From advanced automation to smart factories

Global manufacturers are moving beyond robotics and task automation toward smart factories that can operate with limited human presence.

In brief

• Repetitive, high-volume work processes are strong candidates for autonomy, but the greatest value comes from end-to-end applications of autonomous systems.
• Preparing brownfield assets for autonomous operations requires significant investment in equipment, connectivity, instrumentation and controls.
• People remain essential in autonomous environments. Organizations must redesign workflows, decision rights and escalation paths to keep humans “in the loop.”

Manufacturers have been pursuing autonomous operations for years, using robotics, digital automation and process redesign to shift human roles from hands-on production to supervision and exception handling. Around the world, there are increasing examples of highly autonomous plants across industries that apply these principles. When executed well, smart factories can improve throughput and quality, enhance safety, and sustain more consistent production. With costs rising, supply chains remaining volatile, and workforce constraints persisting, many organizations view autonomy as a practical path to resilience and long-term competitiveness.

Despite the momentum, most manufacturers still have significant work to do before autonomous operations can be realized at scale. Restrictive physical layouts, gaps in instrumentation, ineffective technology stacks, limiting technology architecture, and inconsistent or low-quality data all pose restraints on what advanced systems and processes can safely run unattended. Increasingly, the differentiator is not just adopting digital tools quickly, but building the infrastructure, operating model and workforce capabilities required for sustained autonomous performance.

What it takes to scale autonomous operations

To move forward and realize smart, autonomous factories at scale, organizations will need to step back and take a different approach to automation. Beyond specific, asset-level or process-level automation and robotics use cases, companies need to think through end-to-end workflows, decision-making processes, supporting technology, data foundations and architecture, and future labor structures and operating models. Incrementally building capabilities over time is no longer a viable path: technology is evolving more quickly than in the past, and competitors are accelerating investments. Speed to action is becoming a critical success factor. The gap between leaders and laggards is widening, meaning a holistic vision and bold investment decisions will be required to achieve autonomous operations at scale.

The data foundation behind smart factories

Whether an organization is building a greenfield site or retrofitting a brownfield operation, a primary challenge in both cases is the data layer. Most manufacturers have developed robust methods for collecting data from assets, but the data is often fragmented and lacks context. Without context, relationships are hard to define, and scaled solutions become unachievable.

A foundational data strategy — and people who understand the latest developments in the manufacturing and operations technology (OT) data landscape — are an important starting point for evolving from traditional automation to a smart, autonomous factory.

Rethinking work management processes

Most work processes in today’s manufacturing environments were originally designed around people. As traditional automation solutions have been implemented, many work management processes were modified incrementally. Moving to an autonomous smart factory will require a different approach: companies will need to review critical processes and reinvent them for unattended operations. This means rethinking steps, approvals and verification, and shifting the point of view from process execution to process and outcome verification.

This rethinking will also be required for supervisory and decision-making processes. Autonomous operations mean not only execution without people directly involved, but also some level of closed-loop decision-making. The deployment of AI agents and decision intelligence solutions will streamline work processes and, in some cases, require full redesign. The addition of physical AI solutions will also push organizations to evaluate existing processes, establish that documentation exists, and then reinvent processes to accommodate new technologies and solutions.

Why OT cybersecurity matters in autonomous operations

As digital density increases on the shop floor, cybersecurity concerns rise exponentially, and protecting the plant from cyberthreats becomes a core operational risk that few manufacturers are currently organized to manage effectively. This will require updates to existing operating models to establish that OT assets, smart physical assets, and robotic solutions operate safely on the right networks, with the right fail-safe mechanisms in place. It will also require tighter collaboration between enterprise cybersecurity teams and site-level groups responsible for the installed base at each plant.

How workforce transformation supports smart factories

Since dark factories can run with limited on-site human intervention, the human role shifts toward oversight. This can reduce frontline labor while creating new positions in robot maintenance, data interpretation and systems oversight.

The role of indirect labor — especially supervisory roles — will shift significantly.

Most people who work at manufacturing sites — managers especially — will need to learn new skills. In the past, even with highly automated operations, engineers could still walk the floor, troubleshoot, and rely on undocumented, experience-based skills to solve problems. In an autonomous operation, engineers, planners and managers will need to learn how to read data instead of physical cues. They must trust instrumentation rather than direct observation and learn how to detect workflow anomalies by interpreting data feeds. They will also need to think through how to manage people and the agents deployed to support operations.

The journey toward autonomous and smart manufacturing is neither simple nor instantaneous. It demands a thoughtful approach to process improvement, cybersecurity, data integration and workforce adaptation. Organizations that succeed will be those that not only invest in advanced technologies but also foster cross-functional collaboration, develop clear data strategies and help their teams evolve alongside the changing operational landscape. Manufacturers that address both the technical and human challenges with equal care will be best positioned to unlock the full potential of the digital transformation required to adopt smart factories at scale and achieve long-term resilience and growth.