KM Component 50 – Cognitive Computing and Artificial Intelligence

Stan Garfield

Stan Garfield

March 24, 2022

This post will define key terms, provide background, and list the key uses and benefits of cognitive computing and artificial intelligence as related to knowledge management.

Cognitive computing is defined by machine intelligence – a collection of algorithmic capabilities to augment employee performance, automate complex workloads, and provide cognitive agents that simulate human thinking and engagement. Artificial intelligence comprises technologies capable of performing tasks normally requiring human intelligence. 


  • Cognitive computing: simulation of human thought processes in a computerized model, involving self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works; makes a new class of problems computable, addressing complex situations that are characterized by ambiguity and uncertainty
  • Artificial intelligence: the capacity of a computer to perform operations analogous to learning and decision making in humans, as by an expert system
  • Expert systems: computerized systems that emulate the decision-making ability of a human expert; designed to solve complex problems by reasoning about knowledge, represented mainly as if–then rules rather than through conventional procedural code
  • Natural language processing: a branch of artificial intelligence that deals with analyzing, understanding and generating the languages that humans use naturally in order to interface with computers in both written and spoken contexts using natural human languages instead of computer languages
  • Machine learning: giving computers the ability to learn without being explicitly programmed; a method of data analysis that automates analytical model building; using algorithms that iteratively learn from data to find hidden insights without being explicitly programmed where to look
  • Deep learning: part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms
  • Neural networks: computing systems made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs; artificial neural networks (ANNs) are algorithms or actual hardware that are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales
  • Intelligent agents: algorithms that interpret requests and provide responses for specific domains and tasks, and produce unique results to questions that have not been pre-programmed
  • Chatbot: a computer program that conducts a conversation via auditory or textual methods; often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test.


Cognitive computing and artificial intelligence are very hot topics. Many people seem to think that AI is a relatively new technology, but it is not. AI has been around for a long time, having gone through several hype cycles.

Awareness of AI is widespread. Movies such as 2001: A Space Odyssey (1968), Colossus: The Forbin Project (1970), and A.I. Artificial Intelligence (2001) have portrayed AI somewhat realistically.

The promise of AI has always been tantalizing, but it has struggled to deliver on that promise. They key is to create, promote, and implement cognitive computing killer apps that are markedly more capable than existing alternatives. In a television commercial for Amazon Alexa, Alec Baldwin asks Alexa to check the traffic. This is not likely to convince many people to buy one. More compelling use cases for AI are needed.

One of the early areas where AI was applied was expert systems for medicine. AI can help diagnose illnesses, prevent problems with drug interactions, and detect new associations not yet seen by humans. My radiologist friend, Dr. David Osher, told me that computers will soon be able to read most x-rays better than he can. Now that is a compelling use case.

Uses and Benefits

Cognitive computing and artificial intelligence can be used to support decision-making, deliver highly relevant information, and optimize the available attention to avoid missing key developments. Implement cognitive computing to help achieve these desirable results:

  • Help people make better decisions, take action more quickly, and achieve more successful outcomes.
  • Deliver relevant information and advice at the time of need.
  • Reduce information overload and optimize people’s available attention span.
  • Allow people to act more efficiently and effectively.
  • Reduce errors, minimize loss and damage, and improve health and safety.

Cognitive computing can perform operations analogous to learning and decision making in humans. Intelligent personal assistants can recognize voice commands and queries, respond with information, or take desired actions quickly, efficiently, and effectively. Artificial intelligence can simulate human thought processes and mimic the way the human brain works, addressing complex situations that are characterized by ambiguity and uncertainty.

Using these approaches can enhance the capabilities of humans by augmenting their powers of observation, analysis, decision making, processing, and responding to other people and to routine or challenging situations. Cognitive computing tools such as IBM Watson, artificial intelligence tools such as expert systems, and intelligent personal assistant tools such as Amazon AlexaApple SiriGoogle Assistant, and Microsoft Cortana can be used to extend the ability of humans to understand, decide, act, learn, and avoid problems.

Cognitive computing and artificial intelligence are the key elements of an Augment Strategy for knowledge management. Here are three examples of such a strategy:

  1. Non-Profit Organization: provide chatbots and voice recognition for contacting and responding to donors
  2. Manufacturing Company: implement expert systems for designing, engineering, and building new products.
  3. Consulting Firm: automatically determine the specialties, roles, and interests of consultants and automatically deliver important information relevant to their work at the time of need.

Content from Lucidea

Stan Garfield

Stan Garfield

Please enjoy Stan’s additional blog posts offering advice and insights drawn from many years as a KM practitioner. You may also want to download a copy of his book, Proven Practices for Implementing a Knowledge Management Program, from Lucidea Press.  And learn about Lucidea’s  Presto and SydneyEnterprise with KM capabilities to support successful knowledge curation and sharing.

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