Designing Autonomous Enterprises for Industry 5.0

Across industries, organizations are at an inflection point. For more than a century, they have been built on two primary assets: labor and capital. Now, a third has emerged—data intelligence. Real-time data flows, AI agents and the embedded knowledge they generate are rapidly becoming the foundations of autonomous activities that create value. This new asset class is forcing leaders to rethink not just how their companies operate, but how they are organized, led and governed.

Autonomy is not automation. While automation executes predefined tasks, autonomy (enabled by AI) perceives context, analyzes situations, makes decisions and acts—without human intervention. Autonomy reorganizes decision-making itself, shifting away from human bottlenecks and toward intelligent systems that operate at speed and scale.

By 2030, our research indicates nearly all operational activities will be either AI-augmented with human oversight or fully autonomous. Already, 63 per cent of companies have integrated autonomy into their strategic plans, and 25 per cent are actively implementing it.

In companies scaling autonomy, we’re seeing results that reach every corner of operations.  Industrial organizations, for example, can now understand and address disruptions in four days, down from 11. Production lines reconfigure themselves to keep work flowing, cutting lead times by almost a third. Orders arrive on schedule 97 per cent of the time, and assets that once sat idle are now running at more than 80 per cent capacity—all because AI systems can sense change and adjust in real time.

To unlock a future with these and other gains, companies must re-architect themselves around three key shifts: (1) adopting platform-centric structures, (2) embedding human-AI collaboration and (3) modernizing governance for a world where intelligent systems are independent actors.

This is not about tweaking processes. It is about reimagining the very shape of the enterprise for an age where data stands alongside labor and capital as a defining source of advantage.

Transformation drivers

Autonomy is the capability that will allow companies to thrive in the new social, industrial and geopolitical world of today. There are three main reasons for this.

First, disruption has become relentless. We’ve entered an era of persistent volatility defined by compounding shocks: geopolitical instability, climate extremes, supply chain fragility, abrupt policy swings and erratic demand. Since the COVID-19 pandemic, slow or rigid responses have cost industrial companies an average of 8 per cent in sales and 3.5 per cent in earnings. This dramatic downswing reinforces how important it is for companies to be able to reconfigure, replan and adapt to any change quickly in ways that legacy organizations with siloed functions cannot. Autonomy precisely fits with this new context, offering, for instance, materially faster recovery from disruptions, with some organizations reducing recovery times by 65 per cent.

Second, real time agility will become the norm as complexity outpaces human capacity. AI-driven systems can evaluate hundreds of real-time tradeoffs—balancing cost, resilience, sustainability and regulatory constraints—and deliver forward-looking decisions far beyond the reach of even the most experienced human teams.

Traditional performance levers like Lean, Six Sigma, process reengineering and even conventional automation are reaching their limits. They can’t provide the expected next performance frontier because humans are in the loop on every small decision, preventing sufficient scale and speed to face today’s challenges. At the same time, an aging workforce is taking critical know-how with it, forcing companies to rediscover solutions they once mastered.

“Just as steam, electricity, computing and data analytics defined earlier industrial revolutions, autonomy is defining Industry 5.0, where intelligence and decision-making are delegated to systems.”

Finally, autonomy itself is no longer a distant concept. From robots and workflows to entire organizational processes, autonomous capabilities are already taking hold in ways that reduce entry barriers and open the door to increasingly more start-ups. As autonomous natives, these new players pose significant competitive threat to legacy organizations.

Just as steam, electricity, computing and data analytics defined earlier industrial revolutions, autonomy is defining Industry 5.0, where intelligence and decision-making are delegated to systems. In previous revolutions, enterprise structures largely remained the same because humans stayed at the center of every decision. Industry 5.0, by contrast, will fundamentally reshape how organizations are designed and run.

Harnessing the full potential of the autonomous enterprise calls for new approaches in creating value, structuring work and governing intelligent systems, as we detail below.

Platform-centric operating models: Design for outcomes

Unlike labor or capital, data intelligence scales without fatigue and is not confined by hierarchy or function. Yet most companies remain constrained by structures designed for another era. Decades of acquisitions have left them fragmented by functions and business units, and their data infrastructure was built for record-keeping rather than decision-making. Cultural habits reinforce human-centric control, even when intelligent systems could act faster and more effectively.

Because legacy structures can no longer keep pace with scalable intelligence, organizations must increasingly organize around outcome-based platforms or cells. Each platform serves a specific purpose or result (like product line, materials sourcing or logistics delivery) and operates with its own data-AI architecture and a lean team of multidisciplinary experts. A product platform might manage the full product lifecycle—from initial design to after-sales service and end-of-life. A delivery platform could coordinate everything from N-tier suppliers to the final customer. A risk platform might anticipate and mitigate operational exposures, while a materials platform could manage the full journey of raw materials, including circular sourcing and recycling.

