AI and process mining, when merged, can help businesses extract data from operational systems and event logs to understand business processes.
Process mining is a method of extracting knowledge from event logs that are generated by enterprise systems such as CRM (customer relationship management) and ERP (enterprise resource planning). Since process mining uses event logs for extracting knowledge about processes, it makes it possible to gain insights based on facts and not conjectures. By pairing and comparing event logs data and existing process models, process mining helps to detect various flaws in the model and recommends redesigns wherever needed.
For extracting useful insights from event logs, process mining uses several data mining techniques and visual analytics capacities. With the help of these techniques, process mining identifies trends, patterns, and details contained in the event logs. And, after extracting insights and knowledge from logs, process mining provides output process models.
The output process models are redesigned models that are recommended by process mining. Enterprises can enhance the designs of output models by merging AI and process mining. Machine learning algorithms can determine the sequence of tasks for maximizing the process efficiency. Also, to replace the existing models with recommended models or to implement changes into existing ones is a complicated task. And even in this scenario, AI technology can add to process mining by automating the implementation of new process models without causing much disruption in operations.
Using AI and process mining together
Process mining can use predictive analysis capability of AI to gain better insight into data and create models that have the most useful impact on businesses. And, AI algorithms can also determine the need for resources for different tasks and help allocate them optimally.
Leveraging predictive and prescriptive analytics
With real-time content monitoring, ML algorithms, and deep learning neural networks can predict the sequence of events that would occur next. For instance, how many pages will a person visit before landing on a checkout page in an e-commerce website. Along with finding the probability of a task in a process getting completed, ML algorithms can also calculate how much resources will be needed to complete a specific process. And by finding the number of resources required for a task, AI helps to remove non-productive resources for that task to reduce overall cost. For instance, if a simple task is given excess energy resources, then AI can help allocate that excess resources to other tasks where required.
ML can identify which process variables can contribute most to the final outcome of a task and help businesses to focus more on such variables. Process variables are the parameters that can have a major impact on processes ‒ for instance, the pressure, temperature, flow, and others are process variables for a boiler operation.
Connecting tasks into processes
Several simple tasks can be a part of different processes. For instance, sending an email is a task that can be a part of different processes such as providing product information to clients, complaint management, and updating employees about the arrival of any new products. But the integration of a single task into many processes increases the complexity of process models. Vendors are creating AI-enabled bots that can run on the desktop of employees and record discrete tasks with the help of computer vision. For instance, vendors have created a Virtual Process Analyst that can discover and document processes within weeks.
This analyst delivers zero-integration experience by eliminating the need for connecting to any log, database, or API access. Recording various tasks will help to understand a complex process with higher granularity. Further, constant recording gives access to up-to-date process information that can help process mining techniques to create the best output models.
Making process recommendations
Process mining does provide recommendations on processes, but those recommendations are not real-time recommendations as they are based on previous event logs. Hence, to overcome such problems, AI capabilities are now embedded in process mining tools to interpret processes and make real-time recommendations. For instance, an Action Engine has been created that can constantly analyze data from event logs. The action engine can further send signals to everyone involved in a process to recommend actions that can help improve the efficiency and accuracy of the process. Enterprises can also set rules to allow an action engine to make necessary changes by itself.
Thus AI applications such as the action engine can provide real-time recommendations to take appropriate actions right on time. It is important to identify process bottlenecks, but accelerated outcomes can occur if businesses can get recommendations to remove the frictions causing those bottlenecks and create a fluid process flow.
Identifying processes to automate
The process of creating RPA bots is much easier and quicker than other technical approaches such as API. And there are many situations where APIs fall short to RPA. Hence, enterprises are trying to implement the use of RPA and automate processes wherever applicable. However, it can be difficult to identify the processes that can be automated. For instance, some processes where the costs of automating are greater than the benefits should not be automated. Also, some processes where the human factor is important should not be automated.
Similarly, there are many other things that a CIO should know about AI before automating a process. But, to manually identify processes that can be easily automated is time-consuming and challenging. And that’s where AI comes into the picture. With the help of computer vision, enterprises can identify processes that can be automated and implemented quickly.
Several companies have started creating AI tools that can help identify processes that can be easily automated. For instance, an AI-enabled process mining tool called Process Discovery can help automatically map all processes of an organization and help to identify the processes that can be automated.
Identifying deviations
Deviations in processes are differences between observed and expected value for a process or product. These deviations may occur during the manufacturing of a product or its acceptance by consumers. And they may also be triggered while customer complaints. Since deviations may occur at various stages of a process, traditional process mining tools cannot identify deviations from event logs, and it requires human analysts to observe and identify them.
AI has the potential to uniquely identify patterns of deviations in any process and prevent their reoccurrence. Further, ML algorithms can categorize deviations into minor, major, and critical based on the attributes they affect. For instance, if a deviation does not affect any quality attribute or a critical process parameter, then it would be categorized as a minor deviation. On the other hand, if a deviation affects any quality attributes or critical parameters, then it can be categorized as a major or critical deviation. Based on such categorization, enterprises can schedule which deviations need quick management and which ones can be managed later.
Process mining uses data from event logs, but with the help of ML algorithms, it can also use historical data of processes to make future predictions. Dr. Lars Reinkemeyer, head of global process mining services at Siemens, said, “The innovative algorithm enables us to make complex processes transparent–something that simply couldn’t be done before.” These words of Dr. Lars Reinkemeyer reflect that a synergic mix of AI and process mining can enhance the efficiency of processes in organizations like never before.
