Bridging the Gap: Aligning Operational Goals with Consumer Behavior via AI-Driven Services

Abstract

AI integrations into software-as-a-service utilities are aimed at enabling flexible and high-quality support experience for each service consumer dedicated to engaging a service provider. To accurately fulfill service promises of delivery times, uptime, and risk mitigation, management of each service consumer is complex and subjected to consumer behavior and production capabilities. In traditional process management approaches, the lack of representative benchmarks and means to balance quality and risks between consumers and providers comes with restricting automation potentials in consumer-prioritized high-paced service domains. AI-based consumer behavior estimation and service utilization prediction allows for complementing existing management paradigms in response to varying engagement-wise operational conditions. Aligning operational goals with consumer behavior towards guaranteeing service promises can be achieved through the accounting of quality affecting circumstances.

The application of AI in services has dramatically escalated, with methods from back-office operations to front-line customer care engagements now being utilized. AI-based service management provides increasingly individualized service while reducing unpredictability and mistakes [1]. In Zoho, for instance, millions of transactions are examined each day to anticipate issues, and Airbnb, the backend image processing of each image added is purely delayed due to content moderation requiring AI checks, before the image is recommended to consumers or other site visitors. The post-pandemic period has seen an increased need for driverless food delivery robots, whether due to resuming social lives or as a preventive measure against the spread of infection.

The monitoring of massive numbers of food delivery orders during the epidemic period is critical. In the face of pandemics, new AI support technologies require AI powers to provide real-time delivery capabilities through human intelligence support. To pre-screen delivery areas and times prior to daily order generation, running cohorting algorithms based on historical data is required. For orders from schools, hospitals, and offices well-specified in locations and preparation, working on batch orders is essential. For consumers’ common ones, serving ones through clustering based on pickup address area is crucial. Exploring each type of pre-screen notice can relieve peaks and efficiency improve [2].