Help Employees Get Better—Not Just Faster—with AI

2026-06-15 · Source: Feeds - HBR.org · Field: Business & Management — Human Resources & Workforce Development, Corporate Strategy & Leadership, Operations & Process Management · Depth: Intermediate, long

Summary

The article, published on June 15, 2026, by David S. Duncan and Tyler Anderson, argues that as AI simplifies content generation, human judgment becomes the critical skill. Most organizations currently focus on AI tool fluency rather than developing critical thinking with AI. The authors propose a four-step process to cultivate this "hybrid skill" of combining human judgment with AI capabilities. This model helps professionals move from tacit intuition to explicit articulation, enabling them to evaluate, steer, and contextualize AI outputs effectively. The process involves establishing an initial point of view, collaborating with AI across multiple modes (generate, critique, compare, simulate, challenge), analyzing differences between human and AI perspectives, and delivering the output with an explanation of the judgment applied.

Key takeaway

For Directors of AI/ML or VPs of Engineering/Data tasked with workforce development, you should prioritize training programs that cultivate critical judgment alongside AI tool fluency. Implement a structured approach, like the proposed four-step process, requiring your teams to articulate their initial views, actively critique AI outputs, and explain their reasoning trails. This ensures employees develop deeper expertise, catch subtle errors, and truly get "better," not just "faster," with AI, maximizing its strategic value.

Key insights

AI shifts expertise from tacit intuition to explicit judgment, requiring articulation for effective collaboration.

Principles

• AI-era expertise rewards explicit articulation of judgment.
• Traditional mastery moves from rules to intuition; AI reverses this.
• Directing AI can reveal gaps in one's own thinking.

Method

A four-step process: establish initial view, collaborate with AI across generation, critique, compare, simulate, and challenge modes, analyze differences, and deliver with a reasoning trail.

In practice

• Form a preliminary view before using AI tools.
• Prompt AI to generate multiple versions and critique its assumptions.
• Compare AI outputs to surface tradeoffs and test against stakeholders.

Topics

• AI Training
• Employee Development
• Critical Thinking
• Judgment Skills
• AI Collaboration
• Workforce Transformation

Best for: Director of AI/ML, VP of Engineering/Data, HR Professional

Related on AIssential

• Everyone Is Learning AI, So Why Will Most Still Fail? — Surface-level AI tool proficiency is becoming a baseline, not a differentiator, requiring deeper skills for true value.
• Workers are getting paid to teach AI how to do their jobs - CBS News — Expert human input is crucial for fine-tuning large language models to improve reasoning, creativity, and factual accuracy.
• I Stopped Sending My Team AI Tutorials. Here’s What Actually Worked — Direct, integrated AI demonstrations on real tasks drive adoption more effectively than passive education.
• Everyone has AI tools, a Few has AI judgment — As AI capabilities become commoditized, human judgment and structured thinking frameworks are the new competitive edge.
• The AI Labor Shift: Why We Are Transitioning from ‘Doers’ to ‘Reviewers’ — Generative AI shifts labor from task execution to human review and system orchestration, demanding contextual judgment.
• Webinar: Better Judgment in the AI Era – May 21 — AI-driven simulations can effectively develop legal judgment in junior lawyers amidst workflow automation.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Feeds - HBR.org.