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2018 Will Bring Convergence of AI and Process Automation

In this special guest feature, Alain Gentilhomme, Chief Technology Officer at Nintex, explores the top AI and process automation trends we can expect to see throughout the rest of the year. One of the most useful developments in artificial intelligence this year will be the convergence of artificial intelligence and process automation. Alain Gentilhomme brings more than 25 years of experience with product development, product management and technical leadership experience to Nintex. Prior to joining Nintex, Alain was senior vice president of engineering and product management at Parallels, where he provided technology leadership and led the company’s product strategy. Alain has an electrical engineering degree from the Grenoble National Polytechnic University, and a master’s degree and PhD in electrical engineering from Grenoble INP, the PhD focused on the use of artificial intelligence (AI) technologies.

One of the most useful developments in artificial intelligence this year will be the convergence of artificial intelligence and process automation.

High-value targets in AI – such as speech recognition and synthesis, image recognition and bots — were identified years ago but have only proliferated in recent years with the availability of cloud computing platforms, big data, and machine learning algorithms. In parallel, process automation solutions have evolved to enable “self-service” operation that lets business users automate their own processes using drag-and-drop interfaces, without any code or IT involvement.

With this combination of machine learning and do-it-yourself automation, people can target the processes that take most of their time, are least efficient or most annoying, and so on. In other words, they can focus on the processes that will make the biggest difference in their workday.

What makes this convergence so powerful is that both elements — machine learning and process automation – are capable of rapid iteration. The machine learning systems get smarter about identifying what’s important to you, and your automated processes can become steadily more sophisticated in scope and efficient in operation.

Customer interaction and support is one of many promising areas for this type of convergence. Companies recognize the need to improve customer service, but “live” support can be prohibitively expensive. Accordingly, many businesses have invested in chat services or chatbots that incorporate machine learning to ingest massive amounts of input and become smarter at interpreting what customers need. Many can answer basic questions, such as “what is my balance” or “when is my next payment due,” but often the next steps still require human interaction. At this point the customer has to wait in a queue to speak with a live representative.

A better solution would be to employ sophisticated process automation to deliver satisfying outcomes more quickly and at less expense. For a wireless carrier, this might start with automated analysis of the customer’s history. Next steps could include various levels of loyalty rewards and incentives. If a customer is close to being eligible for a new phone, an intelligent system might notice that and offer the new phone ahead of schedule. A fully automated system could arrange shipping, update relevant records, manage inventory and replenishment, and so on. Such a system would not just be faster; it also could significantly cut costs and errors, improving the bottom line and customer satisfaction near-term and also increasing customers’ lifetime value to the carrier.

Scenarios like this only scratch the surface of what is possible with systems that combine machine learning, and intelligent process automation. Key to the value of such solutions is that they aren’t systems and they aren’t static; machine learning and self-service automation interact continuously to become smarter and more efficient, and ultimately deliver process optimization.

 

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