The 5% Who Direct Intelligence Differently
Three signals on what separates sophisticated AI use from routine prompting, why AI collaboration erodes motivation, and the kind of knowledge machines can’t replicate.
01 / Only 5% of users consistently direct AI with sophistication. Now we know what they do differently.
KPMG and UT Austin’s McCombs School of Business analyzed 1.4 million real workplace AI interactions and published the results in Harvard Business Review. The researchers studied eight months of back-office operations, evaluating more than 30 behavioral characteristics of how people actually use AI at work.
The finding that matters: the sophisticated users, the ones who consistently produced high-impact outcomes, weren’t the most frequent users or the most technically skilled. They were the ones who excelled at framing problems clearly, directing the AI’s reasoning toward specific tasks, and iterating with purpose across their work.
About 5% of users demonstrated these behaviors consistently.
This is the first large-scale empirical study I’ve seen that confirms what we’ve been building toward at humanskills.ai: the skill isn’t prompting. It’s directing. Problem framing, task orchestration, evaluative judgment, purposeful iteration. These are the behaviors that separate someone who uses AI from someone who directs intelligence. And the fact that only 5% do it consistently tells you everything about the gap between access and capability. Every organization I work with has solved the access problem. Almost none have solved the capability problem.
02 / AI makes the work better. Then it makes the worker feel worse.
A study published in Nature’s Scientific Reports ran four experiments with 3,562 participants and found something that should unsettle anyone designing AI workflows. Collaborating with generative AI improved immediate task performance. But when participants transitioned from AI-assisted work back to working alone, their intrinsic motivation dropped significantly and their feelings of boredom increased. The performance boost did not carry over into subsequent independent tasks.
The researchers call it a “psychological deprivation effect.”
Read this carefully. AI collaboration makes the output better in the moment. Then it makes the human feel less capable and less motivated when the AI isn’t there. The performance gain doesn’t transfer to the next task. This is the cognitive atrophy that educators have been warning about, now measured in a controlled setting across thousands of participants. The implication for anyone designing how people work with AI is serious: if you don’t deliberately build in moments where the human does the hard thing alone, you’re training dependence, not capability. The Automate/Augment/Keep Human framework exists for exactly this reason. Some tasks should stay with the human even when the AI could do them faster. Not because the AI can’t. Because the human needs the practice.
This is also why I still hand-chop onions instead of using the food processor. The food processor is faster. But I lose something when I stop using the knife.
03 / AI replicates what you learn from textbooks. It amplifies what you learn from experience.
The Dallas Federal Reserve published research drawing a distinction that deserves to become part of how every organization thinks about AI capability. The researchers separate codified knowledge (established information from textbooks, training materials, documented procedures) from tacit knowledge (understanding gained through experience, pattern recognition built over years of practice, judgment formed by doing the work).
AI automates codified knowledge. It complements tacit knowledge. Employment is falling for young workers in AI-exposed fields, not through layoffs but through a collapsing job-finding rate. Wages for experienced workers in those same fields are rising.
*This reframes how we should think about developing AI capability in any organization. The 5% of sophisticated users in the KPMG study aren’t better at AI. They’re better at the underlying work, and that’s what makes their AI use sophisticated. Tacit knowledge is the operating system. AI is the application. You can install the application on any machine. But it only performs well on a machine with a strong operating system.*
*If you’re building an AI capability program and you’re only teaching the application, you’re building on sand.*
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