For decades, technology has been framed as something we use. Software assisted us. Machines sped up what we could achieve. Automation helped us scale. Even as AI has advanced, its role has largely been analytical: processing vast amounts of data, identifying patterns and generating insights, while humans remained responsible for judgment, verification and final decisions.
That dynamic is not disappearing. What is changing is where intelligence operates. Today, intelligence is moving beyond dashboards and into the physical world, embedded in machines that can sense, interpret and act in real time, under human supervision. These systems do not replace human responsibility; they extend human reach. They reduce cognitive load, increase situational awareness and allow people to focus on higher-value decisions.
This evolution marks the beginning of the silver collar era: a new model of work where humans and intelligent systems operate side by side. Not as operators and tools, but as closely coordinated teammates – combining human judgment, accountability and creativity with machine speed, precision and consistency.
Why this moment is different, and why it’s accelerating
Much of the recent conversation around the future of work has focused on hybrid “purple collar” roles that combine digital tools with operational expertise. This shift brought software and data into physical environments and moved human value toward oversight, judgment and decision-making, while machines handled execution.
That balance does not change in the silver collar era. Humans continue to set intent, define priorities and make final decisions. What is changing is the level of autonomy with which intelligent systems carry out tightly scoped, operational tasks.
Today’s systems can sense conditions and execute predefined actions in real time, verifying whether components meet specification, adjusting processes within tolerance, or responding to routine variation. For example, a machine may determine whether a fastener is the correct size or whether a process parameter needs minor adjustment, while humans remain responsible for defining acceptable limits and deciding when broader intervention is required. These actions translate human intent into consistent, repeatable outcomes at speed and scale.
As a result, productivity is being redefined. It is no longer driven solely by automating individual tasks, but by how effectively people and intelligent systems collaborate. This is the essence of the silver collar workforce. These professionals do not hand over authority to machines; they train them, supervise their behavior, intervene when needed and remain accountable for outcomes in the real world.
When accountability enters the real world
When robots adjust production or AI optimizes critical infrastructure, actions have immediate consequences for safety, cost, uptime and trust. In these environments, performance cannot be judged by outputs alone. Accountability depends on how authority is assigned, how actions are governed and how human intervention is built into everyday operations.
This is where physical AI raises the stakes. Intelligent systems increasingly exercise operational agency, sensing conditions and acting autonomously within defined boundaries. Moral agency, however, remains human. It is people who interpret context and are able to balance competing priorities and take responsibility when outcomes diverge from expectations.
Embedding accountability into these systems therefore requires intentional design of the human role. Authority must be explicit and oversight continuous, with intervention treated as a normal part of operations rather than an exception. Within the silver collar workforce, humans guide how autonomy is applied and remain accountable for its effects in the real world.
Making this work at scale depends on tools that keep human judgment connected to live conditions. Decision frameworks combined with real-time simulation technologies such as digital twins provide a practical foundation. By mirroring physical systems virtually, digital twins allow teams to explore risk, test interventions and refine control strategies before actions are executed, reinforcing accountability where it matters most.
The reality gap, and how to close it
The greatest barrier to scaling physical AI and achieving a true silver collar workforce is the reality gap: the disconnect between how intelligent systems are trained and the conditions they encounter in the real world.
Simulations and synthetic data are powerful tools, but they cannot fully capture the unpredictability and nuance of real environments. Systems that perform flawlessly in virtual settings often struggle when exposed to real-world variability, from sensor noise to unexpected human behavior.
In operational contexts, this gap erodes trust and makes failures harder to predict, diagnose and govern. For intelligent systems to operate alongside humans with increasing autonomy, learning and validation must remain grounded in reality. That requires a continuous link between AI models, physical assets and human oversight. This is where digital twins become foundational rather than optional.
A true digital twin connects live sensor data from the physical world with models and decision logic, creating a shared, evolving reference that reflects current conditions. It allows intelligent systems to adapt based on what is actually happening, while giving humans visibility into performance and risk as it unfolds. Learning can be tested against real constraints, and changes can be explored before they are applied in production.
In this way, digital twins support the kind of collaboration that defines the silver collar workforce. They allow people to remain closely involved as systems learn and adapt, ensuring responsibility stays clear even as autonomy increases. By anchoring intelligence in reality, digital twins help close the gap between capability and trust, making physical AI a dependable part of real-world work.
Preparing for the silver collar era
The silver collar era is not a distant, future scenario. It is already unfolding across factories, infrastructure and industrial environments, where humans work alongside machines that operate with increasing independence. Success depends on keeping intelligence firmly anchored in real-world conditions.
Preparing for this shift demands leadership and human skills that look beyond automation alone. Despite what many might think, as systems become more autonomous, the human role becomes more and more important. Organizations must invest in lifelong learning and reskilling and collaborative innovation, equipping people to understand system limits, oversee intelligent behavior and apply judgment as conditions change.
In an age of intelligent teammates, progress is measured by the quality of collaboration between people and machines. The organizations that lead the silver collar era will be those that recognize autonomy must be matched with responsibility – and that accountability, ultimately, remains human.
By Burkhard Boeckem, CTO of Hexagon