Robotic Process Automation and Cloud Technology – Challenges and Opportunities

The original article was published on IT Toolbox on July 23, 2019. 

RPA holds incredible promise for organizations looking to drive greater efficiency and cost savings; however, the industry must overcome several crucial challenges before it can truly live up to its potential. This article unpacks those challenges and explores the opportunities ahead.

Robotic Process Automation holds incredible promise for organizations looking to drive greater efficiency and cost savings; however, the industry must overcome several crucial challenges before it can truly live up to its potential. This article unpacks those challenges and explores the opportunities ahead.

By now, you’ve probably heard about Robotic Process Automation (RPA). It is not especially a new idea that’s suddenly gaining attention as businesses strive to become more digital. The promise of RPA is providing quick and significant cost savings through automation of human tasks with software robots. In fact, PwC estimates that “45% of work activities could be automated, and that this automation would save $2 trillion in global workforce costs.”

Challenges Faced by Organizations

Today, there are thousands of software robots automating everything from simple tasks like order entry and invoice preparation, to complex interactions, like issue resolution and customer service. But there are challenges awaiting many organizations, who have rushed to deploy robots.

1. Cloud Infrastructure Challenge:

First, there’s the matter of the cloud. Before RPA came along, those same organizations were busy planning multi-year efforts to reap the benefits of cloud computing. Moving IT to the cloud offers a similarly enticing cost benefit, but it is a long term project, requiring the deployment of new and emerging technologies.

Much has been invested in containers, orchestration, microservice and service mesh architectures, etc., as we lay the foundations for a serverless, data center-less future. However, RPA has some catching up to do. It is still confined to the desktop—the Windows desktop, to be precise.

The majority of software robots currently deployed are of the ‘attended’ type. This means that they exist on your Windows desktop, much like the little ‘Clippy’ assistant in bygone versions of Microsoft Office, where they do things like, move rows of data from a back office database to a spreadsheet, so that you can focus on more important things.

In the recent years, RPA has evolved to enable ‘unattended’ bots to manipulate your enterprise data behind the scenes, on Windows servers. That’s a step in the cloud direction, but still far from the notion of cloud-native bots that can cruise around your hybrid cloud and fix whatever needs fixing.

When will we see containerized bots, orchestrated by standard platforms like Kubernetes and Istio? Well, presumably not until RPA vendors realize the central role that Linux plays in modern cloud architectures. But more importantly, not until RPA goes open source. Why? Because open source software is the central pillar of modern cloud stacks, and if RPA is to have a role in hybrid cloud infrastructure, it must be open source too.

However, today, there is very little in the way of open source RPA. There are a few open source RPA-like projects such as, TagUI, Robot Framework and Sikulix, but these are very bare-bones compared to the market leading proprietary products in the market currently. The opportunity for these proprietary vendors to play in the hybrid cloud market is immense if they can embrace open source business models.

2. Cost Challenge:

The second challenge for users of RPA looking to save on labor costs is that today’s bots just aren’t all that smart. They don’t measure up to their human counterparts in their ability to figure out how to get the job done when some part of it turns out a little differently. Some bots are simple macros, repeating the same series of steps over and over. Others may have a little more intelligence, perhaps a rules engine to handle complex scenarios, but very few have anything close to actual intelligence.

The world of AI and ML is currently separate from RPA, and although some bots may be able to utilise AI services, like IBM Watson, none of them have the in-built ability to learn from past experience so they can do a better job the next time. Consequently, the anticipated cost savings don’t always materialize, and bots can be limited to highly structured and repetitive tasks. Just like with the cloud, though, there is opportunity here. I expect a marriage of RPA and AI/ML will likely happen soon, and will open up a new landscape of possibility for automated business.

3. Implementation Challenge:

Finally, there’s the implementation challenge—how to deploy RPA technology so that it supports your IT strategy rather than hobbles it. It’s easy to be tempted by RPA’s promise of a quick fix into attacking the symptoms of your problems rather than the root cause. Some organizations deploy bots as ‘band-aids’ to relieve bottlenecks in semi-automated processes, when the real problem is an ageing infrastructure that can’t accommodate new business requirements. This may solve the immediate problem, but will continuously break again with every minute change in operating processes, applications or infrastructure. Partly, it’s the ease with which an RPA bot can be deployed that’s to blame. Why go to the trouble of creating APIs for critical applications when it is easier to just have a bot screen-scrape, say, an accounts receivable app to get the one extra data field needed for the new invoices?

The answer, of course, is because this problem is just a symptom of a larger issue within the IT infrastructure, RPA does not fix a spaghetti tangle of applications, data and integration strategies. Organizations with this problem need to focus on building a cloud-native foundation first. Otherwise, if the team continues to throw bots at every new business request, the entire data center will eventually collapse from unmanageable complexity.

Automating Human Work

RPA is a valuable technology when it is used to automate human work, and not to patch holes in IT systems and applications. It is made more powerful when it can integrate effectively into a modern application environment—monitoring events, using cloud services to gather information and interacting with applications via APIs.

The opportunities for automation are huge, but the supporting IT infrastructure is critical. I believe those enterprises that are able to combine a modern cloud-native application environment with open source, intelligent, cloud-native bots will have an unparalleled competitive advantage.

Business Process Management Reimagined – New Services for Application Developers

You may be familiar with Business Process Management (BPM).  It is a discipline in which people use various methods to discover, model, analyze, measure, improve, optimize, and automate business processes. Today BPM, or more specifically the technology that supports BPM, is widely used in organizations large and small to automate business operations. The story of BPM is long, with roots going back to the early 1990’s, and it has constantly reinvented itself to meet the evolving needs of enterprises.  Once focused on driving efficiencies into back-office functions, BPM platforms have evolved into essential tools for enterprises looking to digitally transform operations, and to deliver a personalized customer experience that’s integrated across points of interaction.  

BPM in the Application Environment

The focus on digital transformation has led to the modern role of BPM solutions in application development, and to the rearchitecting of the old monolithic BPMS as a set of middleware services that developers can easily incorporate into applications.  Now often referred to as a Digital Application Platform (DAP), the BPMS has become part of the application environment – a catalog of components that can be included in applications requiring process management, decision management or optimization capabilities.  Now, for example, when building an application that requires, say, to make a determination of whether an insurance application complies with underwriting rules, a developer can quickly locate the corresponding decision service within their app environment and include it in their application.  Conversely, it’s the new DAP solutions that enable such application services to be quickly created from models provided by the business. Business friendly tools support the creation of a range of model types – from decision models, built with the new graphical Decision Model & Notation (DMN) standard, to models of entire business processes constructed in Business Process Model & Notation (BPMN).  DAP technology today is truly making it possible for business users to contribute to application development alongside developers.

Cloud-Native Digital Automation

The advantages of modern digital automation middleware are not limited to application development, however.  Once built, those new applications must be deployed on a variety of cloud platforms, they must scale automatically to meet varying demands, they must be secure, and they must be easy to replace or upgrade without impacting the user experience.  At Red Hat, the application environment, in which DAP services are included, is designed from the ground up to be cloud-native. DAP services are deployed in containers, and managed by Kubernetes, to provide the scalability and resiliency that enterprises need.

Digital automation today is an essential part of modern applications, and its importance is only likely to increase as we look to the future role of the Digital Automation Platform as the logical home for emerging technologies like Robotic Process Automation (RPA), and artificial intelligence / machine learning.  At Red Hat, we are focused on growing our application environment to support this widening technology landscape, so that our customers can succeed in an ever more digital world.