arXiv:2607.06017v1 Announce Type: new Abstract: We study a human-AI service system in which tasks arrive sequentially and are processed through a two-stage architecture: an automated chatbot followed, when necessary, by a human agent. We consider $T$ sequentially arriving tasks, each belonging to one of $K$ heterogeneous types. For each task the decision maker chooses how many resources to allocate to the chatbot, whose type-dependent success probabilities are initially unknown. Tasks not resolved by the chatbot enter type-dependent human-service queues, where they are processed by a human age
The proliferation of AI chatbots and large language models necessitates research into optimal integration strategies with human workflows to maximize efficiency and customer satisfaction.
Optimizing human-AI collaboration in service systems is crucial for businesses aiming to scale operations, reduce costs, and improve service quality in an increasingly AI-driven landscape.
This research provides a framework for dynamically managing resources between automated and human agents, allowing for more adaptive and efficient service delivery based on real-time performance data.
- · Businesses adopting human-AI hybrid service models
- · AI service providers
- · Customer service sectors
- · Companies relying solely on fully human service models
- · Inefficient AI-only customer support systems
Companies will begin to implement more sophisticated dynamic routing and resource allocation strategies between chatbots and human agents.
This will lead to improved customer satisfaction, reduced operational costs, and higher employee efficiency in service departments.
The success of these models could accelerate the adoption of similar human-AI collaboration frameworks across other white-collar workflows, potentially redefining job roles and skill requirements.
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Read at arXiv cs.LG