generative ai – DK Consulting of Colorado

Human-in-the-Loop as a Leadership Practice: A Framework for Better AI Workflows

May 31, 2026June 2, 2026Debra W Kahn (@debrawk.bsky.social)

A shift is underway that has implications for leaders developing AI-assisted workflows. In recent months, even some technology leaders have begun revisiting an idea that once seemed unfashionable: the enduring value of the humanities. As AI becomes more capable, qualities often associated with disciplines like psychology, philosophy, and literature are being reframed as professional advantages rather than academic luxuries.

In a recent New York Times opinion piece, columnist Maureen Dowd explored the renewed interest among AI technologists in liberal arts education. In the piece, sources suggest that a deeper understanding of human behavior, ethics, history, and enduring narrative themes might help younger professionals gain an edge in an AI-rich workplace. The notion is striking: the more sophisticated AI becomes, the more valuable distinctly human capabilities become.

The implications for leaders engaged in workflow design or redesign run deeper than the superficialities sometimes associated with Human-in-the-loop (HITL).

In this space, I have written about HITL as a safeguard for ensuring human judgment remains central to AI-assisted work. In a previous blog post, I argued that effective HITL requires cognitive friction—intentional pauses for questioning, verification, and reflection that are distinct from mere review and approval.

The challenge for today’s leaders is twofold: First, to ensure meaningful human engagement in AI-assisted workflows; and second, to ensure that humans drive the continuous improvement of those workflows.

That is where HITL as a leadership practice comes in.

In this blog post, I offer a practical HITL leadership model—a repeatable process for keeping human engagement intentional, your team’s relationship with AI collaborative, and AI-assisted workflows continuously improving.

Human Judgment vs. AI Insight: Rethinking Strategy in an Automated World

March 31, 2026April 3, 2026Debra W Kahn (@debrawk.bsky.social)

Visionaries have given us products that disrupted markets, but they have always had a strategy to back up the vision. Steve Jobs gave us a cellular phone (the iPhone) with a touchscreen keyboard because he hated mechanical keyboards. It also played music like Apple’s popular iPod and offered a world of apps you could download from Apple itself.

When Herb Kelleher took Southwest Airlines nationwide, he had a vision for making air travel affordable for all: he would model it after Greyhound bus lines. For better or worse, that led Southwest to implement its less expensive point-to-point flight patterns, distinct from the other airlines’ hub-and-spoke patterns.

The vision drove the strategy, and, no doubt, many project managers and communications professionals made it work.

In recent months, I have heard a subtle but important shift in how professionals talk about strategy. Increasingly, teams are not just using AI to support execution; they are asking it to suggest direction. Prompts such as “What should our strategy be?” or “What is the best approach?” crop up more and more in both project environments and content strategy discussions.

This shift raises an important question: Are we improving strategic thinking, or are we outsourcing it?

This post explores the following:

What Strategy Really Is
Features of Experience-Based Strategy
Features of AI-Influenced Strategy
Comparison of the Two Approaches
The Blended Approach—And Its Risks
Caveat: HITL Is Not a Panacea
Conditions for Effective Blending
Structuring Strategy in an AI Environment: A Model
Practical Applications
Strategy Still Requires Human Ownership

Critical Thinking and GenAI: Why Human-in-the-Loop Needs Cognitive Friction

February 28, 2026April 2, 2026Debra W Kahn (@debrawk.bsky.social)

After viewing my recent International Project Management Day presentation on Human-in-the-Loop (HITL) practices, an attendee asked a simple but profound question:

“This all makes sense. But how do we actually implement it?”

That question has stayed with me.

I expended a lot of energy in 2025, through blog posts and presentations, describing the limitations of generative AI (GenAI) in practical applications. But it’s one thing to agree that generative AI introduces risk. It’s another to design workflows that preserve human judgment in the presence of fluent, confident, probabilistic systems.

Now the designers of GenAI have jumped into the fray. Recently, Anthropic issued a public statement regarding the U.S. Department of Defense’s use of Claude. The statement included this line:

“…without proper oversight, fully autonomous weapons cannot be relied upon to exercise the critical judgment that our highly trained professional troops exhibit every day.”

The domain there is defense. Ours is content, strategy, and project leadership. But the principle transfers cleanly.

AI systems do not exercise judgment. Humans do.

The risk in everyday professional environments is not that GenAI will launch weapons. The risk is quieter: that we gradually outsource evaluation, synthesis, and dissent. That we begin to accept fluency as understanding. That we mistake coherence for truth.

In last month’s post, I examined the effects of cognitive shortcuts—automation bias, and confirmation bias—that can crop up in our use of GenAI. But the deeper concern isn’t simply bias. It is the potential erosion of critical thinking.

If GenAI reduces friction, we must intentionally reintroduce the right kind of friction.

In this post, I’ll explore:

Why AI-assisted workflows can quietly weaken critical thinking
Where Human-in-the-Loop fits along the spectrum of human–AI collaboration
What Cognitive Forcing Functions (CFFs) are—and what recent research says about their impact
Practical ways to design cognitive friction into professional workflows

The goal is not to slow AI adoption. It is to ensure that efficiency does not come at the expense of judgment.

Designing Content for AI Summaries: A Practical Guide for Communicators

November 30, 2025April 30, 2026Debra 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.)

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.

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