Editorial
White Paper
The Future of Leadership Development: AI-Driven Manager Enablement
A common concern I hear from CHROs, CFOs and other executives lately is that their CEO or founder is falling prey to Twitter / X FOMO. By “Twitter FOMO,” I mean the pressure to act on every AI trend as it happens. A founder posts about becoming “AI-first.” A company announces a headcount reduction tied to automation. A tech CEO predicts the end of entire job categories. A viral thread claims every employee should be measured on AI usage. Within hours, the executive team is asked, “Why aren’t we doing this?”
The pressure is understandable. BCG’s 2026 AI Radar found that companies expect to double their AI spending this year, from 0.8% to roughly 1.7% of revenue, and that half of CEOs believe their job stability depends on getting AI strategy right. No CEO wants to be the leader who waited too long. But the answer to that anxiety cannot be copying what others do.
I am not saying CEOs should ignore AI thought leaders. They should absolutely be paying attention. The problem is when external narratives become internal mandates before they have passed through the filter of strategy, evidence, context and values.
The Narrative Economy Around AI
Executive AI literacy now entails not just understanding the technology but also the narrative economy around it. Founder interviews, investor presentations, product launches, viral demos, layoff announcements, productivity claims and AGI timelines are all public signals produced inside a market. Sometimes they reflect genuine learning, and sometimes they are shaped by capital raise cycles, customer pressure, regulatory scrutiny, recruiting needs, competitive positioning or personal brand. These signals can be useful, but they can also be dangerous if treated as gospel.
Consider the job loss narrative. For years, OpenAI CEO Sam Altman made stark comments about AI’s likely impact on jobs. Time recently noted that he had said AI would “probably replace most of the jobs people do today” and that entire job categories would be “totally, totally gone.” In May, Altman said he was “delighted to be wrong” about the feared jobs apocalypse and that his intuition about entry level white collar job losses had been off.
Maybe that was an honest learning. Maybe it is also strategically convenient as OpenAI moves toward the scrutiny that comes with public markets. I don’t claim to know the motive, but public statements from frontier AI leaders should not become workforce strategy. Their incentives are not your incentives.
The Announcement Is Not the Whole Story
The same issue shows up in company announcements. Klarna’s AI customer service story was widely cited because it was so concrete. In 2024, the company said its AI assistant was doing the equivalent work of 700 full-time agents and handling two-thirds of customer service chats. A year later, coverage focused on Klarna reinvesting in human customer service and using AI as a supplement, not a full replacement, for staff.
In another version of the same lesson, Duolingo had positioned AI use as part of its “AI-first” strategy, including evaluating AI usage in performance reviews. Then, CEO Luis von Ahn said the company had backtracked because employees feared being pushed to “use AI for AI’s sake.” He clarified that Duolingo valued job performance over AI usage.
These reversals aren't proof of AI failure, but they do illustrate that the first announcement was not the whole story. The splashy announcement travels farther than the correction. The “we replaced humans with AI” story gets shared in board decks. The follow-up nuance, that quality suffered, customers wanted humans, employees misunderstood the incentive or the metric created the wrong behavior, gets treated as a footnote.
The announcement is the press release, and the reversal is typically a niche follow-up story. The problem with AI news is that too often, leaders are only being shown the press release.
Copy the Question, Not the Conclusion
That does not mean CEOs should tune out Twitter, LinkedIn, podcasts or industry commentary. They do, however, need a better filter. Sometimes the herd is right, and history is littered with examples of companies that did not transform themselves fast enough. The discipline is to copy the question, not the conclusion.
If a company announces an AI-related reduction in force, instead of jumping to, “Should we reduce headcount too?” CEOs should be asking, “Where is AI changing our unit economics, cycle times, service model or work design?”
If a company starts evaluating AI usage, the next step is not, “We should add AI usage to performance reviews.” Instead, executives should be asking themselves, “What behaviors and outcomes would prove our people are using AI effectively?”
This is where CHROs can play a critical role as the executive who helps the organization separate urgency from imitation. AI discourse creates “borrowed urgency,” i.e., the pressure to act because someone else’s context, incentive structure or announcement has made inaction feel irresponsible. Sometimes that urgency is useful, but often, it needs to be translated before it is acted on. That discipline should cut both ways. “Our context is different” can be a thoughtful strategic objection, but it can also become HR’s excuse for avoiding hard change. The goal is not to protect the status quo but to make better bets.
The AI Signal Filter
A practical starting point is what I call the AI Signal Filter. Before turning an external AI story into an internal mandate, leadership teams should ask the following questions:
- Strategy: What business problem are we trying to solve?
- Value: How will this create measurable value for customers, employees or the enterprise?
- Context: What was true about the company we are copying that may not be true about us?
- Evidence: Is this a proven practice, an experiment, a PR narrative or an investor signal?
- Incentives: Who benefits if we believe this story?
- Operating model: What work, workflows, roles, decision rights and capabilities need to change?
- Values: Would we still be proud of this decision if it became public?
- Reversibility: Is this a low-risk experiment or a high-stakes organizational bet?
The reversibility question may be the most important one. AI transformation cannot be reduced to the mood of the market. One month, the conversation is about replacing workers. The next, it is about preserving the human part of work. One month, companies celebrate token consumption. The next, they confront the cost of usage and the limits of activity metrics.
Engaged, But Not Convinced: What's Eroding Beneath Your Strongest Engagement Numbers
Watch Now
Retention Is a Communication Problem: New Data from 6,000+ Employees
Watch Now
CEOs and boards are right to feel urgency, but acting because something makes sense for your business is different from taking action because a thought leader you respect posted about it on social media. The most effective leaders understand that this moment is about translating, not ignoring, external signals. They study the market without being governed by it, and they experiment without outsourcing judgment. They ask what others are learning, but they make decisions based on their own strategy, capabilities, customers, values and risk tolerance. They pay attention to the news cycle, including the sensational headlines, but they consistently come back to first principles when running their businesses. In an era of AI FOMO, that may be one of the most important leadership disciplines of all.
Editor's Note: How else can leaders align AI potential with their workplace reality?
- 5 Questions Every Leader Should Ask Before Building AI Solutions — AI isn’t the enemy — or the magic fix. Most failures come from leaders skipping the hard questions. Here are 5 that separate hype from real impact.
- An AI Roadmap for the Next 5 Years — Developing an AI business plan with clear, practical goals for the one, three and five-year horizons is the key to delivering real-world business results.
- From AI Ambition to ROI: Where Leaders Are Getting Stuck — Most companies are experimenting with AI, yet few are profiting from it. The gap isn't about technology. It's about how humans lead and decide.
Learn how you can join our contributor community.