The Human+AI Equation: Letting Go of the Effort Illusion 

Generative AI has created a leadership dilemma few expected. For decades, those who manage knowledge workers have gauged progress by a familiar set of observable cues—quick responses, polished documents, constant activity. Psychology explains why: people instinctively equate visible effort with value, a bias known as the effort heuristic.

By producing emails and deliverables, GenAI changes what managers once assumed reflected human effort behind visible work. This shift creates the effort illusion, the belief that artifacts still represent human endeavor even when they often do not.

Sure, GenAI may accelerate tasks, but organizations create value through outcomes—not artifacts. 

Mistaking AI-Generated Productivity for Progress

While GenAI gives workers back time, most organizations will squander that capacity unless they redirect it deliberately toward real value creation. But the effort illusion makes this even more challenging to do. When leaders mistake output for progress, they reward teams unintentionally for producing more artifacts than advancing meaningful outcomes.

It’s like trying to turn a stripped screw. You twist the screwdriver harder and feel movement, but the screw goes nowhere. Squandering generative AI is akin to applying a power drill—it accelerates rotation without any evidence that real progress is happening. 

According to Microsoft’s 2024 Work Trend Index, about 75% of global knowledge workers now use GenAI at work, and more than half use it multiple times a week. Content creation dominates this adoption, with writing, analysis, and communication growing fastest. Leaders, then, can no longer reliably infer human effort from AI-generated outputs, yet many still cling to these visible artifacts because they are psychologically reassuring. Yet, in an AI-enabled workplace, what is easiest to see does not always reflect what creates the most value.

The Enduring Value of Slow Thinking and Relational Work

This dynamic raises a critical challenge because the work GenAI cannot yet perform is slow thinking and relational work—two practices inherently hard to observe.

Psychologist Daniel Kahneman describes slow thinking as integrative, conceptual, deliberate, and deeply effortful—a cognitive process that gives space to access situations more comprehensively. Relational work, meanwhile, is what we do when we create and sustain interpersonal relationships. It’s what keeps teams aligned—based on trust and judgment to distinguish noise from what truly matters.

From the outside, slow thinking and relational work often look like inactivity. That’s because the brain’s most complex integrative processes occur in the Default Mode Network (DMN), which activates during quiet reflection. In a torrent of AI-generated artifacts, these deeper forms of work become even easier to miss—unless leaders intentionally look for them. And GenAI only heightens this tension by making it simpler to generate the outward signs of reasoning without anyone doing the underlying cognitive work. 

The Effort Illusion’s Effect on Talent Acquisition

Few functions expose the effort illusion more than talent acquisition.

On the surface, talent acquisition appears susceptible to automation because much of the visible work is repetitive and structured—think resume screening, scheduling, boilerplate communications, funnel management. These tasks generate a constant stream of output and polished artifacts, making them ideal candidates for GenAI acceleration. 

As GenAI automates more of TA’s visible tasks, leaders need to shift their attention to the intangible human work (judgment, relationship-building, strategic alignment) that determines hiring quality and business impact.

In fact, when TA executives describe what they look for when hiring talent, AI and automation skills are not at the top of their list. Our own research shows that talent leaders instead prioritize critical thinking, learning agility, and collaboration—capabilities that are relational, conceptual, and inherently human. They are also hard to see, quantify, and infer from GenAI-generated output. And those activities that differentiate hiring performance—assessing long-term fit, exercising critical judgment, and aligning talent decisions with business strategy—are more difficult and nuanced to measure.

This mismatch creates a paradox: AI may boost the appearance of productivity while the function’s most important human contributions become harder to see and even easier to undervalue. 

Reclaiming Focus in a World of AI Excess

To thrive in the AI era, leaders should welcome the discomfort of not being able to see work happening. They should look for outcomes, not optics, rewarding clarity over volume, discernment over velocity, and alignment over activity.

Leaders should also:

• Trust teams more, even with fewer signals to verify short-term progress.
• Set strategic direction with greater precision by explicitly stating outcomes that should be achieved through work.
• Discourage low-value productivity theater by optimizing for outcomes, not activity.
• Coach employees to develop their thinking, not just create artifacts.
• Recognize and legitimize deep work that mimics inactivity or appears to move slowly.

This, of course, requires leaders to rethink what they believe about work. They will need to see it not as visible activity and constant production, but instead as sound judgment, insight, and outcomes—even when those are not immediately observable.

In organizations that embrace this shift, productivity will begin to look like:

• A decrease in the number of meetings focused on status updates, because teams build alignment through trust, not performative presence.
• An increase in protected time for deep work, understood as a driver of value rather than a luxury.
• More thoughtful, accurate, and strategically relevant outputs aligned to business outcomes.
• AI-generated artifacts as inputs to thinking and early drafts, not evidence of work itself. 

Letting go of the effort illusion is only the beginning. As AI accelerates work in every direction, leaders will face an even harder task: deliberately deciding what not to do.

The rest of our Human+AI Equation series will explore how discerning what matters—and what doesn’t—will become today’s defining leadership skill. Missed our first installment? Read it here.