Artificial Intelligence in Business Process Management

For example, Artificial Intelligence can support process simulation by analyzing historical process data. Through the use of machine learning and deep learning, AI algorithms can identify patterns and dependencies within the process data and integrate them into a simulation model. Within this simulation model, various scenarios and process variants can be simulated and tested. This allows companies to calculate key performance indicators (KPIs) such as process costs and lead times, as well as identify bottlenecks and estimate the impact of process changes in advance. They can quickly see the duration of each process step, where delays occur in the process, how often a process step is executed, and the cost of individual steps as well as the entire process. By varying the parameters, a wide range of use cases can be represented to develop an optimal setup without using real resources. Thanks to AI, particularly complex relationships and interactions between different process variables can be modeled, contributing to a better understanding. Overall, companies can gain a better understanding of their processes, identify risks and opportunities, and make informed decisions.

Artificial Intelligence can also support the analysis, visualization, and improvement of business processes in the context of process mining. One possible application is automated process discovery. AI can help automatically identify and model processes by analyzing data from various source systems (e.g., CRM or ERP systems). By analyzing log data or event logs from these sources, AI can identify patterns and relationships to visualize the process flow and identify individual steps.
Based on this data, AI can also detect deviations from defined processes by uncovering unusual activities, delays, or errors. This enables companies to identify and address potential issues in a timely manner. In addition to process deviations, AI can discover weaknesses, redundancies, and bottlenecks in business processes and provide suggestions for process optimization.
The significant advantage of AI-driven process mining is that processes can be monitored in real-time, not just analyzed after their completion. With real-time monitoring, companies can obtain up-to-date information about the process status and intervene immediately if necessary to solve problems or seize opportunities.

Artificial Intelligence also plays an increasingly important role in the field of process automation. The use of AI in the form of software bots allows for the programming of automated sequences to execute manual, repetitive, and rule-based workflows. These digital robots independently handle tasks that would typically be performed by an employee, significantly speeding up processes, reducing costs, and minimizing errors. This also allows employees to focus on more demanding tasks and utilize human resources more effectively.
Workflows such as submitting vacation requests, processing invoice receipts, preparing employee onboarding, and reviewing purchase inquiries can all be automated using this approach. With continuous advancements and developments in the field of Artificial Intelligence and machine learning, not only simple and one-dimensional processes but also complex end-to-end processes involving multiple stakeholders, departments, and various systems can be automated - tasks that previously required human intelligence.

Only a few employees are satisfied with how decisions are made in their companies. This is because significant decisions are often time-consuming and involve multiple stakeholders. AI can support complex decision-making processes by providing extensive information, simulating alternative scenarios, or suggesting optimal decisions. It processes structured and unstructured data from internal and external sources, extracting all the necessary information to identify patterns relevant to the decision-making process. This enables decision-makers to make better-informed decisions and consider potential risks or impacts.
It is even possible to automate process decisions using AI without the need for further human intervention. Based on predefined rules, patterns, or algorithms, AI can autonomously make decisions and trigger actions. The result: fewer errors and faster decision-making.

Another application area of AI includes process and risk predictions. For this purpose, historical data on process flow and outcomes are collected and analyzed to identify patterns and trends. This information is then used to make predictions about how the process will unfold in the future. This allows companies, for example, to proactively detect and address bottlenecks to avoid delays in the process flow. It also enables companies to respond more quickly to market changes and demand and adjust their processes accordingly. Overall, AI enables a proactive and forward-looking approach. Typical examples include:

• Detect credit risks and fraudulent activities in the financial and insurance sectors.
• Determine pricing strategies, projected sales volumes, and optimal inventory levels in supply chain management.
• Predict when machines need to be serviced or replaced.
• Identify potential applicants, reduce employee turnover, and increase employee engagement.
• Detect dissatisfied customers to initiate appropriate countermeasures at an early stage.