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The term reference model is already well established within systems engineering, enterprise architecture and other engineering disciplines. It generally refers to an abstract representation of a domain that defines concepts, their relationships and a common frame of reference while remaining independent of a particular implementation.

For readers unfamiliar with the concept, the Wikipedia article provides a concise overview of the commonly accepted definition:

https://en.wikipedia.org/wiki/Reference_model

This Practice Note does not attempt to redefine what a reference model is. Instead, it establishes the perspective adopted throughout this body of knowledge.

Many reference models successfully describe what exists. They identify concepts, components and relationships. They provide a shared vocabulary and a structural understanding of a domain.

In practice, however, organizations face a different challenge. Over time, the structure often survives while the understanding behind it gradually disappears. Documentation remains available, yet the reasoning that shaped it becomes difficult to reconstruct. New team members can read the model without fully understanding why it exists, what assumptions influenced it or under which circumstances it should evolve.

From the perspective of these Practice Notes, a reference model is therefore valuable not only because it represents a domain, but because it enables the continuity of understanding of that domain.

Three questions become particularly important.

• Does the model preserve the intent behind its concepts and relationships?
• Can its meaning be reconstructed when context has been lost?
• Can the model evolve without losing the reasoning that originally made it useful?

These questions extend beyond documentation. They concern the preservation of organizational knowledge and the ability to make informed decisions as organizations, products and capabilities evolve.

The Practice Notes therefore build upon the established concept of a reference model rather than replacing it. Their contribution is to explore how reference models can support continuity of understanding, deliberate development and defensible assessment throughout the lifecycle of organizations, capabilities and products.

Future reference documents in this body of knowledge adopt this perspective as their foundation.

While continuing to add new Practice Notes to the growing body of knowledge on this blog, I found myself wondering how each new insight would affect the publications we were in the process of creating.

A single new observation might strengthen an argument in a derived theoretical paper. Another might influence the capability methodology. A third might require changes to the product guidance, consulting offering or training material.

Each new Practice Note had the potential to affect several different publications.

Initially, this seemed perfectly reasonable. Then another thought occurred to me.

What if I had to perform all of those updates manually?

Every new insight would require reviewing multiple publications to determine whether they should be updated. Every update would require checking whether the same insight should also appear elsewhere. Every publication would gradually become another artifact requiring maintenance. As the body of knowledge continued to grow, so would the effort required to keep all of those representations aligned.

At that moment I realized something important.

The challenge was not maintaining the knowledge. The challenge was maintaining all of its representations.

The previous Practice Note proposed maintaining a single canonical body of knowledge and generating all other knowledge assets from periodic Knowledge Snapshots.

Conceptually, this governance model is straightforward: practice Notes become the authoritative source, Knowledge Snapshots provide stable baselines. Publications, methodologies, training material and consulting offerings become derived representations of that knowledge.

While this may work in theory, the question is whether it is practical. Historically, the answer would often have been no. The bottleneck wasn’t the creation of knowledge, but the repeated synthesis of that knowledge into many different representations.

Every publication required manual interpretation. Every revision required manual consistency checking. Every new insight increased the cost of keeping the knowledge assets aligned. The governance model was sound, but the economics were not.

Recent advances in AI fundamentally change the economics of knowledge synthesis. AI is exceptionally well suited to transforming an existing body of knowledge into multiple coherent representations for different purposes and audiences.

This is a fundamentally different role from creating knowledge. Knowledge continues to originate through experience, observation, experimentation, discussion and reflection.

AI enters the process only after that knowledge exists. Its contribution is synthesis.

This distinction is important.

Organizations learn through experience. People make observations. Teams discover better ways of working. Experts refine concepts and challenge assumptions. Those activities create knowledge.

Once that knowledge has been captured, however, another activity begins. The knowledge must be organized. Synthesized. Presented. Explained. Adapted for different audiences. Connected to existing concepts. Translated into methodologies, publications, training material and consulting guidance. This is knowledge synthesis.

