The world of Knowledge Management is changing rapidly. For those that can seize the moment and embrace the change, this presents an incredible opportunity. For those that instead react with fear or resistance, they will quickly find themselves in obsolescence. From the viewpoint of knowledge management consulting, we are seeing firsthand both of these viewpoints from our partners, and moreover from the clients that have asked us to define (or, rather, redefine) what Knowledge Management means in “the age of AI.”
The KM experts that can leverage advancements in AI effectively, fill gaps in their organization’s AI strategies, and connect the organization’s groups and assets to make enterprise AI a reality, can find themselves more at the center of the organization than they ever have been, becoming the indispensable resources they deserve to be by directly supporting the highest-priority initiative of their organization.
At the same time, organizations that do not actively engage KM in their AI efforts are beginning to see other functions, particularly engineering, data, and technology teams, expand their scope to address knowledge-related challenges. As these teams recognize the strategic importance of KM for AI success, they are starting to take ownership of areas that have traditionally fallen within the KM domain. Without clearly-defined roles and responsibilities and “marketing” of KM departments and expertise, this may well lead to overlapping responsibilities, duplication of effort, and the unfortunate replication of capabilities that KM teams have spent years developing and refining. Organizations that invest in KM roles and treat it as a strategic partner in their enterprise AI efforts are better positioned to avoid these inefficiencies, speed up AI adoption, and leverage the knowledge and capabilities they already have rather than starting over.
I’ve divided these new KM roles into five personas, each of which requires a unique skillset and background. Of particular note, all five personas represent a specific subset of knowledge management skills and expertise that KM professionals have held for years. The difference in the current AI-boom is the context and the prominence. This work has always been of critical importance, but in many cases that has historically been difficult to prove. In the last two decades of KM work, a great deal of our time has been spent helping people connect the dots and understand the value of KM and how it can impact the bottom line of the organization. Given the success factors for AI, the dots are a lot easier to connect, making it much easier for KM professionals to successfully market themselves and their skills.
Number 1: The Knowledge Gap Filler
Any enterprise AI is only as good as the information to which it is connected. The best AI solutions require the right combination of quality data, content, people, and other knowledge assets to represent the organization’s knowledge and expertise, delivering complete, correct, and contextualized answers and outcomes. This only works when the organization has proactively captured and managed their knowledge assets in a digital form that is readily consumable by the AI solution. This goes beyond simply capturing lessons learned and after-action reports of an organization. Rather, this is true knowledge capture, documenting not just the “how,” but the “why” from an organization’s experts and incorporating it into the digital knowledge assets. Embracing the concept of semantic layers, organizations can now systematically extract missing information and knowledge discontinuity, creating mechanisms for KM professionals to, for the first time ever, proactively fill gaps.
Traditional KM Skills: Knowledge Capture, Knowledge Transfer
Number 2: The Knowledge Asset Curator
As with the previous persona, the Knowledge Asset Curator is focused on ensuring the AI solutions have complete and quality sources of information. Whereas the first persona is largely focused on generating new knowledge assets, this one is focused on their maintenance and management, ensuring they are up-to-date, properly structured, and effectively managed to stay current and secure. It’s important to note these sources transcend the traditional unstructured information of knowledge bases, intranets, and corporate portals. The most mature AI solutions will use a rich combination of different types of knowledge assets, ranging from structured to unstructured, but also encompassing people, products, and processes. To play this role effectively, KM professionals will need to be adept and working with other information managers, helping to guide and connect the unique assets managed across an organization.
Traditional KM Skills: Content Strategy, Content Governance, Content Management, Digital Community of Practice (CoP) Moderation, Library Management
Number 3: The Knowledge Engineer
The connective tissue for many organizations, as well as the critical differentiator to achieve high-functioning and reliable AI, is semantics: namely, taxonomies, ontologies, and business glossaries. Taxonomies and ontologies have commonly fit within the knowledge management domain in many organizations, whereas business glossaries often fell more on the data side. In the age of AI, all of these semantic components are critical foundations to delivering context, avoiding hallucinations, and delivering complete and correct AI results. KM professionals focused on semantics have spent a lifetime convincing their organizations of the value of their work, but are now enjoying a moment of prominence as the value of semantics for AI becomes apparent. The important complementary skills for the semantic specialist are facilitation and business alignment, as simply being a semantic design expert is not enough to help an organization prioritize use cases and build competency questions that will ensure a focused, iterative design approach.
Traditional KM Skills: Taxonomy Design, Ontology Design and Modeling, Metadata Management
Number 4: The AI Steward
Even in the best AI implementations, there are real risks of errors, an inability to answer a query, or at least incomplete results. This is a major part of where AI governance comes in. In the ideal case, AI governance should be proactively sourcing knowledge assets, assuring quality of assets and sources, and identifying gaps before they’re a problem. At times, however, an issue will occur and it will be critical to respond completely with communications and remediation (as in, share the problem and the fix, and ensure the fix addresses the root cause). KM professionals have commonly been the “gap fillers” of organizations, playing this very role for years before AI. In these cases, that role should now be more official and more prominent.
Traditional KM Skills: Governance, Communications, Systems Thinking, Product Ownership
Number 5: The Bridge Builder
The most successful AI efforts will be those that bridge the disparate sources, systems, people, and assets of an organization, bringing complete answers powered by the complete knowledge, information, and data of the organization, in context, to those who need it. This requires more than a great technical solution, and even more than a semantic design or reliable knowledge assets. Successful AI requires choreography and coordination between parts of the organization that have historically been siloed. The concept of data and content existing as separate disciplines and departments, for instance, must be vanquished. Coordination across an organization, aligned security and semantics, is what AI requires to flourish. This, at its core, is a human challenge in most organizations. The KM professionals have been seeking to build these bridges through their entire career, silo-busting along the way. The skills of facilitation, coordination, and communication they’ve honed should now be put toward the purpose of organizational alignment for AI readiness and operating models for long-term sustainability.
Traditional KM Skills: Facilitation, Governance, Operational Planning, Product Management, Change Management, Organizational Design
There are a lifetime of techniques and hard-won skills possessed by KM professionals. In truth, all five of the personas and roles I list above have long existed within the domain of knowledge management. The roles and responsibilities aren’t new; rather, the potential prominence of them in the context of enterprise AI is what’s changed. The KM professionals that embrace the role they can and should play in an organization’s AI efforts may find themselves both with new authority and greater ability to impact their organizations.
It will be heavily dependent on these KM professionals to market their own value and set new KM strategies that help organizations realize the potential business value of AI. If your organization is seeking help to build a KM strategy to serve as the foundation for enterprise AI, contact us.