Article • 4 min read
Three ways an AI-powered knowledge base changes the game
By Mark Smith and Chandler Sopko, Content marketing manager at Zendesk <br>& Content marketing manager at Guru
Last updated July 1, 2020
If long-term predictions about artificial intelligence bear out, the way we work will soon be upended in ways not seen since the Industrial Revolution—but in the meantime, AI is already helping humans work better and smarter. And in customer service, AI stands to help support teams emphasize that most human of skills, empathy—a trait that no software program can match (well, at least until the singularity).
For many people, AI’s role in customer service brings to mind bots, which indeed play an increasingly effective role in giving customers the human-free interactions they crave for low-complexity issues. But intriguingly, AI can empower human reps to deliver better customer experiences and accelerated responses while also driving self-service, a channel that customers prefer and support leaders love, since a curated knowledge management base can reduce operational costs by 25 percent.
So why have AI-powered knowledge bases become vital to providing high-quality customer service? Here are a few reasons.
AI enables faster service by surfacing relevant knowledge
When reps are served knowledge proactively by an AI system, they don’t have to go searching for the answers they need—and that means customers spend less time waiting. While a customer won’t know whether an agent is being aided by AI or not, they will know the difference between an immediate answer and “I’ll have to get back to you on that.”
Because AI can operationalize a knowledge base and make information from different teams available instantly, reps don’t have to pass customers from department to department when a question is out of their purview. After all, customers don’t care which department they’re speaking to, they just know that they’re speaking to your company and need an answer.
When an agent doesn’t have to put the customer on hold or transfer them to another department to find an answer, it’s an opportunity to grow and nurture the customer relationship. “An agent has to be empowered with all the right data and knowledge to be able to answer a customer’s question as quickly as possible,” says Kate Leggett at research firm Forrester. “Customers say that valuing their time is the most important thing. And that’s a really difficult proposition.”
AI helps keep content accurate and relevant
Beyond serving content to agents so they can quickly resolve customer issues, AI can ensure that a company’s knowledge base actually stays relevant—and studies have shown that companies with an agile approach to updating content have higher self-service ratios and better search results. In this era of complex products and services, curating a help center can be surprisingly difficult, but support teams can lean on AI to make that process run smoothly.
For example, AI can flag content for review at regular intervals, leveraging machine learning to identify articles that need updated titles, new content, and better search labels. Yet perhaps the most powerful feature of an AI-powered knowledge base is its ability to suggest new content based on what customers are asking for in support requests. That empowers internal subject matter experts to focus their contributions on what will impact customers the most—and that, in turn, frees up agents to focus on white-glove service.
Agents onboard faster and train better with AI
While seasoned agents may know exactly which piece of knowledge they need to answer a particular question, newer reps spend precious time—the customer’s—searching for that same knowledge. A knowledge base augmented with AI, however, can surface relevant knowledge to agents based on the context of an ongoing conversation, eliminating the need to search altogether. That helps rookie agents—who at first tend to tackle lower-tier tickets—hit the ground running, without long-term shadowing by more experienced colleagues.
Being served knowledge in the moment also helps with contextual coaching. Gordon Ritter and Jake Saper, partners at Emergence Capital, have explored this concept at length and developed a thesis around what they call coaching networks, which use machine learning to coach workers on how to do their jobs better as they do them, rather than relying on training before or after an interaction.
Because an AI-driven coaching network collects data from a distributed network of agents and then identifies the best solutions, it effectively aggregates human intelligence in a way that increases the entire organization’s expertise. “By one person anywhere in the world doing their job, and just by doing their job, they can inadvertently be training everyone else in the network,” Saper says. For example, imagine that a customer poses a complicated security question to an agent, and the AI-powered knowledge base serves up a potentially relevant article. What happens if he or she continues to search and pulls up a different piece of knowledge that answers the customer’s question? Without AI, that learning moment happens in a vacuum. However, with AI, the creativity and success of that agent’s actions can be captured for the collective good of the support team—and better yet, for the benefit of customers.
