Revolutionize process mining with the AI advantage

In today’s rapidly evolving business landscape, where competition is fierce and challenges come out of nowhere, enterprises are actively seeking avenues to optimize customer outcomes and process efficiency. Artificial intelligence (AI) has emerged as a critical tool to enable businesses to achieve these objectives. According to a survey by McKinsey, 59% of the respondents said they saw revenue increase due to AI adoption. The survey also revealed that more than two-thirds of them expected their organizations to increase investment in AI.

The AI advantage in process mining

Process AI combines automation with real-time information and event processing.This combination drives innovative process optimization, leading to enhanced business performance.

Process mining is a data-driven approach that empowers organizations to discover, monitor, and improve business processes. By extracting valuable process data from the event logs of various applications, process mining generates a dynamic representation of the current processes and maps how they are being executed. This facilitates comparison with reference models. Enterprises can use the information to analyze actual processes to identify bottlenecks, inefficiencies, and areas of improvement. Taking this a step further, process mining infused with AI, i.e., process AI, combines automation with real-time information and event processing. This combination drives innovative process optimization, leading to enhanced business performance.

Following are some ways in which AI can elevate the value derived from process mining, steering organizations towards enhanced efficiency and excellence.

Predictive analysis

Process AI, powered by machine learning and neural networks, excels in predicting the sequential flow of events within a process. It also has the ability to calculate the resources required to complete a process, ensuring optimal resource allocation. It can also highlight the most critical process variables for the success of a process, allowing businesses to proactively manage them for superior outcomes. For example, a logistics firm can use process mining to detect delivery delays and determine factors causing them. Predictive analytics can be used to issue alerts regarding the probability of the occurrence of these factors, enabling timely and appropriate interventions.

Prescriptive analysis

Prescriptive analytics is the process of using data to determine an optimal course of action. Process AI involves using machine-learning algorithms for prescriptive analytics. These algorithms can parse large amounts of data faster and more efficiently than humans can. Using a combination of prescriptive business rules and insights derived from more extensive datasets, process AI facilitates next-best actions and real-time decisioning.

Process automation

Deviations in processes are differences between observed and expected results. They can occur at any stage of the process, resulting in inefficiencies. Process AI can capture deviations, compliance violations, exceptions, etc., from event logs and historical data. ML algorithms can detect outliers, patterns of deviations, and exceptions similar to given examples. Further, they can classify the outliers quickly and efficiently. These insights allow enterprises to determine and implement process changes and optimize processes.

Digital twins

According to Gartner, “A digital twin is a digital representation of a real-world entity or system.” Process AI can be used to develop digital twins, i.e., dynamic visualizations of models, processes, and data across business functions. Digital twins can be used for advanced simulations and what-if analyses using real-time data. Further, these data-driven digital twins can be used by enterprises to study performance issues and identify possible improvements in costs and performance. They can implement process improvements to generate greater business value.

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