The hottest topic in service today is generative AI, especially in the contact center. 84% of IT leaders we surveyed in a recent study say generative AI will help their organization better serve customers, and every day I speak with service leaders who are excited about the potential for generative contact center AI.
Yet, only 24% are actually using any form of contact center AI. What’s in the way? 66% say that their employees don’t have the right skills to successfully put generative AI to use. So let’s look at the four ways you can use contact center AI, along with example use cases and tips that will help you get started.
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1. Generating service responses to customers
Your contact center provides multiple ways for customers to contact your business —from phone to email to chat to SMS. While many customers still use the phone, 57% now prefer to use digital channels. Your agents staffing these digital channels need to give accurate and relevant information, reply in a timely manner, and resolve the customer’s issue quickly.
So how can generative AI help? The large language models powering generative AI can automatically generate a human-like reply to any question. When grounded in your customer data and knowledge base, you can personalize these generated replies, making them more trustworthy. Agents can review the suggestions from the model and easily send. For agents working on several cases all at once, contact center AI can be a real timesaver.
Let’s look at an example for a fictional internet company we’ll call Nation-Wide Web.
Jane is a Nation-Wide Web customer and notices an unusual charge to her bill. Jane opens up a chat message on the company’s website and is soon connected to an agent, Katie.
Katie has a few messaging windows open from customers, one of them is Jane. Jane shares her concerns about her bill. Apparently, Jane went over her data package for the month. The contact center’s AI tool uses Jane’s question and the context of her account status to generate a personalized message that explains this charge in an empathetic tone, but also that it’s within company policy to waive the fee given the circumstances.
Katie reviews the message and confirms the policy, then sends the message and removes the charge from Jane’s account. Jane is happy she got a quick and easy solution and Katie can focus her attention on customers with more complex issues .
Tip: Taking the time to review any customer communication for accuracy and tone helps to avoid misunderstandings.
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2. Generating case summaries
To provide your customer with a great experience, you need accurate data to track and optimize your business’ service interactions. This makes the wrap-up summary your agents do after a case is closed one of the most crucial pieces of service data your business can collect.
The challenge? This is a time-consuming task that keeps your agents from helping other customers.
But contact center AI can take the most complex email and chat conversations and generate a proposed wrap-up summary. Your agent just needs to review these summaries before they’re saved to the case log. This saves agents a ton of time and effort on data entry.
Let’s go back to our Nation-Wide Web example.
If you remember, Katie’s AI tool generated a response to Jane and all Katie had to do was review the message, press send, and waive the fee from Jane’s account. In the meantime, the AI is using the data from the message thread and the actions that Katie took in Jane’s account to generate a case summary.
After the conversation with Jane is complete, Katie can read over this proposed summary, adjust some details, and save it to the case record. Reducing after call work helps Katiemove on to help other customers faster.
Tip: Create a template for your case summaries so that your contact center AI tool can easily pull conversation data into the CRM without missing important details.
3. Generating knowledge articles
Salesforce research shows that 59% of customers prefer self-service tools for simple service issues. However, to do that, a business needs a large knowledge base that customers can search through to find a solution.
Service agents are often tasked with publishing knowledge articles after resolving a case. But it takes time for agents to manually create, review, and publish an article, which keeps them from helping customers in need.
Contact center AI can automatically generate a knowledge base article after a support case is closed by pulling from case notes, message history, and data from other service tools. From there, your agent just needs to review the article to ensure accuracy and add it to the queue for approval. This takes the pressure off agents to write articles from scratch.
Going back to our Nation-Wide Web example, Austin has slow internet and calls to troubleshoot. He’s connected to Tawni who asks for his router and modem details. Tawni runs through a few common scenarios based on similar cases, but none work for Austin’s setup.
Tawni decides to try something new. She asks Austin to do a full-system reboot through the Nation-Wide Web mobile app. After this is over, Austin’s internet speeds are back to normal and the case is closed. Tawni logs all of this information into the company’s service console, including his router and modem setup and how she solved this issue with a reboot.
Because this was a unique case, the contact center’s AI tool uses the details of the Tawni’s conversation with Austin and the context of Austin’s issue to generate a new knowledge base article. Tawni adds some extra detail and pushes it into the approval queue.
Tip: Include as much detail as possible in your knowledge base articles so customers have all the information they need to solve their problems.
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4. Generating answers
When your agents are in the middle of a service interaction, they don’t have time to read pages of documentation or every detail of a knowledge base article. But, they still need to find the right information to solve your customer’s query.
The same is true for self-service. Reading article after article to find the information you need is not a good customer experience.
Generative AI can help agents and customers get the answers they need faster and easier. Rather than getting a list of pages that may (or may not) have the answer, AI can pull the relevant details from a knowledge article and answer a question directly as plain text.
For our final example, we’ll go back to our Nation-Wide Web customer, Austin.
A few months after his interaction with Tawni, his internet is slow again. He remembers they used the mobile app to fix the issue last time, but now he’s locked out of the mobile app. But instead of calling for assistance, he takes a look at the company’s Help Center. Austin uses the search function to ask the following question: “How do I fix a slow internet connection when I’m locked out of my mobile app?”
