How artificial intelligence is fundamentally revolutionizing business process management

Deploying AI's discovery and automation capabilities in BPM can drive progress in front-end processes, process data analysis, business process mapping, and process modeling.

Business process management has a long history of helping enterprises with process engineering efforts and digital transformation initiatives. Now, BPM is getting a huge boost from AI

Jeff Springer, principal consultant at data and analytics consulting firm DAS42, said: "AI technology is advancing rapidly, making development more complex and more complex. Effective AI-driven process discovery and automation solutions are possible. He added that many of these advances are due to the increasing availability of data from many sources such as enterprise systems, sensors and social media, leading to larger-scale AI deployments. .For example, the development of deep learning algorithms enables artificial intelligence systems to learn from data and identify patterns that are difficult or impossible for humans to recognize.

How is AI changing BPM?

In BPM, AI-enabled deployment is becoming more and more popular. This deployment method plays an important role in many application scenarios. It can be used to optimize front-end processes, analyze process data, and map business processes, and even leverage generative AI process modeling capabilities

Front-office processes

##Gryphon, the provider of intelligent platform for call centers, has stated that the deployment of artificial intelligence in front-end processes is driving sales, improving customer satisfaction, and enhancing employee dedication. For example, in contact centers, AI in business process management enriches customer interactions, reduces call waiting time, provides personalized recommendations, and offers real-time sales assistance.

Process Mining

Process Mining is a key enabler of BPM, helping companies discover improved processes, create value and cost reduction opportunities. “Artificial intelligence helps make process mining faster and easier to use,” explains Chris Monkman, vice president of product management, artificial intelligence and knowledge at business process SaaS provider Celonis. “In contrast, process mining enables manual The data the intelligence (system) is being trained on becomes even smarter, thereby unlocking its true power. But when it comes to training large language models (LLMs) and generative AI’s battle against hallucinations, innovations in process intelligence will require improvements in real-time structured data and semantic knowledge.

Object-centric process mining

Celonis and RWTH Aachen University are combining artificial intelligence and object-centric process mining to to better understand and control business processes. For example, as real objects like shipping orders or invoices move through a business process, AI can continuously update expected delivery times, send alerts when delays occur, and even take action to resolve issues

Large Process Model

Enterprise management software company SAP Signavio is using labeled data from LLM to train a so-called Large Process Model (LPM) , to analyze process data more accurately. SAP and academic researchers have released the SAP Signavio Academic Models LPM dataset, a collection of hundreds of thousands of business models, primarily in business process modeling notation. Dee Houchen, head of global market impact at SAP Signavio, said LPM can be deployed in many use cases, such as best practice recommendations, process analysis, content creation and process data enhancement.

Data extraction and enrichment

ABBYY optical character recognition software provider ABBYY is exploring artificial intelligence technology, said Bruce Orcutt, senior vice president of product marketing at ABBYY How to extract more data from client documents and correspondence to speed up decisions on enrollment, funding and approval processes. AI can also be used to enrich data insights and improve process outcomes. “Data is king,” Orcutt said, “but AI helps make sense of all data and bring context and meaning to all data in ways that are impactful to the business.

Low-Code/No-Code Development

Traditionally, low-code and no-code tools have been combined with BPM analysis tools to help streamline business re-engineering efforts. John King, business process partner at Lotis Blue Consulting, said AI is using GitHub Copilot capabilities to enable more low-code/no-code development. This feature can promote decentralization of application development and promises faster changes and more A/B testing type deployments to meet customer needs. Enterprises can also develop and support applications that automate critical business processes with just IT department infrastructure and platform support

WORKING NETWORK ANALYSIS

Network analysis is a method of using graph theory to understand the structure and function of complex systems. King believes these same concepts can be extended to the enterprise through work network analysis, which can handle work content from meetings, phone calls, instant messages and emails. Through artificial intelligence's identification of behavioral and collaboration patterns and comparing them to company expectations and best practices, productivity can be improved when needed

digital twin

A digital twin is a working model that connects digital threads to real-world physical environments and complex processes. AI technology can help transform raw data captured from sensors and workflows into more relevant digital twins. In addition, King pointed out that artificial intelligence can also be applied to these models to provide different scenarios and decision-making analysis. He believes this will help save time and money and allow companies to model rare or expected events before they occur, thereby understanding the impact of the event in a safe but objective environment and developing contingency measures

Business Process Mapping

According to DAS42’s Springer, artificial intelligence and machine learning models are already being applied to automatically map business processes and identify areas for improvement and automation. Chance. He noted that one manufacturing company was successfully increasing output by 10% by monitoring its production lines in real time, identifying potential bottlenecks and other issues, and providing operators with corrective actions.

Business Process Analysis

Traditionally, business process analysis is done manually by process experts. Stephen Ross, head of business development for the Americas at cybersecurity consulting firm S-RM, said AI in BPM can accelerate business process analysis results for tasks involving modeling, collaboration, process mining, and risk management and compliance.

Chatbots, Virtual Assistants and NLP

Although chatbots and virtual assistants have been around for nearly 60 years, their commercial value did not exist until This has only been achieved in the last ten years. Powered by generative AI, natural language processing (NLP) opens up new business opportunities for chatbots and virtual assistants that can be integrated into BPM systems to handle queries, guide employees through processes, and improve customer interactions. NLP is also good at analyzing unstructured data sources, such as customer feedback and social media posts, to extract valuable insights.

The advantages of AI in BPM

Taking Gryphon’s Steele as an example, he pointed out that in BPM Applying artificial intelligence in China can identify opportunities to optimize processes, improve efficiency, reduce costs and create value, as follows:

• Identifies and automates repetitive tasks, freeing up call agents to focus for more complex tasks, increasing customer satisfaction.
• Route customers to the correct agent or department to reduce call wait times and ensure customers receive the best service.
• Provide real-time assistance to agents to resolve customer service issues faster and more efficiently.
• Analyze data to identify customer sentiment, trends and patterns to improve customer experience.

Challenges of Artificial Intelligence in Business Process Management

The benefits of deploying AI in BPM applications come with challenges, risks and ethical issues , including the following content starting with :

• Lack of overall overview. There is currently no consensus on how generative AI can contribute to BPM more broadly.
• Weaknesses of generative artificial intelligence. Concerns about accuracy, bias, reproducibility, data privacy, and hallucinations of LLM need to be addressed by vendor homogeneity.
• Data quality. Data used to train and operate AI systems must be clean, accurate and complete.
• New data risks. There needs to be greater scrutiny of siled AI within organizations and an understanding of where organizational data resides, what is made of it, and how it is used.
• Lack of skilled workers. Artificial intelligence and BPM require specialized skills and knowledge, which will require additional investment in professional training or hiring employees with the necessary skills.
• Afraid of jobs being replaced. Many organizations want generative AI and automation technologies to work in tandem, so they need to keep their employees in the loop and at the center of the transformation.
• Moral issue. Transparency, accountability and responsible use, as well as potential bias and illusion, are just some of the ethical considerations when applying AI to BPM.

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