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Power Couple: Artificial Intelligence and Business Process Management

27 Nov 2017 - Mickey Farrance

Process Improvement Daily explains BPM and AI complementarities based on Bonitasoft vision.

There are simple but critical tasks – coordinating resources, automating manual assignments and allowing for self-service where it previously wasn’t available – that comprise a bulk of the methodology utilized through business practice management, or BPM.

BPM is known for taking advantage of technology to assist with modeling and, ultimately, executing crucial processes.

But what if those processes could be managed through artificial intelligence?

Digital transformation – a movement to enhance business operations through the addition of intelligent software – might make that a reality sooner than previously thought.

By implementing intelligent software, companies can quickly automate key processes, such as tracking sales, assisting logistics and better managing the customer experience.

Artificial intelligence, or AI, allows businesses to be proactive and anticipate issues well before they occur, giving the company time to direct resources.

Identifying Problems Before They Occur

AI applications not only are capable of detecting process-flow patterns, guiding fixes where needed and updating other applications while they are running. They also can infer from existing patterns where constraints might soon appear, according to software company Bonitasoft’s CEO Miguel Valdés Faura, writing on the website Information

By merging AI with existing business process management platforms, a company can extract data that helps develop predictions. That information can then be used to construct a predictive model that anticipates and identifies possible blockage points to a task being completed.

One example offered by Faura that illustrates this concept: Imagine that you operate a bank that’s responsible for reviewing, approving and processing loan applications which must be filed within a specific time period. Using an application that incorporates AI with existing BPM, a manager could not only monitor the flow of applications through the bank’s system, but receive an alert from a predictive model if one or more loans don’t appear likely to be completed within the required window. That predictive model would make that determination based on historical and current data regarding the loan application process.

As more companies open the door for a digital transformation of existing business processes, the discussion about integrating AI with existing BPM tools is turning to critical issues, such as cost-savings and efficiency, as well as an enhanced customer experience and better end-product quality.

Combining Human, Machine Skills

AI already has a foothold in customer relationship management (CRM), and a recent report on the website anticipates that this may be ground zero for the introduction of AI-enhanced BPM tools.

Say you own a company and you want to identify a specific high-value set of customers to target with a personalized digital campaign. A BPM tool could be programmed to navigate multiple online dashboards to identify those best customers while the AI analyzes patterns in the dashboard process to pinpoint other relevant customers that a human operator might miss or not have the time to burrow down and locate.

Once programmed, the BPM tool could function autonomously until it encounters a situation or data set that wasn’t included the first time. Then it would alert a human member of the team.

What this does is allow the people working at your company to pay more attention to the specific details of the campaign, which can result in a higher return of successful engagements.

While the advantages of digital transformation are promising, the reality is that a successful and thorough change-over isn’t inherently simple. Time is needed for the technology to improve and particular business models to embrace the concept before AI can be used to perform complex tasks.

Many companies may find it easier to prioritize specific areas, such as repetitive, low-level tasks, to target for an immediate AI-BPM automation.

By understanding where a worker’s skills are not being fully optimized, due to a mundane but necessary task, businesses can free up time and energy for those workers to apply in more complex areas.