These platforms are designed to operate mostly autonomously, with minimal hierarchy and decision latency. Some activities, such as shared administrative services, already function in this way. The shift now is to extend this logic to core business operations, using data intelligence as the connective tissue between people, processes and systems.

Renault Group, for example, has built a real-time digital environment that links products, machines, tools, logistics, suppliers and sensors into a single and unified end-to-end operations platform. The next step in its evolution is to deploy AI agents across the platform to enhance numerous autonomous processes. This will allow the company to reconfigure operations quickly when needed and to immediately mitigate any disruption in the network. The physical asset—the factory—will become software defined, and therefore more resilient.

Human-AI collaboration: Return to industrial craftsmanship

In autonomous enterprises, every employee—planners, buyers, engineers—will have an AI agent at their side. Accenture, for example, is already helping several of its clients implement a touchless supply chain where AI agents constantly monitor for demand-supply imbalances and autonomously adjust planning, fulfillment and procurement. If the AI agents detect a potential surplus or shortage, they analyze scenarios, develop responses and increasingly act on their own to avoid any rupture or overconsumption of parts and materials. Instead of doing the work, humans supervise the system and are available to react if they detect abnormal behavior.

The human role must shift from executor to strategic steward—someone who ensures the system stays aligned with purpose, ethics and culture. This isn’t a reduction in human work; it’s a return to industrial craftsmanship in the sense that humans, like craftsmen, can see the full impact of their efforts, from raw material to value to a client, and quality increases as a result. It’s the end of fragmentation of tasks and hierarchical control of Taylorism that broke work into narrowly defined repetitive tasks. With this shift, humans use their entire skill set including empathy, problem solving and creativity; not just their two hands or legs.

As result, even though our research shows companies can achieve as much as a 30 per cent increase in labor productivity in autonomous enterprises, it’s unlikely they will reduce their workforce accordingly. Instead, leaders are preparing to reskill employees for higher-value roles that emerge from this human-technology collaboration. In short, AI will not eat the pie of human work, it will expand it.

Corporate shape and governance: Build and manage what matters

The rise of platform-based operating models demands a new corporate shape. In today’s structure, executive committees of corporate or business units are typically composed of heads of Finance, HR, Operations, or Sales, breaking down companies by functions with their own resources. Over time, these structures have grown increasingly complex—many companies now operate with double or even triple matrixed organizations. This complexity stifles agility and impedes the ability to respond to fast-changing customer needs.

Platform models flip this logic such that teams, data and technology align around value creation, not bureaucracy. This shift requires companies to rethink the role of the corporate center—not as a controller of every piece of the company, but as an orchestrator of intelligent delivery platforms.

Some companies are already showing the way with different corporate shapes. Amazon, a “delivery monster,” operates decentralized, localized delivery platforms governed by a central supply chain team largely composed of data scientists responsible for analyzing deviations. Netflix follows a “freedom within a framework” model, where corporate sets standardized core principles but empowers local platforms to innovate. Meanwhile, “industrial innovators”, use a dual-platform approach—one platform focuses on innovation, while the other ensures global go-to-market delivery compliant with FAA regulations. These organizational forms, tailored to the specific needs of each company, enable a laser focus on customer outcomes at scale.

As agentic AI will increasingly power platforms, corporate governance must evolve in parallel. In addition to managing human talent, the corporate center will oversee a new workforce: AI agents. Organizations will need experienced experts in intelligent systems to create, manage and retire AI agents as needed and to conduct audits for bias, hallucination, cybersecurity and compliance with ethical AI standards.

The organizations that get this right are already gaining competitive advantage. They’re building structures that are simpler, smarter and engineered to scale responsible autonomy and sustained value creation.

A different endgame

This is not a trend to watch for; it’s a reality unfolding now. One that calls for an entirely new mindset.

Industry has evolved for centuries by improving how humans and machines work together. But today, we are entering a fundamentally new paradigm—one in which data intelligence joins labor and capital as a core asset class and platform models give rise to multiple structural formats better tailored to each business.

If leaders apply yesterday’s logic to today’s transformation, they will get it wrong. The end goal is no longer incremental improvement—it is structural reinvention. AI-enabled autonomy is here, marking a Darwinian moment for industry. The future belongs to those who can transform faster, smarter and more autonomously than the rest. Those able to scale AI will enter a virtuous cycle of innovation and leadership. Those who cannot risk irrelevance—or extinction.