CXNext Live: Today’s Biggest Knowledge Base Challenges and Opportunities
Here it is nearly 2020, and knowledge bases are still a hot topic. In fact, Forbes and CIO Review recently published articles about why knowledge bases (KB) are the next big thing, and how AI can help CIOs make knowledge management (KM) systems more impactful, respectively.
Knowledge bases have been around for a while, but it seems many organizations are still struggling with how to catch up, build, connect, consolidate, manage, or even harness their knowledge bases to optimize and grow their businesses. Why is a knowledge base important? Because when you have a centralized source of all company data, you can point someone, anyone, to that foundational source of truth for consistent and up-to-date information.
Now with AI on the scene, the importance of a knowledge base is even more of a hot topic because now it’s a hot opportunity. AI helps solve many of the challenges around the current knowledge management model by taking a much more analytical approach.
Current challenges around knowledge base management
Traditional models of knowledge base management are, well, traditionally not doing organizations any favors. Company data is only getting more complex and having multiple repositories to handle that data is problematic. Fragmented systems owned by different teams makes it hard to find information, and that data can easily become outdated.
Knowing the importance of a knowledge base, a lot of organizations take a stab at building FAQs before they’ve even seen any questions. When your knowledge base is built on assumptions, you aren’t really serving the needs of your customers.
On top of that, many organizations take a one-size-fits-all approach, which is like constructing all roads in a city the same size, regardless of the dynamic traffic needs. If you’re putting the same amount of resources against an article that only gets used five times a year as you are against another one that’s your #1 hit item, you’re not utilizing your resources wisely.
Where does this lead? New research finds that businesses are wasting millions of dollars on knowledge management solutions that add little or no value and take much longer than expected to deploy. Considering the importance of a knowledge base, a change in approach is needed to capture a better ROI and improve the customer experience.
Taking a more analytical approach
A knowledge centered services (KCS) approach, where you create and evolve knowledge as agents solve customer issues, is the most used approach, but this puts a lot of extra responsibility on support teams. It may be successful for some organizations, but creating content on the fly is a heavy lift for agents day to day, and it requires appropriate staffing to ensure people have the bandwidth to do the work.
With AI technology and natural language understanding, the onus isn’t on the agent to figure out where there’s crossover and where there are gaps or to assign the right tags and labels. You see clusters of questions around what customers are trying to ask (their intents), even if they ask it differently. AI can guide you where you most need to focus your efforts, so that you can spend more time on high-value intents. It provides the right navigational organization and creates a rich feedback loop, which leads to more conversational flows.
Then there’s the accessibility of the knowledge. If it’s hard to search, whether that be because the engine doesn’t understand the customer input or the customer (or agent, for that matter) is flooded with results to sift through, your metrics are going to let you know this isn’t a good thing. Recent research shows that 25% of an agent’s time is spent just finding information. That’s a day and a half every week. For agents who serve customers face-to-face, this is especially awkward and problematic.
That’s why a centralized knowledge base is important, one that serves the needs of both customers and agents, especially when your company has multiple locations or field service agents. But it’s not enough just to have everything in one place. AI using natural language processing understands user intents to pull the right information from that single source. Everyone gets the same information, regardless of where they are or how they ask.
Opportunities to improve KM going forward
We have a diagram we like to use here that shows how AI connects the customer and agent feedback loops.
Bringing these two together with intelligence, instead of bifurcating them, allows you to learn from one to influence the other. Let’s say your agents are seeing a lot of the same question. Clearly that’s not something being handled by your self-service channels, but with this infinity feedback loop model with AI as the nucleus, you can turn around that agent insight and create an appropriate answer on the customer-facing side.
Knowledge management upkeep will always need to be done as policies, regulations, products, or any other information changes. If you’re not a beast of an organization with unlimited resources, taking a more analytical approach with AI helps you maximize the resources you do have.
The importance of a knowledge base simply can’t be understated in today’s digital market. Be sure to check out 5 tips for knowledge management success for more ways to make the most of AI technologies. And watch our LinkedIn live video Architecting Your Knowledge Base for Long-Term Success, where we dig deeper into why a knowledge base is important, and the current challenges and opportunities of knowledge management today.