KM Triversary Forum Workshop Outputs – Workshop E, Eastern Hemisphere
By Stuart French, Facilitator, Workshop E
This workshop was one of three solutions and resolutions workshops facilitated at the end of the KM Triversary Forum. For further information on the role of the workshops in the Forum Program, please see KM Triversary Forum Workshops – Overview.
Source information
Through the various Eastern Hemisphere presentations (sessions E1 and E2), participants were asked to fill in their thoughts on a shared spreadsheet with three columns: Ideas, Issues and Information.
These were stored and processed in a Google Sheet.
During the last 10 minutes of the final session presentation, these concepts were run through a Google AppScript that used Gemini Pro 2.5 to name and theme each concept. These were quickly reviewed real time before the activity phase of the workshop began. In general, the data was left as found with spelling and grammar issues intact. Out of the 274 concepts, three seemed to be critical of speakers so were not included in the data.
Feedback examples
| Name | Type | Description | Theme |
| Deep Expert | Ideas | Grudge match between Deep Expert and ChatGPT | Approach |
| Knowledge-Action Gap | Issues | KM is often sidelined in organizations key decision making | Knowledge Barrier |
| Tacit Knowledge | Ideas | Because of this tacit nature, a simple form or survey will not do. | Knowledge Capture |
| Cross-Disciplinary Communication | Ideas | The team needed to speak the language of engineers and geologists and project managers | Language |
| Actionable Knowledge Gap | Ideas | Academic versus Practitioner view: Process and method versus Output and outcomes | Outcome focus |
| Skills Gap | Issues | Shortage of skills & human resource | Skills |
| Skills shortage | Issues | Skills shortage | Skills |
| Deep Domain Knowledge | Ideas | Achieving Synthesis (Bennet & Shelley 2024): (1) Thorough knowledge of one or more research domains. | Synthesis |
Methodology
The workshop used a distributed cognition technique to populate the edges of a knowledge graph with the nodes representing the captured concepts including title, description (as entered by the participants) and the theme for each.
We then used wisdom of crowds to review a significant percentage of the concepts and identify relationships using an AppScript driven App designed to overcome the low bandwidth constraints of a significant proportion of the audience. As shown in the screenshot below, the app presents pairs of concepts to each user, allowing them to identify any relationships, describe the link and hit save. Clicking Generate New Pair presented the next random pair.
From the 271 concepts, in the space of 15 minutes, the participants identified and documented 211 relationships. A pleasantly successful level of engagement considering the various cultures, time-zones, (not to mention very tired people) involved.
Feedback on the methodology: participants commented on the novelty of the approach and the way the almost live visualisation of this complex dataset led to insights that no amount of manual reading of the spreadsheet would have surfaced. One attendee with considerable experience with a similar approach suggested that we might have received even higher engagement if the app gave a drop down of possible connections – rather than a plain text input. This would have removed ambiguity and inspired confidence right from the start.
The back end spreadsheet created the upload file for Polinode visualisation on the fly as users entered their relationships.
Graph visualisation
Whole
Individual terms
Artificial intelligence
Collaboration
Responsible KM
Breakdowns
Feedback by type
| Ideas | 152 |
| Issues | 84 |
| Information | 35 |
Top five themes (total)
| Theme | Ideas | Issues | Information | Grand Total |
| Definition | 6 | 8 | 3 | 17 |
| Responsible KM | 2 | 9 | 11 | |
| Collaboration | 9 | 1 | 10 | |
| AI | 6 | 3 | 1 | 10 |
| Practice-Research Gap | 7 | 1 | 8 |
Top five ideas
| Theme | Ideas |
| Collaboration | 9 |
| Synthesis | 7 |
| Practice-Research Gap | 7 |
| Knowledge Transfer | 6 |
| Definition | 6 |
Top five issues
| Theme | Issues |
| Definition | 8 |
| Gap Analysis | 5 |
| Research Praxis | 4 |
| Indigenous KM | 4 |
| Risk | 3 |
Top 5 information
| Theme | Information |
| Responsible KM | 9 |
| Definition | 3 |
| Knowledge translation | 2 |
| Knowledge Transfer | 2 |
| Theory | 1 |
Review of emergent trends in identified relationships between issues and ideas
Creating the Polinode result took around 3 minutes then the URL was distributed to all participants do they could review the results. They were given a quick tutorial on setting themes by colour and layout, then given a few minutes to browse the graph looking for clusters, interesting relationships and descriptive groupings.
While they did that, I isolated relationships where one was an issue and the other was an idea, then used ChatGPT to identify 5 emergent trends, then show which relationships supported each point.
