Nicolas BEHBAHANI

Nicolas BEHBAHANISep 6, 2026

713,564 employee AI prompts changed one question for me this week. Not: “How many people have we trained on AI?” But: “What can they actually do differently 60 days later?” That distinction stayed with me. Because across the research I explored this week, I kept seeing the same challenge: We measure training. But do we measure capability? We measure AI adoption. But do we measure value created? We measure jobs affected by AI. But do we ask what happens to the human capacity AI gives back? We measure days in the office. But do we measure whether the way people work is actually working? 👋 Happy Sunday everyone! Welcome to the 𝟭𝟳𝟬𝘁𝗵 𝗲𝗱𝗶𝘁𝗶𝗼𝗻  of Weekly People Research. If you missed one or several of my Daily HR Metrics videos this week, I've brought the six research stories together in one place: 6 studies. 6 practical HR metrics. One week of research in 6 minutes. 🤖 AI Learning 🧠 Human Ingenuity 👥 Leadership & Employee Experience 💼 AI, Jobs & Workforce Transformation ⚡ AI Productivity & Effectiveness 🏠 Work Flexibility Effectiveness Each started with a different piece of research. But together, they left me with one much bigger question: As AI gets better at doing the work, what should humans get better at doing? Judgment? Creativity? Empathy? Leadership? Domain expertise? Or something we aren't talking about enough yet? In the full edition, I've brought together the research, the numbers worth remembering, the six practical HR metrics, my take on each and: ☕ 3 questions to take into Monday morning. The 170th edition of Weekly People Research is below 👇 6 HR Studies You May Have Missed This Week: AI, Jobs, Human Skills, Leadership & WFH And before you read it, I'm curious: If you could put ONE new people metric on your CEO's dashboard tomorrow morning, what would you want it to reveal that your current HR dashboard doesn't? I'd love to read your ideas. 👇 Happy Sunday and take care! ☕️ Dave Ulrich #PeopleAnalytics #FutureOfWork #HumanIngenuity #HR

Nicolas BEHBAHANISep 7, 2026

259 senior HR leaders were asked about their biggest concerns. And one challenge stood out: 💡 Leadership readiness. That caught my attention. Because organizations are investing heavily in AI. But technology can move much faster than leadership capability. We measure: 🎓 Leadership training completed 📚 Development programs attended ⭐ Potential ratings 🔄 Succession coverage But there's another question I'd want on the CHRO dashboard: Are our leaders actually ready to lead the organization we're becoming? New HRO Today research across North America, EMEA and APAC shows the pressure HR leaders are facing. ✅ 52% are concerned about the availability of skilled workers. ✅ 51% report limited or no progress on upskilling. ✅ 80% are extremely or very concerned about HR burnout.  And more than three-quarters of organizations are already using AI.  So today's Daily HR Metric is: 📊 Leadership Future-Readiness Rate (LFRR) Leaders demonstrating the critical capabilities required for future business needs ÷ Leaders assessed × 100 But I don't want to stop at the metric. If I were discussing this with an HR leadership team, I would ask three things: 1️⃣ READY FOR WHAT? Define the capabilities your future business strategy requires not another generic leadership competency model. 2️⃣ WHERE IS THE RISK? Don't only look at the company average. Find where leadership capability gaps intersect with your most critical roles, businesses and succession pipelines. 3️⃣ WHAT DECISION WILL WE MAKE? Development? Coaching? Mobility? Succession? External hiring? Because I increasingly believe: 👌 HR earns influence not by putting another metric on the dashboard, but by helping the business make a better decision because of it. And I'd really like your perspective: What ONE capability would you add to your organization's leadership model today? Judgment? AI fluency? Empathy? Adaptability? Something else? Dave Ulrich 👇 #DailyHRMetrics #Leadership #FutureOfWork

Nicolas BEHBAHANISep 4, 2026

AI is supposed to save us time. So why are workers spending 20 workdays a year trying to make it work? That's the number that caught my attention in new research from BambooHR. They surveyed more than 1,600 full-time US workers about how they're actually using AI at work. Employees reported spending: ⏱️ 87 minutes per day with AI 📅 Equivalent to roughly 47 workdays per year But here's where it gets interesting. 42% of that AI time goes to troubleshooting errors and iterating prompts. Only 35% goes to productive work. That translates into roughly 20 full workdays per year troubleshooting AI. And this makes me question one of the metrics we're using to measure AI transformation: Adoption. ➡️ How many employees are using AI? ➡️ How frequently? ➡️ How many licenses? ➡️ How many prompts? Useful information. But... AI usage ≠ AI productivity. That's why today's #DailyHRMetrics is: AI Efficiency Rate (AER): Productive time using AI ÷ Total time spent using AI × 100 If employees spend 100 hours using AI but only 60 hours actually move their work forward: AER = 60% AER is my proposed management metric inspired by this research — not a BambooHR metric. Because the question for leaders is rapidly changing. Not: “Are our people using AI?” But: “Is AI actually giving time back to our people?” That's the metric I'd want to see on the dashboard. What are you measuring today: AI adoption or actual AI value? Dave Ulrich #AIAtWork #Productivity