Identifying and Exploiting the Potential for Optimization in Business Processes
Over half of automated business processes fail to accurately reflect the “happy path”—the ideal scenario—in practice. As a result, companies miss out on significant opportunities for excellence, efficiency, and quality. Artificial intelligence (AI) can help optimize automated processes, fully leveraging the benefits of business process automation.
Several factors contribute to why automated processes often deviate from the ideal path. Often, those responsible have differing opinions on what constitutes the correct approach. Deviations are then never far behind, particularly when an increasingly complex process is involved. Other obstacles include inaccurate data, incomplete information, or missing key details. These issues compromise the excellence of the automated process and diminish the anticipated benefits of increased efficiency and time savings.
“Artificial intelligence can address these challenges,” says Andreas Balsiger, Head of Product Management at Axon Ivy. “AI helps to better understand the workflows seen in business processes and to optimize their efficiency, speed, and quality during implementation.” A prime example is process mining, which examines, reconstructs, and analyzes business processes based on digital traces in IT systems. It uses data automatically collected during process execution, such as logs of processes, events, activities, or transaction data. This data is then used to map workflows, identify patterns, deviations, and weaknesses, and optimize processes.
The effective implementation of AI in a company requires an automation platform as a central hub, which ensures seamless processes and optimal synchronization of the various technologies involved. “AI offers tremendous potential for automating business processes across industries. Take machine capacities and maintenance in the construction machinery or production industry, for example. Among other aspects, automated data transmission can signal capacity limits and maintenance cycles. This allows machines to be operated without downtime and serviced at optimal times based on workload. As a result, benefits such as efficient equipment operation and minimized downtime can be quickly and easily achieved,” explains Andreas Balsiger.
