Robotic process automation in 2022

In 2021, enterprise teams turned to robotic process automation (RPA) to simplify workflows and bring some order to office tasks. The next year promises to bring more of the same sophisticated artificial intelligence and task optimization so more offices can liberate their staff from repetitive chores.

The product area remains one of the poorly named buzzwords in enterprise computing. There are no robots in sight. The tools are generally deployed to fix what was once known as paperwork, but they rarely touch much paper. They do their work gluing together legacy systems by pushing virtual buttons and juggling the multiple data formats so that the various teams can keep track of the work moving through their offices.

Here are the 10 ways that RPA marketplace will shift and adapt in 2022:

Better Integration

The main job for RPA is to knit together some hundreds of legacy systems that now make up the backbone of many companies. The main challenge for each RPA company will be strengthening the connections between systems. That means more modules or bots in the marketplaces and better versions of the existing ones.

Lower Code

One of the major selling points for many RPA vendors is that their tools can come close to programming themselves through what some call “process discovery.” While this may never be as magic as anyone wants, the tools will continue to simplify this job. It may even approach “no-code” level automation for some simple tasks.

Higher Code

It seems contradictory to imagine that RPA platforms will simultaneously get easier to program and harder, but these changes will be seen in different levels of tasks. While the interns and managers will be able to automate more simple tasks, the developers will be called to customize the RPAs for more complex integrations. In many cases, RPA tools make good frameworks that sophisticated programmers can revise and extend. The RPA handles 95% of the work and the development team handles the last 5%. This is why some companies are reporting that RPAs are more complicated and expensive to maintain than they thought. Companies are asking them to do more and more sophisticated jobs, and that means bringing in better programming talent.

More AI

Craig Le Clair at Forrester Research predicts that every RPA company will either embrace AI or “become a dinosaur”. While this may never become strictly true, there’s no doubt that RPA is one of the simpler vectors for inserting AI into corporate DNA. The standard modules tackle tasks like optical character recognition, machine learning, and machine vision. RPA firms that ship better, smarter AI modules will be able to win more contracts. The accuracy and depth of the AI algorithms will rise in importance.

Divergence

Some firms need all the cleverness that AI scientists can deliver. Some firms, though, do not. Many of the AI options are aimed at dealing with older, paper interfaces or other tasks that require adaptability. One popular job for AI is to convert paper documents into digital form and then search for relevant data like the invoice number or the expiration date for a driver’s license. Some workflows, though, are pretty mature and don’t need this extra dose of smarts. Companies that process little paper or don’t need the extra intelligence may find they’re not as interested in AI-based innovations.

Smarter

Not all innovations will be obvious. The machine learning algorithms and machine vision algorithms will continue to learn over the years. The best platforms will be slowly rolling out better versions of the algorithms with better performance, knowing that these algorithms are doing more and more of the work.

The intelligence level is slowly rising at all parts of the stack. Some, for instance, are touting “semantic automation” to highlight the ability to intuitively understand what jobs must be done. The first generation of RPAs succeeded by simply tying together user interfaces, and that often meant that automation developers needed to know the names and locations of buttons on screens. Semantic automation promises to be smarter about guessing that these buttons do, a process that can help if and when user interfaces are redesigned.

Blockchain

While many cynics dismiss cryptocurrencies and blockchains as solutions looking for problems, there’s no doubt that they can bring some assurance and order to workflows. RPA tools will begin to add better cryptographic algorithms to offer better authentication and mathematical certainty. When the workflows link together disparate groups or different companies, good blockchains and trustworthy ledgers can prevent disputes and reduce the need for human intervention.

Sustainability

Good automation saves time, staffing, and energy. Groups charged with saving the environment are noticing that good automation can help. Expect more pitches to explicitly highlight green concerns.

Governance

Once the work is flowing through the systems successfully, the various issues and sticking points become more obvious. The idea of “governance” will become increasingly important to RPA teams, who can begin to worry more about who is authorized and who exerts control over each step.

Merging

Some companies and developers make distinctions between marketing terms like “intelligent automation,” “robotic process automation,” “RPA-plus,” “low code application platforms” and general AI tools. The differences are disappearing as the tools and many of the other options for delivering code converge. Top RPA tools are rapidly adding new intelligent options, making them more capable. They’re also becoming more adept at process discovery and other low-code and no-code options, making it harder to be certain which acronym to deploy on PowerPoint slides. The software, though, doesn’t care what it’s called.