Before, Austin would have first needed to find the article on resetting his password and then find the article on using the app to perform a full-system reboot. Now, the contact center’s AI tool generates a personalized response to Austin’s question, pulling together information from multiple articles. “First, click here to request a new password to your mobile app. Once you are logged in, here’s how to use the app to perform a full-system reboot…”
Austin solved his issue without interacting with an agent and still got a personalized experience. If an agent was the one who needed to find specific information within the knowledge hub, they’d have this same experience.
Tip: Making your self-service content easy to find and navigate builds customer trust.
By adding generative AI to your contact center, you’re helping everyone get the most out of every service interaction. Your agents get more done with less busy work and your customers get a quick and easy resolution to their problems while having a personalized experience.
What’s the best way to set up for success with generative AI? Start slowly and build your contact center AI program out as your business skills-up on AI. For example, have your agents take Einstein Reply Recommendations for Service on Trailhead and then practice what they learn with one another. Once they’re comfortable, check out how else you can apply generative AI across your contact center.
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FAQs
How does generative AI affect contact centers? ›
Real-time Sentiment Analysis:
Generative AI algorithms can analyze customer sentiment in real-time by interpreting text, tone, and even voice cues. This invaluable feature helps contact centers identify and address customer dissatisfaction promptly, leading to improved customer retention and loyalty.
Several key steps must be performed to build a successful generative AI solution, including defining the problem, collecting and preprocessing data, selecting appropriate algorithms and models, training and fine-tuning the models, and deploying the solution in a real-world context.
What are the different types of generative AI? ›What are some examples of generative AI tools? Generative AI tools exist for various modalities, such as text, imagery, music, code and voices. Some popular AI content generators to explore include the following: Text generation tools include GPT, Jasper, AI-Writer and Lex.
What is generative AI and what can we do with it? ›What is Generative AI? Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data.
What is generative AI for customer service? ›Generative AI is a powerful tool that (when built into a broader automation or CX strategy) can help companies to deliver faster, better support — from offering a more conversational experience for customers, to assisting agents and supporting bot builders.
What is generative AI in customer support? ›Layering generative AI on top of Einstein capabilities will automate the creation of smarter, more personalised chatbot responses that can deeply understand, anticipate, and respond to customer issues. This will power better informed answers to nuanced customer queries, helping to increase first-time resolution rates.
What is generative AI used for in healthcare? ›Generative AI can create realistic virtual patient populations, which can be used to test and optimize medical interventions, conduct clinical trials, and train healthcare professionals. This will be the final frontier of Generative AI in patient care.
What are the prerequisites for generative AI? ›Learning Generative AI requires a strong foundation in machine learning, deep learning, and probability theory, as well as proficiency in programming languages such as Python and experience with deep learning frameworks such as TensorFlow or PyTorch.
How are companies using generative AI? ›For instance, companies may use generative AI models to analyze large amounts of data and make predictions about prices or improve the accuracy of their services.
What are the 4 types of AI models? ›According to the current system of classification, there are four primary AI types: reactive, limited memory, theory of mind, and self-aware.
What are the four models of AI? ›
There are four main types of AI: reactive machines, limited memory, theory-of-mind, and self-aware.
What is an example of a generative model? ›A generative model could generate new photos of animals that look like real animals, while a discriminative model could tell a dog from a cat. GANs are just one kind of generative model.
What are the most interesting uses of generative AI? ›Improve Quality. Generative AI can help improve the quality of generated content. It may produce high-quality, attractive photographs and films that are preferable to those made manually. In addition, it can be used to generate text that is more accurate and relevant than text created by humans.
What are the benefits of generative AI in business? ›Generative AI can be an excellent asset for businesses, as it can automate various processes, including data analysis, customer service, and content creation, increasing efficiency and cost savings and allowing businesses to focus their resources on other important tasks.
What is an example of AI for customer service? ›Intelligent chatbots can do more than just chat; they can be programmed to complete certain transactions. For example, some businesses allow customers to place orders, update contact information, or find nearby locations from a customer support chatbot on their website.
What are three ways to improve customer experience using AI? ›- Provide customer service. ...
- Present customized recommendations. ...
- Use more engaging customer surveys. ...
- Streamline marketing and sales journeys. ...
- Generate content easily. ...
- Engage in multiple languages. ...
- Create customer segment-driven experiences.
A reimagined AI-supported customer service model therefore encompasses all touchpoints—not only digital self-service channels but also agent-supported options in branches or on social-media platforms, where AI can assist employees in real time to deliver high-quality outcomes.
How is AI used for customer engagement? ›It helps brands quickly and responsibly use data to understand and predict customer needs and improve the quality of AI chatbots to serve the right information to customers at the right time.
What are the four uses of AI in healthcare? ›Deep learning AI can be used to help detect diseases faster, provide personalized treatment plans and even automate certain processes such as drug discovery or diagnostics. It also holds promise for improving patient outcomes, increasing safety and reducing costs associated with healthcare delivery.
