Current Landscape and Future Outlook for AI in KM

Speaker: Anthony Rhem, CEO/Principal Consultant, A. J. Rhem & Associates

Session Description: What are the leading AI programs and technologies that are facilitating knowledge sharing and management today? What can we expect from machine learning in the future? Our knowledgeable speaker, Rhem, shares a look at the current landscape and future outlook for AI in KM.

[These are my notes from the KMWorld Connect 2021 Conference. I’m publishing them as soon as possible after the end of a session so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

Notes:

• He is currently working with startups that are immersed in both AI and KM
• His focus is “how to infuse KM with AI.”
• Tenets of AI-Infused KM
  • connects tacit and explicit knowledge through ontology management and knowledge mapping
  • addresses the reality that tacit and explicit knowledge are constantly updating in your information lifecycle management system
  • AI enables predictive analytics and knowledge flow optimization
  • provides dynamic, accurate, and personal knowledge delivery
    • dynamic = as the content updates, it connects with the appropriate subject matter experts who can provide additional insights on that updated knowledge
    • accurate = helps identify the authoritative voice and authoritative source for that knowledge
    • personal = personalized knowledge is tailored to what an individual needs to make a decision and is presented in a way that is consumable by that person.
• Why KM needs AI
  • Volume = there is so much information and we need to make sense of it efficiently — humans can’t do this unassisted
  • Timeliness = delivers knowledge in a timely manner
  • Access = improves access to the collective knowledge of the organization 
  • Personalization = workers need knowledge tailored to their individual needs
  • Productivity = increases productivity by helping workers operate more efficiently and effectively
  • Innovation = depends on effective collaboration and knowledge sharing.
• Current Landscape of AI in KM
  • Predict trending knowledge areas/topics to meet knowledge worker needs
  • Improve content by leveraging machine learning to identify what content will be best suited to address the situation
  • Identify which content will be most useful to your knowledge workers based on real- time engagement and content consumption
  • Improve search relevance, precision, and efficiency 
  • Personalize knowledge based on individual preferences
  • AI more effectively interprets user intent, enabling it to provide more useful search results. This is based on AI’s enhanced understanding of the intended use of the content.
  • Chatbots with Natural Language Processing (NLP) provide cognitive capabilities to understand, interpret and manipulate human language. This enables bots to anticipate the needs, attitudes and aspirations of users to aid in decision making and improve outcomes
• Leading AI programs facilitating KM
  • Knowledge-as-a-Service (KaaS) incorporates AI into the access and delivery of knowledge. (Typically implemented through a Digital Workplace). Examples:
    • IBM Digital Workplace Hub
    • axero
    • Sift
  • Search with Natural Language Processing (NLP) and Machine Learning(ML)Semantic Search engines are incorporating NLP and ML in understanding the intent and contextual meaning behind the search query. This, in essence, is figuring out what a user means when executing search. Examples:
    • Enterprise NLP
    • Ontology Builder
    • elastic
    • lucid works
  • Intelligent Chatbots with NLP/ML provide cognitive capabilities to understand, interpret and manipulate human language in a way that enables the bots to anticipate the needs, attitudes and aspirations of users. Examples:
    • Landbot.io
    • Microsoft
    • IBM Watson
    • ManyChat
• Future AI/KM Paradigms and Technologies
  • Ethical AI in knowledge delivery — ideally, the resulting decision making will be ethical and free from bias. 
    • [But as with other ethical AI considerations, we should not assume that the knowledge selected and delivered by AI will be free from bias. The devil is in the developer and design details.]
  • Cognitive Digital Twins (CDT) — a digital twin is a digital replica of a physical system and runs in parallel with that physical system. Adding cognitive ability provides enhanced predictive capabilities regarding the digital twin and, ultimately, the physical system.
  • Augmented Intelligence — where AI assists human reasoning and decision making rather than replacing it.