Think Your Business Processes Are Fine? AI Process Mining Says Otherwise

Author(s): Konstantin Babenko

Originally published on Towards AI.

Process mining is an advanced data analytics technique to visualize, analyze and optimize business processes. It extracts knowledge from event logs recorded by information systems to offer a transparent view of ‘as is’ process execution, and frequently reveals inefficiencies and deviations not discovered by traditional methods.

AI is helping process mining become faster and more accurate by automating the extraction and analysis of huge amounts of complex data. Recent industry reports suggest that the market for process mining worldwide will increase from $1.8 billion in 2023 to more than $12.1 billion in 2028, with a compound annual growth rate (CAGR) of 45.6%. Process mining has been a game changer in the industry but without the aid of AI, it is raw data that just sits there, waiting to be interpreted. The following sections will discuss how the application of artificial intelligence is changing the fundamentals of process mining, from process discovery to prediction and more.

Key AI Technologies Powering Process Mining

Process mining combined with AI helps businesses get more out of process data and thus gain greater efficiency and flexibility. Key AI techniques transforming process mining include:

Predictive Analytics in Process Mining

Data mining techniques are useful to see patterns and trends, which gives insight into what may take place in the future. One of the most important benefits of employing artificial intelligence in process mining is the so-called predictive analytics capability. Using predictive analytics, businesses can identify risks such as workflow interruption, work backlog, or system breakdown. It also enables them to prevent such issues from occurring in the first place.

Using predictive analysis for manufacturing activities can reduce operational costs by 10–20%. This is done through greater transparency into operations, and the capacity to act on this information in real time. As a whole, machine learning algorithms empower important predictive solutions that can help businesses forecast risks and problems in advance.

Incorporating Unstructured Data with NLP

Traditional process mining tools rely on structured event log data provided by enterprise systems including ERP or CRM platforms. Yet, a large portion of organizational data is unstructured (emails, customer service interactions, contracts, etc.). AI-powered process mining can include unstructured data through natural language processing (NLP). It analyzes text-based data using NLP algorithms to extract meaningful information that is subsequently used to enrich the analysis.

This has therefore made NLP a vital tool for businesses looking to improve their processes from all angles. One of Deloitte’s studies revealed that many companies using NLP in process mining saw an increase in process efficiency by 15–25%, largely because NLP allows for integrating unstructured data into standard process workflows.

AI-Powered Event Log Analysis for Process Efficiency

Event log analysis has advanced significantly with the rise of AI. Event log data (all events and transactions in the system) makes up the foundation of process mining. It is important to note that AI in event log analysis is not limited to traditional dashboard making, it also analyzes the root cause of process non-compliance and its surface symptoms.

A success story in this regard is that of Zurich Insurance which incorporated AI technology in event log analysis to enhance efficiency in claims handling. Of particular note, AI spotlighted processes that were ineffective and pointed out time delays in the processing of claims. This activity helped Zurich insurances to reduce the efficiency of claim processing by 18% and better organizational effectiveness. Thus, in industries with a heavy flow of business activities, AI comes in handy due to its advantage of identifying variations in the given operational processes.

Continuous Process Improvement via Reinforcement Learning

Reinforcement Learning (RL) is becoming a powerful technique for continuous process improvement. While most conventional methods of training incorporate parameters of the model or input/output pairs, RL aims to identify the ideal action through feedback. Reinforcement learning can be deployed in process mining for ongoing improvement, as the system will be continuously learning from real-time feedback and addressing changing environments.

For instance, Reinforcement Learning algorithms were used by global telecom provider Telefónica to optimize their customer onboarding process. AI continuously analyzed real-time customer interactions and outcomes, figuring out what variations to the process were most effective, and then adjusting workflows on the fly, leading to notable reductions in time and enhancements in customer retention.

Process Simulation and Scenario Analysis using AI

AI also changes process simulation and scenario analysis. Businesses use AI-backed simulations to predict what will happen when the process changes and make decisions with more confidence.