For many organizations, synthesis has traditionally been the most expensive part of knowledge management.

The governance model described in the accompanying Practice Note becomes practical because the economics of synthesis have changed.

Rather than manually maintaining many independent artifacts, organizations can maintain one canonical body of knowledge and periodically regenerate derivative assets from approved Knowledge Snapshots.

Those assets may include:

• conceptual publications;
• capability descriptions;
• product guidance;
• assessment methodologies;
• consulting playbooks;
• training material;
• onboarding guides;
• indexes;
• traceability matrices;
• AI assistants.

Human effort shifts accordingly. Instead of maintaining documents, people improve understanding. Instead of synchronizing publications, they enrich the canonical body of knowledge. The cost of regeneration decreases dramatically.

The governance model itself remains technology independent.

Organizations still require:

• stewardship;
• critical thinking;
• peer review;
• traceability;
• professional judgement;
• continuous learning.

AI does not determine what becomes organizational knowledge. It does not decide which observations are significant. Those remain human responsibilities.

AI helps synthesize the resulting knowledge into coherent and consistent representations.

Without governance, AI simply produces more content. With governance, AI enables organizations to preserve continuity while continuously communicating an evolving understanding.

This observation extends beyond the framework described in these Practice Notes.

Any organization maintaining professional disciplines, methodologies, capability frameworks, operating models or enterprise knowledge faces the same synthesis challenge.

As AI reduces the cost of synthesis, organizations gain an opportunity to redesign how they govern knowledge.

Rather than maintaining many independently evolving artifacts, they can maintain one authoritative body of knowledge and regenerate the representations required for different purposes.

This improves consistency. It improves traceability. Most importantly, it improves continuity of understanding.

We speak naturally about product development. We also speak about capability development, although in practice organizations often use terms such as capability improvement, capability maturity or organizational change.

While developing the body of knowledge based on the Practice Notes in this blog, I noticed that exactly the same pattern appeared again: the body of knowledge was not simply being documented. It was evolving.

New observations were captured. Existing concepts were refined. Connections emerged between previously unrelated ideas. Publications became richer through successive Knowledge Snapshots. Although the subject had changed, the nature of the work felt remarkably familiar.

This raised a question:

Are product development, capability development and the development of a body of knowledge fundamentally different activities, or are they different expressions of the same underlying pattern?

The terminology suggests they are different. Product development is associated with creating new products. Capability development is often associated with improving organizational abilities. Developing a body of knowledge appears to be something else again.

Yet each involves the same essential activity. An intended future state is envisioned. The current realization is understood. Intentional changes are made. The result is assessed. Learning from that assessment guides further evolution.

The subject changes, yet the pattern remains remarkably consistent.

This suggests that development should not be understood primarily as the creation of something new.

Rather:

Development is the intentional evolution of a subject toward its intended state.

Development encompasses every intentional effort to establish, improve, adapt, extend or modernize a subject.

For example:

Product development includes creating new products, extending existing products and modernizing mature products.

Capability development includes establishing new organizational capabilities, improving existing ones and adapting them to changing circumstances.

Development of a body of knowledge includes capturing observations, refining concepts, incorporating experience and periodically publishing new versions of derived documents.

Each represents the same underlying activity viewed through a different domain.

Regardless of the subject, development follows a similar lifecycle:

Intent → Reference Model → Development → Realization → Assessment → Learning

The intended state provides direction. A reference model makes that intended state explicit. Development changes the realization in the direction defined by the reference model. Assessment evaluates the current realization against the intended state. Learning then influences future development.

Importantly, learning may affect two different things. It may reveal that the realization should improve while the intended state remains valid. Or it may show that assumptions embodied in the reference model itself should be reconsidered.

Sustainable development therefore requires openness to improving both the realization and the model that guides it.

