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In 2021, enterprise teams turned to robotic process automation (RPA) to simplify workflows and streamline office tasks. Next year promises to bring the same sophisticated artificial intelligence and work optimization so that more repetitive offices can free their staff from work.
The manufacturing sector is one of the most notorious buzzwords in enterprise computing. There is no robot in sight. Tools are commonly used to fix something called paperwork at one time, but they rarely touch more paper. They do this by pressing virtual buttons and juggling multiple data formats, gluing legacy systems together so that different teams can keep track of the work going on in their office.
Here are 10 ways to shift and adapt to RPA Marketplace 2022:
The main task for RPAs is to weave together hundreds of legacy systems that now form the backbone of many companies. The main challenge for each RPA company is to strengthen the connection between the systems. That means more modules or bots in the marketplace and better versions of existing modules.
One major selling point for many RPA sellers is that their tools may come close to programming in what some call “process discovery.” While this may never be as magic as one would like, tools will continue to make this task easier. It can also approach “no-code” level automation for some simple tasks.
It may seem counterintuitive to assume that RPA platforms will be easier and harder to program together, but these changes will be seen in different levels of work. While interns and managers will be able to automate more simple tasks, developers will be called upon to customize RPAs for more complex integration. In many cases, RPA tools create a good framework that sophisticated programmers can modify and extend. RPA handles 95% of the work and the development team handles the last 5% of the work. This is why some companies are reporting that RPAs are more complex and expensive to maintain than they expected. Companies are asking them to do more and more sophisticated jobs and that means bringing in better programming talent.
Craig Le Clare at Forrester Research predicts that every RPA company will either accept AI or “become a dinosaur”. While this may never be strictly true, there is no doubt that RPA is a simple vector for inserting AI into corporate DNA. Standard modules handle tasks such as optical character recognition, machine learning, and machine vision. RPA firms that send out better, smarter AI modules will be able to win more contracts. The accuracy and depth of AI algorithms will increase significantly.
Some companies need all the intelligence that AI scientists can offer. Some companies don’t do that though. Many AI options aim to deal with older, paper interfaces, or other functions that require adaptability. A popular task for AI is to convert paper documents to digital form and then find relevant data such as invoice number or expiration date of driver’s license. Some workflows, however, are very mature and do not require this extra amount of smart. Companies that process less paper or do not need extra intelligence may find that they are not interested in AI-based innovations.
Not all innovations will be obvious. Machine learning algorithms and machine vision algorithms will continue to be learned over the years. The best platforms will gradually introduce better versions of the algorithms with better performance, knowing that these algorithms are increasingly working.
The level of intelligence is slowly increasing in all parts of the stack. Some, for example, are using “semantic automation” to instinctively highlight the ability to understand what jobs should be done. The first generation of RPAs succeeded simply by combining the user interface together, and that meant that automation developers needed to know the names and locations of on-screen buttons. Semantic automation promises to be smarter about guessing what these buttons do, a process that can help if the user interface is redesigned and when.
While many cynics dismiss cryptocurrencies and blockchains as solutions to problems, there is no doubt that they can bring some confidence and order to the workflow. RPA tools will begin adding better cryptographic algorithms to provide better authentication and mathematical precision. When workflows bring together different groups or different companies, a good blockchain and reliable ledger can prevent disputes and reduce the need for human intervention.
Good automation saves time, staffing and energy. Groups charged to save the environment are paying attention to the fact that good automation can help. Expect more pitches to highlight green concerns clearly.
Once the task successfully flows through the systems, the various issues and sticking issues become more apparent. The idea of ”rule” will become increasingly important for RPA teams, who may begin to worry more about who is authoritative and who controls every move.
Some companies and developers differentiate between marketing terms such as “intelligent automation,” “robotic process automation,” “RPA-Plus,” “low code application platforms,” and general AI tools. Differences are disappearing as tools to distribute code converge and many other options. Top RPA tools are rapidly adding new intelligent options, making them more capable. They are also becoming more adept at process discovery and other low-code and no-code options, making it difficult to decide which acronym to use on PowerPoint slides. The software, however, does not care what it is called.
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