The following year promises to bring more sophisticated artificial intelligence and task optimization so more offices can free up their staff from exhausting drudgery. Here are the ten ways that the RPA marketplace will shift and adapt in 2022:
Craig Le Clair at Forrester Research predicts that every RPA company will either embrace AI or “become a dinosaur.” Undoubtedly, 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 computer vision. RPA firms that ship better, more intelligent AI modules will win more contracts. The accuracy and depth of the AI algorithms will rise in importance.
Some firms need all the cleverness that AI scientists can deliver. Some firms, though, do not. Many of the AI options aim to deal 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. However, some workflows are pretty mature and don’t need this extra dose of smarts. Companies that process little paper or don’t need the additional intelligence may find they’re not as interested in AI-based innovations.
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, which 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.
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.
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.
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 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 report that RPAs are more complicated and expensive to maintain than they thought. Companies are asking them to do more sophisticated jobs, which means bringing in better programming talent.
Good automation saves time, staffing, and energy. Groups charged with protecting the environment are noticing that good automation can help. Expect more pitches to highlight green concerns explicitly.
Once the workflows through the systems successfully, the various issues and sticking points become more apparent. The idea of “governance” will become increasingly crucial to RPA teams, who can begin to worry more about who is authorized and who exerts control over each step.
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 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.
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 other options for delivering code converge. Top RPA tools rapidly add new intelligent options, making them more capable. They’re also becoming more adept at process discovery and other low-code and no-code opportunities, making it harder to know which acronym to deploy on PowerPoint slides. The software, though, doesn’t care what it’s called.