Tag: content management

Structured Content: The Missing Piece in AI Architecture

Structured Content: The Missing Piece in AI Architecture

Debra W Kahn (@debrawk.bsky.social)

You can’t scroll through LinkedIn these days without encountering elaborate diagrams of AI architecture. They are everywhere. With spooky colors and black backgrounds, they illustrate how large language models (LLMs), retrieval systems, AI agents, orchestration tools, and governance mechanisms work together to deliver business results.

I’ve been looking at a number of these models recently. Some are remarkably detailed, with layers devoted to everything from data management to security and human oversight. But something stands out: Many acknowledge the importance of enterprise data and knowledge, yet few explicitly address structured content or the professionals who manage it.

That’s a consequential omission. For decades, technical communicators, content strategists, and information architects have developed methods to organize, classify, maintain, and reuse information. These methods have renewed relevance as organizations build trusted AI systems that depend on reliable organizational knowledge.

Overlook that relevance at your own risk.

Structured Content: Established Principles, New Relevance for AI

Structured content has been extensively defined, described, and standardized by international bodies and leading institutions. It is information that is organized into modular, discrete, self-contained components that are:

  • Consistently architected for human and machine readability
  • Identifiable
  • Semantically tagged (without regard to formatting)
  • Version-controlled
  • Reusable
  • Made relative and relevant with component-level metadata
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Effective Content in the AI Era: Thriving in a Three-Force Audience Ecosystem

Effective Content in the AI Era: Thriving in a Three-Force Audience Ecosystem

Debra W Kahn (@debrawk.bsky.social)

When I created the Three-Force Audience Model for my July blog post, I focused on the endpoints.

The reader. The AI system. The community.

But afterward, I found myself thinking more about the spaces between them.

After all, these forces don’t operate independently. A reader asks an AI system a question. Someone brings the AI-generated answer into a professional discussion. That community discusses, challenges, or reinforces the idea and seeks further sources. Each interaction can change how information is understood—and what happens to it next.

That makes the spaces between the endpoints at least as interesting as the endpoints themselves.

So, for this follow-up, I went back to the Three-Force Audience Model and added arrows to represent the multi-way interactions among the three forces.

What they reveal is a more dynamic picture of today’s content environment—and some important implications for those of us who create and manage content.

How the Three Audience Forces Interact

The audience ecosystem I introduced last month should move us, as content creators, beyond the notion of a single audience type for our content. Instead, our content exists within an ecosystem shaped by three very different audience forces.

Given that, we must accept that there is no single type of interaction with our content—or even a single audience flow. Audiences interact with our content at any point along their journeys to or from it. As they do, they can add meaning that, in turn, can help us improve and even expand the original concept.

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Why Your Company Needs a GenAI Policy for Content Contributors

Why Your Company Needs a GenAI Policy for Content Contributors

Debra W Kahn (@debrawk.bsky.social)

“Wikipedia Bans AI-Generated Content,” or some variation of that headline, captured online newsfeeds on March 26, 2026. But Wikipedia’s announcement, while consequential (impacting 7.1 million articles), wasn’t that unusual.

In 2025, several large publishers released policies governing the use of generative AI (genAI) in content development and editorial workflows. Organizations such as Elsevier, John Wiley & Sons, and SAGE Publishing recognized the growing reality: AI-assisted content creation had already entered the workplace, often faster than governance and guidance could keep pace.

The concern is practical rather than theoretical. GenAI tools introduced new questions about factual accuracy, fabricated citations, copyright exposure, confidential data, manipulated images, and growing challenges with authorship and ownership.

Small companies and organizations outside the publishing industry face many of these same risks.

A content department generating online content through AI prompts, a software company creating AI-assisted chatbots, or a nonprofit drafting donor communications with AI tools all face important questions:

  • What kinds of AI use are acceptable?
  • What kinds of AI use should be restricted or prohibited?
  • When should AI use be disclosed?
  • Who remains responsible for validating accuracy?
  • How can we safeguard against bias?
  • How should confidential information be protected?

For content managers and project managers, particularly in organizations that outsource content creation, an AI policy for content contributors is more than a legal safeguard. It is a governance tool that helps preserve content quality, establish accountability, and maintain trust with audiences. In this blog post, I outline the key elements of AI policy.  

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Agent vs Agency in GenAI Adoption: Framing Ethical Governance

Agent vs Agency in GenAI Adoption: Framing Ethical Governance

Debra W Kahn (@debrawk.bsky.social)

Everywhere I look these days, I uncover new terms related to Generative AI (GenAI), some of which have competing definitions. I get lost in the details. My confusion is partly my fault for trying to knit together meaning from too many sources, but it is also due to the evolving nature of GenAI and its application to real-world work environments.

Ay, there’s the rub, as Hamlet would say—GenAI’s nature versus the real world.

Odd isn’t it? To think of GenAI having a “nature” since it is a thing that has been nurtured. Equally perplexing is thinking of the usually ordered world of human work flailing in the face of a single new technology. But that is where we find ourselves these days.

Hamlet’s famous “to be” speech finds him in a moral dilemma, caught between acting—or not—to avenge his father’s death. He contemplates existence versus non-existence and the known world versus the unknown world beyond death, an experience he labels “the undiscovered country.” (Star Trek fans, anyone?) The speech offers a foreshadowing of what is to come in the play.

While not all of us are paralyzed by fear of the unknown, as Hamlet is, many of us struggle with the tensions inherent in the adoption of GenAI by our organizations and content teams. In this blog post, I examine these tensions, share some definitions, and offer suggestions for the ethical governance of GenAI in the content workplace.

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Safeguarding Content Quality Against AI “Slop”

Safeguarding Content Quality Against AI “Slop”

Debra W Kahn (@debrawk.bsky.social)

We are privileged these days to be able to roll our eyes still at fakery created by generative AI. Think of the blurred hands and misaligned clothes in the Princess of Wales’ infamous 2024 Mother’s Day family photo. More recent and brazen examples exist in fake citations included in some lawyers’ depositions and even in the first version of the U.S. government’s 2025 MAHA (Make America Healthy Again) report.

But we likely won’t have that easy eye-roll privilege for long.

The recent iterations of generative AI models, such as ChatGPT 4o, Claude 4, and Google’s Gemini, include even more sophisticated reasoning and huge context windows—thousands of times the size of the original ChatGPT release. Generally, the longer the context window, “the better the model is able to perform,” according to quiq.com.

As I mentioned in my most recent blog post (“Leveling an Editorial Eye on AI”), the omnipresence of AI has the capability—and now the model power—to compound inaccurate information (and misinformation) a thousand-fold, collapsing in on itself. This endangers the whole concept of truth in our modern society, warns my colleague Noz Urbina.

Given this capability, what are reasonable steps an individual, an organization, and the content profession as a whole can take to guard against even the subtlest “AI slop”?

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