The long-term vision for industrial operations is compelling: equipment that monitors itself, diagnoses its own failures, and initiates corrective action — while humans stay in the loop for judgment and governance, not routine execution. Zero-touch industries: A dream most manufacturers share and few are close to realizing.
Agentic AI is what makes that dream achievable. Not immediately, and not by deploying a single tool — but by making it possible, for the first time, to build a loop where operational experience continuously improves the entire system.
In this article, you’ll learn:
- About the two ways that agentic AI creates value for industrial operations
- How these two ways reinforce each other in a loop of continuous improvement
- Why that loop is the path that leads — over time — to zero-touch industrial operations
What Does “Zero-Touch” Actually Mean?
Industrial manufacturers have been automating processes for decades — and at the lower layers of operations, they have been remarkably successful. PLCs control machines without intervention. SCADA systems monitor production lines continuously. Conveyors, assembly cells, and CNC machines run unattended around the clock. The physical processes at the base of operations are, for many manufacturers, already largely autonomous.
The automation pyramid describes this reality. From bottom to top:
- Field devices, sensors, actuators — deterministic, local, fully automated
- Control and supervisory systems — highly automated, rule-based
- Manufacturing operations, maintenance, quality decisions — partially automated, increasingly manual
- Business planning, supply chain, strategic decisions — largely human-driven
The further up the pyramid, the less automated the processes become — not because the engineering ambition is lacking, but because the nature of the problems changes fundamentally.
Lower-level processes are generic and context-independent. A PLC deciding whether to open a valve doesn’t need to consult the ERP.
Higher-level processes are the opposite: maintenance scheduling requires production history, spare parts availability, technician capacity, and service contract terms — simultaneously. Automating these processes means reasoning across many heterogeneous systems at once, and that complexity made viable automation at the top of the pyramid prohibitively very hard to build and maintain.
The bottom of the automation pyramid is already largely automated. Zero-touch industries is the vision of taking that all the way to the top.
How Does Agentic AI Enable Zero-Touch Industries?
Agentic AI is the first technology that can pull context from multiple systems, reason across them, and suggest or even directly tigger action. Beyond that, it dramatically lowers the cost of building automation itself — making it viable, for the first time, to automate processes that were previously too complex or niche to justify the effort. These are two distinct ways of pushing automation higher up the pyramid.
In operations, when a new issue occurs, agentic AI helps teams respond faster and with less manual effort — detecting anomalies, diagnosing causes, and progressively taking over more of the response.
However, you cannot just ask AI to constantly monitor every incoming data points. There are two important constraints that shape how far this can go:
- Economic: every agent invocation results in significant cost; value only materializes when the benefit — typically human time saved — exceeds it.
- Trust: AI decisions are not deterministic, and wrong actuation in industrial environments can be catastrophic. Autonomy must be earned incrementally, with governance in place.
Why Is Design Time the Larger Near-Term Opportunity?
What gets far less attention — and is, in many ways, the larger near-term opportunity — is using agentic AI at design time. It is already proven today, and it is what makes increasing the level of automation over time actually achievable.
At design time, agentic AI compresses the cost of building and improving operational solutions — connecting data sources, generating analytics logic, writing integration code. What previously required months of implementation work is becoming achievable in hours.
How do the two join into a compounding loop?
The real magic happens where the two ways of using agentic AI stop being parallel tracks and become a single compounding system. A problem surfaces in the field; an operator resolves it; and then the system itself is improved so the same issue will not recur. Without AI, that last step was often not commercially viable.
When building becomes cheap, closing the loop will become the routine. This leads to operations in which every incident makes the system more capable — and that compounding is the architecture that leads, over time, to zero-touch operations.
What Does This Look Like in Practice?
One of Cumulocity’s customers operates a fleet of over 30,000 wind turbines across multiple geographies. A single turbine offline costs €8,000 per day. Here is how the loop works in practice.
Which foundation is required to get started?
Before any automation was possible, the groundwork had to be laid: Every turbine securely onboarded with remote device management in place and streaming live data. And, equally important a semantically enriched data model, giving agents the context to reason across turbine history, maintenance records, and operational patterns.
That data foundation is the precondition for everything that follows.
How Do You Start? Automate the Trivial Decisions First.
With the turbine data accessible in real-time, the team started with the most frequent recurring workflow: turbines that stop automatically when certain fault conditions are detected for safety reasons.
The traditional process: an operator receives an alarm, reviews the turbine state, determines there is no ongoing fault or damage, and manually triggers a restart. Every turbine waiting in a service queue is a turbine not generating money.
In most cases, the decision is straight-forward — a handful of conditions confirm a restart is safe. The team encoded this as an Analytics Builder rule using Cumulocity: when those conditions are met, the turbine restarts automatically, without human intervention.
The result: around $3.7M in additional revenue and cost savings from autonomous resets.
How Does Agentic AI Handle the Complex Cases?
For the harder cases, that cannot yet be automated, agentic AI makes the operator’s life dramatically easier. Instead of investigating — pulling maintenance history, cross-checking manuals, comparing against similar incidents across the fleet — they receive a briefing with a recommended action already prepared.
How Does the Loop Close?
The loop closes at design time. Development and operations teams review the incident data together: which recommendations are operators approving without modification? Which fault types are now well understood? These conversations become the next automation rules — and with every cycle, more is handled autonomously.
That is zero-touch operations in action — not a switch that gets flipped, but a loop that compounds over time.
If you are exploring how to build this loop in your operations — where to start, what the foundation needs to look like, or how to identify the first workflows worth automating — we’d love to talk.