Table of contents
- What is AI in customer service?
- What are the benefits of AI in customer service?
- 8 examples of AI in customer service
- How to use AI to improve customer service
- 3 things to consider when implementing AI in customer service
- The future of AI in customer service
- How to get started with AI in customer service
If you asked any customer service professional to describe how the last few years have been, they’d probably say “intense.” With budgets in flux and customers expecting more, service teams are constantly figuring out how to answer an important question: how do you actually do more with less? The answer is AI in customer service.
Since the pandemic, customer service has been a rollercoaster ride. Customer expectations are higher than ever — 72% of consumers say they will remain loyal to companies that provide faster service. And 78% of service agents say it’s difficult to balance speed and quality, up from 63% since 2020. All of these pressures have led to a turnover rate of 19% in service organizations.
While predictive AI is not new to customer service, generative AI has recently stepped into the spotlight. With the powerful potential of this new technology, service professionals and customers alike are curious how AI-powered customer service will impact their experience. Let’s dive into what AI does, its benefits, and how you can get started.
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What is AI in customer service?
There are many different ways you can use AI in customer service. For example, you can embed AI-powered chatbots across channels to instantly streamline the customer service experience. Beyond answering common questions, these chatbots can greet your customers, serve up knowledge base articles, guide them through common business processes, can send out a field technician for field requests, and can route more complex questions to the right person.
Imagine this from the customer perspective: you want to return a pair of shoes and you need some help. You start an online chat with an agent, but then wait 30 minutes for a response.
With customer service AI, you get a personalized response in seconds. Think of it like a virtual buddy who’s not only knowledgeable, but also understands your exact needs and preferences. All you have to do is tell it what you need help with, and it will take care of the rest. No need to find your tracking number, provide your email, or explain the details of your purchase, it already has all that information and knows exactly what to do.
So many organizations are already using AI for customer service. In fact, the share of service decision makers who report using AI has increased by 88% since 2020 — up to 45% from 24%.
What are the benefits of AI in customer service?
Let’s look at five ways AI in customer service can help your team, especially if you’re interested in getting started with generative AI:
- Higher productivity: We found that 84% of IT leaders believe AI will help their organization better serve customers. Case in point: AI-based conversational assistants can increase productivity by 14% for support agents.
- Better efficiency: Manual processes can be a heavy lift for service agents. This includes tasks like swiveling back and forth between systems and screens to view customer history, searching for knowledge articles, routing field workers to service locations, and manually typing responses — all of which tend to be error-prone when done by a human. AI in customer service can give customer service workers intelligent recommendations across knowledge bases, conversational insights, and customer data. Our recent research found that 63% of service professionals say AI will help them serve their customers faster.
- A more personalized service interaction: When a customer initiates a conversation with a chatbot, AI can populate important information — such as the customer’s name, location, account type, and preferred language in real time. If the request requires a field service technician, AI can send all of the important information to the field worker so they can provide personalized service the moment they walk in the door.
- Less burnout and improved morale: AI allows agents to eliminate repetitive, time-consuming work and focus on situations that require creative problem solving, social intelligence, and complex critical thinking — activities that will move the needle on overall customer experience. It’s not a surprise that 79% of IT leaders say generative AI will help reduce team workload and thereby reduce burnout.
- A proactive service experience: AI can draw info from your customers’ contracts, warranties, purchase history, and marketing data to surface the next best actions for agents to take with your customers — even after the service engagement is over. For instance, AI can let customers know that it’s almost time to renew their subscription, remind them when it’s time to book a maintenance appointment, or that a product upgrade or discount is available. And taking that to the next level, generative AI can even summarize customer conversations and produce knowledge base articles for future reference.
8 examples of AI in customer service
Whether you’re in the contact center or in the field, AI in customer service can transform the customer experience. Here are a few examples:
1. Content Generation: Generative AI can analyze customer messages, extract relevant details, and generate human-like replies to customer questions, improving response times and overall customer satisfaction. This is especially true when the AI pulls from CRM data and knowledge.
2. Chatbots: AI-powered chatbots can handle basic customer inquiries, provide instant responses, and assist with tasks such as order tracking, product recommendations, and troubleshooting. They are available 24/7, reducing response times and improving customer service accessibility.
3. Natural Language Processing (NLP): NLP is a technology that enables AI systems to understand and interpret human language. It helps in analyzing customer sentiment, identifying customer needs, and providing relevant responses. You can use NLP in chatbots, voice assistants, or sentiment analysis tools.