Viewing development in this way broadens its meaning considerably. Products, capabilities and bodies of knowledge are all examples of intentional subjects.

Each has:

• an intended state;
• a current realization;
• evidence that can be assessed;
• opportunities for learning;
• the ability to evolve over time.

The same conceptual pattern therefore applies regardless of the specific subject under consideration.

This does not eliminate the differences between domains. Product development, capability development and knowledge development each have their own practices, techniques and terminology.

What they share is a common developmental structure. This perspective provides a common conceptual foundation for the remainder of this framework:

• Reference models represent intended states.
• Development represents intentional movement toward those states.
• Assessment interprets evidence about the current realization.
• Learning improves the realization and may, where appropriate, revise the reference model itself.

The governance model proposed for maintaining a living body of knowledge is therefore not separate from the framework. It is one example of the broader development pattern.

This framework is developed using the same principles that it proposes for developing products, capabilities and other intentional subjects. In that sense, the framework serves as both theory and demonstration.

Development is often understood as creating something new.

A broader perspective is possible:

Development is the intentional evolution of a subject toward its intended state.

Whether the subject is a product, an organizational capability or a living body of knowledge, the same fundamental pattern emerges.

The subject changes.

The developmental structure remains.

Recognizing this common structure provides a foundation for understanding how reference models, assessment, learning and continuous improvement relate to one another across different domains.

As the number of Practice Notes on this blog increased, I began consolidating them into a series of larger publications. The idea seemed straightforward:

• Practice Notes would capture individual observations, hypotheses, examples and reflections.
• Publications would then synthesize those notes into coherent narratives for specific purposes and audiences.

Initially, this worked well. However, after producing the first publications, I noticed an unexpected problem.

During the process of writing, I naturally refined explanations, discovered new connections, introduced stronger examples and developed concepts that had not yet appeared in the original Practice Notes.

While the publications improved, the Practice Notes did not. Over time, the two began to diverge.

At first, this appeared to be a documentation problem. The obvious solution was to copy the improvements back into the Practice Notes.

As the number of publications increased, however, this quickly became impractical. Each improvement created new questions. Should it be added to one Practice Note or several? Should existing Practice Notes be rewritten? Should historical notes remain unchanged? Which version now represented the current understanding?

Most importantly:

How do we prevent valuable knowledge from becoming trapped inside publications instead of becoming part of the body of knowledge itself?

This led to an important realization that the real problem was governance.

The Practice Notes and the publications had become two independently evolving representations of the same knowledge. As more publications were created, maintaining consistency between them became increasingly difficult. The framework itself was beginning to drift away from the knowledge it was intended to represent.

This divergence can be understood as framework debt: the growing gap between an evolving body of knowledge and the frameworks, methods and publications intended to express it.

This pattern is not unique to this blog. Many organizations experience the same problem. They maintain methodologies, standards, white papers, training material, consulting guides, presentations and knowledge repositories.

Each begins as a faithful representation of organizational understanding. Over time, they are revised independently. Eventually, nobody can answer with confidence which version is authoritative.

The problem is that knowledge continues to evolve in multiple places without a common source of truth. When this happens, continuity of understanding gradually deteriorates. Organizations inherit artifacts but lose confidence in the knowledge those artifacts are intended to represent.

My approach is this: A living body of knowledge should have a single canonical source. Everything else should be derived from that source.

Within this framework, the Practice Notes constitute the canonical body of knowledge. Each note captures an observation, proposition, example, question or reflection. Published notes remain part of the historical record. Later notes may qualify, supersede or challenge earlier propositions without silently rewriting how the understanding developed.

Collectively, the active Practice Notes and the relationships between them provide the canonical basis from which the current state of understanding is synthesized.

Publications are generated from dated Knowledge Snapshots rather than maintained as independent products. A Knowledge Snapshot freezes the state of the Practice Notes at a particular moment and provides the basis for creating derived publications.

