Machine Learning is a game changer for business process optimization – enabling organizations to achieve levels of cost and quality efficiency never imagined previously. For the past 30 years, business process optimization was a tedious, time-consuming manual effort. Those tasked with this effort had to examine process output quality and review a very limited set of operational data to identify optimization opportunities based on historical process performance. Process changes would require re-measurement and comparison to pre-change data to evaluate the effectiveness of the change. Often, improvement impacts were either un-measurable or failed to satisfy the expectation of management.

With modern machine-learning capabilities, process management professionals are able to integrate a broad array of sensors and monitoring mechanisms to capture large volumes of operational data from their business processes. This data can be ingested, correlated and analyzed in real-time to provide a comprehensive view of process performance. Before machine learning, managing the signals from instrumented processes was limited to either pre-defined scenarios or the review of past performance. These limitations have now been removed.

Machine learning enables the instrumentation of a greater number of activities because of its capability to manage large volumes of data. During the past, process managers had to limit what monitors they set up to avoid information overload when processing the data being collected. Cloud-scale services combined with machine learning provide greater flexibility for process managers. They are able to collect data for “what-if” scenario modeling, as well as the training of the machine-learning system to “identify” relationships and events within the operational processes much more quickly than users are able to identify them manually.

One of the most promising potential benefits of machine learning is the “learning” aspect. Systems are not constrained to pre-defined rules and relationships – enabling them to adapt dynamically to changes in the data set from the business process and make inferences about problems in the process. These inferences can then be translated into events and incidents – potentially leading to automated corrective action and/or performance optimization of the process.

Even if companies are not ready to fully embrace machine-learning systems making decisions and taking actions without human intervention, there is tremendous near-term value in using machine-learning capabilities for correlation analysis and data validation to increase confidence and quality of data being used to drive operational insights. Manual scrubbing of data can be very costly and, in many cases, can offset (and negate) the potential benefits that data insights can provide to business process optimization. Machine learning is enabling higher quality insights to be obtained at a much lower cost than was previously achievable.

In business process optimization, there is an important distinction to be made between “change” and “improvement.” Machine-learning systems can correlate a large diversity of data sources – even without pre-defined relationships. They provide the ability to qualify operational (process) data with contextual (cost/value) data to help process managers quantify the impacts of inefficiencies and the potential benefits of changes. This is particularly important when developing a business justification for process optimization investments.

Machine learning is a true game changer for process optimization and process management professionals. Process analysis is now able to involve an exponentially larger volume of data inputs, process the data faster and at a much lower price point, and generate near-real-time insights with quantifiable impact assessments. This enables businesses to achieve higher levels of process optimization and be more agile to make changes when they are needed.