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AI In Business Process Management Automation

Artificial Intelligence (AI) is often considered to be the future of computing and technology. In fact, it’s already showing promising results in cars, voice controlled digital assistant, like Siri and Alexa, and devices with facial recognition. This is only the beginning. According to Koos Du Preez, CTO at K2, AI’s power to support human knowledge and ingenuity and change our perception of business challenges is game-changing when we look at how it can be applied to processes throughout an organization.

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AI technology is a partner for Business Process Management (BPM) efforts: it can help automate manual, routine business tasks, improve user interfaces and analyze huge amounts of data in short spurts of time.

Adding AI to BPM, otherwise known as Intelligent Business Process Management (iBPM) helps businesses further by planning and automating their complex business processes by building a dynamic technology environment based on value-added knowledge work. Since its introduction by Gartner in 2012, businesses have been indicating iBPM as the next big thing in the enterprise BPM domain

McKinsey & Company, a global management consulting firm that helps organizations make significant and lasting improvements to their organizational performance, estimates that AI can automate as much as 45 percent or more of any particular job, allowing workers to focus on higher level, mission-critical activities.

Preez provides two examples of this already in use: 1. AI-driven natural language interfaces make interacting with applications much easier and speeds up many individual steps within a process.

“For example, natural language processing can allow doctors to dictate clinical notes into a device, which then automatically populates appropriate forms, lab orders and prescriptions. Or it can summarize long blocks of text from medical journal articles or studies by identifying key concepts and phrases,” Preez explains.

The second example mentioned is the utilization of machine learning, which is a component of AI. Machine learning analyzes and identifies patterns within large quantities of data by iterating through data to identify relationships between data and the resulting decisions made from the data.

“With each iteration, the system acquires a deeper understanding of why decisions within an organization are made and applies statistical analysis to develop rules around these decisions. In the case of a complex area of business activity, supply chain management, machine learning can do things like predict when stock will run out or recommend which products are at surplus and automatically reduce their price to clear inventory,” Preez  adds.

In these ways, it’s easy to see how AI is starting to play an important role in process automation and optimization efforts. As adoption of AI technologies become more widespread, they’ll become easier to implement and have more applications within BPM.

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