2026-05-22T00:00:00.000Z

# We Have Been Here Before

Artificial Intelligence is forcing organizations to confront a problem that enterprise architects, search engineers, and information architects have wrestled with for decades: how to structure knowledge in ways that preserve meaning, authority, and institutional memory at scale.

For the better part of the last two years, the technology industry has spoken about Artificial Intelligence as though it represents a complete break from the past. Every week introduces a new vocabulary and a call to learn a new technology. RAG, CAG, Semantic Retrieval, Embeddings, AI Agents, Contextual Memory, the list goes on.

Listening to these discussions, one might conclude that we have entered an entirely new era of computing. In some ways, we have. Considering that the capabilities of large language models are genuinely transformative. Yet as I watch organizations rush to integrate AI into their operations, I find myself with an increasingly strange sensation:

I have seen this before

Fifteen years ago, I contributed chapters on Content Types and Enterprise Search for books published by Wrox Press during the SharePoint 2010 era. At the time, we were wrestling with many of the same concerns now resurfacing in modern AI systems:

- how knowledge is structured,
- how meaning is categorized,
- how information is discovered,
- how relevance is determined,
- how institutional memory is preserved,
- and how organizations retrieve truth from increasingly massive collections of content.

The terminology changed, but the architectural problems did not. Back then, we spoke about Managed Metadata, Content Types, Crawled and Managed Properties, Search Scopes, and Relevance Tuning. Today it’s Embeddings, Semantic Retrieval, Vector Databases, Ontologies, RAG, and AI Memory.

Underneath the language, the underlying challenge remains the same: How do human beings structure knowledge in ways that allow computational systems to retrieve, contextualize, govern, and reuse meaning?

For a time, the industry attempted to answer that question through enterprise knowledge architecture. Systems like SharePoint were built around the idea that organizational knowledge should become durable institutional memory. Documents were structured in content types that exposed metadata. The taxonomy mattered.

Then something changed.

Over the last decade, organizations increasingly abandoned structured knowledge systems in favor of communication-first platforms like Slack and Teams. Knowledge architecture slowly gave way to conversational fragmentation. Institutional memory dissolved into channels, notifications, reactions, and buried threads. Stakeholders stopped asking whether information was structured correctly and instead assumed that search would somehow compensate for the entropy.

Now AI has arrived, and organizations once again expect technology to reconstruct meaning from chaos. But AI systems inherit the structure of the knowledge environments beneath them. A language model cannot reliably reason over knowledge that an organization itself no longer understands. And so, after more than a decade, the old problems have returned.

Only now, they matter more than ever.

## The Original Promise of Enterprise Knowledge Management

To understand why AI is forcing organizations to rediscover knowledge architecture, it is important to remember that this is not the first time the industry attempted to solve the problem of institutional knowledge at scale. Long before large language models, organizations were already drowning in information.

Documents accumulated across file shares, intranets, email systems, and departmental applications (many of which were access databases and excel spreadsheets). The problem was never the absence of information. The problem was that organizations increasingly struggled to locate knowledge, preserve context, determine authority, and distinguish signal from noise.

Enterprise knowledge management systems emerged as an attempt to solve this problem.

At the center of many of these efforts was Microsoft SharePoint Server 2010. Whatever frustrations people may remember about SharePoint implementations, the underlying vision was ambitious and, in many ways, correct. The idea was not merely document storage. The idea was institutional memory.

Documents would become more than isolated files sitting in folders. Documents could be categorized, structured, versioned, governed, searchable, and connected to organizational processes. Metadata was not an afterthought. It was central to knowledge management.

In the SharePoint era, concepts like Content Types, Managed Metadata, Taxonomies, Search Scopes, Relevance Tuning, and Federated Search were all part of a larger architectural philosophy. The assumption beneath these systems was deceptively simple:

> knowledge becomes valuable when it can be reliably discovered, contextualized, trusted, and reused.