Also feedback generated while reviewing or applying in practice, are first captured as additional Practice Notes. Only after the canonical body of knowledge has evolved is a new Knowledge Snapshot created and the publications regenerated.

In this model:

Practice Notes → Knowledge Snapshot → Publications → Feedback and Learning → New Practice Notes

Knowledge evolves in one place. Everything else is regenerated from that source. This preserves consistency while allowing continuous learning.

Although this governance model emerged while developing this framework, it is not specific to it. Any organization that maintains a conceptual framework, methodology, architecture, operating model or professional body of knowledge faces the same challenge: as understanding evolves, independently maintained representations tend to drift apart.

The governance pattern proposed here addresses that challenge through four principles:

1. Maintain a single canonical body of knowledge.
2. Publish stable snapshots of that knowledge.
3. Generate derivative assets from those snapshots.
4. Feed learning back into the canonical source.

This approach changes how a body of knowledge is maintained. Rather than treating publications as the primary assets, it treats them as synthesized representations of a continuously evolving body of knowledge.

The authoritative source is no longer the latest document. It is the canonical collection of Practice Notes from which that document was derived.

As new observations emerge, the body of knowledge evolves first. New publications are then generated from updated Knowledge Snapshots rather than edited independently.

The objective is not to preserve documents. It is to preserve the continuity of understanding that those documents represent.

By maintaining one canonical source and regenerating derivative publications, learning remains cumulative, traceable and consistent over time.

As a consequence of using AI in my writing, the cost of writing has decreased dramatically. An idea that once required several evenings of outlining, drafting, rewriting and polishing can now become a coherent paper much faster. Arguments can be tested in different forms. Weak transitions can be repaired. Examples can be added. Alternative structures can be explored without starting again from scratch. For that, I am incredibly grateful.

But one important cost has not decreased nearly as much: the cost of serious review. A reviewer still has to read the paper carefully. They still have to understand the argument rather than merely recognize the words. They may also use AI, but that’s not enough.

They still have to compare the claims with their own experience, identify hidden assumptions, notice contradictions, challenge weak reasoning and decide whether the model is actually useful. That takes time. More importantly, it takes qualified attention.

AI can help produce more documents, or more incremental versions of the same documents. It does not automatically create more people willing and able to judge whether those documents deserve to be trusted.This changes the bottleneck.

Previously, writing itself limited how much material could be produced. If creating a paper took months, review requests were naturally infrequent. Now drafting is cheaper. A paper can be improved repeatedly, and new versions can appear almost whenever a new insight emerges. That sounds positive. Until every small improvement is sent to the same colleagues for another serious review.

The cost of producing the new version may have become low. The cost imposed on the reviewer has not. That creates a new responsibility for the writer. Cheap drafting should not lead to expensive review churn.

I may revise a paper privately many times. I may collect new examples, conceptual distinctions, field observations and corrections in a backlog. But a new version should only be published when those changes together create a significant improvement.

Not merely better wording. Not one extra paragraph. Not version 1.04 because I had another thought on Tuesday. A new review should be worth the attention it asks for.

This suggests a simple structure. Field notes can remain the exploratory layer. They capture observations while they are fresh. Several notes may begin pointing in the same direction. The stable insight then enters the backlog for the relevant paper. Only when enough important changes have accumulated does the formal paper absorb them and become a genuinely new version.

The production cycle becomes faster. The release discipline should become stricter. This applies far beyond my own papers.

Organizations can now produce reports, proposals, strategies, requirements, policies and presentations at unprecedented speed. Much of it will be polished, plausible and professionally written. But polished language is not the same as sound reasoning. And increased output does not create increased capacity for careful judgment.

The danger is not only that AI will produce poor documents. It is that it will produce more documents than anyone can seriously assess. The scarce resource is shifting from creation to judgment.

The cost of producing documents has collapsed. The cost of deciding whether they deserve to be trusted has not.