Machine Learning Contributing to BPM Innovation
Blog: Appian Insight
Automation is central to the conversation surrounding the Internet of Things (IoT). The IoT brings connected devices together to gather large quantities of data throughout the enterprise. From there, software can automatically act on that data or send alerts to end users based on what information is discovered. Business process management (BPM) software has already evolved to facilitate this process, and machine learning could take this functionality to another level.
A recent report from the Harvard Business Review explained that businesses have long been optimizing and automating processes based on the data they discover across the organization. In most cases, however, data is used to inform process changes and decisions that are handled manually. Moving forward, machine learning will allow many process changes to be handled automatically.
Using machine learning alongside BPM
According to the news source, machine learning capabilities are allowing software to gather data, analyze it and adjust processes. A study performed by the HBR, but not yet published, found that 96 percent of respondents agreed or strongly agreed that machine learning will lead to automated process changes moving forward. At this point, early adopters are using the strategy in two key ways:
- Self-adapting: Adjusting and fine-tuning processes automatically as data patterns reveal opportunities to make small changes.
- Self-repairing: Identifying trends in data that highlight inefficiencies within process roadmaps and make changes to fix them.
Taking advantage of machine learning hinges on establishing a BPM software framework that can get data to the right places at the right times.
“Automation isn’t just about doing work faster.”
Balancing automation and humanity to create value
Automation isn’t just about doing work faster. It’s also about empowering your employees to spend more time on high-thinking tasks that can’t be automated or shifted to machines. Introducing machine learning to BPM creates an opportunity to offload more work from overburdened employees, letting them put more time on the most important tasks they handle each day, creating more value potential for businesses.
All of this is only possible, however, if data is properly sorted and shifted between different user groups and software systems. Modern BPM solutions combine traditional process automation tools with app platform functions that let companies create unique apps that can incorporate machine learning and human interaction into different processes to streamline changes. Being able to unify machine learning gains with human operations can empower companies to improve decision-making as they work to use data from diverse parts of the organization.