The evolution of process automation brings autonomous operations & enterprises within reach

On the surface it seems the automation of business processes is progressing fast. Celonis’ annual Process Optimization Report reveals almost three-quarters (72 percent) of business processes have already been at least partially automated, as part of a quest to improve efficiency, productivity and quality.

Dig deeper, however, and it’s clear most enterprises are still a long way from end-to-end automation or even fully autonomous processes. What I mean by that is there are no organizations where core, end-to-end business processes can already self-optimize against key metrics to maximize their performance without some degree of human intervention. At least I haven’t seen any while working in this space for the last 25 years. That’s because most businesses are still in the early stages of real process automation, relying on relatively basic or disjointed automations of tasks to streamline their workflows.

The evolution of process automation can be thought about as three key stages.

Phase one: Robotic process automation (RPA)

RPA is the simplest form of automation. Contrary to what the name suggests, it doesn’t actually automate entire end-to-end business processes like order-to-cash or purchase-to-pay but discrete tasks or actions within those processes.

RPA bots are used to automate repetitive, rules-based tasks, using consistently structured data. Generating routine reports, matching purchase orders to goods receipts or automatically updating stock levels are all good candidates for RPA. They can increase efficiency, reduce human error and free up the workforce for activities that add more value.

However, RPA bots can’t think for themselves, they can only do what they’re told. They can’t interpret information outside of their specific automation parameters or react to changes in their process ecosystem unless instructed. For these more advanced capabilities you’ll need to move to phase two.

Phase two: AI-powered automation

The application of artificial intelligence (AI) brings judgment and learning capabilities to workflow automation. While AI has been used in a variety of ways to make automation more intelligent, the recent rise of AI agents is taking process automation to another level. These agents can be defined as autonomous systems that perceive dynamic environments, process information and take actions to achieve specific goals. Gartner predicts that by 2028 a third of enterprise software applications will include agentic AI, enabling 15 percent of day-to-day work decisions to be made autonomously.

As AI agents increasingly become owners and managers of more complex operations there will inevitably be a transfer of decision-making from humans to machines. For example, an agent that identifies inconsistencies between contract payment terms and purchase orders may start by recommending remedial actions to an accounts payable clerk, but then move onto triggering those actions itself. Equally, an agent that monitors inventory across multiple production plants may start by suggesting how spare parts could be reallocated to most efficiently meet orders, but then progress to arranging stock transfers autonomously.

The advent of agentic AI doesn’t mean every RPA bot should be replaced by an AI agent. Far from it. While agents are suited to more complex processes, where decision-making and real-time learning is required in dynamic scenarios, RPA bots are still useful for taking rules-based action. AI agents can trigger RPA bots to implement their recommendations and, in return, RPA bots can be used to make AI agents more effective, perhaps by gathering and normalizing training data to fuel them or monitoring their performance to flag errors or issues.

While AI agents are a significant step forward for enterprise automation, it’s still not possible to hand an entire process over to an agent to optimize. That’s because not everything can be automated. There will always be exceptions, edge cases and situations where human judgment is required. No matter how advanced AI becomes, it’s still artificial. It lacks full context, empathy, and the ability to weigh trade-offs in ambiguous situations the way humans can.

To automate full end-to-end processes you’ll need process orchestration, because it isn’t just about automating tasks – it’s about making sure people, systems and AI can work together. Seamlessly, intelligently and with the right balance of autonomy and control.

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Phase three: Process orchestration

Process orchestration technology acts as an intelligent management layer with an overview of systems, processes and automations and the ability to coordinate it all. It acts as a guiding hand for complex, end-to-end enterprise workflows that rely on a team of AI agents, RPA bots, other system automations and often humans in the loop.

Effective process orchestration requires real-time process visibility across all systems, combined with business context. This context may include, among other things, the structure of the organization, codified rules, policies and procedures and the mechanisms for calculating KPIs. In short, process orchestration requires process intelligence, ideally gained from a digital twin of business processes.

Informed by process intelligence, process orchestration can direct AI agents and RPA bots to detect anomalies and opportunities, adjust processes in real-time to resolve those issues or seize those opportunities and maximize performance against key metrics. Orchestration technology can also monitor the results that are achieved, enabling continuous process improvement. As the advent of AI boosts organizations’ automation ambitions, process orchestration powered by process intelligence becomes an essential part of the puzzle.

Autonomous operations and enterprises are coming

The evolution of business process automation is accelerating, moving beyond simple task-based automations like RPA towards more sophisticated, intelligent systems. As discussed in my previous article, the next great leap is the age of autonomous operations. To get there, organizations must progress beyond RPA and AI-powered automation and reach process orchestration.

This is where a process intelligence platform plays a crucial role. With its digital twin of business operations, this platform acts as the intelligent management layer that orchestrates all forms of automation-from RPA bots to AI agents-by providing the essential business context and process visibility they need to function effectively. This allows organizations to move from disjointed automations to a cohesive, self-optimizing system that drives value and innovation, making the promise of autonomous operations and eventually enterprises a reality.

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