Click to learn more about author Nitesh Dudhia.
There is a treasure-trove of knowledge that lives in the communications infrastructure of your business. Every day, and in multiple ways, valuable knowledge flows through conversations between employees and customers. Unfortunately, however, most of this mountain of data remains unused and gets buried in archives.
Typically, this information is lost to the tides of constant incoming information. Between emails, texts, phone calls, meetings, and online docs (you get the picture), there is an ongoing onslaught of data coming to each employee’s doorstep. Artificial intelligence can help you consistently and reliably extract and organize knowledge from this unstructured conversational data. This codified insider knowledge is easy to reuse — transforming the potential of processes and generating an exponential return on investments.
AI for Enterprise Knowledge Management Systems
“Knowledge operates on a principle of abundance — the more we share, the more we learn,” says Consortium, a library resource for service innovation. “Knowledge is the by-product of an interaction or experience, and no one leaves an interaction with less knowledge than they came in with.”
Traditional knowledge management systems are designed to increase productivity, minimize risk, create competitive advantage, and help businesses make better, faster decisions. But a significant amount of time, energy, and attention go into managing a business’ knowledge management system. The effort spent on finding, curating, and updating knowledge systems negates the benefits of knowledge management.
As of 2020, more than 9 in 10 leading businesses have ongoing investments in artificial intelligence, according to New Vantage. While every business is investing in AI, they should consider the value of using AI in their knowledge management to truly fuel their competitive business edge. The four most significant ways that AI can enhance your knowledge management system are as follows.
The first is through simplifying knowledge discovery within your business. AI can add metadata like tags, classifications, and context information without taking breaks. Contextual metadata will make information easier to search and knowledge discovery more relevant, thereby delivering more intuitive search capabilities and making information more accessible across operations.
The second advantage of AI is that it can be used to create additional context from unstructured data. Once the metadata has been added to incoming conversations, you can store this information into a knowledge graph that creates links between seemingly disparate data points. Modeling information in a similar fashion to the way a human brain does, knowledge graphs can factor in context and relational data. The knowledge graph creates a node with a piece of data, connects another node, and over time, the connections build strength and construct relevant information highways.
Thirdly, AI helps to keep your content up-to-date. Data flows in and out of an organization every day yet never remains static. As more interactions come in, the knowledge graph grows, and the system gets better and more relevant. This is important because if employees are accessing stale information, it can be detrimental both to your business and to your employees’ trust in the system.
The last, and one might argue most pertinent, advantage of AI, as applied to your knowledge base, is that AI tools provide important knowledge management metrics.
With good data practices and, most importantly, employee participation — valuable knowledge is created and curated through the AI tool. This helps to create statistics that prove through hard numbers that AI is working to improve your system, significantly impacting both productivity and operational costs.
All of these benefits help to outline the potentialities of AI implementation in your knowledge management system. But the reality of AI is that it is only as good as the data that it is being served.
So exactly what kind of data should AI be feeding off of in your business?
How AI Is Used to Create Knowledge from Data
There are two types of data from intercompany knowledge that AI can excavate.
The first is explicit knowledge, which is structured data codified and digitized in books, documents, reports, memos, etc. — documented information that can facilitate action. This is knowledge that can be easily identified, articulated, shared, and stored in various systems. This is information that is easy to search and understand.
The second and more underutilized form of knowledge that AI can use is what is known as tacit knowledge. This is knowledge embedded in the human mind through experience. Tacit knowledge comes in the form of personal wisdom, insights, and intuitions, which are context-specific and highly unstructured — making it much more difficult to extract and codify. It is this knowledge that is commonly exhibited in conversations and interactions within a company’s team and makes up a businesses’ “tribal knowledge.”
Tacit knowledge data from these conversations can come in the form of voice recordings, text and chat logs, video recordings, and the content of emails. Most of this conversational data flows through various platforms, and if AI is given permission to access this treasure-trove of data to analyze, it can extract and organize the data. The AI tool then cultivates this information and enhances a living breathing knowledge repository of tribal know-how, further informing your businesses’ knowledge system.
The remote work explosion of this last year has meant that teams are now more distributed than ever before, and there’s even more data flowing through communication systems. If you aren’t tapping into an AI source to codify your tribal tacit knowledge — then you’re simply missing out.
You already have the data — don’t let it go dark — and rather use it to create better experiences and a competitive advantage that fuels your enterprise. AI implementation enhances the tools that your company already possesses and the strengths and experiences of each of your employees, which is more important than ever in an increasingly digital workplace.
How Tribal Knowledge Can Be Deployed to Support CX
Before the pandemic, customer service expectations were already at a high, with 93 percent of customers reporting that customer service was an important driver of brand loyalty, according to HubSpot Research. In order to meet both the challenges brought by a pandemic economy and the overall upward trend in online interactions across industries, employing the help of AI is vital.
Chatbot deployment helps take over some of the more repetitive work that can come in with CX queries. Tightly scripted chatbots can be made more intelligent if you allow AI to tap into the living, breathing knowledge graph that is unique to your company. By applying this tacit knowledge to your chatbot, you give it superpowers, connecting it to the repository that is enriched by everyday conversations. This information can be used over and over again and is updated and evolved as more data is brought in, constantly improving the CX experience.
The AI derived from your team’s tacit knowledge can also be used to create Q&A systems inside your most used platforms, such as Slack or Microsoft Teams, to help employees find better answers in real-time when they need help in their efforts to drive CX. Archived interactions can help the AI to recommend relevant information when similar contexts recur in the future. Most importantly, this knowledge is shared evenly across departments to create consistent CX experiences. Anyone who has permission to access the knowledge can use it.
Internally, AI also prevents knowledge drain when people leave the organization by extracting and retaining context from their interactions and making that available whenever needed. This comes into play especially when new employees can simply ask the AI questions, as they would an appointed onboarding buddy to quickly learn how things work. This speeds up onboarding processes and keeps a running tab of retained tribal enterprise know-how that is easily accessible and reusable.
Finally, AI naturally helps with increased volume and varying times of demand in online CX because it works 24/7. AI bots can power improved customer experience by enabling intelligent self-service, with no lag-time on responses. A well-trained AI management system will bring consistency to tagging, classification of issues, and the overall improvement of analytics for your data — thereby enriching your customer management experience.
The Future of Better Business
In any organization, there are systems, rules, and operating procedures. The hidden and most fundamental part of an organization is its people. Their “tribal knowledge” is the soul of the business, and that experiential understanding hasn’t necessarily been documented in enterprise communications to date.
With each person’s input, a knowledge graph can grow its parameters daily. This can become overwhelming to the individual, but with the implementation of AI can only inform and increase proficiency within the business. Providing an encyclopedic resource to both employees and customers alike, AI can stimulate both success and return on investment.
Implementing the help of AI in your business can turn your otherwise lost communications into an oracle for success.