This was the real purpose of Information Architecture. Information Architecture was an attempt to impose semantic structure on organizational knowledge. In my own work during that period, I spent a great deal of time focused on Content Types and Enterprise Search. In one chapter, I described Content Types as:

> “a conceptual container for content and processes in the system.”

Looking back now, that description feels remarkably AI. Because modern AI systems increasingly depend upon the same underlying semantic objects that bind together content, metadata, workflow, context, authority, and retrieval behavior. The terminology has evolved, but the architectural concern remains the same.

At the time, enterprise search itself was also evolving beyond simple keyword matching. Search systems like FAST Search Server 2010 for SharePoint introduced concepts that now sound strikingly familiar in the age of AI: contextual search, managed properties, semantic refinement, relevance tuning, property extraction, and personalized search experiences.

In one of my FAST Search chapters, I wrote:

> “The main function of the query engine is to strike a balance between recall and precision.”

That remains one of the central challenges of modern AI retrieval systems. The problem was never simply retrieving information. The challenge was retrieving the right information. And that required more than indexing documents, it required understanding meaning.

Enterprise search teams spent years wrestling with information architecture. It was a difficult and often frustrating problem. Governance was tedious, metadata strategies were frequently ignored by stakeholders, taxonomy discussions could become surprisingly political, and search quality was directly tied to the discipline of the organization itself.

But despite the frustrations, there was an important recognition embedded in these systems:

> organizational knowledge does not become useful simply because it exists.

> It becomes useful when it is architected.

For a time, the industry understood this. And then, gradually, it began to forget.

## The Collapse of Knowledge Discipline

Somewhere along the way, the industry stopped believing in Information Architecture.

Not openly. No one stood up in a conference room and declared that structure, taxonomy, and governance no longer mattered. The shift happened gradually through a long series of practical compromises. Collaboration became more important than curation. Speed became more important than structure. Convenience became more important than stewardship.

Platforms like Slack and Microsoft Teams quietly changed the center of gravity inside organizations. Communication became immediate in ways older enterprise systems never fully achieved. Teams could organize themselves quickly. Remote work became easier. Conversations moved faster than email ever allowed. The friction that once slowed collaboration largely disappeared.

But something else disappeared with it. The older generation of enterprise systems were built around the idea of durable organizational knowledge. A document was meant to become part of institutional memory. It had structure, metadata, context, and a lifecycle. It lived somewhere intentionally. Modern collaboration systems approached the problem differently. The conversation itself became the workspace.

At first this felt liberating. Nobody wanted to spend an afternoon debating taxonomy. Stakeholders rarely cared about metadata strategy. Governance discussions were seen as bureaucratic overhead standing in the way of “real work.” The assumption became that information no longer needed to be carefully structured because search would simply find it later.

I watched this happen in real time over the course of a decade.

Entire knowledge architecture initiatives slowly unraveled because the organization no longer valued the discipline required to sustain them. Documents became attachments inside channels. Decisions disappeared into threads. Critical operational knowledge became trapped inside conversations that were meaningful only within the moment they occurred.

There was a growing belief that search would simply find what you needed when you needed it. That assumption turned out to be deeply flawed. Search can improve discoverability, but it cannot reliably reconstruct missing context. It cannot fully resolve inconsistent terminology, fragmented authority, or organizational ambiguity. It cannot distinguish between institutional truth and conversational noise when both are scattered across disconnected systems.

Users adapted to the growing disorder. They searched repeatedly. They asked coworkers where information lived. They recreated documents they could no longer locate. Knowledge became increasingly transient, but the organization continued functioning well enough that few people questioned what was being lost underneath the surface.

> What was actually disappearing was institutional memory.

Over time, organizations transformed durable knowledge into streams of communication. Important decisions became buried beneath notifications, reactions, and side conversations. Context dissolved. Ownership blurred. Information spread across SaaS platforms faster than governance models could adapt to them.

And now AI has arrived.

