cognitive friction – DK Consulting of Colorado

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

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.

Tag: cognitive friction

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

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

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