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GenAI in Professional Settings: Adoption Trends and Use Cases

September 30, 2025February 28, 2026Debra W Kahn (@debrawk.bsky.social)

Some content and project professionals are making their GenAI wishes come true, some are still contemplating their first wish, and some feel trapped in the genie’s bottle. Such is the current state of GenAI use within organizational boundaries.

In the past few weeks, I have been engaging with practitioners through events and private discussions on the application of GenAI to everyday work. Most notably, I recently delivered a recorded presentation on Human-in-the-Loop for IPM Day 2025, set for release on November 6; led a virtual session for the PMI Chapter of Baton Rouge on September 17, 2025, titled “GenAI: The Attractive Nuisance in Your Project”; and participated in an October 2 webcast, “An Imperfect Dance: Responsible GenAI Use.”

What folks told me didn’t always surprise me.

What they told me matched, for the most part, some of the GenAI adoption patterns I’ve been researching. I’ll share those trends, as well as common and emerging use cases and persistent drawbacks, in this month’s blog post.

A New Code for Communicators: Ethics for an Automated Workplace

August 31, 2025March 31, 2026Debra W Kahn (@debrawk.bsky.social)

What happens when you’re asked to document a product that doesn’t exist—or to release content before it’s been validated? Those of us who have been outside of corporate culture for a while forget that our still-enmeshed colleagues regularly make ethical decisions about their content work. But I began recalling some of my own experiences recently, cringing the whole time.

Early in my career, a colleague at a small manufacturing firm quietly informed me that our newest product, recently presented to the firm’s most important client, was a prototype, not the final design. So, I was basically documenting vaporware. Later in my career, the manager of our small but busy editorial and production group at a large high-tech company stopped by my cubicle one day to tell me that I had to “change my whole personality.” Apparently, the larger department was no longer as concerned about content quality as she perceived I was.

Of course, nothing beats the ethical situation I found myself in as a fledgling business owner, which I described in last month’s blog post. But you get the point.

Fast forward to today. The ethical complexities presented by GenAI in the workplace are multifold. I discussed some of those complexities in my June 2025 blog post. Luckily, we don’t have to face the wave of complexities alone.

We can use existing ethical frameworks for GenAI development, adoption, and use to inform a new ethical code for communicators.

Agent vs Agency in GenAI Adoption: Framing Ethical Governance

June 30, 2025September 1, 2025Debra 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.

Safeguarding Content Quality Against AI “Slop”

May 31, 2025May 1, 2026Debra 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”?

Leveling an Editorial Eye on AI

April 30, 2025June 5, 2025Debra W Kahn (@debrawk.bsky.social)

A colleague and I once pioneered using levels of edits to help manage the workload through our content department at a large high-tech firm. We rolled out the concept and refined it over time, all in the name of efficiency and time to market. What we were really trying to do was save our sanity.

We failed.

Or rather, the whole endeavor of developing and releasing educational content through a single in-house unit failed. All the work—from course design to release—was eventually outsourced. But I learned something valuable from the experience. (And I hope others did, too.)

You can’t outsource quality.

I think that’s as true in today’s world of generative AI as it was “back in the day” when I was a technical editor. But how does editorial refinement work in today’s hungry market for “easy” content? Let’s look at how it used to work, how people would like it to work, and how it might work better.

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