Suddenly organizations expect language models to synthesize meaning from environments that human beings themselves no longer fully understand. They expect AI to retrieve institutional knowledge from fragmented repositories filled with duplicated content, conflicting terminology, missing context, and years of unmanaged conversational debris.

In many cases, AI is not entering well-architected knowledge ecosystems. It is entering archaeological ruins. And yet the expectation remains the same as it was during the collapse of enterprise search discipline: somehow, the machine will figure it out.

## AI is Exposing the Cost

One of the more persistent myths surrounding modern AI is the idea that large language models somehow eliminate the need for structured knowledge systems. In practice, the opposite appears to be happening. AI is not removing the importance of knowledge architecture. It is exposing how dependent organizations have always been upon it.

When organizations first began experimenting with enterprise AI, many assumed the hardest part would be the models themselves. Attention focused almost entirely on model size, context windows, GPU requirements, and which vendor appeared furthest ahead in the race.

But once organizations moved beyond demonstrations and attempted to integrate AI into operational environments, a different reality emerged. The AI could only reason over the information it was given, and in many organizations that information existed in fragmented, contradictory, poorly governed states. Documents existed in multiple versions across multiple systems. Terminology varied between departments. Conversations contradicted formal documentation. Important operational decisions were buried inside years of chat history. Ownership of knowledge was often unclear. Context had long since eroded.

> The language model did not create these problems. It simply made them visible.

This is one of the reasons so many organizations encounter disappointing results when attempting to build retrieval-based AI systems. There is often an assumption that if enough content is fed into a vector database, semantic search will somehow reconstruct organizational understanding automatically. But retrieval systems inherit the architecture, and the disorder, of the architectures beneath them.

If the underlying corpus is inconsistent, fragmented, or semantically weak, the AI system will reflect those weaknesses. In some cases, it may even amplify them. A language model is exceptionally good at synthesizing patterns, but it has no innate understanding of organizational truth. It cannot reliably distinguish between authoritative knowledge and conversational residue unless the surrounding architecture provides enough structure to support those distinctions.

This becomes especially dangerous because AI systems present information with extraordinary confidence. Traditional enterprise search at least forced users to interpret lists of documents themselves. Modern AI systems instead synthesize responses directly, compressing ambiguity into a single coherent answer whether the underlying information deserved that confidence or not.

In this sense, many so-called “AI hallucinations” are not purely model failures. They are often failures of information architecture, governance, and retrieval. The model is attempting to construct coherence from incoherent systems.

I have increasingly come to believe that the future success of enterprise AI will depend less on model sophistication and more on the quality of organizational knowledge environments. As models continue to improve and become increasingly commoditized, the differentiator will not simply be who has access to AI. Everyone will eventually have access to capable models. The differentiator will be whose knowledge systems are structured well enough to support trustworthy retrieval, contextual understanding, and durable institutional memory.

This is where many organizations now find themselves confronting the consequences of the last decade.

- They abandoned taxonomy because it felt cumbersome.
- They abandoned governance because it slowed collaboration.
- They abandoned metadata because users resisted it.
- They abandoned curation because search seemed sufficient.

Now they want AI to recover what was lost. But AI can’t magically restore the institutional discipline that organizations themselves stopped maintaining. In many ways, modern AI is functioning less like a revolutionary intelligence and more like a diagnostic instrument. It reveals the hidden condition of the organization’s knowledge systems. In well-structured environments, AI appears remarkably intelligent. In chaotic environments, it becomes unreliable, inconsistent, and difficult to trust.

## Why Knowledge Architecture Matters Again

What makes the current moment so interesting is that many of the disciplines the industry spent the last decade abandoning are suddenly becoming essential again. Metadata matters again. Taxonomy matters again. Authority and lifecycle management matter again. Not because organizations suddenly rediscovered a love for governance, but because AI systems place enormous pressure on the quality and structure of the knowledge environments they operate within.

