AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery
DOI:
https://doi.org/10.21590/Keywords:
Artificial Intelligence (AI), Conversational AI, Customer Experience Automation, Natural Language Processing (NLP), Chatbot Interactions (Personalized), Artificial Intelligence (AI) Customer Support, Intelligent Virtual Assistants.Abstract
The systems of AI customer service have advanced very far in the field of Conversational AI, Natural Language Processing (NLP) and Intelligent Virtual Assistance, but complete systems still cannot handle complex, ambiguous or emotive customer requests. The article proposes a Hybrid Human-Machine Collaboration Model (HHMCM) of the real-time service delivery depending on the speed and adaptability of the AI-driven automation with the degree of judgment, empathy, and awareness of situations in human agents. The model includes four main layers starting with the AI-driven Interaction Engine, which recognizes an intent and customizes chatbots using transformer-based NLP models and identifying high-risk or ambiguous cases; a Knowledge Orchestration Layer, which dynamically retrieves product, policy, and contextual information; a Human-in-the-Loop (HITL) workflow, which routes high-risk or unclear cases to expert agents and a Continuous Feedback Reinforcement Module, which corrects and improves the system using agent feedback and customer feedback.
The HHMCM was applied on a 50,000 anonymized interaction log dataset of a financial services helpdesk and compared to a baseline of fully automated chatbot. The metrics that were measured were five and these included average resolution time, first-contact resolution (FCR), customer satisfaction (CSAT), accuracy of intent classification, and frequency of escalation. The results have shown that the hybrid model allowed reducing the mean resolution time by 46, FCR by 38, CSAT by 29 and reducing escalations by 41. Another advantage that the model had was a stronger accuracy of intent detection particularly where query has multiple intent and context-intensive query. The findings can validate the concept of a hybrid AI-human solution as the potential to enhance the customers-delivering service in real time, and offer a scalable, reliable, and customer-centric automation solution.
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