Machine Learning and BPM - KB| Comidor Low-Code Platform

How Machine Learning Transforms BPM

How Machine Learning Transforms BPM 789 443 Comidor Low-code Automation Platform

If you already use BPM software in your business you already see the benefits: proper risk management, transparency, employee satisfaction, measurability, and sustainability of business processes are some of them.
Now, imagine using all the data from the BPM so as to automatically optimize processes and improve decision-making. With Machine Learning and BPM combined, you can accomplish just this. 

What is Machine Learning? 

Machine Learning (ML), according to Wikipedia, is the scientific study of algorithms and statistical models that computers systems used in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. In the 21st century, many enterprises have realized that Machine Learning will bring the revolution to every business sector, making the study of machine learning of vital importance. . Information technology could not be an exception. Machine learning is transforming the future of IT while it makes business software smarter and recovers valuable hours during the day of an employee. 

How Machine Learning and BPM can be combined? 

Plenty of data are stored in BPM software. Combining Artificial Intelligent and Machine Learning in Business Process Management, with these data, makes decision-making easier by identifying patterns as the process advances through the workflow. Existing Machine Learning tools already include libraries ready to be implemented in combination with BPM solutions so processes are improved immediately.

Machine learning | Comidor Low-Code BPM Platform

Below, we share 6 applications of BPM Machine Learning that help you understand the power of this combination:

        1. Process Scheduling 

Machine learning could be applied during process execution. For example, BPM Software triggers a new process or reroutes running processes according to predictions. After Machine Learning’s predictions, the BPM reacts immediately (task reallocation, form update) or in the long term (redesign the process or the user interface (UI), analyze UI performance). 

       2. Decision Recommendation

Managers have to make decisions, like “approve/reject a partnership proposal”,  “authorize/decline a new project”, and so on. The final decisions for cases like these can be analyzed through sophisticated Machine Learning algorithms (such as the decision trees and neural networks) in order to find the best decisions for other similar ones. 

        3. Recruiting 

A combination of Machine Learning techniques can be used also by the Human Resources department to optimize their business processes. Through Natural Language Processing (NLP) techniques, recruiters can analyze the CVs and create summaries of them. Also, they can predict how well a candidate will fit into the company based on additional interviews or other data. 

       4. Project Management 

Going beyond the above benefits, intelligent BPM with Machine Learning functionalities allows businesses to automate project management. So, the BPM can assign tasks to the most suitable team member, correct task estimates, and recommend corrective actions to meet deadlines. 

       5. Marketing 

Businesses target the campaigns right and organize a successful marketing strategy btaking advantage of the power of Machine Learning. For example, sales and marketing teams often spend hours figuring out which leads are the highest-value targets for sales promotion email campaigns. By incorporating Machine Learning in Business Process Management, a BPM tool is “taught ” to navigate lead dashboards and find the most valuable targets. 

      6. Process Mining and Machine Learning 

Regarding Wikipedia, process mining is a family of techniques in the field of process management that supports the analysis of business based on event logs . This new trend aims to improve process efficiency and comprehension of them. The data that arise from process mining can be used as input to create future predictions. For example, process performance can be monitored by evaluating how ongoing cases flow through the process until they are completed. 

Wrapping up

BPM is a process that is used in many industries. It has been used for decades to automate business processes and make them more efficient. With the help of Machine Learning, BPM has been able to advance and become more efficient than ever before. Machine learning can be incorporated into a company’s BPM system by using it for predictive analytics, anomaly detection, and optimization of workflow processes.

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