Originally published October 12, 2023

Stan Garfield

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This is the 96th article in the Profiles in Knowledge series featuring thought leaders in knowledge management. Rachad Najjar specializes in generative AI, knowledge exchange, knowledge management, artificial intelligence, and organizational learning. Based in Grenoble, France, he leads GE Renewable Energy in organizational learning, knowledge sharing, and virtual collaboration. In this role he is responsible for defining a renewable knowledge architecture with the right set of business-driven knowledge communities.

Rachad has extensive experience in the fields of knowledge management and organizational learning. His professional experience is coupled with scientific discipline and research-based models that have been published by specialized scientific conferences. He earned a PhD in industrial engineering with a focus in knowledge management. His thesis detailed collaborative methods and tools to remotely develop multi-site engineering standards using the case of GE Renewable Energy.

Rachad has been a regular contributor to the SIKM Leaders Community. He has given presentations, participated in calls, answered questions, and shared useful insights.

Recently a member of my local Detroit KM community, Susan Genden, asked me for help in finding a last-minute replacement for a speaker for the community she supports. Based in Farmington Hills, Michigan and serving adults 55 and better throughout the metropolitan Detroit area, SOAR Lifelong Learning Institute offers a year-round, diverse selection of classes, lectures, and explorations along with a large variety of social activities for those with a curious mind and a passion for learning.

Based on my suggestion of Rachad, Susan contacted him, and was delighted when he immediately agreed to step in. On October 20, 2023 at 10 am EDT, he will present via Zoom on AI Trends Today and Tomorrow. Here is the class description:

Artificial Intelligence (AI) has been making significant strides in recent years and is poised to have a substantial impact on various industries and everyday life. Generative AI technology focuses on creating new content, such as images, videos, text, and music, using machine learning algorithms. This class will explore AI and provide examples of current trends such as image generation, text generation, deep fakes, and generative AI tools that are empowering artists and designers.

Background

Profiles

Education

  • Grenoble INP — UGA: Doctor of Philosophy (Ph.D.), Industrial Engineering, 2013–2016
  • Université Grenoble Alpes: Master’s degree, Computer Software Engineering, 2011–2012
  • University of Balamand: Master of Science (M.Sc.), Computer Hardware Engineering, 2003–2008

Experience

  • GE Vernova
  1. Organizational learning leader, 2020 — Present
  2. Global Knowledge Sharing Leader, 2013 — Present
  • LIG Grenoble Informatics Laboratory — Business Process Analyst, 2011–2012
  • Intelligile Excellence — Knowledge Management Specialist, 2008–2011

Content

3R Knowledge Consulting

Medium

  1. Preliminary Understanding of Generative AI: What & How?
  2. Why virtual collaboration is different from remote working or home working
  3. A conversation between a knowledge sharing advocate and a knowledge sharing skeptic
  4. What my day resembles as a Knowledge Manager

RealKM

  1. AI integration strategy for learning and knowledge management solutions [Generative AI & KM series part 1]
  2. AI-based KM features for social learning and personal capabilities [Generative AI & KM series part 2]
  3. AI-based KM features for knowledge co-development and exchange [Generative AI & KM series part 3]
  4. AI-based KM features for knowledge retention and reuse [Generative AI & KM series part 4]
  5. AI-based KM features for expertise discovery and dissemination [Generative AI & KM series part 5]
  6. AI-based KM features for knowledge discovery and generation [Generative AI & KM series part 6]
  7. AI-based KM features for knowledge-centered services [Generative AI & KM series part 7]
  8. AI-based KM features for knowledge analytics and intelligence [Generative AI & KM series part 8]
  9. Summary and conclusion [Generative AI & KM series part 9]

SIKM Leaders Community Posts

1. Continuously invest in education and learning whether the employer sponsored it or not

It’s important to learn how to learn, I chose micro-learning that enables me to acquire micro-skills. I usually choose project-based learning that focuses on achieving a well-defined set of tasks. The theoretical knowledge is already acquired from the academy and the experiential knowledge is acquired in practice. I have formed a conviction that free knowledge is about the know-why and know-what. Free knowledge is a ‘sales pitch’ to some extent. On the other side, paid knowledge offers the know-how and the expert opinion (the consulting industry). I advocate open-source and open-access communities as I believe knowledge should be made available so we can collectively co-construct new knowledge.

2. Three main challenges when trying to implement a Knowledge Graph (KG)

  1. Limited awareness of the knowledge graph applications and use cases: Some business leaders have perceived KG as a mind map for their databases and IT applications. Other leaders thought that KG is only applicable for health sciences where it involves the discovery of a new drug for a disease or repurposing an existing one.
  2. Siloed data, lack of APIs (interfaces), lack of unified standards for KG representation: The ‘Mille-feuille’ effect of multiple, competing, fragmented, siloed systems within the organization make it difficult to work with KG especially when dealing with data with different formats and sources.
  3. Technical expertise, cross-collaboration, and knowledge sharing: KG requires expertise in data modeling, ontology development, and graph database management. It also requires the collaboration of different functions to integrate their knowledge into a unified schema. knowledge hoarding and self-preservation can quickly hinder the successful implementation of KG.

3. Where and how Generative AI is accelerating and impacting knowledge use cases, applications and processes?

You can follow the progress through this evaluation spreadsheet. To get familiar with the evaluation grid that I have constructed here are some key elements:

  • Column B: The KM platform/ tool that has integrated Generative AI into its offering.
  • Column C: A brief description of the platform/ tool.
  • Column D: What is the main use case/ area where KM is applicable?
  • Column E — Column AM: The evaluation criteria embedding an AI feature.