What are the 4 key principles of AI development? ›- Inclusive growth, sustainable development and well-being.
- Human-centred values and fairness.
- Transparency and explainability.
- Robustness, security and safety.
- Accountability.
How did generative AI start? ›
The history of generative AI dates to the 1950s and 1960s, when researchers first began exploring the possibilities of artificial intelligence (AI). At that time, AI researchers were focused on developing rule-based systems that could simulate human thinking and decision-making.
What are three 3 main categories of AI algorithms? ›There are three major categories of AI algorithms: supervised learning, unsupervised learning, and reinforcement learning. The key differences between these algorithms are in how they're trained, and how they function.
What are the two main types of generative AI models? ›So far, there are two prominent frameworks of generative AI: Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT).
How can generative AI improve productivity? ›Generative AI can help product designers reduce costs by selecting and using materials more efficiently. It can also optimize designs for manufacturing, which can lead to cost reductions in logistics and production. Improved product testing and quality.
What are the 5 main groups of AI? ›- Text AI.
- Visual AI.
- Interactive AI.
- Analytic AI.
- Functional AI.
Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.
What are the 5 big ideas in AI? ›In this fun one-hour class, students will learn about the Five Big Ideas in AI (Perception, Representation & Reasoning, Learning, Human-AI Interaction, and Societal Impact) through discussions and games.
What are common ways to be generative? ›Generativity is expressed in many forms: parenting, mentoring, community service, ecological conservation, and political activism are just a few examples. The common thread is that generative acts allow us to leave a positive legacy that benefits society in a lasting way.
What is a generative process? ›Generative processes use a cutter in the form of gear or a rack cutter to generate gear teeth. Module of the cutter should be the same as that of the gear to be machined. Gear hobbing, gear shaping, and gear planning are generative processes for gear manufacturing.
Where are generative models used? ›Last but not least, generative models are also used in image translation where our goal is to learn the mapping between two image domains. Then, the model is able to generate a synthetic version of the input image with a specific modification like translating a winter landscape to summer.
How is generative AI being used today? ›
Generative AIs are beneficial in producing new music pieces. Generative AI-based tools can generate new music by learning the patterns and styles of input music and creating fresh compositions for advertisements or other purposes in the creative field.
What are the benefits of generative design? ›- Improve Product Performance.
- Reduce Cost.
- Expand Innovation by Exploring New Design Concepts.
- Faster, Smarter Technologies.
- AI drives down the time taken to perform a task. ...
- AI enables the execution of hitherto complex tasks without significant cost outlays.
- AI operates 24x7 without interruption or breaks and has no downtime.
- AI augments the capabilities of differently abled individuals.
A.I. has been proven to be helpful in a variety of areas related to hiring more diversely, including anonymizing resumes and interviewees, performing structured interviews, and using neuroscience games to identify traits, skills, and behaviors.
What is the role of AI in contact center? ›Artificial intelligence (AI) in contact centers
- Chatbots - chatbots may be the most visible use of artificial intelligence (AI) in the customer service process. When customers choose to chat online with a business, chatbots greet them, collect some background information, and try to solve the customer's issue.
- Increased Efficiency. Generative AI can be used to automate tasks that would otherwise require manual labor. ...
- Improved Quality. ...
- Faster Results. ...
- Cost Savings. ...
- Improved Decision Making. ...
- Increased Creativity. ...
- Improved Customer Experience.
Without proper governance and supervision, a company's use of generative AI can create or exacerbate legal risks. Lax data security measures, for example, can publicly expose the company's trade secrets and other proprietary information as well as customer data.
How is AI used in data centers? ›Better physical security at data centers
AI can help in this regard by analyzing data that can help to detect physical intrusions. For example, by parsing video streams in real time, AI could potentially identify individuals who pose a risk.
AI Customer Service is an artificial intelligence system that interacts with customers on behalf of a company. The AI system is programmed to respond to customer queries and requests, and it can simulate a human conversation by using natural language processing.
What are the common applications of generative AI? ›Generative AI can help companies find information more easily within their own documents, which is known as enterprise search. Generative AI can securely read through all of a company's documents, such as research reports or contracts, and then answer questions about them.
What are 3 advantages of AI? ›
- AI drives down the time taken to perform a task. ...
- AI enables the execution of hitherto complex tasks without significant cost outlays.
- AI operates 24x7 without interruption or breaks and has no downtime.
- AI augments the capabilities of differently abled individuals.
Generative AI enables industries such as manufacturing, automotive, aerospace, and defense to design optimized parts to meet specific goals and constraints such as performance, materials, and manufacturing methods. For example, automakers can use generative design to create lighter designs.
What are the 4 risks dangers of AI? ›Some of the biggest risks today include things like consumer privacy, biased programming, danger to humans, and unclear legal regulation.
What are the weaknesses of generative AI? ›Generative AI can't generate new ideas or solutions
One of the key limitations of AI is its inability to generate new ideas or solutions.