Understand. Anticipate. Improve. How Cognitive Computing Is Revolutionizing Knowledge Management
For decades, organizations have tried to unlock the collective knowledge contained within their people and systems. And the challenge is getting harder, since every year, massive amounts of additional information are created for people to share. We’ve reached a point at which individuals are unable to consume, understand, or even find half the information that is available to them.
Artificial intelligence (AI) and cognitive computing technologies have emerged as a disruptive and innovative force across many industries. These new technologies are now helping to simplify, modernize, and automate knowledge solutions. By bringing together traditional knowledge management tools with advanced computing intelligence, organizations can vastly simplify the process of creating, finding, and improving knowledge. This powerful combination, which we call cognitive knowledge, is a more natural and effective way for businesses to connect people to knowledge.
Advancements in AI now enable people and machines to interact more naturally to extend human expertise and cognition. In fact, AI and cognitive computing can augment your knowledge management program by helping you understand what people are searching for, anticipate follow-on requests, and continually improve the information that you provide in the future. Let’s take a closer look at each activity.
The most fundamental component to a successful knowledge management application is the ability to easily search for information. AI can understand the nuances in how people ask questions and search for answers. Like our brains, AI disambiguates words and concepts based on context. Sophisticated AI algorithms leverage mathematics underpinned by an extensive body of linguistic data. Similar to the human experience, advanced knowledge management systems can understand the correct meaning of search terms, even if they are phrased in different ways. These applications can analyze content semantically to determine contextual relevance to a user’s request, pinpointing solutions more accurately than non-cognitive approaches.
Beyond simply understanding a user’s current request, cognitive computing can anticipate the answers users may need and predict what they might ask next. AI-based knowledge management is both proactive and predictive, making inferences based on the conceptual understanding of the knowledge base to deliver useful information when and where people need it.
Similar to human memories, these applications cluster related concepts and recall this knowledge to predict next questions. Essentially, knowledge applications can mimic a person’s train of thought, answering questions before they are even asked. For example, an initial inquiry about the symptoms of a medical condition might trigger suggestions on relevant treatment options, medications, and lifestyle changes.
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