For years, organizations were able to tolerate increasingly chaotic information ecosystems because human beings are remarkably adaptive. Employees compensate for weak systems constantly. They learn where information “really” lives. They remember which coworker knows the answer. They develop informal maps of organizational knowledge that exist nowhere in the architecture itself. Human organizations survive enormous amounts of informational disorder because people instinctively fill in the gaps socially.

> AI cannot do this.

An AI system has no intuitive understanding of organizational context beyond what the surrounding architecture allows it to infer. It does not know which documents are outdated unless lifecycle governance exists. It does not know which terminology is authoritative unless semantic structure exists. It does not know whether a buried Teams conversation should outweigh formal policy documentation. It does not know which department’s definition of a term reflects institutional truth. All of these distinctions must come from the knowledge architecture itself.

This is why retrieval quality is rapidly becoming one of the defining problems of enterprise AI. The challenge is no longer simply storing information or making it searchable. Organizations are increasingly facing deeper questions:

- What information should the AI trust?
- What content is authoritative?
- How is knowledge categorized?
- How is context preserved over time?
- How are conflicting definitions reconciled?
- How does institutional memory survive personnel turnover?
- Which information belongs in conversational systems and which belongs in durable repositories?

These are not model questions. They are architectural questions.

In many ways, the current AI cycle is forcing organizations to rediscover the importance of semantic structure. During the height of enterprise search initiatives, there was at least some recognition that information required intentional organization. Managed metadata, content types, and taxonomy strategies existed because search relevance depended upon them.

> Modern AI systems depend upon them even more.

The common assumption that embeddings and vector databases somehow eliminate the need for structure misunderstands what these systems actually do. Semantic retrieval improves flexibility. It helps systems locate related concepts even when terminology varies. But semantic similarity is not the same thing as organizational understanding. Two documents may be semantically related while still differing radically in authority, accuracy, recency, governance status, and/or operational importance. A language model cannot reliably infer those distinctions in isolation.

This is where knowledge architecture re-enters the picture.

Knowledge architecture is ultimately the discipline of creating environments in which meaning can survive scale, time, and organizational complexity. It is the work of establishing enough semantic structure that both human beings and computational systems can navigate information with confidence. For a long time, this discipline was treated as secondary to speed and convenience. Governance became synonymous with friction. Taxonomy became synonymous with bureaucracy. Metadata became something users ignored whenever possible.

AI is changing that equation. Once an organization begins relying on AI systems to assist with operations, analysis, retrieval, and decision support, the cost of weak knowledge architecture becomes immediately visible. Poor structure no longer results merely in inconvenience. It results in unreliable outputs, inconsistent reasoning, and loss of trust in the system itself.

This is why I increasingly suspect that the future leaders in enterprise AI will not necessarily be the organizations with the largest models or the most aggressive automation strategies. They will be the organizations that best preserve contextual integrity, semantic clarity, authoritative knowledge, and durable institutional memory.

In other words, the organizations that relearn how to architect knowledge.

## The Return of the Knowledge Architect

For most of the last decade, the role of the Information Architect slowly faded into the background of enterprise technology. The industry shifted its attention toward:

- agility,
- cloud adoption,
- SaaS integration,
- DevOps,
- automation,
- and collaboration tooling.

Knowledge architecture increasingly came to be viewed as a legacy concern from the era of intranets and enterprise portals. The people who worried about taxonomy, metadata, governance, and retrieval quality were often treated as though they belonged to an older generation of enterprise computing.

In some ways, perhaps they did. But history has a habit of returning to unresolved problems. And AI is returning us to one of the oldest problems in computing: how human knowledge can be structured in ways that remain meaningful at scale.

What is changing now is that organizations are beginning to realize that AI systems are not merely software tools. They are interpretive systems. They operate by traversing, retrieving, synthesizing, and contextualizing information drawn from increasingly vast collections of organizational knowledge. That changes the nature of enterprise architecture itself.

