Conversational AI language models like Chat GPT, Claude, and Bard have opened up a world of possibilities and opportunities for utilizing AI in your business. AI is being used in applications like customer support services, personalizing marketing and customer experiences, predictive analytics, process automation, and fraud detection. In this post, we will focus on using AI to automate and extend customer support functions, i.e., answering frequently asked questions, assisting with basic inquiries, and handling simple transactions.

First, what exactly are Open AI Chat GPT, Anthropic Claude, and Google Bard? They are conversational AI language models, created to understand and generate human-like text based on context. Their ability to simulate human conversation and provide responses to prompts or queries in a natural language format makes them well-suited to customer service interactions.

Let’s look at how you can leverage AI in your customer service operation.

AI Customer Service Use Cases

The first step is to choose a use case. Pick 1-2 initial ways you want to use an AI assistant in your business. Good options include a chatbot on your website, automated email responses, or basic call center support. Start small and focused.

Customer service cases:

Set up a chatbot on your website. Any of these AI models can be deployed as an chatbot on your company's website to answer basic questions from customers. Questions like business hours, contact information, product details, and more. AI will be able to better understand varied queries and provide more relevant answers than templated systems that rely on limited canned responses. Thanks to this higher level of sophistication, AI solution will free up more of human agents then current systems.

Automate email responses. AI can generate automatic email responses to common customer questions and requests. Things like order confirmations, thank you emails, password reset emails, and simple FAQs. These responses can go beyond templated fixed responses and customize the response to the customer's actual query.

Handle basic call center queries. If customers call into your call center with simple, repetitive questions like "What's my account balance" or "When will my product ship", AI can instantly provide the answer using account data. AI can go beyond the pre-scripted responses of current systems and interact with the client to provide information tailored to each customer’s specific query. Only passing the call to an agent if needed.

Assist with billing and account support. Using customer account data, AI can provide quick answers to inquiries related to billing, payments, account statuses, charges, and more. AI can handle rudimentary account support and escalate to an agent if a complex issue arises. Here again, AI can go beyond the pre-scripted responses of current systems and provide a conversational highly customized experience for each customer.

Provide 24/7 customer support. By deploying AI on your website, email, and call center, AI can provide basic levels of customer support 24 hours a day, 7 days a week. Your human teams can then focus on more advanced issues during normal business hours.

Scale during peak periods. During peak customer service periods, times of crisis, or seasonal rushes, I can dynamically scale to handle a surge of additional contacts. Human teams can stay focused on high-priority or complex matters while I address temporary rises in traffic.

Capture data to improve service. Through automating queries and interactions, AI systems can capture customer data, questions, feedback, and more to help improve your customer service over time. By seeing common questions and requests, you'll gain valuable insights.

Building and Deploying Your AI Application

Once you have a use case determine the scope and objectives: Identify the specific types of customer service queries and interactions you want to automate. Determine the primary goal, such as reducing response time, handling FAQs, or providing basic support.

Prepare the training data: Gather a dataset of customer queries and corresponding responses that represent the scenarios you want to automate. The dataset should cover a range of typical customer inquiries and their appropriate responses. This should include information on your specific business and segment, process and procedures, case studies and any documentation that a CSR would use to respond to an inquiry.  Ensure the dataset is diverse and representative of different scenarios.

Fine-tune the model: Fine-tuning is necessary to make the base language model more specific to your business and its customer service requirements. OpenAI has a fine-tuning guide that provides detailed instructions on how to prepare your data, set up a training environment, and fine-tune the model using your dataset.

Define input-output format: Determine the format in which customers will interact with the model. For example, you can set up an API where customers send their queries and receive responses in a conversational manner.

Build an API integration: Develop an interface or integration that connects your customer-facing platform (e.g., website, chat widget, messaging app) with the fine-tuned model. This integration should enable users to send their queries to the model and receive responses seamlessly.

Implement error handling: While these AI models are powerful, they occasionally produce incorrect or nonsensical responses. Implementing error-handling mechanisms to detect and handle such cases is essential. For example, you can set up a fallback mechanism to redirect queries the model can't confidently answer to a human customer service representative. You can also guide the user through the construction of a query so that the prompt is engineered to ensure a relevant and accurate response.

Test and iterate: Thoroughly test the system to ensure it meets your requirements and provides accurate responses. Iterate on the fine-tuning process if necessary, using additional training data or adjusting hyperparameters to improve performance.

Monitor and gather feedback: Continuously monitor the system's performance and gather feedback from users to identify any shortcomings or areas for improvement. Incorporate this feedback into your ongoing training and improvement processes.

Deploy and scale: Once you are satisfied with the system's performance, deploy it to your customer service platform. Monitor its scalability and handle increased user load as needed. Regularly update and retrain the model with new data to keep it up to date.

Provide human backup: While automation can handle many customer queries, ensure there is a human support system in place to handle complex or sensitive issues. Customers should have the option to escalate to human support when necessary.

Potential Problems and Challenges

While Conversational AI language models like Chat GPT, Claude, and Bard appear to be ‘smart’, they lack true understanding and possess zero creativity. They rely on patterns and examples in training data, which means they may struggle with novel or out-of-context queries. This can result in incorrect and nonsensical responses. It is critical to proactively address these limitations through proper training, monitoring, error handling mechanisms, and providing clear instructions to the model.

Potential problems:

Contextual understanding: While AI is proficient at generating text based on context, it can sometimes struggle with accurately understanding complex or nuanced queries. It may misinterpret certain phrases or fail to grasp the underlying intent, leading to incorrect or irrelevant responses.

Lack of real-time updates: Training data has a knowledge cutoff, meaning it's not aware of events or information that occurred after its training period. This can be problematic for customer support, as it may not have the latest updates or be aware of recent changes in your business, products, or policies.

Incorrect or nonsensical responses: for the reasons outlined above AI chatbots can occasionally produce responses that are incorrect, nonsensical, or lack factual accuracy. It may generate plausible-sounding but incorrect answers, potentially leading to confusion or misinformation for customers.

Poorly constructed prompts: The quality of responses heavily depends on the quality and clarity of the prompts or queries provided. If the prompts are ambiguous or poorly constructed, it can result in inaccurate or nonsensical responses. Ensuring clear and specific prompts is crucial to get accurate answers.

Conclusion

Designing and building a system that takes into account the domain training needs, limitations, capabilities and prompt engineering requirements of the technology and your use case will ensure a successful result.

Remember to adhere to ethical guidelines and privacy regulations while implementing automated customer service systems. Regularly review and update the system to align with changing customer needs and business requirements.