Comparing AI and Human Talent

When it comes to using AI to match candidates with jobs, more accurate/predictive AI is better, right? Not necessarily. One data-driven study would suggest the answer is no. I recently read Co-Intelligence, Living and Working with AI by Ethan Mollick, which I highly recommend. In the book, Ethan features a study by Fabrizio Dell’Acqua titled, "Falling Asleep at the Wheel: Human/AI Collaboration in a Field Experiment on HR Recruiters" in which 181 experienced recruiters were hired to collectively review nearly 8000 resumes for a software engineering position. Of note: the recruiters were incentivized to be as accurate as possible. The recruiters received algorithmic recommendations about the job candidates but the quality of these AI recommendations was randomized between 1) perfectly predictive AI; 2) high-performing AI; 3) lower-performing AI; and 4) no AI. Of critical importance to the study, recruiters were aware of the type of AI assistance they would be receiving. Key findings include: 1. Recruiters with higher-quality AI performed worse in their assessments of candidates in relation to the job than those using lower-quality AI. They spent less time and effort in their evaluations of each candidate, and they tended to blindly trust the AI recommendations. 2. Recruiters with lower-quality AI "exerted more effort and spent more time evaluating the resumes, and were less likely to automatically select the AI-recommended candidate. The recruiters collaborating with low-quality AI learned to interact better with their assigned AI and improved their performance." These findings suggest that when users have access to high-quality AI (or at least believe they do), they are indeed in danger of "falling asleep at the wheel," where they become overly reliant on AI, and reduce their attention, effort, and critical thinking - which can negatively impact outcomes for all involved. As we increasingly integrate AI into work, it's important to maintain a balance between technological support and human skill/expertise. Instead of aiming for (or claiming to have!) "perfect" AI, perhaps our goal should be to develop systems that enhance human decision-making and keep users actively engaged and thinking critically. What do you think? Check out the full details of the study here: https://lnkd.in/eGaTmTEi #AI #matching #criticalthinking #futureofwork

Last month, a Fortune 100 CIO said their company spent millions on an AI decision system that their team actively sabotages daily. Why? Because it optimizes for data they can measure, not outcomes they actually need. This isn't isolated. After years advising tech leaders, I'm seeing a dangerous pattern: organizations over-indexing on AI for decisions that demand human judgment. Research confirms it. University of Washington studies found a "human oversight paradox" where AI-generated explanations significantly increased people's tendency to follow algorithmic recommendations, especially when AI recommended rejecting solutions. The problem isn't the technology. It's how we're using it. WHERE AI ACTUALLY SHINES: - Data processing at scale - Pattern recognition across vast datasets - Consistency in routine operations - Speed in known scenarios - But here's what your AI vendor won't tell you: WHERE HUMAN JUDGMENT STILL WINS: 1. Contextual Understanding AI lacks the lived experience of your organization's politics, culture, and history. It can't feel the tension in a room or read between the lines. When a healthcare client's AI recommended cutting a struggling legacy system, it missed critical context: the CTO who built it sat on the board. The algorithms couldn't measure the relationship capital at stake. 2. Values-Based Decision Making AI optimizes for what we tell it to measure. But the most consequential leadership decisions involve competing values that resist quantification. 3. Adaptive Leadership in Uncertainty When market conditions shifted overnight during a recent crisis, every AI prediction system faltered. The companies that navigated successfully? Those whose leaders relied on judgment, relationships, and first principles thinking. 4. Innovation Through Constraint AI excels at finding optimal paths within known parameters. Humans excel at changing the parameters entirely. THE BALANCED APPROACH THAT WORKS: Unpopular opinion: Your AI is making you a worse leader. The future isn't AI vs. human judgment. It's developing what researchers call "AI interaction expertise" - knowing when to use algorithms and when to override them. The leaders mastering this balance: -Let AI handle routine decisions while preserving human bandwidth for strategic ones -Build systems where humans can audit and override AI recommendations -Create metrics that value both optimization AND exploration -Train teams to question AI recommendations with the same rigor they'd question a human By 2026, the companies still thriving will be those that mastered when NOT to listen to their AI. Tech leadership in the AI era isn't about surrendering judgment to algorithms. It's about knowing exactly when human judgment matters most. What's one decision in your organization where human judgment saved the day despite what the data suggested? Share your story below.

🎨 Can AI be truly creative—or just brilliantly combinational? This question hit me hard the other day when I was discussing with an artist. We’ve all seen AI generate jaw-dropping art, haunting music, and prose so beautiful it felt human. And yet… I can’t shake the feeling that it’s just the most sophisticated cut-and-paste machine in history. The numbers are fascinating: → 90% of creators say AI sparks new ideas — yet over 50% fear it’s making all ideas look the same. → Across 28 studies, AI matches human creativity… but when humans + AI work together, creativity jumps significantly. → AI can generate more ideas, but humans still win on originality and diversity. → With AI, writers boost novelty by 8% and usefulness by 9% — but risk creative convergence. → Creativity scholars call this “artificial creativity” — outputs that may be original and effective, but lack the self-actualization, emergence, and human context that define true creativity. It reminds me of the 4P and 6P theories of creativity: it’s not just the product that matters—it’s the person, the process, the environment. AI can simulate the product, but without human intent, the process feels hollow. It reminds me of the 6P theory of creativity: Creativity isn’t just about the output (product) — it’s also about the person creating it, the process they follow, and the environment they’re in. AI can generate an output, but it doesn’t have a lived experience, emotions, or intent, which are what give creativity meaning. In IRREPLACEABLE, we call this the “Creative Co-Pilot” approach: ✅ Let AI generate combinations at scale. ✅ Filter through our uniquely human ethics, emotions, and lived experience. ✅ Add intent—because meaning is what turns remixing into originality. For me, the future of creativity isn’t AI or human. It’s AI + human. One brings infinite combinations. The other brings meaning. 💬 So here’s my question to you: When AI “creates,” do you see true creativity… or something brilliant yet hollow without us? #AI #Creativity #Innovation #HumanPlusAI