4 Ways Your Contact Center Can Get Started with Generative AI

Generative AI has emerged as the most scorching topic in the service industry today, particularly within the contact center domain. According to a recent study, a staggering 84% of IT leaders we surveyed believe that adopting generative AI will significantly enhance their organization's ability to cater to customers. Excitement is palpable among service leaders as they explore the vast potential of generative contact center AI.

Surprisingly, despite the enthusiasm, a mere 24% of businesses have implemented any form of contact center AI. What obstacles stand in their way? A considerable 66% cite the lack of appropriate skills among their employees to effectively harness the power of generative AI. Let us now delve into the four key ways you can leverage contact center AI, complete with practical use cases and valuable tips to help you embark on this transformative journey.

Generating Service Responses to Customers

In your contact center, customers have various channels available to reach your business, ranging from phone calls to emails, chats, and SMS. While traditional phone usage remains significant, there is a notable shift, with 57% of customers now preferring digital channels. It places a crucial responsibility on your agents handling these digital channels to provide accurate, pertinent information, respond promptly, and swiftly resolve customer issues.

Enter generative AI, the game-changer. Powered by large language models, generative AI can automatically generate human-like responses to any customer query. When integrated with your customer data and knowledge base, these generated replies can be personalized, instilling greater trustworthiness. Agents can review these model-generated suggestions and seamlessly send them. This proves to be a time-saving boon for agents simultaneously managing multiple customer cases in the contact center.

To illustrate this, let's consider a fictional internet company called Nation-Wide Web. Imagine Jane, a Nation-Wide Web customer, who notices an unfamiliar charge on her bill. She initiates a chat on the company's website and connects with Agent Katie. Among the various messaging windows, one belongs to Jane.

Jane shares her concerns about the bill, revealing that she exceeded her data package for the month. The contact center's AI tool leverages Jane's question and her account status context to craft a personalized message that empathetically explains the charge while also highlighting the company's policy to waive the fee under such circumstances.

Katie reviews the message, confirms the policy, and then sends it to Jane, promptly removing the charge from her account. Jane is delighted with the swift and hassle-free solution, while Katie can now focus her attention on customers facing more complex issues.

Tip: Prioritizing the review of all customer communications for accuracy and tone helps to prevent misunderstandings and fosters better customer interactions.

Read More: How is AI redefining Call Centers in India?

Generating Case Summaries

To deliver a stellar customer experience, obtaining accurate data to monitor and optimize your business's service interactions is paramount. This makes the wrap-up summary compiled by your agents after resolving a case one of the most vital pieces of service data your business can gather.

However, this task poses a challenge as it consumes considerable time, hindering your agents from assisting other customers promptly.

Thankfully, contact center AI comes to the rescue by tackling even the most intricate email and chat conversations and generating proposed wrap-up summaries. All your agent needs to do is review these summaries before saving them to the case log. This efficient process saves agents a significant amount of time and effort in data entry.

Now, let's revisit our Nation-Wide Web scenario. As you may recall, Katie's AI tool produced a response for Jane, and all Katie had to do was review the message, hit send, and waive the fee from Jane's account. Meanwhile, the AI utilizes the data from the conversation thread and Katie's actions in Jane's account to generate a comprehensive case summary.

Once Katie concludes the conversation with Jane, she can peruse the suggested summary, make any necessary adjustments, and save it to the case record. Minimizing after-call work empowers Katie to swiftly move on to assist other customers.

Tip: Streamline the process by creating a template for your case summaries, enabling your contact center AI tool to seamlessly extract conversation data into the CRM without overlooking essential details.

Interesting Blog: Discovering the Prospects of AI in Customer Service in India

Generating Knowledge Articles

According to research, 59% of customers prefer self-service tools for simple service issues. To cater to this preference, a business requires an extensive knowledge base that customers can easily search through to find solutions.

Traditionally, service agents are burdened with the task of creating and publishing knowledge articles after resolving a case. Unfortunately, this manual process is time-consuming and prevents agents from promptly assisting customers in need.

However, contact center AI emerges as a powerful solution by automatically generating knowledge base articles once a support case is closed. It accomplishes this by extracting relevant information from case notes, message history, and data from other service tools. After the AI generates the article, your agent simply needs to review it for accuracy and submit it for approval. This liberates agents from the pressure of crafting articles from scratch.

Let's return to our Nation-Wide Web example, where Austin faced slow internet and reached out for troubleshooting assistance. Tawni, the service agent, interacted with Austin, obtaining details about his router and modem setup. After trying common scenarios without success, Tawni proposed a novel solution—performing a full-system reboot through the Nation-Wide Web mobile app. This approach successfully restored Austin's internet speeds, and the case was resolved. Tawni diligently logged all this information into the company's service console, including the steps she took to address the issue.

Given that this was a unique case, the contact center's AI tool leveraged the specifics of Tawni's conversation with Austin and the context of his issue to generate a new knowledge base article. Tawni added some additional details and placed them in the approval queue.

Tip: When creating knowledge base articles, include as much detail as possible, empowering customers to have all the necessary information at their disposal to solve their problems effectively.

Also Check: AI Chatbot - A Game Changer in Customer Service

Generating Answers 

During service interactions, your agents face time constraints that prevent them from delving into lengthy documentation or reading every detail of a knowledge base article. Nevertheless, they must still access the right information to resolve customer queries effectively.

The same holds true for self-service scenarios. Customers seeking solutions shouldn't have to read through multiple articles, leading to a less than satisfactory experience.

Here's where generative AI comes to the rescue, facilitating faster and easier access to answers for both agents and customers. Instead of presenting a list of potentially relevant but not entirely helpful pages, AI can extract pertinent details directly from a knowledge article and provide a plain text response to the query.

For our final example, let's revisit our Nation-Wide Web customer, Austin. After experiencing slow internet again, he recalls the previous mobile app fix but finds himself locked out this time. Rather than reaching out for assistance, Austin turns to the company's Help Center and employs the search function, posing the question: "How do I fix a slow internet connection when I'm locked out of my mobile app?"

Previously, Austin would have had to locate an article on resetting his password and then another one on using the app for a full-system reboot. However, with the contact center's AI tool, Austin receives a personalized response that combines information from multiple articles. The response guides him to "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 successfully resolves his issue without engaging an agent, enjoying a personalized experience. If an agent had needed to find specific information within the knowledge hub, they would encounter a similarly efficient experience.

Tip: Ensure your self-service content is easily accessible and navigable to build customer trust.

Read More: How Does AI Chatbot Customer Service Enhance Your Customer Experience?

Get Started with Generative Contact Center AI

By integrating generative AI into your contact center, you empower everyone to make the most of every service interaction. Your agents can accomplish more without being bogged down by unnecessary tasks, and your customers benefit from swift and effortless issue resolution, all while enjoying a personalized experience.

To ensure a successful implementation of generative AI, it's best to proceed gradually and expand your contact center AI program as your business becomes proficient in AI utilization. A practical approach is, to begin with small steps, such as having your agents undergo Einstein Reply Recommendations for Service on Trailhead. They can then put their learnings into practice by interacting and supporting each other. As they become more comfortable with the AI tools, explore additional opportunities to leverage generative AI throughout your contact center.

Tip: Starting with measured steps and continuous learning allows your business to maximize the benefits of generative AI effectively.