Preparing For AI Disruption In The Workforce

AI Adoption is Stalling in Your Org—Here’s Why (And How to Fix It) AI isn’t the future. It’s now. And yet, in too many organizations, ambitious AI initiatives hit an invisible wall—cultural stall. A client of mine, a fast-moving, high-change-tolerance exec, recently found himself in this very situation. He saw AI as a catalyst for transformation. His company? More like a fortress of tradition. The result? A slow crawl instead of a sprint. So, why do even the smartest AI strategies grind to a halt? Three core reasons: 1. Fear: “Will AI Replace Me?” AI doesn’t just change workflows—it challenges identity. Employees fear obsolescence. Leaders fear looking uninformed. Unchecked, fear turns into passive resistance. 🔹 What smart leaders do: Flip the narrative. AI isn’t a job taker; it’s a value amplifier. Show—not tell—how AI makes work more strategic, not less human. Make AI upskilling a leadership priority, so people feel empowered, not endangered. 2. The Status Quo Stranglehold Big companies have institutional memory. “This is how we’ve always done it” isn’t just a mindset—it’s a roadblock. AI disrupts deeply ingrained habits, and people default to what’s familiar. 🔹 What smart leaders do: Instead of forcing AI as a hard pivot, position it as an acceleration of what already works. Connect AI adoption to existing business priorities, not as a standalone experiment. Find internal champions—people with credibility who can shift the narrative from the inside. 3. No Quick Wins = No Buy-In AI often feels abstract—too complex, too long-term, too risky. If employees can’t see immediate benefits, skepticism spreads. 🔹 What smart leaders do: Deploy fast, visible wins. Start with low-friction, high-value applications (automating reports, enhancing decision-making). Make results tangible and celebrated. Small victories create momentum—and momentum is everything. Bottom Line? AI Adoption Is a Mindset Shift, Not Just a Tech Shift. Your strategy isn’t enough. Your culture has to move at the same speed. The leaders who win with AI aren’t just tech adopters—they’re behavior shapers. So, if your AI initiative is stalling, ask yourself: Are you implementing AI, or are you leading AI adoption? The latter makes all the difference. 🔹 In my next post, I’ll share real-world success strategies from leaders who’ve cracked the code on AI adoption—so their teams aren’t just accepting AI, but accelerating with it. Stay tuned.

Companies aren't telling you they're replacing jobs with AI. And that's your biggest opportunity right now. While headlines focus on the 41% of companies planning AI-driven workforce reductions, there's a fascinating pattern emerging: Organizations are hiding their AI adoption behind terms like "reorganization" and "optimization." Here's the counterintuitive truth: This corporate silence is your early warning system. Three ways to leverage this moment: 𝟭. **𝗥𝗲𝗮𝗱 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲 𝗟𝗶𝗻𝗲𝘀** When a company announces "operational efficiency" with healthy profits, that's your signal. They're likely testing AI integration. Study these moves - they're showing you exactly where to position yourself. 𝟮. **𝗧𝗮𝗿𝗴𝗲𝘁 𝘁𝗵𝗲 𝟭𝟬% 𝗚𝗮𝗽** Companies are discovering AI can handle 90% of certain tasks, but that critical 10% still needs human expertise. This is your sweet spot. While others fear replacement, position yourself as the essential human element that makes AI solutions work. 𝟯. **𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗔𝗜-𝗛𝘂𝗺𝗮𝗻 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼** Don't just learn to use AI - learn to fill its gaps. Focus on: - Strategic decision-making - Complex problem-solving - Stakeholder management - AI output quality control The real opportunity isn't in competing with AI - it's in becoming the professional who knows how to make AI truly valuable to an organization. Remember: By the time companies start being transparent about AI adoption, the early advantage will be gone. What signals are you seeing in your industry? #careeralchemy #AI #innovation #careers #creativity

Over the past few months, I’ve had the privilege of working with an extraordinary group of AI leaders on a new white paper: “Proactively Developing and Assisting the Workforce in the Age of AI," recently published by University of Notre Dame - Keough School of Global Affairs and Americans for Responsible Innovation. While there's still much we don't yet know about AI’s impact on work, our paper offers recommendations and questions to consider—grounded in the data and evidence we have—to help workers and organizations prepare, including: 🧩 Reimagine work at the task level Jobs aren’t monoliths—they’re bundles of tasks and skills. AI will automate some, augment others, and leave some distinctly human. Leaders who approach job transformation as an exercise in unbundling and rebundling jobs can move past blunt job cuts and more intelligently design future roles. 🏗️ Treat AI training as infrastructure, not overhead Companies invest heavily in AI systems but underinvest in the people who use them. Training should be treated as infrastructure, no matter how it’s classified on a balance sheet. Tools like digital skill wallets are tough to scale, but AI can help dynamically map, match, and "stack" skills as roles evolve. 🤝 Integrate dignity into AI adoption AI is stress-testing the social contract at work: the expectation that people will be treated fairly, respected, and share in the benefits of progress. Several warning signs are already here: Workers turning to "shadow AI," pocketing time savings rather than reallocating it back to their organization, and staging “AI productivity theater” to climb dashboards built on superficial usage metrics. Dignity isn’t just the right thing to do. It’s the foundation for lasting adoption and real value, not short-lived theatrics. I'm honored to be headed to Washington, D.C. in a couple of weeks with a few of my co-authors to share our findings with policy members. Link to the paper—co-authored by—Yong Suk Lee, John Babak Soroushian, Justin Bullock, Michaela Carroll, Jane Dokko, Jacob Dominski, Harry Holzer, Mike Horrigan, Zanele Munyikwa, Matthias Oschinski, Courtney Radsch, PhD, Daniel Rock, Maria Rossi, Rob Seamans, Alexandra M. Towns, PhD, and Baobao Zhang—in the comments👇. If you're in D.C., please join us for a live session on the findings (link also 👇)