directly. “This requires a different way of thinking. In the past there were many reasons why a decision like this might not have been made, but we are placing a bet. We intend to combine technology and expertise as a learning organisation.” His emphasis on adaptive systems is not rhetorical. It reflects a growing expectation that automation will evolve well beyond static control.
Why software-defined industrial operations matter now
The compelling part of this development is not the scale of the building, but the architecture behind it. Manufacturers are moving toward software-defined control in the same way that data centres migrated from fixed infrastructure to virtualised and programmable environments.
A software-defined plant changes how engineering teams work. Instead of episodic upgrades, systems can be configured, validated, and deployed at speed. Model-based control strategies can be tested virtually before implementation. AI agents can adjust process parameters, optimise energy consumption, and detect anomalies across entire production lines rather than isolated machines.
Industrial firms have been waiting for this convergence for years. Yet fragmentation across the technology stack has slowed adoption. A greenfield site creates a different scenario. The control layer, data layer, visualisation layer, and execution layer can be designed as a single organism. That foundation makes autonomous workflows and predictive intelligence achievable rather than aspirational.
This is why the project has strategic significance. It signals an era in which factories must behave as responsive systems rather than static assets. It demonstrates the expectation that industrial organisations will need architectures capable of ingesting and acting on high-volume data streams without exhausting their human workforce.
A test-bed for the next generation of industrial robotics
Robotics is moving through a similar shift. Traditional automation relied on fixed-position robots programmed for narrow processes. The emerging model centres on mobile robotics, collaborative systems, and autonomous movement that adapts to real-time conditions.
Rockwell Automation has already demonstrated how autonomous mobile robots can reshape production layouts. In Cleveland and Singapore, AMRs have been deployed to move materials, optimise floor space, and eliminate manual handoffs that slow throughput. Those deployments sit upstream of the Wisconsin project, and they underpin the company’s broader theory of the future factory.
A factory designed around dynamic robotics must treat movement as a data problem rather than a mechanical constraint. Paths become optimised on demand. Material flows adjust to production conditions. Human-robot ergonomics can be redesigned to reduce repetitive strain and enable workers to oversee more complex operations.
Executives responsible for digital transformation will recognise how this converges with AI-driven orchestration. When robots become mobile agents within a unified data environment, the plant gains a fluidity that is difficult to achieve with fixed equipment. Tasks such as replenishment, component delivery, and line changeovers can be automated in ways that previously required large teams.
A shift from automation to autonomy
One of the most important themes in Moret’s remarks is the shift from automation to autonomy. The distinction matters. Automation performs tasks. Autonomy interprets context, predicts requirements, and adjusts behaviour without prompting. Achieving autonomy at scale requires coordination across devices, control systems, data repositories, and enterprise platforms. It also requires a workforce that understands how to guide, monitor, and refine autonomous behaviour.
Moret is explicit about the implications. “Systems can be more performant throughout their lifecycle past the first day they are commissioned,” he says. “Learning systems, learning organisations, and technology that enables adaptation will sort out the winners and the losers over the coming years.”
This is a candid acknowledgement of the strategic divide forming across manufacturing. Organisations with coherent data infrastructure, adaptive control, and model-driven processes will outpace those that rely on manual tuning and isolated expertise. The Wisconsin facility becomes the testing ground for what that model looks like when scaled.
Why the workforce remains central to the new factory
A recurring tension in discussions about autonomous systems concerns the role of the workforce. The Wisconsin project does not treat labour as an afterthought. It places workforce capability at the centre of the architecture. Moret addresses this directly. “Change management plays a larger role than many of us recognise. You need your people ready for the journey,” he adds. “The combination of technology and workforce expertise is essential.”
This reflects a broader shift in executive thinking. The move toward autonomous operations does not diminish human judgement. It amplifies its value. Engineers become system designers, analysts, and orchestrators rather than line supervisors. Workforce development becomes an operational requirement, not an HR agenda item.
For senior leaders, the lesson is clear. Building autonomous factories without developing autonomous workers results in brittle operations. The organisations that succeed will be those that treat talent, architecture, and technology as an integrated system.
A blueprint for industrial resilience
Supply chain volatility, energy constraints, and geopolitical tensions have pushed resilience to the top of executive priorities. A factory that can adapt to fluctuating demand, rapid design changes, or supply interruptions holds a strategic advantage. The Wisconsin project is positioned as both an operational investment and a resilience strategy.
A software-defined architecture can reroute production flows when disruptions occur. AI can forecast energy needs, optimise load distribution, or detect process drift before it impacts quality. Digital twins allow teams to stress test scenarios without halting production. Mobile robotics support flexible layouts when supplier variability forces last-minute adjustments.
Executives across manufacturing are increasingly aware that resilience is not a defensive concept. It is an enabler of sustained competitiveness. The Wisconsin plant functions as a physical statement of that principle.
What this means for the wider manufacturing sector
The deeper importance of this project lies in its applicability beyond Rockwell’s own operations. Manufacturers across automotive, food and beverage, consumer goods, pharmaceuticals, and heavy industry face the same systemic challenge: legacy architectures cannot deliver the responsiveness demanded by modern markets.
A factory designed from first principles, with autonomy in mind, provides a blueprint. It demonstrates how data, software, robotics, AI, and human expertise can form a cohesive system rather than a patchwork of adjacent tools. It creates a testing environment where new models of orchestration can be refined before being replicated across other sites.
Moret closes his remarks on that note. “We are combining technology and expertise as we learn along the way. The opportunity to optimise workflows and layouts from the ground up is enormous. We cannot wait to work with you to create the future of industrial operations.”
Senior executives will read the Wisconsin announcement as more than a factory launch. It is a structural commitment to a different form of industrial organisation. The project represents a turning point in how factories will be designed, operated, and continuously improved. It reflects a recognition that manufacturing is shifting from mechanical optimisation to systemic intelligence.
The sector is entering an era where factories are not only automated but adaptive. Those that embrace coherent system architecture, workforce capability, and autonomous technologies will define the competitive landscape of the next decade. The Wisconsin facility is a signal of that future and a reminder that the most significant innovations in manufacturing now lie in the spaces between machines, data, and the people who guide them.
