Tag: Content strategy

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 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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Don’t Let Your AI Proof of Concept End With the Demo

Don’t Let Your AI Proof of Concept End With the Demo

Debra W Kahn (@debrawk.bsky.social)

In a September 24 letter to The Wall Street Journal, Sparq CEO Ingrid Curtis makes an important distinction between experimenting with AI and being ready to deploy it enterprise-wide. “A successful pilot proves little,” she writes. AI earns trust when it performs reliably “within the messy reality of a working business.”

One widely circulated 2025 statistic, based on IDC research for Lenovo (and reported by CIO), suggested that 88% of AI proofs of concept (PoCs) fail to result in widespread deployment. BCG reported in late 2024 that 74% of companies struggle to achieve and scale value from AI. But deployment may not be the only measure of a successful PoC. What did the organization learn from the experiment, and what happened to that knowledge?

Curtis argues that companies need to better prepare their AI applications, data, infrastructure, systems, and employees for that reality. I would add that the two most important contributors to this preparation are documentation and communication.

In my last two posts, I’ve focused primarily on external content—and the AI-mitigated audiences that interact with it. But demand for enterprise-ready AI is also creating demand for important internal content: the knowledge organizations generate as they experiment with the technology.

That knowledge shouldn’t disappear when the PoC ends.

An internal AI PoC is not complete when an organization determines whether the technology can perform as expected. It is complete when the organization documents, communicates, and applies what it learned to the decisions that come next.

Documentation creates organizational memory. Communication creates organizational knowledge. Both are essential if an AI PoC is going to become more than a successful experiment.

In this blog post, I answer these questions:

  • What is an AI PoC?
  • How can AI PoC documentation capture knowledge?
  • How can communication about AI PoC findings serve future efforts?
  • What kind of organizational knowledge can an AI PoC yield?
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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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Rethinking Content Audiences in the AI Era: The Three-Force Audience Model

Rethinking Content Audiences in the AI Era: The Three-Force Audience Model

Debra W Kahn (@debrawk.bsky.social)

Thanks to a recent experience, I’ve been thinking about how the audience for my content has changed—and how, in some ways, it has stayed the same.

During a recent group book discussion, one of my friends asked me to characterize the university students I taught in the mid-1990s and compare them with today’s students. Those students lived in a very different information environment. Access to knowledge was slower, more deliberate, and often mediated through libraries, instructors, and printed materials.

Today’s students inhabit an ecosystem where information is abundant, instantly searchable, algorithmically recommended, and increasingly summarized before it is ever read.

These changes don’t simply alter how people consume information. They change what it means to be an audience.

The traditional idea of a content audience as a stable set of personas or demographic segments no longer reflects how people encounter information online. Today, content consumption is shaped by three interacting forces: the individual reader, the AI system that discovers, summarizes, or interprets the content, and the community that shares, evaluates, or discusses it.

This shift requires content professionals to think differently.

Read on to learn about these three forces and how today’s content creators and managers can best work with the emerging challenges:

  • Three Forces Now Shape Content Consumption
  • Beyond Designing for AI Summaries: What a Blended Approach Really Adds
  • Solving the Audience Conundrum: What Content Professionals Can Do
  • A New Focus: The Audience Ecosystem
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Designing Content for AI Summaries: A Practical Guide for Communicators

Designing Content for AI Summaries: A Practical Guide for Communicators

Debra W Kahn (@debrawk.bsky.social)

There’s a certain irony in admitting this, but I recently struggled to write the introduction to one of my blog posts, “Agent vs Agency in GenAI Adoption: Framing Ethical Governance.” I wanted to frame the topic with a reflection on evolving terminology, a nod to Hamlet, and a meditation on AI’s “nature.” On top of that, I introduced the idea of the “ghost in the machine” only a few paragraphs later. In hindsight, I had written two introductions to the same post without meaning to.

At the time, the ideas felt connected. But when I later ran those paragraphs through an AI summarizer, the summary focused almost entirely on Hamlet’s moral dilemma and the mind–body problem—interesting concepts, certainly, but hardly the point of the post. The AI confidently reported that the blog was “about comparing the adoption of GenAI to Hamlet’s struggle with death.”

Not exactly the message I intended.

To be fair here, the most recent version of Google’s Gemini gave me a much more comprehensive summary. That summary mentions, as I did, “the tensions inherent in adopting Generative AI” and my proposed “governance framework.”

But looking back, I realize I had made two classic mistakes in writing that introduction—mistakes that human readers can forgive with patience but AI summarizers absolutely cannot. First, I opened with a metaphor instead of a clear point. Second, I layered multiple conceptual frameworks (terminology, nature vs. nurture, Hamlet, Koestler, agency) before stating my purpose. I know better. Many of us do. But as I’ve written elsewhere, expertise doesn’t exempt us from the structural pitfalls that now matter more than ever.

That experience became the seed of this post.

If our writing can be so easily misinterpreted by a summarizer—and thus by downstream readers who rely on that summary—then it’s worth rethinking what it means to write clearly and responsibly in an AI-influenced world. Good writing has always been about serving our readers. Now, increasingly, it must also serve the machine readers that bridge the gap between our content and those readers.

In this post, I explore why AI summarizers can distort meaning, how machines “read” what we write, and how we can design content that preserves accuracy, nuance, and intent—even after it’s digested by AI. (Note: Some content in this blog post was generated by ChatGPT.)

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