Autonomous or obsolete – the new industrial imperative
Traditional automated process, by its very nature, meticulously follows instructions. But imagine a system that doesn’t just follow but pursues objectives. The fundamental distinction between automated and autonomy is rapidly becoming the defining factor in who holds the competitive edge in today’s industrial landscape.
The shift is already underway. Deloitte’s “State of AI in the Enterprise” report reveals a compelling trend:
74% of organizations anticipate moderately adopting agentic AI within the next two years.
This isn’t just about automating tasks anymore; it’s about becoming truly autonomous, where agentic AI collaborates with other intelligent agents, making data-driven decisions with unprecedented speed and accuracy – far surpassing human capabilities.
The upcoming competitive divide won’t simply separate companies that embrace AI from those that don’t. Instead, it will draw a clear line between organizations that remain automated and those that step into the autonomous future. True success will demand more than just deploying AI tools; it will necessitate tackling the foundational data challenges and meticulously architecting a robust industrial AI fabric.
This blog will cover the distinct differences between automated and autonomous, why it’s the non-negotiable shift businesses must make, why data is the most important piece of building the autonomous enterprise and what makes Siemens the leader in industrial AI solutions.
The shift from automated to autonomy
Despite rapid advances in data, AI and automation, many organizations remain stuck in environments that weren’t built for the speed and scale today’s business demands. It’s not uncommon for companies to sit on enormous amounts of data spread across product development, engineering, manufacturing, supply chains and operations, with critical business information existing in separate operational platforms.
This results in fractured insight, limited collaboration and stalled AI initiatives that depend on reliable, connected data. That leads to teams spending valuable time reconciling data, duplicating effort and operating in an automated mindset instead of focusing on innovation.
Automated is when a human says, do this, then do that – a sequence of well-defined steps clearly defined by the human and is fully deterministic – that’s automated, but not autonomous
Tobias Malbrecht, Head of Product Management, Data & AI
Agentic AI introduces goal-driven intelligence, shifting the human role from direct instruction to setting objectives within defined guardrails and supervising – the human sets the goal and the AI agent determines the best path to achieve it.
The autonomous enterprise, by design, is more cost-effective, can make better decisions faster and process information at a scale impossible for humans alone.
An AI agent that can work 24/7, process terabytes of data in minutes and identify patterns or root causes – tasks that would take humans days or weeks – translates directly into significant cost savings, increased efficiency and a profound competitive advantage.
Agentic AI drives automation even further because, instead of requiring humans to implement automated prescribing steps, agents can figure out the “how”. They can mix human-like reasoning with executing tasks at scale – ultimately driving autonomy.
“Automated is great,” Malbrecht says. “But autonomous is even better and goes way beyond, as this enables agents to do way more than what humans can do alone – which is true for a single agent, but even more so if you think about scaling to a workforce of agents.”
The biggest obstacle isn’t AI; it’s the data
Organizations typically have data distributed among different data sources – from PLM systems and manufacturing execution systems to operations management and IoT platforms. AI agents are only as effective as the data and context available to them. If that information remains fragmented, AI inherits the same limitations that have traditionally slowed human decision-making.
This is why the AI agent needs context.
By creating a contextual layer across the enterprise, one that connects distributed information and establishes meaningful relationships between data sources, organizations can make data available and understandable to people and intelligent systems. This includes knowledge graphs, which connect entities, relationships and context across the entire data landscape and replaces fragmented silos with a single, trusted foundation, so agents can reason across the entire value chain or organization.
Successful companies will solve the data complexity challenges by creating a connected foundation of enterprise knowledge so their AI systems can operate with true autonomy.
Siemens’ long-standing industrial expertise provides deep understanding of complex industrial environments. Siemens’ solutions are built with the deep domain connectivity and openness that allows customers to operate as they have been without moving data to other systems or foundational agentic enterprise systems. Companies can build within their environment, customized to what they want and how they want to work, while achieving the real value agentic AI.
Building the industrial AI fabric is critical
Success isn’t about isolated AI projects but about building an entire connected ecosystem, or AI fabric, where AI permeates all business processes and drives autonomous decision-making.
