Implementing Safe and Accurate Generative AI for Customer Experience
Generative AI refers to algorithms trained to predict data sequences based on training information. Two of the most common sequences they generate are text and images. Large Language Models (LLMs) are a subset of generative AI focused on text and image generation. LLMs are unique because they have been trained on enormous data sets. This training gives them capabilities beyond any AI seen before. LLMs excel in reading and writing language and answering questions from the information used to train them. They achieve this by training in vast amounts of data and iterative fine-tuning. The AI learns language nuances and patterns to predict words and generate meaningful responses. The fine-tuning process involves human interaction, where experts score responses and provide corrective feedback. This human-AI synergy refines the model’s ability to provide accurate answers. While LLMs exhibit what appears to be knowledge, they lack genuine comprehension. They respond based on their training and feedback loops, blurring the lines between knowledge and understanding.
LLM capabilities are widely recognised due to their ability to understand questions and generate well-written responses. However, because of the recency of the training data, users may encounter incorrect answers and limitations. There are two overarching concerns in AI-powered customer support: accuracy and safety. Industry leaders in LLM-powered conversational AI utilise the LLM’s skills to read and write language while disabling the LLM’s ability to answer questions based on its training data. This is referred to as giving the LLM amnesia, similar to a person with amnesia who can communicate with language but cannot remember anything. This is a protective mechanism. The AI uses only the company’s information rather than relying on its extensive training data. The vast training information is not useful for answering questions like “Where is my order?” The objective is to insert company-specific information and make the LLM forget all it knew before. This methodology empowers businesses to provide secure, accurate, and brand-specific responses without hallucinations.
Just as important as answering questions with the correct information (accuracy) is ensuring that the AI does not say something embarrassing (safety). Businesses want the AI to carefully answer questions such as product limitations or comparisons with competing products. Another aspect of brand safety is ensuring that AI answers reflect the tone and voice of the brand. While this all sounds daunting, it should not diminish enthusiasm for what the latest AI can accomplish. With the right AI platform and expert partner, businesses can harness the power of the LLM to provide accurate and safe answers to customers.
Building Effective AI Assistants
Using the latest AI may seem as easy as developers using APIs in commercial LLM options. But it is much more than enlisting engineers to call LLM APIs. It takes a diverse team to build and deploy an effective AI assistant to answer customer questions. Developing an LLM AI assistant involves multiple ingredients. Beyond engineers, various roles contribute to the AI assistant’s creation. Development requires conversational designers, UX engineers or product managers to map the customer journey and identify the types of questions customers are asking, determine how to answer those questions, and identify where the required data lives.
It is essential to know how well AI is doing and where it can be improved. Understanding performance requires data analysis by business analysts or data scientists. Crafting a user-friendly conversation design starts with mapping the customer journey to solutions. LLM interactions differ from prior generations of chatbots, which required scripted interactions. The LLM is skilled in language, so the conversation design emphasises instructing the LLM rather than scripting each utterance. Building an AI assistant follows defined steps, with the design and experience creation phases being the most critical.
All AI assistants follow a similar pattern to answer customer questions. The process starts with a customer asking a question. The AI divides the question into various characteristics, creating features to find later information. For instance, if an AI assistant were answering questions about car insurance, details like the customer’s location, policy status, and whether the individual is a prospect or current customer would be helpful, in addition to the intent behind the query. Once characteristics are identified, it may be helpful to tease out additional elements based on what is known.
Using the car insurance example, if the customer is asking about claims, it may be necessary to determine whether the inquiry relates to a new or existing claim. Determining the information hierarchy that the LLM should collect is part of the journey-mapping design process. After obtaining all necessary characteristics, the company’s information is used to get the answers to the customer’s question. This information can include static knowledge and data, often obtained through knowledge bases and APIs. The AI does not gather the information itself, eliminating the risk of inaccuracies.
The AI assistant employs the LLM’s language generation capabilities to create a tailored response for the specific question as opposed to simply returning static text. Fact-checking and safeguards are applied to ensure accuracy and brand safety. This step verifies that the LLM’s response is accurate and appropriate and uses only the company’s information in the response. By following these steps, the AI assistant can deliver responses that are accurate and highly personalised. This approach leverages the capabilities of LLMs for powering customer experience technology while mitigating potential risks.
One of the most appealing aspects of creating an AI assistant is the relatively swift timeline. The processes have sped up because using LLMs eliminates much of the laborious training necessary in prior generations of AI. It takes about a month to finish crafting an AI assistant, including design, review and inevitable human-centric delays. Complex user journeys with user-specific internal data only extend the timeline by one to two months. Contrary to perceptions of lengthy projects, building an AI assistant is a streamlined journey, thanks to leveraging LLMs for language generation and comprehension.
As the path of AI assistant creation is navigated, it becomes evident that roles extend beyond engineers. Timelines are far more reasonable than one might expect. A fusion of creativity, strategy and technology marks the process itself. Expertise lies in merging asynchronous messaging with advanced AI capabilities, offering businesses a comprehensive platform. The future of customer interactions is at the intersection of technology and human expertise. With the right approach, businesses can embrace AI’s potential while ensuring that the essence of human connection remains intact.
A blend of vision, knowledge, meticulous design and execution is paramount to excelling in customer experience. Partners work with global brands, guiding them in crafting and implementing CX strategies. This is achieved by empowering and inspiring teams, instituting sound processes grounded in best practices, and employing cutting-edge technology. An array of assessment, transformation and enablement services can be offered to address CX technology challenges. By leveraging a fusion of technology and expertise, organisations are empowered to design and deliver exceptional service journeys.
The evolution of AI assistants is an exciting chapter in the realm of customer experience. With the right technology solution and expert guide, businesses can explore this next generation of AI with confidence, purpose and a clear roadmap. By leveraging LLMs for language generation and understanding, the focus shifts from creating training data to crafting the customer experience. Using advanced platforms, businesses can embrace AI’s potential while ensuring that the essence of human connection remains intact. Maintaining brand tone and safety is a delicate balance between AI capabilities and human guidance. Instructions provided to the AI, fact-checking and safeguards ensure that AI responses are consistent, appropriate and aligned with the brand’s identity.
Mastering AI integration into the customer experience requires customer experience, operational, and technology expertise. Consultants working closely with technology solution providers help organisations successfully navigate the intersection of AI, technology and human interaction. As businesses continue embracing AI, the convergence of human expertise and AI capabilities promises a dynamic future for customer interactions. While AI enhances efficiency, the human element remains vital for building connections. The path forward lies in striking a harmonious balance between automated AI interactions and personalised human engagement.