Enterprise architecture will increasingly revolve around the stewardship of organizational meaning. That may sound abstract, but the operational implications are very concrete. Organizations are beginning to confront questions they largely ignored during the collaboration-first era:

- What constitutes authoritative knowledge?
- How should institutional memory be preserved?
- Which systems are trustworthy?
- How should semantic conflicts be resolved?
- What information belongs in conversational systems versus durable repositories?
- How should AI systems distinguish policy from discussion?
- Who governs organizational knowledge over time?

These are no longer theoretical governance discussions, but the operational requirements for trustworthy AI. This is one reason I believe the next phase of enterprise AI will place increasing importance on disciplines that once sat quietly beneath the surface of enterprise systems: information architecture, records management, taxonomy design, search relevance engineering, metadata governance, and knowledge lifecycle management.

Ironically, many of the people best prepared for this transition may not come from the current AI hype cycle at all. They may come from the generation of architects, librarians, search engineers, records managers, and enterprise practitioners who spent years wrestling with the difficult realities of organizational knowledge systems long before large language models entered the picture.

Because beneath all the excitement surrounding AI lies a truth much older than the technology itself:

> An organization can only reason as effectively as it remembers.

And memory must be curated, structured, maintained, and governed. This is the work knowledge architecture has always attempted to do. The difference now is that the consequences of neglecting it are becoming impossible to ignore.

For years, weak knowledge architecture primarily produced inefficiency. Employees wasted time searching for documents. Information became duplicated. Teams struggled to locate authoritative answers. Frustrating, certainly, but survivable. AI changes the stakes. When organizations begin delegating retrieval, synthesis, operational guidance, and decision support to computational systems, weak knowledge architecture no longer produces mere inconvenience. It produces unreliable reasoning.

This is why I increasingly believe that the next generation of enterprise leadership will need to think less like software administrators and more like custodians of institutional cognition. The systems we are building are no longer merely storing information. They are shaping how organizations remember, retrieve, and reason. In that environment, knowledge architecture stops being a forgotten enterprise discipline.

It becomes foundational again.

## Conclusion: We Have Been Here Before

There is a tendency in the technology industry to speak about each new wave of innovation as though history has somehow been reset. Artificial Intelligence is often framed this way. The language surrounding it carries the feeling of rupture, as though we have crossed into an entirely new epoch of computing disconnected from everything that came before it. But the deeper I explore modern AI systems, the more I find myself returning to older questions.

Questions that enterprise architects, librarians, records managers, search engineers, and information architects have been wrestling with for decades - Questions about meaning, structure, authority, discoverability, memory, and trust.

The tools have changed dramatically. Large language models represent a genuine leap in capability. Semantic retrieval is vastly more sophisticated than the enterprise search systems many of us worked with fifteen years ago. The scale and flexibility of modern AI systems would have seemed almost unimaginable during the SharePoint and FAST Search era.

And yet, beneath all of the new terminology, the underlying architectural concerns remain strikingly familiar. Organizations still struggle to preserve institutional memory. Knowledge still fragments over time. Context still erodes. Authority still becomes ambiguous. Information still decays into entropy unless deliberate effort is made to structure and govern it. AI did not create these problems, it simply removed our ability to ignore them.

For years, organizations compensated for weak knowledge systems through human adaptability. Employees learned where information “really” lived. Teams developed informal knowledge networks. Search engines masked structural decay just well enough to keep the organization functioning. But AI systems force organizations to confront the condition of their knowledge environments directly. They reveal whether institutional knowledge is coherent, trustworthy, contextualized, and architected, or whether it has dissolved into disconnected fragments spread across years of unmanaged collaboration systems.

This is why I believe the future of enterprise AI will depend far less on the sophistication of models than many currently assume. The true differentiator will be organizational knowledge quality. The organizations that succeed will not simply be the ones with the best AI tools. They will be the organizations that relearn how to preserve semantic structure, contextual integrity, and durable institutional memory. In other words, they will be the organizations that rediscover knowledge architecture.

We have been here before.

The names have changed.
The interfaces have changed.
The scale has changed.

But the problem itself remains the same.