4. Sentiment Analysis: AI-powered sentiment analysis tools monitor and analyze customer feedback, reviews, and social media interactions to gauge customer sentiment. This helps companies identify areas of improvement, respond to customer concerns, and provide personalized experiences based on customer preferences.
5. Recommendation Systems: AI-driven recommendation systems analyze customer behavior, purchase history, and preferences to provide personalized product or content recommendations. By understanding individual customer preferences, companies can enhance cross-selling and upselling opportunities.
6. Predictive Analytics: AI-based predictive analytics uses customer data to anticipate customer needs, behavior patterns, and potential issues. This helps companies proactively address customer concerns, optimize resource allocation, and personalize customer interactions.
7. Self-Service Solutions: AI-powered self-service solutions, such as knowledge bases or FAQs, leverage natural language processing to understand customer queries and provide relevant information or troubleshooting steps. This allows customers and agents to find answers quickly without requiring human assistance.
8. Intelligent Routing: AI-based intelligent routing systems analyze incoming customer inquiries and route them to the service representative or department with the most relevant experience or knowledge. This ensures that customers are connected to the right person who can address their needs efficiently.
How to use generative AI to improve customer service
Here are a few ways that AI can help organizations provide even better service to their customers:
Quickly generate personalized replies to service inquiries: This technology can help agents respond to service questions with personalized prompts. AI can generate these responses based on relevant customer data, knowledge articles, or trusted third party data sources on any channel.
Create work summaries and mobile work briefings: Customer service AI can drive agent productivity by automating the time-consuming but crucial task of writing wrap-up summaries based on case data and history. This is especially helpful in the field. You can summarize the most relevant data to start the job — saving your frontline workers time.
Preserve and share knowledge across your business: You can connect a generative AI tool to your service console and have it create the first draft of your knowledge base article based on conversation details and CRM data for your experienced agents to review. This will save you time and help you get your articles out faster. An extra bonus: you can also use these knowledge base articles to help customers find their own answers to questions in a self-service portal.
Search for answers: As your agents or customers are looking for answers to a question, AI in customer service can surface a generated answer from your knowledge base, directly into the search page — saving everyone time.
3 things to consider when implementing AI in customer service
Despite the benefits of AI in customer service, there’s still a ways to go in terms of adoption. According to recent research, less than half (45%) of service decision-makers told us they’re using AI. So what’s holding organizations back?
1. Impact on the workforce: Since AI, especially generative AI, is a new field, service leaders are struggling with a skill gap. For example, 66% of leaders believe that their team doesn’t have the skills needed to handle AI. And similarly, service professionals are concerned that AI could take over their jobs, which can make them apprehensive about embracing the technology. As you bring AI into your service organization, communicate how AI will help your teams get more done and that their human-skills are still very much needed to provide a great experience for your customers.
2. Trust and reliability issues: AI technology, although rapidly advancing, is not perfect. For one, most learning language models are trained on data that’s almost two years old. Similarly, there may be concerns about the accuracy of AI systems in understanding and resolving complex customer queries or handling sensitive information. Similarly, concerns around privacy and trust should be taken seriously — and must be managed carefully to keep your business and customer data secure. When the data for AI is grounded in your trusted CRM data and knowledge base, you can solve this challenge.
3. Investment and implementation: Depending on whether or not you decide to develop your own AI or bring in an AI tool into your organization, it may require significant investment in technology infrastructure and training. Small businesses or organizations with limited resources may find it difficult to fund AI implementations or lack the technical expertise to deploy and maintain such systems.
The future of AI in customer service
As AI in customer service rapidly evolves, more use cases will continue to gain traction. For example, generative AI will move from the contact center into the field. This technology will ensure frontline field service teams have the right customer, asset, and service history data for the job at hand. Through AI in customer service, field service teams will offload more of the mundane work — through automated work summaries, knowledge articles, and more.
How to get started with AI in customer service
AI in customer service doesn’t have to be difficult to understand — or implement. The first step is learning more about what it can do for your business.
Begin by learning more about how generative AI can personalize every customer experience, boost agent efficiency, and much more. Then check out how you can make the most of AI in customer service.
Supercharge your customer service with generative AI
You can scale your customer service with the power of generative AI, paired with your customer data and CRM. See how this technology improves efficiency in the contact center and increases customer loyalty.