Now we have Generative AI, we don’t need communities of practice; or not?

This is a very frequent and relevant question for the current and future state of AI integration within knowledge management. This question merits some reflections to better understand the opportunities and limitations when interweaving generative AI technology with any knowledge-driven event or activity.

A Simulation

To develop more on this question, I’m going to simulate a dialogue between opposing opinions and hopefully, we can find the right balance. In this dialogue, I’ll consider John as “pro generative AI” and Bob as “pro communities of practice”. John and Bob are both fictional personages illustrating the motivations and the challenges towards the integration of generative AI within knowledge management activities.

Prologue

Character 1 – John (Pro Generative AI):

John argues that Generative AI can efficiently generate solutions and information, reducing the need for traditional communities of practice. He believes that AI can provide instant insights, improving decision-making processes. For example, in healthcare, AI can analyze vast datasets to recommend personalized treatment plans, minimizing the reliance on traditional medical communities.

Character 2 - Bob (Pro Communities of Practice):

Bob contends that communities of practice foster collaboration, shared expertise, and a deep understanding of the context that AI lacks. He highlights the value of human connections in fields like education, where teachers exchange practical knowledge and experiences. Bob argues that the nuanced aspects of certain domains require human intuition and empathy, which AI cannot replicate.

These characters represent contrasting views on the role of Generative AI in replacing or complementing communities of practice.

Act 1: Generative AI can replace traditional communities of practice?

John: I think yes! Generative AI offers quick data analysis and solution generation, reducing the need for prolonged and divergent community discussions. In fields like medicine, AI can provide personalized insights, minimizing the reliance on traditional practices.

Bob: While generative AI has its merits, communities of practice foster collaboration and shared human experiences. In education, for instance, teachers exchange valuable insights that go beyond data, enhancing the learning environment.

Act 2: Where has Generative AI significantly outperformed traditional communities of practice?

John: In finance, AI-driven algorithms can analyze market trends swiftly, enabling faster decision-making than traditional financial communities. This efficiency can be a game-changer in dynamic markets.

Bob: While AI can enhance efficiency, the financial landscape is shaped by human behavior and unpredictable events, where human intuition and collective insights play a vital role.

Act 3: Are there instances where Generative AI may fall short despite its efficiency?

John: Well, in creative fields like art, AI may struggle to capture the depth of human expression and emotions. However, advancements in sentiment analysis and multimodal generative AI are continually pushing these boundaries.

Bob: Creativity and emotional intelligence are inherently human qualities. In fields like psychology, understanding subtle nuances and emotions is crucial, and AI may not fully grasp these complexities.

Act 4: Can Generative AI truly replicate the nuanced aspects and human connections found in communities of practice?

John: Currently, generative AI can excel in data-driven tasks, however, there is ongoing research on integrating human intuition and emotions into artificial intelligence. On another note, do humans not possess biases and egos that hinder and complicate the establishment of human connections?

Bob: Communities of practice thrive on empathy, trust, and emotional connection among members, which are challenging for AI to replicate authentically. Generative AI struggles to grasp the serendipitous connections of human interactions, social norms, power dynamics, and unpredictable situations. AI cannot replicate the unpredictability and spontaneity of human interactions that lead to unexpected discoveries.

Act 5: Can generative AI provide solutions to a novel and an original problem?

John: Highly possible! Generative AI can facilitate rapid experimentation by automatically generating and testing hypotheses. This accelerates the iterative process of refining and improving solutions, enabling more efficient problem-solving in domains where experimentation is feasible. In addition, generative AI can identify patterns and correlations from disparate sources to uncover insights embedded in the data that humans might overlook.

Bob: This is where communities of practice shine! They leverage collective intelligence, iterative learning, and cross-disciplinary collaboration. By integrating experiences, perspectives, and insights of multi-disciplinary profiles, members can iteratively refine their understanding of the problem based on feedback and reflection to tackle complex problems and propose diverse solutions.

Act 6: What are the ethical concerns in relying heavily on Generative AI?

John: Ethical considerations arise in AI decision-making processes. They can be addressed in several ways, for example: fairness and bias mitigation, transparency and explainability, scenario simulation, and privacy preservation. With these measures, generative AI allows stakeholders to understand how decisions are made and ensure accountability.

Bob: Ethical concerns highlight the importance of human oversight. Communities of practice can collectively shape ethical guidelines, ensuring responsible AI use across various domains. Ethics and responsible AI should not be confused with checklists, every AI application is a case study for ethical evaluation.

Act 7: How can generative AI and communities of practice elicit tacit knowledge?

John: In fields like engineering, AI can sift through historical data to uncover hidden insights, and extract implicit knowledge complementing the tacit knowledge shared within communities of practice.

Bob: Communities of practice are instrumental for sharing tacit knowledge, as they provide a space for social interactions. Through these interactions, members engage in informal discussions, storytelling, lessons learned, and experiential insights. Novices within communities of practice learn tacit knowledge by observing and emulating the behaviors and practices of experienced practitioners.

After this dialogue between John and Bob, I'm interested to get your thoughts and comments on the role of Generative AI within communities of practice. What do you think?

At 3R Knowledge Services we can help you design, build, and operate AI-augmented communities of practice. We've done this before! Engage with us at [email protected]

Would you like to check previous dialogues between John and Bob? You may check A conversation between a knowledge sharing advocate and a knowledge sharing skeptic. | by Rachad Najjar, Ph.D | Medium