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

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

Debra 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.

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

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

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

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

Debra 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.

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Cognitive Bias in GenAI Use: From Groupthink to Human Mitigation

Cognitive Bias in GenAI Use: From Groupthink to Human Mitigation

Debra W Kahn (@debrawk.bsky.social)

“When you believe in things you don’t understand, then you suffer; superstition ain’t the way.”

–Stevie Wonder, “Superstition,” 1972

I thought of the words of Stevie Wonder’s song “Superstition” the day after I spent a late night doomscrolling social media, desperate for news about a recent national tragedy that touched a local family. I ended up taking a sleeping pill to get some reprieve and a decent night’s sleep.

While doomscrolling on social media is a uniquely modern phenomenon, the desire to seek confirmation and validation through affinity is not. It’s a form of Groupthink. After all, we choose to “follow” folks who are amused (or perhaps “consumed”?) by the same things we are. Cat video, anyone?

In the 21st century, Groupthink isn’t limited to groups anymore. It’s now personal and as close as your mobile phone or desktop. The intimate version of Groupthink began with social media memes and comments and has quickly expanded to include generative AI (GenAI) engagement.

Intellectually, we have mostly come to understand that Groupthink drives our social media feeds—with the help of overly accommodating algorithms. Now, similar dynamics are quietly emerging in how we use GenAI. Cognitive biases that seep into GenAI engagement, especially automation bias and confirmation bias, can warp our content and projects unless we understand what these biases are, how they manifest, and how to manage them.

A Quick Refresher on Groupthink

Irving Janis, an American professor of psychology, first defined the term ” Groupthink ” in 1972 as a “mode of thinking that people engage in when they are involved in a cohesive in-group, when members’ strivings for unanimity override their motivation to realistically appraise alternative courses of action.” In other words, we go along to get along, as the American idiom goes.

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