As a customer service leader, you know this is true: customer expectations are steadily increasing. They want more personalized service, faster responses, and a smooth experience on whatever support channel they choose.
But while customer expectations are loftier than ever before, your resources don’t match up. You’re likely grappling with inadequate staffing, burned-out agents, or a lower budget — or maybe even a combination of all of the above.
For that reason (and many more) generative AI feels like a welcomed silver bullet for the customer service industry, with many leaders jumping on the bandwagon:
Generative AI can streamline your operations, boost agent productivity, improve accuracy, and even enhance customer satisfaction. However, it’s not a panacea. And, those compelling advantages are balanced with plenty of concerns.
For starters, implementing generative AI feels like another thing on your towering to-do list when you’re already spread thin. And, of course, there are questions about how it will impact your customer experience — particularly when 64% of customers say they’d prefer that companies don’t use artificial intelligence in customer service.
The reality is that, while generative AI isn’t without its flaws, it’s here and it’s here to stay. It’s your responsibility to determine how to effectively and responsibly implement it on your customer service team.
That starts with understanding generative AI — what it is, how it works, its potential use cases, and how you can best leverage AI technology alongside your human agents. Sound daunting? It doesn’t need to be. This guide covers what you need to know.
There are all sorts of different types of AI — and generative AI (often abbreviated to “gen AI”) is just one of them. Generative AI is a type of artificial intelligence that’s focused on creating (or, you know, generating) new content using patterns from user inputs or existing data.
You don’t need to understand the nitty-gritty of how generative AI works to make the most of it. So, here’s the gist: generative AI uses advanced AI models like large language models (LLMs) and natural language processing (NLP) to analyze data and then use generative pre-trained transformers (GPT) to generate human-like content or responses.
Those suggestions Gmail makes when you’re typing an email? That’s generative AI. The detailed response you get when you type a question into ChatGPT? That’s generative AI too.
While it requires some human intervention in terms of feeding inputs or training AI systems, the main value proposition of generative AI is that, once that foundation is set, it’ll do the rest of the legwork and create something for you.
With that basic premise in mind, it’s not hard to see how these AI capabilities can help you optimize your support operations.
There are seemingly endless use cases of AI tools for contact centers and support teams — from chatbots that handle customer interactions entirely to pre-drafted responses that your human support agents can edit and refine.
Let’s take a closer look at some of the most common applications of AI platforms in customer service.
Automation is likely one of the first examples to spring to mind, as it can remove entire service interactions from your customer service agents’ plates. Once an AI chatbot is trained on your FAQs and brand guidelines, it can draft and send personalized responses to common customer questions — without any further intervention from your agents.
Generative AI can also improve the customer service experience by analyzing customer data and using sentiment analysis to recognize a customer’s current state and deliver more personalized interactions. An AI assistant can craft and send tailored responses on your team’s behalf. Or, it can serve as an agent copilot for your support team, providing real-time information about preferences, history, and needs that your agents can use to improve their messaging.
Customers often use a variety of channels in a typical support experience. And, according to a recent Gartner survey, they want seamless transitions between those channels. Generative AI can automatically draft consistent and accurate responses to customer queries across your different support channels — so your customers get a cohesive and reliable experience, regardless of how or where they reach out.
There are several different ways your call center or support team can get more done in less time with generative AI, including:
The time savings can be meaningful, and it can have a direct impact on your customer experience and your response times. For example, using Assembled Assist, Tithely was able to reduce average handle time by up to 26% and Honeylove increased solves per hour by 54% after just five months.
Service level agreement (SLA) breaches can happen, especially if you aren’t realistic about what you and your team can accomplish. Machine learning algorithms can analyze your historical data and real-time inputs to predict your ticket volumes, forecast accurate staffing, prioritize urgent cases, and use virtual agents to automate responses to common questions. This helps you work strategically and meet your agreed-upon service levels — even when things get busy.
Many generative AI tools can connect with the apps and tools your team is already using (like Salesforce or another CRM), which allows you to further streamline your workflows — like automatically categorizing support tickets or escalating issues based on priority. Your team benefits from smoother processes along with more time and energy to focus on complex or high-value interactions.
