To run a successful business, you do not only need to have motivation and drive, but you must also be a master multitasker. Having the ability to either perform or oversee multiple tasks as your company grows is an important skill every entrepreneur must have if they want to succeed.
However – and this is true for all industries – not all the tasks you have to control are glamorous or interesting. The reality of business is that a lot of what takes place is repetitive and, in some cases, mundane. Fortunately, as has been the case for the better part of two decades, technology is changing the game – largely through robotic process automation (RPA) and artificial intelligence (AI).
Thanks to RPA management software, repetitive tasks can be controlled by a robot. By using a rule-based system, these robots can process information more efficiently than humans. In fact, today’s RPA tools can offer business owners a one-stop platform that allows them to control their robots with a few clicks. Once RPA software is installed, process discovery functions can create a complete overview of the user’s network and identify where automated processes can be used to speed things up or provide increased accuracy.
Not surprisingly, with RPA software making automation more accessible for business owners, the market is starting to evolve; experts expect it be worth $2.9 billion by 2021. However, with business needs constantly changing, RPA is set to evolve in order to fulfill this evolution will undoubtedly continue, helping automation fulfill its underlying promise of making our working lives easier and more efficient.
Although AI and RPA occupy the same spectrum, they’re essentially at opposing ends. In simple terms, RPA tools try to mimic human behavior by using set rules to follow set patterns. In contrast, AI systems attempt to simulate human intelligence by creating a system that can learn, evolve, and be creative. Still, despite the differences, both sectors are striving for similar goals.
For those on the inside, artificial intelligence (AI) will be way in which RPA tools improve. Based on this, it’s hardly surprising that today AI is being used to enhance RPA software. Here are three key examples:
Smarter Customer Service Bots
Customer service is an one key area where AI could make RPA tools more effective. As it stands, RPA software is able to answer simple customer queries by reading through a database of common complaints and /issues and generating stock responses. AI systems are now being developed tTo tackle more complex issues and, in essence, give the impression of a human operator, AI systems are now being developed.
Chatbots are capable of going beyond a set database of questions and responses. With an RPA-powered customer service assistant, any deviation from standard phrasing or queries will upset the system. AI, however, can address the issues of synonyms, non-common phrases, and more, thanks to machine learning. Because the software can be taught to learn that “cat” is the same as “kitty,”, it can tackle more questions and provide more accurate answers.
Analyzing Data Instead of Simply Reading It
Just as AI can learn to deal with obscure queries, it can also make inferences based on the data it captures. At this point, RPA tools are great for scraping documents for information and processing data in a routine fashion. What this basic RPA technology can’t do on its own, however, is make assumptions based on the material it is reading. This is where AI can help –and is increasingly doing so.
Being able to take an established set of rules (i.e.such as a database of knowledge) and then analyze documents based on that it is the start of machine learning. From this, the AI system can use its previous experience to constantly refine how it performs. The upshot of this is that companies could use AI AI-infused RPA tools to make business decisions about new data protection laws or market data, for example.
Creating a More Efficient System
The final third way area in which AI can revolutionize RPA technology is efficiency. One of the main reasons AI systems can perform so well is because they’re constantly looking for weaknesses and improving. A great example of this is the AI poker bot Libratus, which – despite starting a series of matches as a relative novice – quickly identified its own flaws and those of its opponents, and beat a team of pros.
The same logic could be applied to a business. For example, by allowing AI technology to review a network of RPA tools, it AI technology could learn to make the processes even smoother, slicker and suggest optimization solutions. The upshot of this would be this would likely result in a better working environment and, in turn, more useful RPA software, thanks to the power of AI.
