Improving Efficiency and Effectiveness in Industrial Support Business Processes through Low-Code Conversational AI: Evidence from a Workflow-Embedded Case Study
Industrial support business processes often involve work outside core production activities, including record retrieval, spreadsheet checking, supplier communication, and follow-up of operational events. We examine these issues in a maintenance-support case where a low-code conversational Artificial Intelligence (AI) layer was connected to existing information and communication routines. Two agents were configured: ManuBot, for querying and updating maintenance-history data, and MailBot, for recurrent supplier-email handling. The empirical sequence covered baseline diagnosis, prototype testing and implementation-stage evaluation, drawing on workflow observations, user feedback, task comparisons and records from the implemented tools. The clearest measured changes were task-specific. MailBot reduced supplier-email preparation from about 12-15 min to 2-3 min per message. ManuBot reduced maintenance-data retrieval and querying time by approximately 50%. Users also reported easier access to historical malfunction records, better visibility of recurrent events, and more structured email routines. The case remained constrained by incomplete ERP (Enterprise Resources Planning) integration, data-structure quality, platform permissions and differences in user readiness. The evidence points to a task-specific use of low-code conversational AI: gains were observed when the agents were tied to specific records, supplier-email workflows and human validation points.