Business Process Management (BPM) is evolving rapidly. Building on a rich research portfolio that spans from early workflow automation to robotic process automation (RPA), large language models (LLMs) are the most recent disruptive technologies to influence all BPM capability areas. Particularly, these technologies fuel the emerging research fields of AI-enhanced business process management systems and autonomous process execution. In a design science research approach, we develop a modular LLM agent architecture that effectively enables the adaptation and execution of business processes. The architecture integrates a Frame Agent that can generate process descriptions and an Operational Agent that autonomously executes processes based on the Frame Agent’s process descriptions. It further enables a future Tactical Agent for autonomous process adaptations. We demonstrate the architecture’s applicability and utility with the real-world case of a meter-to-cash process, comparing it to the performance of an RPA bot. Our findings provide early insights into the challenges faced by autonomous process execution and its relation with RPA in terms of adaptability, flexibility, and complexity in autonomous process execution. While researchers can build on our findings to establish modular LLM-agent architectures for autonomous process execution, practitioners can derive early insights into the design of LLM agents for process automation. • Properties and perspectives of AI-enhanced business process management systems. • Modular LLM agent-based system design with process frame for adaptive and autonomous process-aware execution. • Evaluation based on real-world meter-to-cash process data.

High streets across Europe continue to lose consumers to online retail, leading to business closures and the decline of city centres, impairing cities’ overall liveability. To counter this vicious cycle, our study presents smartmarket2, the first instantiation of a digital actor engagement platform designed specifically for high streets. smartmarket2 enables hybrid online-offline customer journeys by connecting consumers to stores and other high street service providers. In an action design research (ADR) project, we design, implement and evaluate smartmarket2, involving 150 high street operators and 2,300 citizens in three cycles of building, intervention and evaluation. We derive four design principles that contribute prescriptive knowledge on the design of digital actor engagement platforms. Our results reveal that such a platform is able to increase engagement, but that it is subject to actors’ engagement dispositions.

The diffusion of electric vehicles suffers from immature and expensive battery technologies. Repurposing electric vehicle batteries for second-life application scenarios may lower the vehicles’ total costs of ownership and increases their ecologic sustainability. However, identifying the best – or even a feasible – scenario for which to repurpose a battery is a complex and unresolved decision problem. In this exaptation research, we set out to design, implement, and evaluate the first decision support system that aids decision-makers in the automobile industry with repurposing electric vehicle batteries. The exaptation is done by classifying decisions on repurposing products as bipartite matching problems and designing two binary integer linear programs that identify (a) all technical feasible assignments and (b) optimal assignments of products and scenarios. Based on an empirical study and expert interviews, we parameterize both binary integer linear programs for repurposing electric vehicle batteries. In a field experiment, we show that our decision support system considerably increases the decision quality in terms of hit rate, miss rate, precision, fallout, and accuracy. While practitioners can use the implemented decision support system when repurposing electric vehicle batteries, other researchers can build on our results to design decision support systems for repurposing further products.