The mix of AI, RPA is turning business process automation smart
Companies are getting better results by combining AI and RPA to optimize their process flow.
Developing artificial intelligence into business process management isn’t an easy task. Many companies add AI to processes by building or buying single-task bots, such as NLP systems or vision recognition tools, and adding them to processes using traditional, non-AI methods.
But human intelligence is still needed to tease out processes, disparate systems into a single coherent process, to change processes as the business evolves, and to spot and fix problems.
According to McKinsey, AI, machine learning, and related technologies are now making inroads into this territory via robotic process automation (RPA). This combination of AI and RPA adds up to intelligent process automation (IPA). In addition to RPA and machine learning algorithms, IPA also includes process management software, natural language processing and generation, and cognitive agents, or “bots.”
IPA can add up to to 20 to 35 percent improvement in efficiency, 50 to 60 percent reduction in process time, and returns on investment in triple-digit percentages, according to McKinsey. However, it’s still early, as most companies are in early-stage development, using individual pieces of AI, and rarely connecting them into a complete end-to-end automated process.
“There are no use cases which will go all the way across yet,” says Gartner analyst Moutusi Sau, referring to RPA adoption in the financial services industry. “There have been some chatbot engines out there, and AI decisioning tools, but you cannot build momentum on one particular solution. Banks want to do more than one thing.”
The humble bot
Germany’s ZF Group, an automotive supplier which began applying intelligence to its business processes just over a year ago and began with the creation of a bot to answer the most repetitive questions.
“In our corporate communications area, we have a lot of repetitive work,” says Andreas Bauer, the company’s IT manager. “We have a lot of emails coming into our inboxes with a lot of repetitive questions.”
But once most of the steps of a business process are automated, then a different level of intelligence can be applied. So, when picking vendors for its bots, the company had an eye towards that future.
“We are heading in the direction of automating the whole process chain,” says Bauer. “We weren’t looking for just a bot. What we have been looking for is an orchestration and integration platform, where we could easily adopt these technologies and combine them with intelligence.”
So while automated integration and orchestration is the end goal, the company also wanted a platform with built-in checks and balances. “There was the fear of something going crazy and us not being able to control it,” he says. “You have to be careful, you have to keep an eye on the technology. It’s not like the technology maintains itself. You have to put effort into it.”
ZF Group chose Vizru, a bot platform that offers management, governance, and language support layer underneath the bots, called stateful network for AI process (SNAP), which will stop a bot if it demonstrates anomalous behavior. The SNAP layer can also flag or halt a transaction if there are compliance violations or sensitive data is being shared inappropriately between processes, according to Vizru.
Another way is to add intelligent decision points into a traditionally-automated business process.
That is what American Fidelity Assurance, an insurance policy provder is doing. One challenge the company faced was automatically routing the many emails that come in each day to the correct destination. In the past, a human would decide where each email should go.
“Is there a way to get advanced machine learning to learn from past data, from past decisions, and make the same decision that a human would make?” asks Shane Jason Mock, the company’s VP of research and development, who was inspired by a tour of Amazon to do just that.
American Fidelity turned to UiPath, an enterprise RPA vendor, and AI platform DataRobot, to add intelligence to its processes.
“In the new email process, we combined the RPA component with the machine learning component, and the combination of the two decides where the email needs to be routed,” he says.
In many cases, traditional approaches to RPA will hit a decision point that is too complex for simple automation. The organisation is also looking at using AI for process mining to automate process discovery, rather than have business analysts figure out what happens in the company.
The traditional approach to business process management includes business analysts talking to managers and employees, carrying audits, then creating charts that illustrate the organization’s various business processes.
“Many client engagements where we go in, there’s a process workflow on the wall,” says Sumeet Vij, director in the strategic innovation group at Booz Allen Hamilton. “But is that how things actually happen? You’ll find that how things actually happen is different, the bottlenecks are different. Using machine learning to do process mining helps people get a picture of how things are actually happening.”
As the business evolves, these tools can update the processes and also spot anomalous behaviour in real time.
One company that already has an intelligent process mining system in place is Chart Industries, a manufacturing firm serving the energy industry, headquartered in Ball Ground, Georgia.
A manufacturing firm serving in the energy industry–Chart Industries struggled a few years ago. The company’s stock price dropped, and the top execs were replaced and new leadership wanted to make changes. For example, Chart had three main divisions, and even though they shared a single ERP system from Oracle and J.D. Edwards, there were multiple back offices handling accounts payable, accounts receivable, and other back-office tasks — each with their own processes and procedures.
“We were finding that our customers were effectively taking advantage of paying us later than they should,” says Bryan Turner, Chart’s executive vice president of IT.
There were also other opportunities for affecting cash flow. For example, in some instances, the company could take advantage of discounts for paying vendors within a certain period of time; in others, it could take advantage to hold on to cash longer. The benefits of better efficiency here can go into the millions, Turner says.
Chart turned to Celonis, a process mining vendor, to help uncover opportunities such as these.
“We have it running on a few custom systems today. As long as it has a database and transactions and time stamps, then you can punch it into Celonis,” Turner says. “A lot of the heavy lifting was how to move data between our organization and the SaaS application or the Amazon back end of Celonis.”
The business process can be viewed in the form of charts, such as Visio diagrams, and managers can drill down into the process, down to the level of individual transactions.
“Just in one example of late payments, we had annual savings of $240,000,” says Turner. “The software has paid for itself several times over and we continue to see that the cost opportunity is definitely working with both our suppliers and our customers.”
Business process analytics
Seann Gardiner, senior VP of business development at DataRobot, an AI platform provider, says that some of the most advanced companies have enough business process data that they can now look at the overall picture of what’s happening, and make analyses and predictions.
“They’re taking the exhaust from the RPA process and trying to capture that and learn from it and make those processes smarter,” he says. “I wouldn’t say that we’re seeing it very broadly in organizations, but we’re starting to see it.”
If a company has a strong focus on process-level automation and can unsilo that data, then it might be ready, he adds. “But you have to have business leaders who believe in automation and in an AI-first mentality, and can make the organizational changes needed.”
Companies in the Fortune 5000 are ready, he says, and have processes in place where they can adopt a combination of AI and RPA, he says. “The question is, do they want to put the work in to be able to make those wholesale changes to the organization.”
The article first appeared on CIO.com