In the automobile industry, for example, Toyota modeled the effect of deploying new production lines with AI-powered process simulation. Toyota simulated different scenarios to determine the most efficient production configurations and reduce setup times.

AI in Process Mining: Use Cases

Automating process analysis, identifying bottlenecks, and making predictions, AI-driven process mining unlocks new levels of efficiency and insight across industries. Below are key use cases of AI-backed process mining.

Workflow Automation in Financial Services

Operational efficiency, customer experience, and regulatory compliance are all areas in which the financial services sector is under severe pressure to improve. Banks and financial institutions can use AI-driven process mining to understand their workflows when dealing with complex transaction data and pinpoint inefficiencies in real time. For example, Deutsche Bank used AI-based process mining to automate its loan approval processes. AI analyzed thousands of loan applications and found process bottlenecks to recommend improvements. The result was a substantial reduction in time required to process a loan and an increase in customer satisfaction. On the other hand, AI was used to identify patterns that increased the risk of operation, which the bank was able to prevent from becoming threats.

Uncovering Bottlenecks to Boost Healthcare Efficiency

Operational efficiency is directly responsible for patient care and outcomes in healthcare. AI-driven process mining has been successfully applied to analyze patient journeys, hospital workflows and administrative processes, eliminating inefficiencies and streamlining the work. For instance, Mount Sinai Health System in New York used AI-enhanced process mining to enhance patient discharge processes. By applying AI to event logs from electronic health records (EHRs), certain inefficiencies in discharging processes were discovered. As a result, patient discharge time was decreased by 20% and hospital beds were freed up more quickly, helping patient flow at Mount Sinai.

Optimizing Production Efficiency in Manufacturing

AI-driven process mining is increasingly being used by manufacturers to optimize production processes, minimize downtime and increase overall equipment effectiveness (OEE). AI can also look at data from production lines to find patterns, predicting potential disruptions before they happen so the lines run more smoothly and are down less often.

Manufacturing leader Siemens used AI-powered process mining to optimize its production lines. Machine data, employee workflows and production schedules were analyzed by AI to unearth hidden inefficiencies. After adopting AI-driven process mining solutions, Siemens reduced lead times and increased production efficiency. Moreover, predictive maintenance insights helped the company to reduce machine downtime and significantly contribute to reduced costs.

Predictive Process Monitoring for Smarter Supply Chain and Logistics

There are many variables in supply chain and logistics operations — from supplier performance to transportation schedules. Companies can get real-time insights with AI-driven process mining, which enables predictive monitoring & optimization.

Global logistics leader DHL used AI-powered process mining to optimize its supply chain operations. AI used historical and real-time data to predict potential delays in deliveries based on traffic conditions, weather and supplier bottlenecks. These insights were used by DHL to automatically reroute shipments, reducing delivery delays and fuel consumption.

Compliance and Risk Management in the Insurance Industry

In heavily regulated industries like insurance, compliance and managing risk are at the top of the agenda. AI-driven process mining enables insurers to automate compliance monitoring and uncover potential risks before they become expensive problems.

One of the world’s largest insurers, AXA, automated its claims management process using AI-enhanced process mining. Thousands of claims were monitored in real-time by AI algorithms, which picked up on patterns that suggested it was probably fraud or noncompliance. This resulted in AXA reducing fraudulent claims, increasing compliance with regulatory standards and saving millions in operational costs.

Final Thoughts

Artificial intelligence is indeed a worthwhile investment to enhance business processes and decisions. AI not only facilitates the identification of improvements but also provides an increased and higher-quality understanding of business processes. With AI, we can bring insights out of complex data that were previously buried, making smarter, faster, more impactful decisions. The algorithms also update and improve the quality of insights over time and provide additional data about what is valuable.

With the right approach, companies can unlock efficiencies, better decisions and eventually build a more agile, resilient organization. From my perspective, those who take that leap now will be well-situated to lead the future.

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Published via Towards AI