Generative AI, the advanced technology behind ChatGPT, Google's Bard, DALL-E, MidJourney, and an ever-growing list of AI-powered tools, has taken the world by storm. And quite literally.
With its ability to replicate human-like responses, gen AI is the next big thing for companies looking to improve the customer experience. Gen AI-based customer service tools can quickly respond to customer inquiries, provide personalized recommendations, and even generate content for social media.
A great example of this pioneering tech is G2’s recently released chatbot assistant, Monty, built on OpenAI and G2's first-party dataset. It’s the first-ever AI-powered business software recommender guiding users to research the ideal software solutions for their unique business needs.
Monty-like gen AI support and service tools significantly reduce response time and improve response quality, translating to a better customer experience. They’re adept at handling recurring customer queries simultaneously, freeing human support agents to focus on more strategic and complex issues.
However, implementing gen AI in customer service comes with its own set of challenges. One of the biggest challenges is training the AI models on different datasets to avoid bias or inaccuracy. The AI must also adhere to ethical standards and not compromise privacy and security.
This article discusses how gen AI has tremendous potential in customer service and how businesses can benefit from its ethical implementation.
What is generative AI?
Generative AI is a branch of artificial intelligence that can process vast amounts of data to create an entirely new output. Depending on the training data you use (and what you want the AI model to do), this output can be text, images, videos, and even audio content.
Thanks to accelerating interest and investment in AI generation companies, the market valuation of this sector is expected to reach $42.6 billion globally in 2023.
Why use generative AI in customer service?
Business leaders resisted implementing automation solutions in the past because customers found bot-to-human interactions frustrating. This was a legitimate concern with clunky, rules-based first-generation bots. But tech has come a long way since then.
Gen AI chatbots' advanced ability to converse with humans simply and naturally makes using this tech in a customer-facing environment a no-brainer. From improving the conversational experience to assisting agents with suggested responses, generative AI provides faster, better support.
The focus has now shifted to empowering customer support teams by liberating them from the mundane so they can focus on what truly matters—solving complex issues and providing exceptional service.
According to a recent study, customer service teams outlined the benefits of using AI as follows: 36% mentioned continuous 24/7 availability, 31% highlighted time savings and task automation, 30% emphasized faster response to support requests, 28% discussed the balancing act of AI collaborating with human efforts, and 25% underscored AI's role as a strategic ally in effective issue resolution.
How to use generative AI in customer service
Generative AI built into a broader automation or CX strategy can help you deliver faster and better support. Here's how.
Create more natural conversations
Adding a gen AI layer to automated chat conversations lets your support bot send more natural replies. This saves you from building dialogue flows for greetings, goodbyes, and other conversations.
Pull updated info from your web pages
Instead of manually updating conversation flows or checking your knowledge base, generative AI software can instantly provide that information to customers. The software accesses the most up-to-date by sifting through your help center, FAQ pages, knowledge base, and other company pages. This information is then conveyed to customers automatically without any further training.
Suppose a customer wants to update the shipping address listed on their account. When you ask your gen AI solution for a response, it’ll search your help articles to find the right answer. Instead of directing customers to the article, the bot consolidates the required information. It sends precise instructions directly to the customer on how to edit their address – solving their query immediately without any back and forth.
Structure support tickets
Gen AI works best when structuring, summarizing, and auto-filling tickets. Not only does this help your support team resolve customer queries faster, but lets them focus on more critical and strategic work.
Gen AI models can even analyze message sentiment and categorize tickets. Categorized support tickets are easy to work with, allowing you to send tailored responses and prioritize tickets.
Use suggested replies
Support agents can prompt a gen AI solution to convert factual responses to customer queries in a specific tone. They remember the context of previous messages and regenerate responses based on new input.
Generate training data
Gen AI accelerates analytical and creative tasks around training and maintaining AI-powered bots. This helps automation managers, conversation designers, and bot creators work more efficiently, enabling organizations to get more value from automation faster.
Don't have the time to work out every single way a customer might ask for a return? Instead of manually creating this training data for intent-based models, you can ask your gen AI solution to generate it.
Provide sample conversation flows
Even the best writers sometimes hit a wall. In such a case, Gen AI can help break writer's block and encourage creativity by creating response templates for your writers. Writers can use the example flows as inspiration for brainstorming dialog flows.
The challenges of using generative AI in customer service
Generative AI is relatively new. And as with every new development, it has a few quirks to iron out. But combining Gen AI capabilities with customer support automation is possible if you address and mitigate the following risks and challenges.
Accuracy
Gen AI models’ impressive fluency comes from the extensive data they’re trained on. But using such a broad and unconstrained dataset can lead to accuracy issues, as is sometimes the case with ChatGPT.
Depending on the prompt you provide, generative AI models draw on their training data to offer their best estimate of what you want to hear. Unfortunately, these estimates might not take facts into account.
Customers who reach out to your support team want accurate responses to resolve their specific issues as quickly as possible. That’s why plugging generative AI straight into your tech stack and letting it loose isn’t a good idea. So how can you ensure generative AI-enabled conversations aren’t derailed?
You don’t want your AI model to make up facts when the data it’s trained on doesn’t contain information about the specific question asked or holds conflicting or irrelevant information. The solution? Creating a system to reshape the AI model.
Here’s how to keep AI-powered support conversations on track:
- Optimize the training dataset. When training data, consider quality over quantity. The gen AI model will be connected to your knowledge base in a customer support setting. To get the most value from implementing it, review your knowledge base, remove old or duplicate articles, and feed current and relevant data to the bot.
- Ground the model with a search engine. You can steer how your model navigates the knowledge base it’s trained on with a custom internal search engine. This model accesses information relevant to the questions asked and streamlines customer interactions.
- Introduce fact-checking processes. If you're concerned about AI accuracy, introducing an extra layer of fact-checking into your automation solution will help produce relevant and useful answers. After using the model to generate a conversational reply, you can use another AI model to verify the response before sending it to the customer.
Setting up these guardrails will prevent the bot from sending rogue responses or coming up with an unrelated topic.
Resource use
Gen AI bots require large datasets to train. This makes maintaining them resource intensive and technically challenging.
You can host your own model, but the running costs can quickly add up. Additionally, many cloud providers cannot offer the storage space these models need to run smoothly.
This can cause latency issues, where the model takes longer to process information and delays response times. With 90% of customers stating instant responses as essential, the response speed can make or break the customer experience.
Using a reasonably sized language model is key to reducing resource usage. Smaller language models can produce impressive results with the right training data. They don’t drain your resources and are a perfect solution in a controlled environment.
“To see the best results with generative AI, we need to think of AI in customer support as not just one neural network, but a whole brain, where different parts of the brain handle different tasks.”
Jaakko Pasanen
Chief Science Officer and AI expert at Ultimate
Rather than relying entirely on big-gen AI models to handle customer support automation tasks, use them as part of a broader automation solution.
Be smart and cautious when implementing gen AI in your business
Generative AI is undoubtedly powerful. However, since it’s new and comes with many challenges and risks, you need to be careful when using it in a customer-facing environment. Instead of looking at gen AI as a silver bullet that will solve all support issues, use it as part of a broader automation system.
Despite the challenges, gen AI has many benefits for customer service. And as it matures, you'll find new and more advanced use cases and a better way to implement it in your tech stack.
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