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Oct 2 2019   5:56AM GMT

RPA series: Micro Focus – the ‘smart’ path from EAI to RPA

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

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This is a guest post for the Computer Weekly Developer Network written by Travis Greene in his capacity as director of ITOM at Micro Focus – and this story forms part of a series of posts on CWDN that dig into the fast-growing topic of Robotic Process Automation (RPA). 

TechTarget defines Robotic Process Automation (RPA) as the use of software with Artificial Intelligence and Machine Learning capabilities to handle high-volume, repeatable tasks that previously required humans to perform — these tasks can include queries, calculations and maintenance of records and transactions.

Greene writes as follows…

Enterprise Application Integration (EAI) is defined as the use of technologies and services across an enterprise to enable the integration of software applications.

According to TechTarget, “EAI is the task of uniting the databases and workflows associated with business applications to ensure that the business uses the information consistently and that changes to core business data made by one application are correctly reflected in others.”

EAI is supposed to link enterprise applications, such as customer relations management or supply chain management, without requiring data structure changes.

In modern compute environments, Application Programming Interfaces (APIs) take on this EAI role, but enterprises face a challenge when integrating legacy applications that may not be supported by the original developers or vendors. Nonetheless, these applications often continue to support significant revenue for enterprises and cannot be replaced without significant risk and cost.

Swivel-chair integration

So, business workers bear the brunt of the workload, performing swivel-chair integration between these applications through menial tasks such as cutting and pasting order numbers from spreadsheets into web interfaces.

Rather than wait on IT to implement a true EAI solution, business users are taking the matter into their own hands with RPA, automating tasks like a user would, through a user interface.

But is this really a revolution in automation, or more of an evolution in EAI?

One way of thinking about this is by analogy to the different approaches to implementing security cameras. Hard-wired cameras (i.e. EAI) are highly stable and secure, but involve higher infrastructural investment, while Wi-Fi cameras (i.e. RPA) are in principle less stable and slower, but are also easily installable and – vitally – more responsive to changing needs.

Beyond RPA screen scrapes

Some capabilities that we associate with RPA have been around for a long time, just in different forms such as screen scrapers and macros. In this sense, RPA can be thought of as an evolution of EAI. So RPA is itself evolving: just as there are ways of making Wi-Fi cameras more secure and reliable, RPA is evolving to meet needs that it previously couldn’t.

For example, task-based automation in a User Interface (UI) is helpful, but limited to what can be performed in a UI.

Newer RPA offerings include API and command line interface integration to expand that capability. Taking the evolution even further is integration with business process management software to expand the use cases across even more processes.

Yet, even as RPA evolves to become more powerful, it risks turning into a maintenance burden like its predecessors. UIs are constantly changing, and this can break the automation if the RPA tool is brittle and unable to handle change automatically. In order for the evolution to continue, RPA must become more resilient, leveraging machine learning and AI to recognise changes and automatically adjust the workflow to accommodate without having to cry for help from a human.

RPA certainly benefits business users who are freed from the mundane tasks of integrating what IT cannot.

But to avoid a future stall in adoption and to fulfill the demands of EAI, it must evolve to integrate more broadly and become more resilient.

Micro Focus’ Greene: More powerful RPA must not become a maintenance burden, so RPA needs AI & ML boosters.

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