Knowledge Base in AI

Building wood blocks on table. Laptop and flowcharts.

The Relationship Between Knowledge and AI

In simplest terms, AI provides the mechanisms that enable machines to “gain knowledge.” It allows them to acquire, process, and use knowledge to perform tasks that display “intelligent” behavior, such as:

  • Perception
  • Learning
  • Knowledge representation and reasoning
  • Planning
  • Execution

There are significant differences between knowledge and intelligence. Knowledge is the collection of skills and information a person’s acquired through experience.

Knowing how to apply that knowledge to problem-solving and decision-making is intelligence. AI knowledge management is a strategic process where an organization creates value from intangible assets.

What Is the Knowledge Base in AI?

A great illustration of knowledge-based AI is AI-powered customer service. When finding a solution to a customer’s problem, customer support agents often search multiple sources of information and seek advice from one or more experts.

AI simplifies the process by using keywords and phrases to quickly scour dozens, if not hundreds, of various types of information to speedily answer an agent’s question.

By placing all information from different siloed teams in a centralized repository, knowledge bases in AI make it incredibly easy for agents to retrieve relevant, useful intelligence.

Customers no longer need to be transferred from department to department in search of someone who has the answer to their question because all agents have access to the same accurate and relevant information.

And agents gain greater job satisfaction from being able to help customers solve their problems.

The fundamental characteristics of an AI-powered knowledge base include:

  • Accurate and Relevant Content. AI extracts insights and patterns from large sets of data and then makes predictions based on that information. It then provides users with real-time and pertinent information that addresses their needs.
  • A Consistent Voice. All agents have access to the same relevant content, which means they’re all working with the same information and providing a consistent customer experience.
  • Faster Service. An AI-powered knowledge base significantly speeds business activities, with customers enjoying faster service from agents.
  • Simplification. Robust doesn’t have to mean complex. Easy-to-navigate dashboards and interfaces make it easy to create and maintain even the most intricate knowledge base.
  • Improved Collaboration. Knowledge base articles are at their best when customers and agents work together. AI gathers feedback and recommendations from agents, customers, and community users to continuously update and improve content.

4 Key Benefits of Knowledge Base in Artificial Intelligence

Knowledge management systems boost productivity, minimize risk, and increase your competitive advantage. A knowledge base in AI delivers many important benefits, including:

  1. Simplifying Knowledge Discovery. Until a few years ago, organizations only had access to two knowledge management practices: capturing and sharing knowledge. Today’s systems add discovering and maintaining knowledge to the mix. Knowledge workers can now use multiple tools, including cloud-based solutions, to share their knowledge and expertise with others. AI makes the entire process easier with new technologies like semantic search, natural language processing, and machine learning.
  2. Connecting Data From Disparate Sources. People working in different departments don’t necessarily capture and share information in the same way. For instance, sales reps may use a CRM tool, and support teams use a ticketing system. AI helps organizations connect and combine knowledge across multiple processes.
  3. Keeping Your Knowledge Base Content Up-to-Date. Over time, a tremendous amount of information ends up stored in a knowledge base. Eventually, some of it becomes outdated, but no one bothers to update or dispose of it. AI supports knowledge maintenance by reminding employees to update knowledge regularly.
  4. Providing Important Knowledge Management Metrics. It’s challenging to prove a knowledge management system is fulfilling its promise. AI makes it far easier to track performance metrics like first call resolution, average wait time, and average call abandonment rate.

What’s Next for Knowledge Base in AI

Nearly 80 percent of enterprises say they plan to prioritize AI and machine learning in 2021, concentrating on initiatives that deliver profits and cost reduction.

Customer experience futurist Blake Morgan says AI has “enabled companies to personalize the customer experience in a way we’ve never seen before.” She urges industries to make digital transformation a top priority for their companies.

Author: Guest Author

Published On: 1st Jul 2021 - Last modified: 6th Jul 2021
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