Intelligent automation

Intelligent automation describes the combination of automation technologies and artificial intelligence to automate business processes more comprehensively and end to end. Where classic automation takes on clearly structured, rule-based tasks above all, intelligent automation widens the scope to tasks requiring understanding, interpretation or decisions.

The term therefore stands less for a single technology than for an interplay of several building blocks. Characteristic is the idea of looking at a process from start to finish and using the right tool at each point, whether rule-based or AI-assisted.

Robotic process automation automates processes by carrying out defined, rule-based steps, such as transferring data between systems. RPA is very capable but hits its limits as soon as tasks contain unstructured content or call for situational decisions.

Intelligent automation extends RPA with AI components. Unstructured documents can thus be analysed, free text input understood or enquiries handled according to context. A purely rule-driven automation thus becomes a process that can also make judgements and respond to exceptions.

The difference becomes vivid in invoice processing: pure RPA can transfer data from a clearly structured form reliably but fails on documents laid out differently. Intelligent automation, by contrast, recognises the relevant information regardless of the layout, classifies it and, where something is unclear, routes the case for review.

With intelligent automation, processes that previously required manual handling can be automated end to end. That includes processing invoices and documents, classifying enquiries, answering recurring customer questions or starting follow-up processes on the basis of content recognised.

The particular value is that judgement-based sub-steps can be automated too. By understanding and classifying content, AI takes on decisions that previously required human intervention. That creates joined-up process chains linking structured and unstructured steps seamlessly.

Processes with high volume, clear goals and a noticeable share of content that has to be interpreted are particularly suitable. In such cases intelligent automation combines the speed of automation with the flexibility of human-like processing.

It can be understood like this: intelligent automation is the method at process level, hyperautomation the overarching ambition at company level. In practice the two interlock, since a broad automation strategy needs intelligent individual components to cover complex processes end to end.

Successful intelligent automation starts with choosing the right processes. It makes sense to begin with processes that are clearly delimited, well understood and at the same time relevant to day-to-day business. That produces recognisable benefit early without getting lost in complexity.

Transparency and control matter just as much. Automated decisions should stay traceable, and critical cases need defined handover points to people. Introduced this way, step by step and under control, intelligent automation grows trust, acceptance and effect alike.

The quality of the underlying data and processes is not to be underestimated either. An automation can only be as reliable as the processes it represents. Before implementation it is therefore worth questioning existing processes and simplifying them where appropriate rather than automating unclear or error-prone ones unchanged. Intelligent automation thus creates lasting value instead of merely speeding up existing weaknesses.

Intelligent automation joins classic automation technologies with artificial intelligence and so enables end-to-end automation of unstructured, judgement-based tasks too. It thereby widens automation's reach considerably and is an important building block on the road to hyperautomation.

At Elisabit we help companies design and implement intelligent automation sensibly. We combine automation and AI so your processes become more efficient, more robust and more joined up.