Business process management has a long history of supporting enterprise process engineering and digital transformation efforts. BPM is now being greatly improved by AI.
“AI technology is advancing rapidly, enabling the development of more sophisticated and effective AI-powered process discovery and automation solutions,” said Jeff Springer, principal consultant at data and analytics consultancy DAS42. ” he said. Many of these advances are due to the increased availability of data from many sources such as enterprise systems, sensors, social media, etc., resulting in massive adoption of AI, he said. added. For example, the development of deep learning algorithms allows AI systems to learn from data and identify patterns that are difficult or impossible for humans to identify.
How will AI transform BPM?
AI-enabled implementations are finding numerous applications for BPM, from improving front office processes to analyzing process data, mapping business processes, and leveraging generative AI process modeling capabilities.
front office processes
Brian Steele, vice president of product management at Griffon, a call center intelligence platform provider, says the introduction of AI into front office processes has increased sales, improved customer satisfaction and improved employee engagement. Masu. For example, in contact centers, his AI in business process management enhances customer interactions, reduces call wait times, personalizes recommendations, and provides real-time sales assistance.
Process mining is a key enabler of BPM, helping companies discover opportunities to improve processes, create value, and reduce costs. “AI helps make process mining much faster and easier to use. Conversely, process mining creates the data that AI needs. [system] “We're training much more intelligently and unlocking real power,” said Chris Monkman, vice president of product management, AI and knowledge at Celonis, a business process SaaS provider. LLM) training and generation When it comes to AI struggles and hallucinations, process intelligence innovations require real-time structured data and improved semantic knowledge.
Object-centric process mining
Celonis and RWTH Aachen University are combining AI and object-centric process mining (representing the actual objects and events in a process) to improve the understanding and control of business processes. For example, as real objects such as shipping orders or invoices pass through a business process, AI continuously updates the estimated delivery date, sends alerts in case of delays, and takes steps to resolve the issue. You can also take steps.
Large scale process model
Process management company SAP Signavio uses labeled data from LLM to train so-called large-scale process models (LPMs) to analyze process data more accurately. SAP and academic researchers have released the SAP Signavio Academic Models LPM data set, a collection of hundreds of thousands of business models created primarily in Business Process Modeling Notation. According to Dee Houchen, Head of Global Market Impact at SAP Signavio, LPM has the potential to be deployed in many use cases, including best practice recommendations, process analysis, content creation, and process data enhancement.
Data extraction and enrichment
ABBYY, an optical character recognition software provider, is using AI technology to improve customer documents and communications to accelerate decision-making in registration, funding and approval processes, said Bruce Orcutt, ABBYY's senior vice president of product marketing. The company is currently exploring ways to extract more data from the data. AI can also be used to enhance data insights and improve process outcomes. “Data is king,” Orcutt said, “but AI can help make sense and bring context and meaning to all your data in ways that are impactful to your business.”
Low code/no code development
Low-code and no-code tools have traditionally been combined with BPM analysis tools to streamline business re-engineering efforts. “AI is using his GitHub Copilot capabilities to enable more low-code/no-code development,” said John King, Business Process Partner at Lotis Blue Consulting. This feature facilitates decentralization of application development, promising faster change rates and the deployment of more A/B test types to meet customer needs. Companies can also rely solely on infrastructure and platform support from their IT department to develop and support applications that automate critical business processes.
work network analysis
Network analysis uses graph theory to understand the structure and function of complex systems. King speculated that these same concepts could be extended to business through workplace network analysis, which processes artifacts from meetings, phone calls, and instant messages and emails. AI can identify patterns in behavior and collaboration, compare them to company expectations and best practices, and improve productivity where necessary.
A digital twin is a working model of a physical environment and complex processes that is connected to the real world through a digital thread. AI helps transform raw data from sensors and workflows into more relevant digital twins. AI can also be applied to these models to provide different scenarios and decision analysis, King added. “This saves time and money, allowing companies to model rare and expected events before they occur, understand the impact of events in a safe and objective environment, and proactively plan for unexpected events,” he said. “We will be able to formulate a plan,” he reasoned. ”
business process mapping
DAS42's Springer said AI and machine learning models are already being used to automatically plan business processes and identify opportunities for improvement and automation. At one manufacturing company, he said, he uses an AI system to monitor production lines in real time, identify potential bottlenecks and other issues, and recommend corrective actions to operators. Production volume increased by 10%.
business process analysis
Business process analysis has traditionally been performed manually by process experts. Stephen Ross, head of business development for the Americas at cybersecurity consultancy S-RM, said AI in BPM has the potential to accelerate business process analysis results for tasks including modeling, collaboration, process mining, risk management and compliance. He said there is.
Chatbots, virtual assistants, NLP
Chatbots and virtual assistants have been around in some form for nearly 60 years, but their business value has only been recognized in the last decade. Natural language processing (NLP) powered by generative AI is a new tool for integrating chatbots and virtual assistants into BPM systems to handle inquiries, guide employees through processes, and improve customer interactions. It brings great business opportunities. NLP is also great at analyzing unstructured data sources such as customer feedback and social media posts to extract valuable insights.
Benefits of AI in BPM
Using the example of contact centers, Gryphon's Steele says AI in BPM can be used to uncover process optimization opportunities, improve efficiency, reduce costs, and create value in the following ways: said that it can be done.
- Identifying and automating repetitive tasks frees up call agents to focus on more complex tasks instead, increasing customer satisfaction.
- Direct customers to the right agent or department to reduce call wait times and ensure your customers receive the best service possible.
- Provide agents with real-time assistance to resolve customer service issues faster and more efficiently.
- Analyze data to identify customer sentiment, trends, and patterns to improve the customer experience.
AI challenges in business process management
Along with the benefits of implementing AI in BPM applications, there are also challenges, risks, and ethical concerns, including:
- Lacking an overall overview. There is no consensus on how generative AI can facilitate BPM more broadly.
- Weaknesses of generative AI. Concerns about LLM, such as accuracy, bias, reproducibility, data privacy, and hallucinations, must be addressed uniformly by vendors.
- Data quality. The data used to train and operate AI systems must be clean, accurate, and complete.
- New data risks. Siloing AI within organizations and understanding where organizational data resides, what it consists of, and how it is used requires closer scrutiny.
- Shortage of skilled labor. AI and BPM require specialized skills and knowledge, requiring additional investment in specialized training and hiring employees with the required skills.
- Fear of leaving the job. Many organizations want generative AI and automation technologies to work in tandem, requiring employees to stay informed and at the center of the transformation.
- ethical issues. Transparency, accountability, responsible use, and potential bias or illusions are just some of the ethical considerations when applying AI to BPM.
George Lawton is a journalist based in London. Over the past 30 years, he has written more than 3,000 articles on computers, communications, knowledge management, business, health, and other areas of interest.