“At a certain level of machine-ness, of immersion in virtual machinery, there is no more distinction between man and machine.” – Jean Baudrillard, Violence of the Virtual and Integral Reality

I attended the KMWorld and co-located conferences held in Washington, D.C., November 18th – 21st. Across the conferences and sessions that I attended and the conversations between sessions, there were several themes which resonated. I’ll detail some of those themes and highlight any talks in particular I thought inspired or captured those trends. I couldn’t attend everything, so please forgive me if I’ve overlooked great talks, of which there were many.

Taxonomies as an Enterprise Strategy

The importance of taxonomies and ontologies as a foundational business and technical program was particularly evident at Taxonomy Boot Camp, but the theme also came up in talks in the other conferences as well. I presented on the topic in my session, “Stand Still Like the Hummingbird: Enterprise Taxonomy Strategy When Nothing Stands Still”. Likewise, Thomas Stilling in his Monday keynote, “Be the Change: Your Taxonomy Expertise Can Help Drive Organizational Transformation”, emphasized metadata as an organizational strategic asset and discussed how to position taxonomy to align with strategic objectives. Similarly, Lindsay Pettai’s session, “Empowering Your Enterprise With a Dynamic Taxonomy Program”, discussed the importance of having an enterprise taxonomy program.

These are just a few examples. Throughout the conferences, the notion that taxonomies and ontologies provide structured, source of truth values for navigation, search, and content tagging was prevalent. The main theme, however, was how taxonomies and ontologies are critical to the success of machine learning (ML) model training and program success. In talk after talk, artificial intelligence (AI) and ML were top topics and various ways to approach projects to make them viable and successful included what needed to be in place. Taxonomies and ontologies were among those foundational requirements.

As a 16-year veteran of KMWorld associated conferences–my first being Enterprise Search Summit West in 2008–I have seen the more recent embrace and understanding of enterprise taxonomies and ontologies beyond simple use cases like navigation. Even just a few years ago, taxonomies seemed to be misunderstood or undervalued by many conference attendees. Now, talk of their use as enablers of AI was ubiquitous.

Artificial Intelligence Is Here to Stay

I don’t believe I saw a talk which didn’t include the keywords “artificial intelligence”, “machine learning”, “AI”, or “ML”. I was unable to attend KMWorld last year, but in years past, any conversation on the role of AI/ML was frequently met with scoffs, eyerolls, or fear. In KMWorld’s Thursday Keynote, KM, Experts & AI: Learning From KM Leaders, Kim Glover and Cindy Hubert captured something important when they said that AI is driving a lot of emotions. Some of these emotions are excitement, skepticism, and fear. Addressing these emotions is going to be essential in addressing the technologies. 

The overarching theme is that AI/ML is here to stay, so what are you going to do about it? Continue to reject it and fall behind the curve? Embrace it without question? Embracing ML models, including LLMs, with guidance and guardrails seemed to be the message most were conveying. While many organizations are still in the proof of concept (PoC) project stage, adoption seems to have already arrived or is pending, so get ready to embrace the change to be successful.

The exponential growth in ML has been fueled by cheaper storage, faster technologies, more robust and accessible models such as ChatGPT, and built-in technologies like Microsoft Copilot. Not only are these tools more accessible, they are more accurate than they have ever been in the past…for the right use cases. And this was another big takeaway: AI/ML is here to stay, but use these technologies for the right use cases. Document summarization and generative AI text generation are big winners while tools used for critical decision making are still improving and must be approached with caution.

Human-in-the-Loop, Cyborg, or Sigh…Borg?

Overwhelmingly across sessions, the consensus was the importance of the human in the use of AI/ML technologies. For both the input and the output, to avoid garbage in and garbage out, human beings need to curate and review ML training sets and the information that an ML model outputs. As noted above, document summarization may be low risk depending on the industry, but decision-making in areas like law and medicine are high-risk and require humans-in-the-loop.

In his session, Evolving KM: AI, Agile, & Answers, the inestimable Dave Snowden discussed storytelling–or, rather, talked about storytelling through the art of storytelling–as an intimately human activity. He noted the deeply contextual nature of human knowledge and the need for knowledge management to generate knowledge rather than store it as codified data. If knowledge can not be fully codified as data, therefore, it is difficult or impossible to transfer that level of human knowing into machines and their models. The connectivity to knowledge and deep metaphor was evident in my three takeaways from his session: his notion of a tri-opticon integrating knowledge between three areas of an organization and the connection to Welsh Triads; forming teams or roles of threes, fives, or sevens, Welsh Rugby, and the number of people on a rugby sevens team; and assemblages, mycorrhizae, and rhizomatic networks in Deleuze and Guattari’s, “A Thousand Plateaus”. These kinds of connections are weak use cases for machine learning and emphasize the importance of human knowledge and inclusion in AI technologies.

And, speaking of human knowledge, of course knowledge retention and transfer were key topics in an era when both age and work-from-home job mobility are creating more job turnover than ever. While human knowledge is difficult to capture, using AI agents to capture knowledge and ML models to parse textual information will assist in ramping up the sheer scale and increasing pace necessary to retain and transfer knowledge.

The human-in-the-loop, the cyborg, and, sigh…Borg, are all going to rely on knowledge and ethics in order to create a human-machine interactive paradigm. If humans don’t use our own ethical boundaries to curate machine content, then bad data, and bad ethics, will spread throughout our information systems.

While there was some fear and loathing in D.C., there was a stronger current of hope, optimism, and curiosity in the many ways we can use taxonomies and ontologies, AI/ML, and human knowledge management together to guide us toward a brighter future of technology use.