Agent vs Agency in GenAI Adoption: Framing Ethical Governance

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”

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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Thistle-Tomes

The following are my 2024 short takes on all things content in life and work:

  1. No one is an expert on AI yet. Take everything with a grain of salt.
  2. Sustainability should be part of every content strategy and content project.
  3. Lack of specificity on websites can be both a friend and an enemy. Vagueness can leave room for negotiation but also misinterpretation.
  4. Jargon in customer-facing content can be a significant barrier to understanding and engagement.
  5. Something I like to call “name theory” says that what you call something matters. And it doesn’t have to rhyme with “oom.”
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How to Develop a Combined Content Audit and Plan

Conducting a comprehensive content audit, also known as an information survey, can be daunting, especially if your organization has a large inventory of content. But is an in-depth audit really needed for every situation?

You would conduct a full content audit if your organization was undergoing a significant transformation, such as a digital transformation, or as part of a continuous improvement effort. But sometimes the moment calls for something more targeted, less exhaustive–something that enables you to quickly develop a plan of action.

I refer to this kind of content audit as a project-based content audit or a combined content audit and plan.

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