| Emerging trend | Supporting relationship texts (quoted exactly as recorded) | Commentary / interpretation (in researcher voice) |
| Bridging as relationship work | “boundary spanning”; “sensemaking”; “Bridging the gap”; “Mentoring”; “Mentoring Gap”; “Learning”; “Ringmaster may help to bridge problematic diversity”; “Build trust and overcome fear”; “Cross-Disciplinary Communication – Take the risk”; “Focus on what’s missing”; “Diagnose gaps using framework”; “Practice-Research Gap – Theme”; “Learning or Doing? – Practice-Research Gap – Theme” | Relationships (mentoring, boundary-spanning, teamwork) are treated as the core mechanism for connecting theory and practice. Gaps are diagnosed and closed through social connection and interpretive sensemaking, not through documentation alone. |
| Tacit knowledge as the nexus | “People assume tacit K can be simply written down. Needs active leadership”; “Tacit knowledge gaps require unique approaches”; “Tacit gaps can cause the most expensive damage, both through impact and through recovery”; “Skills shortages can encourage shortcuts like surveys instead of qualitative techniques.”; “Deep Domain Knowledge – Key for grounding research”; “Deliberate Immersion – Assign a few champions to go deep in KM”; “Apprenticeship Model – Polarity”; “Tacit Knowledge – People assume tacit K can be simply written down. Needs active leadership”; “Tacit Knowledge – Skills Gap – Tacit gaps can cause the most expensive damage, both through impact and through recovery” | The data repeatedly ties tacit knowledge to learning, immersion, and apprenticeship—marking it as the critical connective tissue between expertise, leadership, and practice. Every entry highlights how tacit knowing demands social processes, not codification. |
| Power and culture as hidden variables | “Power/Knowledge”; “Power Dynamics”; “Reduce power dynamics influencing knowledge audits.”; “Create safe spaces”; “Who chooses to prioritise risks?”; “Build trust and overcome fear”; “Cultural diversity across levels”; “Cultural Calibration”; “Requisite Variety – Cultural diversity across levels – Your team needs to be as diverse as the organisations you study”; “Reticence to join communities – Both barriers to engagement”; “Knowledge sharing campaign – Fostering multi-lingual sharing”; “Merit versus ROI – Practice-Research Gap – Theme”; “C-Suite Wilful Blindness” | These relationships expose invisible structural and cultural forces—power, hierarchy, and bias—that shape KM behaviour more than formal systems. Trust, inclusion, and cultural variety are framed as design challenges, not side issues. |
| AI as mirror and amplifier | “AI-based Knowledge Management”; “C-Suite Wilful Blindness”; “AI hype – Keep them separate”; “Content summarisation pulls from many levels and anonymises overcoming bias”; “Knowledge needs culture and tools!”; “RAG – Knowledge-Action Gap – Hypothesis on whether AI can be applied to this issue” | Entries show that AI both reflects existing dysfunctions (hype, executive detachment) and amplifies potential for synthesis (summarisation, bias reduction). The lesson is that AI systems must be anchored in social and cultural understanding to add value. |
| Reframing KM as a living practice | “Knowledge is organic!!”; “Knowledge is not static”; “Sharing the body of knowledge changes it”; “Knowledge translation”; “Agile methodology”; “Knowledge-Sharing Culture”; “Open Access – Access”; “Open Access – Sharing”; “Helping others find resources”; “KM as Doctor – Digital Transformation – Link is absent because KM does not always need technology”; “Knowledge as an asset – AI-based Knowledge Management – Knowledge needs culture and tools!” | These highlight a shift from treating knowledge as an object to be stored toward seeing it as a dynamic social process. The relationships describe learning, sharing, and adaptation as continuous cycles—KM as a living ecosystem rather than a static archive. Involving research as part of that cycle rather than an outside observer may be a key way forward. |
Topics discussed – Stu’s notes
Using this quick analysis as a live facilitation guide, I then led a discussion allowing the participants to guide the flow as much as possible. The notes below try to capture the essence of the four main topics discussed by the group.
- Balancing capture and flow KM sits between documenting knowledge and keeping it alive. Both are vital – definitions bring rigour but meaning and value emerge through ongoing dialogue and use.
- Bridging language and meaning Closing the research–practice gap relies on curiosity about language. KM professionals must speak both the scientific and everyday dialects of their field and act as translators between them.
- Knowledge brokering as a core role Knowledge brokering is central to connection. Whether through people, communities, or AI tools, brokers turn knowledge from a static store into a living network of exchange. KMers need to set the example as knowledge brokers themselves, not just embed knowledge brokers into the decision flows.
- Diversity as strength Global KM efforts like the KM Landscape should reflect the world’s diversity, not define it. Capturing varied meanings and practices strengthens KM’s relevance and adaptability.
Finally, with the topic of AI coming up regularly both as an opportunity and a threat, here is a few of the key relationships identified by the group on the topic:
| Source | Target | Relationship |
| Academic Journal System | AI Thinking Buddy | Getting beyond simple library searches |
| C-Suite blindness | AI-based Knowledge Management | C-Suite Wilful Blindness |
| Create your story | AI hype | Keep them separate |
| Cultural diversity across levels | AI-based Knowledge Management | Content summarisation pulls from many levels and anonymises overcoming bias |
| Requisite Variety | ChatGPT AI guidelines tool | Understand various views of AI not just the one that works for you. |
| Risk Management focus | AI Thinking Buddy | Can AI reveal risks in literature review process |
| Theory of Knowledge Management | Custom AI models assist recall | AI assist in applying KM theory in practice |
Biography:
With a broad background in the oil, water, healthcare and government, sectors, Stuart French is a Melbourne based knowledge strategy consultant and author of the www.DeltaKnowledge.net blog. He combines 23 years of knowledge management (KM) experience with a Master of Knowledge Management (KM) to help companies with expertise identification and management, collaboration, teamwork and knowledge systems to improve their performance and resilience to change. Research on wikis and knowledge cultures led to a fascination with complexity theory, mentoring, and using AI to create experts rather than replace them. He helps facilitate the KM Leaders Forum in Melbourne, which has been meeting monthly since 1998 and speaks at conferences and training events around the world on knowledge, expertise and innovation.
Header image source: Created by Bruce Boyes with Microsoft Designer Image Creator.