4. Indicators of knowledge sharing vitality

For the complete set of the collective’s characteristics, please refer to Tables 18, 19, and 20 in this report.

5. Knowledge itself can’t be quantified, however, you can measure its impact, absence, or reusability.

Here are some resources that I can recommend:

  • My recent co-authored book: Chapter 3 — Section 6: How to Measure the knowledge-sharing members’ engagement and community outcomes
  1. Measuring the communities’ members’ engagement & interactions
  2. Measuring the impact of the community-generated knowledge
  1. Take the estimation as a directive value and not as an absolute value.
  2. It is an indicator for improvement rather than an ROI.

6. How taxonomy supports KM

At GE Renewable Energy, we interpret taxonomy as an expertise and capabilities engine for the engineering and technology community. The taxonomy acts as the curator of different content types, learning sources, and knowledge nuggets. For example, if a control system engineer would like to develop expertise in a specific product component, through the taxonomy it’s possible to pull together people, design practices, standards, codes, and training materials to dynamically form the learning framework around that specific product component. The taxonomy is also considered a conversational process to bring experts together and raise awareness around their skills, areas of interest, and learning topics. We organize taxonomy workshops, on one hand, to refine and reiterate every knowledge-sharing community’ taxonomy and on another hand as a mechanism to bring visibility around what we know and are interested in for technical development and capability building.

7. Does a company have experience, or do its people?

In order to say that the company has experience is when the company becomes a Learning Organization. The transformation from a hero organization to a learning organization goes through phases:

  1. Phase 1: A shift from a Systems Organization to a Networked Organization.
  2. Phase 2: A shift from a Networked Organization to a Fearless Organization.
  3. Phase 3: A shift from a Fearless Organization to a Learning Organization.

Learning organizations are my current occupation.

  • Hero Organization: For example, a medical clinic hires a senior medical doctor and the necessary supporting staff to support the doctor. The expertise is singular and revolves around one senior expert. A startup will recruit a senior sales manager and junior sales team to support an expert. The sales activity is developed around the personal network of the senior sales expert. These individuals at the top are the heroes (rainmakers).
  • Systems Organization: Would like to streamline the development of their expertise through common and repeated processes and tools. They’d like to move from individual expert dependencies to a reproduced set of expertise. However, organizations are complex, and knowledge work evolves across departments, organizations, teams, and locations before the process can be documented or updated. Work is often invisible, and we don’t see it because it’s being completed in the minds of decision-makers and software applications before it can even be documented. It’s often never documented when relative panic sets in as key experts leave or retire.
  • Networked Organization: Works to overcome the challenge of fast-changing work processes by attempting to flatten the organizational hierarchy, connecting expertise and know-how, while working in network design. Expertise is acquired by engaging in common practices, collaborating, and learning on the job, often within knowledge community-type structures. However, networked organizations remain involved in complicated operational issues and pressing customer issues, rarely having the time, vision, or flexibility to question the existing practices and develop a new set of expertise-based approaches.
  • Fearless Organization: provides a degree of flexibility and liberty to question the existing practices by encouraging inquiries and ongoing discovery. Engineers are not afraid to investigate their conventional working methods it’s encouraged (Edmondson, 2018). A safe organizational climate is instantiated within the organization, and this enables the engineers to learn more rapidly through frequent questioning and evolution.
  • Learning Organization: Is based on the capacity of the organization to reinvent and transform itself through the collective learnings of employees. The learning organization IS NOT a university organization that graduates its engineers and workers. Rather it is best characterized as a competitive organization that continuously innovates new products and services, always with a customer focus. Expertise systematically evolves into a new set of competitive skills and competencies. Retaining and re-using knowledge happens in the flow of daily work. It’s embedded.

8. Is KM out of fashion?

As long as people are interacting, reflecting, discussing, and working together, new knowledge will be generated (constructivist epistemology). Knowledge has to be captured, organized, formalized and re-used until it evolves into a new form of knowledge — commonly known as knowledge management. The KM discipline is like a fluid that takes the shape of its recipient, is conditioned by its context, and is profiled by its application. Knowledge management (KM) serves the purpose for which it’s designed.

At 3R Knowledge consulting, we design knowledge management for organizational excellence. We emphasize that KM behaviors are integrated and embedded within the different organizational aspects. 3R Knowledge management methodology addresses 6 organizational areas: Organizational Agility, Organizational Learning, Organizational Performance, Organizational Intangible Assets, Organizational Change, and Organizational Behaviors.

If out of fashion means that KM is no longer a marketing buzzword, that’s fine. KM will continue to exist as people continue to constitute organizations. Organizations express their pain and needs in different ways however they are inherently referring to one shape and form of Knowledge management. Our role as KM leaders is to articulate the organizational needs using their vocabularies.

9. What would you do as Knowledge Manager in your first 90 days?

I can recommend this high-level roadmap as illustrated below. The #1 recommendation remains partnership with leaders, managers and field workers.

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Knowledge Management and Research Innovation in Global Higher Education Institutions edited by Lawrence Jones-Esan, Vipin Nadda, and Kendra S. Albright

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Stan Garfield

Knowledge Management Author and Speaker, Founder of SIKM Leaders Community, Community Evangelist, Knowledge Manager https://sites.google.com/site/stangarfield/