How to automate your customer service with Generative AI? Rise of LLMs!

Think about how many times you have tried to call a customer care number and were kept on hold for at least 10 minutes to just get connected and speak to someone. I am no different from you, I encounter situations like being on hold at least a few times a week when I call a customer care number.

The whole world has embraced conversational interfaces ranging from sending instant messages(IMs) on WhatsApp to asking questions on ChatGPT. But, I wonder, why many companies still struggle to embrace chatbot technologies. In this article, we will explore the challenges from the past with chatbots and how Generative AI technologies can help automate a good amount of customer service without the need to talk to a human every time and improve customer satisfaction.

Why many companies had little success with traditional Chatbots for Customer Service?

Chatbots are an amazing piece of technology that can answer human questions in a conversational interface. If a customer asks questions that are already thought of by the company development team, they can do wonders and answer customer questions precisely. But, unfortunately, customer questions may or may not be thought of beforehand.

Below are some of the common reasons, why many companies had little success with traditional chatbots(without using Generative AI) for customer service:

• Pre-defined flows: Traditional Chatbots are very rigid as they need the conversational flows to be predefined. If a certain path is missed in the flow, they just get stuck.

• Customer Context: The traditional chatbots rely on NLP and NLU, but the biggest issue with them is the ability to bring in the correct context for answering a question. Also, even if the context is somehow managed, due to the way the flows are set up, they often get stuck.

• Customer Perception with Fixed Flows: Due to the rigid nature of chatbots, customers are not very keen on trying them due to the fixed flows and answers not relevant to their context. Unless they see a live human on the other side chatting with them, they often ignore the chatbots.

• Cost to implement: The cost to implement traditional chatbots can be really high as there is a lot of work that needs to be put in for defining the conversation flows, building them, testing them, and deploying them. Large companies may be able to afford the same, but small to medium businesses struggle to justify the cost.

• Responses are not Personalized: Many of the responses that come out of a traditional chatbot are not personalized, which can lead to less customer engagement.

Now that we understand some of the challenges, let’s look at how Generative AI changes the game for Chatbots.

How does Generative AI change the game for Chatbots?

Generative AI has fundamentally transformed the way humans look at AI by producing wonders in generating text, photos, videos, etc. Generative AI’s Large Language Models(LLMs) can do many things such as understand & summarize content, personalize responses, write emails, generate images based on user’s text input, write code, etc.

Unlike building traditional chatbots, you do not need to define flows, when you are creating chatbots using Generative AI technologies. When there are no fixed flows, you do not have limitations or roadblocks to get stuck. Humans can interact with a chatbot in a natural language. As there is no additional effort to define too many flows, the cost to implement Chatbots also reduces significantly.

When you implement Chatbots using Vector databases and LLMs, the responses can be very accurate and precise. The beauty of LLMs is that they can understand the context and can give precise responses with an extreme amount of personalization. Even, if they do not know, they can politely respond rather than a robotic fixed message.

How to Automate Customer Service with Generative AI Technologies?

Customer service can be automated by building Chatbots based on Generative AI technologies. When you are building chatbots with Generative AI technologies, there is no additional overhead from your side to think of the customer flows, responses in chat flow, how to handle exceptions, etc.

Creating a chatbot using Generative AI technologies can be as simple as below:

Step 1 – Identify Content: Identify the content that is relevant for answering customer requests. The content can be in websites, PDFs, word documents, etc. This could be product manuals, FAQs, internal portals, knowledge bases, etc.

Step 2 – Vectorize Content: Feed the content identified in Step 1 to a Generative AI-powered Chatbot solution. They will vectorize it, store it, and make it ready for creating the chatbot. There are many SaaS solutions in the market that offer such solutions. Our company A2O AI also offers the same, you can sign up for a free account and try. If you want to do it on your own, you need to use a vector database such as Pinecone, Weaviate, etc.

Step 3 – Create a Chatbot Widget: Create a chatbot widget based on the solution that processed your content. Again, there are many SaaS solutions that offer this, including A2O AI. If you want to build everything on your own, you need to do some technical work to set up the flow to go from chat to vector database to LLMs.

Step 4 – Embed Chatbot on your Website: Embed the chatbot widget on your website and you are good to go.

If you use a SaaS solution that helps you create chatbots based on Generative AI, your life is very easy and it will meager clicking of buttons and you will be good to go in minutes. But, if you want to do everything on your own, it can be slightly technical, and you may need Generative AI experts to setup the system. Why re-invent the wheel, when you can create an enterprise chatbot using a solution like A2O AI?

Overall, the advent of Generative AI is the death of building traditional chatbots and automation of customer service.