Building this out through platforms and technology enables organizations to develop agents, build agentic applications and tackle use cases with AI and machine learning at scale. This creates an agentic workforce that collaborates just as a human organization would, but with agents augmenting and supervising humans.
As an example, imagine an investigation agent communicating with design and engineering agents to pinpoint the root cause of a product issue in the market. Based on the investigation agent’s findings, another agent could automatically implement a change. This propagation of changes and collaboration among agents across the organization, rather than humans, is how each business process becomes supported and connected by AI.
The four layers of an autonomous enterprise
Siemens has identified four foundational layers for achieving autonomy, which together create a coordinated ecosystem of intelligence of agentic decision-making while humans remain essential for defining goals, supervising and providing vision and direction:
1. Context
AI needs access to connected, understandable data. This is where knowledge graphs and contextual data layers become critical. They unify information that often sits across dozens of systems, creating a shared understanding of the business and removing data silos, connecting it all together in a knowledge graph as context to agents for decision making. This groundwork is necessary for future agentic capabilities.
2. Intelligence
Even with agentic AI, organizations still need machine learning and decision intelligence to uncover patterns, predict outcomes and extract insights hidden deep within enterprise data. While frontier models like Claude or ChatGPT provide common sense reasoning, machine learning provides specialized data understanding. This specialized intelligence ensures AI agents make reason-based decisions autonomously with the full context of information.
3. Action
At this layer, AI agents and agentic applications are integrated into business processes so they can move beyond analysis to execution. Agents can investigate issues, collaborate with other agents to identify root causes and recommend corrective actions. This layer brings the vision of the autonomous enterprise to life, allowing for proactive, intelligent operations.
4. Governance
Ensuring responsible and compliant AI-driven decision-making begins with establishing strong governance that defines the ethical boundaries and operational guardrails for autonomous systems.
Will you lead the autonomous enterprise transformation?
The shift to an autonomous enterprise is, as Malbrecht states, “kind of inevitable. If you want to compete, you have to invest in agentic AI and think through how you can leverage the technology for your benefit.”
The shift to an autonomous enterprise requires four critical components:
- Unify your data: Connect distributed data sources across your enterprise with a contextual data layer, which will help you leverage the power of knowledge graphs.
- Embrace objective-driven AI: When you shift to agentic AI, your organization can move beyond simple automated processes to autonomy and better pursue strategic objectives within defined guardrails.
- Leverage specialized intelligence: By integrating machine learning and decision intelligence, you can uncover deep patterns and insights within your proprietary data. This will enhance the reasoning capabilities of your AI agents.
- Empower AI for action: Build agentic applications that can investigate, recommend and execute actions. When you integrate the agents into your business processes, you’ll drive real-world impact.
Discover how Siemens data and AI solutions can help your organization build its autonomous enterprise.
FAQs about the autonomous enterprise
1. What is the fundamental difference between automation and autonomy in an industrial context?
Automated processes meticulously follow pre-defined instructions, essentially doing what a human has explicitly programmed it to do. In contrast, autonomy, driven by agentic AI, pursues objectives. This means the AI agent, given a goal and defined guardrails, determines the best path to achieve that goal, making data-driven decisions with speed and accuracy far beyond human capabilities.
2. Why is data considered the biggest obstacle, not AI itself, in achieving an autonomous enterprise?
While AI is the engine of autonomy, its effectiveness is entirely dependent on the quality and accessibility of data. Many organizations have vast amounts of data spread across disparate systems and this fragmentation limits the ability of AI agents to make informed decisions. To overcome this, a “contextual layer” is needed, often through knowledge graphs, to connect and unify distributed information. Without this foundational data infrastructure, AI agents inherit the same limitations that have traditionally slowed human decision-making.
3. How does Siemens help organizations build an “industrial AI fabric” for an autonomous future?
Siemens’ approach to building an industrial AI fabric focuses on creating a connected ecosystem where AI permeates all business processes, which include four foundational layers: Context, Intelligence, Action and Governance. Siemens’ long-standing industrial expertise and focus on domain connectivity and openness enable companies to build within their existing environments, customizing solutions to their specific needs while achieving the real value of agentic AI.
