Advancement of artificial intelligence into business process management isn’t a straightforward procedure. Several companies use AI to processes by building or buying single-task bots, for example, NLP systems or vision recognition tools, and adding them to processes using traditional, non-AI methods.

However, the necessity of human intelligence is still in demand. Because it is needed to figure out processes, disparate systems into a single coherent process, modify processes as the business evolves, and also to detect and solve problems.

McKinsey reveals that AI, machine learning, and allied technologies are now making inroads into this area via robotic process automation (RPA). This grouping of AI and RPA adds up to intelligent process automation (IPA). With RPA and machine learning algorithms, IPA also includes process management software, natural language processing and generation, and cognitive agents, or “bots.”

According to McKinsey, IPA can give 20 to 35 percent improvement in effectiveness, 50 to 60 percent fall in process time, and brings in triple-digit percentages on investment. But, it has not been saturated, as most companies are in early-stage development, using individual pieces of AI, and hardly ever connecting them into a complete one-to-one automated process.

“There are no use cases which will go all the way across yet,” says Gartner analyst Moutusi Sau, indicating to RPA implementation 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 started with the conception of a bot to answer the most recurring questions.

"In our corporate communications area, we have a lot of repetitive work," clarifies Andreas Bauer, the company's IT manager. "We have a lot of emails coming into our inboxes with a lot of repetitive questions."

"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 as automated integration and orchestration is the end objective, the company’s necessity was also 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 selected Vizru, a bot platform which provides management, governance, and language support layer below the bots, called stateful network for AI process (SNAP), and it will prevent a bot if it shows abnormal behaviour. According to Vizru, the SNAP layer can also flag or halt a transaction if there are compliance violations or sensitive data is being shared improperly between processes.

Assessment points

One more way is to include intelligent decision points into a traditionally-automated business process. The American Fidelity Assurance, an insurance policy provider is doing the same thing. One challenge the company faced was automatically routing the many emails which come in every day to the accurate destination. But, in the past, a human would choose 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 enthused by a tour of Amazon to do just that.

American Fidelity transformed to UiPath, an enterprise RPA vendor, and AI platform DataRobot, to include 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 several cases, usual approaches to RPA will strike a decision point which is too complex for simple automation. The organisation is also searching at using AI for process mining to automate process discovery, in spite of having business analysts figure out what happens in the company.

Process mining

The usual approach to business process management comprises business analysts talking to managers and employees, carrying audits, and then generating charts that demonstrate the organization’s different 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.”

With the development of business, these tools can modernize the processes with marking abnormal behaviour in real time.

A 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 reduced, top officials were replaced and the new leadership wanted to bring 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.

Chart turned to Celonis, a process mining vendor, to help expose 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 observed in the form of charts, such as Visio diagrams, and managers can drill down into the process, below 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 analytic

According to Seann Gardiner, senior VP of business development at DataRobot, an AI platform provider, 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 sturdy 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.”

He says, companies in the Fortune 5000 are ready, 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.”