It’s tempting to think of generative AI only as a tool for creating customer-facing content, but it’s equally helpful for your internal resources. For example, it can analyze previous interactions and relevant customer metrics to predict future trends in support demand or identify common issues. With a more realistic grasp of peak times and problems, you can generate forecasts or make adjustments in real time for more effective staffing and resource planning.
A whopping 81% of customers say they want more self-service support options, and generative AI can be a big help here too. For example, AI tools or chatbots can:
This helps your customers find the information they need independently and also further improves the efficiency of your support team.
AI tools are trained on the information you provide — including specific brand guidelines. This means it can generate replies that align with your company’s tone, voice, and messaging to deliver a more consistent experience across your different agents and support channels.
With generative AI on your side, you don’t need human agents to address every query or handle every task. By reducing the amount of manual work, you reduce the need for extensive staffing which lowers your training costs, improves your resource allocation, and cuts down your operational expenses. For example, Thrasio saw cost savings of $1.8 million annually since adopting Assembled Assist.
Better personalization, faster response times, and more self-service options — they all lead to a better experience for your customers. So, it’s little wonder why leaders see generative AI as one of the most promising advancements. 65% of customer service leaders expect using generative AI with conversational AI to increase customer satisfaction.
With so many applications in customer service, generative AI won’t remain a competitive advantage — it’ll quickly become table stakes. Here’s how you can jump on board and implement it for your support operations.
Much like with anything else, you don’t want to make a change for change's sake. It needs to have a purpose. So, take a close look at your existing workflows and operations to identify spots where generative AI could add value.
Keep an eye out for repetitive tasks (like answering frequently asked questions, updating knowledge bases, or routing support tickets) as well as pain points and inefficiencies that your team frequently complains about. Those are great spots to strategically implement an AI solution that will make a meaningful impact.
Once you know where AI can make a difference, you need to find the right tools to match those needs. Consider factors like:
You’ll also want to think about specific features or functionality you need — like predictive analytics, AI-driven chatbots, or knowledge base management. By ironing out these criteria upfront, you can narrow your search and find a customer support AI solution that’s the best match for your team.
AI relies heavily on customer data to generate accurate responses, so data privacy and security are top priorities. Data needs to be protected and comply with relevant regulations (like GDPR or CCPA). And, if you’re in a heavily regulated industry, there might be even more requirements you need to meet.
Ask thoughtful questions to understand a solution’s data handling practices, such as whether it offers encryption and access controls. This information will help you be more proactive in managing privacy concerns.
Ultimately, your AI solution is only as good as its training. While one of your goals with generative AI is to be more hands-off, you’ll need to put in some initial elbow grease.
Aim to train your AI tool with as much data as possible from previous customer interactions, support tickets, knowledge bases, and brand guidelines. The more accurate and diverse training data you can provide, the better the AI will be at providing relevant, personalized support and delivering the perceived human touch that customers still desire.
Keep in mind this won’t be a one-time exercise. Commit to continuous training, information updating, and fine-tuning to improve your AI performance over time (especially as your needs and customer behaviors continue to evolve).
Your AI solution isn’t the only thing that needs training — your support team does too. Rolling out a new tool without any guidance or resources will make them feel overwhelmed and potentially even resentful.
Thorough training helps them use and update your AI tool with confidence. Plus, keeping your human agents involved is also one of the best ways to combat inaccuracies or other slip-ups that can creep in with AI. Host frequent and helpful training sessions about topics like:
The work isn’t done once you deploy and start using generative AI. You’ll need to continually monitor and improve its performance by frequently:
Your AI implementation is a one-time process. But optimization? That’s an ongoing one.
It feels like a gross understatement to say that generative AI is transforming customer service. It’s completely changed the game in terms of helping teams deliver more efficient and effective support — and it won’t slow down anytime soon.
Ready to get started on your generative AI journey? With Assembled Assist, your team can automate resolutions for common inquiries, benefit from auto-drafted replies in your brand voice, and design tailored workflows for any scenario. That means more solves per hour, fewer escalations, and an increase in positive CSAT replies.
Put simply, customer expectations are evolving — and your tech stack needs to evolve too. Schedule a demo today to discover how Assembled Assist can help you redefine top-notch customer service for the modern age.
