Knowledge AI vs. Knowledge Management: What’s the Difference?

Knowledge AI vs. Knowledge Management: What’s the Difference?

3 min read

Common Challenges of Knowledge Management Systems

Knowledge management tools were designed to be a centralized location for collective knowledge within a company. Yet, they’ve rarely achieved the results contact centers desperately need. The biggest problem is a human one: legacy knowledge management systems depend on humans to add, update, manage and curate the content within them.

These static repositories can quickly become unwieldy and time-consuming to use—especially for new agents. Over time, content easily becomes fragmented and duplicated across multiple systems both inside and outside of the knowledge base. Silos of information make finding a specific answer to a customer question like finding the proverbial needle in a haystack.

Without dedicated resources to constantly update and share information, a knowledge management system becomes relegated to a generic FAQ—or worse, it becomes a black hole of information that’s lost and unusable to its audience.

How Knowledge AI Differs from Knowledge Management

Knowledge AI complements a knowledge management solution by ingesting data within the system (such as documents, PDFs, Excel tables) in addition to ingesting information from outside the system (such as data from knowledge bases, websites and more). Not only does knowledge AI ingest content from across data sources, but it also updates information dynamically, forgoing the need to manually update every piece of outdated content.

Knowledge AI addresses the long-standing challenges and shortcomings of knowledge management systems to put the right information at the fingertips of customers and agents, without the hassle of searching through pages of related but irrelevant content.

Here’s a quick comparison of the features and capabilities of knowledge AI and knowledge management systems (KMS):

Knowledge AI

  • Search/share information from across data sources, including KMS, web pages, CRM systems and more
  • Uses cognitive or semantic search to extract meaningful information
  • Dynamically updates information across data sources 

Knowledge Management

  • Search/share information contained within knowledge management system only
  • Uses keyword matching, which often yields irrelevant results
  • Requires constant maintenance and curation of individual pieces of content

Why is knowledge AI so Important Now?

In its Fjord Trends report, Accenture highlighted that people expect to have questions answered at the touch of a button and that they are asking more questions because of the ubiquity of information.

“People expect to get answers at points of interaction with the product or service they want to buy and at the point of purchase … A brand is a bundle of promises, and customers want to know more about those promises than ever—they also expect brands to deliver on them.”

This echoes the experience within contact centers. Whether it’s self-service or agent-assisted interactions, generic answers resolve very few customer questions. Instead, finding a relevant, personalized answer to increasingly complex issues requires high levels of customer and agent effort.

That’s because static knowledge management systems and agent assistance tools were never designed to handle:

What Benefits does Knowledge AI Deliver?

Making the search for relevant answers effortless and immediate improves the experience for agents and customers, enhances the conversation and drives operational efficiency. With the right information at the right time, contact centers can:

“Conversation is a natural part of the human experience—it’s how we share and find out information, how we frame who we are and how we grow and learn. We believe brand conversations with their customers might evolve and be used to structurally solve the challenge of providing the right answer at the right time.”

AccentureFjord Trends Report, 2022

Learn more about knowledge AI and how it works

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