In the knowledge economy, we recognize that tangible assets are no longer the primary economic indicators. Ideas have become the raw materials that drive economic advantage, and knowledge management provides the strategic and practical methodologies that support the knowledge economy.
Advancements in KM technology are accelerating at warp speed
Knowledge Management (KM) is an organizational strategy towards more effective and efficient information access and decision making. By leveraging the capacity within people, processes and technology, it also supports continuous improvement and innovation. While KM technology has improved over the last few decades, 2023 will be remembered as the year that conversational AI transformed the field of KM.
While search technologies have made incremental improvements over several decades, with semantic search for example, this technology only helps us find documents. With conversational AI, KM technology can now help us find answers, across repositories, with both structured and unstructured content.
Faster time to knowledge means teams work faster and users find what they need.
Most organizations now manage thousands of documents, knowing that they may have the answers in there somewhere, but that it may be unlikely that search engines will be able to find the right documents for some questions. To make documents more findable, content managers added metadata to each document and tried to organize the information architecture in ways that could increase the search relevance. Then the end users would try navigating through the content and try searching with several keywords to find an answer.
With Conversational AI, the ability to find direct answers within your content means that content managers can spend less time thinking about how to make the content more findable for search technologies. Unless there are very complex relationships between content sources, a knowledge graph may not be required. And end users can spend less time searching, even if they don’t know the exact query terms or how to spell them.
Chatbot interactions create entirely new user experiences.
Research has shown that most users of search engines enter queries with just two or three words. But Conversational AI usually works better when we use our natural language, especially when looking for complex answers. This changes the user experience from entering key words to thinking in terms of having a conversation. Instead of getting search results and starting again, we can simulate a conversation where the context of our query is remembered for each conversation.
Just like rephrasing a question so that a person would understand what you are looking for, users now think about different prompts that can provide answers closer to the context we are looking for. And just as we might provide feedback to a person, end users that provide feedback to the Conversational AI (up/down voting) will help train it to get better at answering similar questions.
