Overview
Since the release of ChatGPT in late 2022, companies have considered many use cases for Generative AI within the enterprise. ChatGPT’s generative AI capabilities ignited significant interest in the potential for Large Language Models (LLMs) within the enterprise, particularly in customer service applications. Customer service became a focal point of GenAI applications because these applications significantly enhance customer service representative productivity. Boston Consulting Group estimates that fully implemented LLM-powered customer service solutions can boost rep efficiency by 30-50%, a compelling prospect for any organization.
Meanwhile, a survey of customer service leaders conducted in 2022 found that 95% of those leaders believed that their customers would be served by an AI bot within 3 years (i.e., by 2025).
Recognizing the substantial productivity gains offered by GenAI, companies are embracing its integration into customer care at an accelerated pace. While risks and challenges remain, the potential benefits continue to drive this rapid adoption.
Examples of GenAI Assisted Customer Service Applications in Production:
- JetBlue embraced cutting-edge AI technology through its collaboration with ASAAP, implementing Generative AI solutions to streamline its customer service operations. This innovative approach yielded significant efficiency gains, saving an average of 280 seconds per chat and freeing up 73,000 valuable agent hours within a single quarter.
- Delta Airlines leverages Generative AI through its “Ask Delta” chatbot, allowing passengers to independently manage tasks like check-in, bag tracking, and flight searches. This user-friendly platform has contributed to a demonstrably positive outcome, reducing call center volume by 20%.
- H&M, the Swedish fashion retailer, implemented a Generative AI-powered chatbot on its website, achieving up to a 70% reduction in response times compared to traditional human agents.
- SmileDirectClub, a leading oral care innovator, known for its democratization of convenient and affordable smile transformations through 3D-printed aligners, embraced Generative AI to optimize its customer care operations. As reported by ciodive, SmileDirectClub is implementing a groundbreaking AI chatbot that listens to and summarizes customer calls. This insightful technology empowers call center agents by providing comprehensive call summaries, enabling them to efficiently review, prioritize, and personalize responses. For agents juggling multiple calls, this AI assistant translates to valuable time saved and ultimately a more streamlined and effective customer experience.
12 Steps to Implementing an LLM-based Chatbot for Customer Service:
- Define Objectives and Scope:
- Determine what the chatbot should achieve (e.g., answering FAQs, guiding users through troubleshooting, handling bookings or orders).
- Establish the scope of the chatbot’s capabilities and limitations.
- When it comes to its limitations — will it escalate to a human agent for complex queries? How will it handle out-of-scope questions or errors?
- Choose the Right AI Model:
- Analyze and select the appropriate AI model, such as Generative AI (GenAI), that aligns with your specific requirements. Evaluate critical factors like language capabilities, adaptability through learning mechanisms, and seamless integration with existing systems.
- Gather and Prepare Data:
- Gather and curate a comprehensive dataset of relevant training materials, including historical customer service interactions, readily available FAQs, and detailed product/service information, to drive optimal AI model performance.
- Customize and Train the Model:
- Customize the AI model to your specific use case. This might involve fine-tuning the model with your data to ensure it understands your business context and terminology.
- Continuously train the model with new data to improve accuracy and relevance.
- Develop Integration and Infrastructure:
- Set up the necessary infrastructure for hosting the chatbot.
- Integrate the AI model with your customer service platform, ensuring it can access relevant databases and systems (e.g., CRM, order management system).
- Ensure Compliance and Privacy:
- Prioritize data security and regulatory compliance by diligently ensuring the chatbot operates within the parameters of relevant data protection regulations like GDPR and CCPA.
- Implement robust data governance measures to safeguard customer information and privacy, maintaining the highest standards of ethical and compliant chatbot development.
- Testing and Quality Assurance:
- Implement a testing protocol to confirm the chatbot accurately responds to user queries across diverse scenarios, ensuring its effectiveness and reliability.
- Execute a testing strategy encompassing various scenarios, error handling mechanisms, and user acceptance testing to guarantee the chatbot delivers optimal performance and meets user expectations.
- Deploy the Chatbot:
- Roll out the chatbot, starting with a limited release to gather initial user feedback.
- Monitor performance closely during the initial stages.
- Collect Feedback and Iterate:
- Proactively gather user insights and track key performance metrics to fuel and elevate the chatbot’s capabilities.
- Empower the chatbot through a feedback loop, capturing user interactions and analyzing performance data to drive ongoing improvements.
- Continuous Monitoring and Updating:
- Regularly update the chatbot based on new products, services, or changes in customer service policies.
- Continuously monitor for any issues and retrain the model as needed to adapt to new types of queries or changes in language use.
- Training Staff:
- Provide comprehensive training for your customer service team on collaborating with the chatbot, covering its functionalities, limitations, and escalation procedures for complex inquiries.
- Empower your customer service agents through targeted training that fosters seamless handoffs from the chatbot, ensuring customers receive efficient and personalized support throughout their interactions.
- Public Communication and Marketing:
- Launch the chatbot with a comprehensive communication strategy to inform customers about its availability, capabilities, and benefits.
- Equip customers with user-friendly resources that clearly explain how to interact with the chatbot and the range of assistance it offers.
Summary
Real-world data demonstrates the tangible benefits of GenAI-powered chatbots for early adopters. While optimal design involves considerations like fine-tuning or Retrieval Augmented Generation (RAG), the underlying framework is established and well understood.
Infinitive’s unique blend of industry expertise, business acumen, and technical prowess empowers companies to harness GenAI’s potential for superior customer service solutions, navigating both opportunities and challenges with confidence.
Call 703-554-5500 or write us don.rippert@infinitive.com to discuss your Generative AI strategy.
