73% of C-suite executives rate themselves as “highly effective” at managing technological change. Yet 68% of their AI projects miss targets or get abandoned entirely. The disconnect isn’t competence—it’s a fundamental misunderstanding of what leadership means in an age of exponential change.
Most executives are optimizing for predictable outcomes when AI success requires rapid iteration.
Through my work across Asia Pacific, I’ve seen this pattern repeat: high-performing leaders who built careers on analytical rigor and strategic planning suddenly find their greatest strengths becoming liabilities. The very skills that made them successful—deep analysis, risk mitigation, consensus building—are now slowing them down when speed and adaptability matter most.
The Control Paradox That’s Crushing Results
Traditional executive training teaches control through prediction. MBA programs celebrate leaders who minimize uncertainty through thorough analysis. Performance reviews reward those who hit predetermined targets with minimal variance.
But modern technology operates on different rules. AI evolves faster than strategic planning cycles. Customer needs shift before market research completes. Competitive advantages emerge and disappear within quarters, not years.
Consider Blockbuster’s leadership in 2007. They had the data showing streaming was growing. They had the resources to compete. What they lacked was comfort with cannibalizing their existing business model fast enough. By the time they built consensus around digital transformation, Netflix had already captured the market.
Today’s equivalent? Financial services executives who spend eighteen months evaluating AI chatbot vendors while fintech startups launch working solutions in six weeks and steal their customers.
The Zoom Revelation: When Crisis Forces Clarity
The Covid-19 pandemic revealed something crucial about leadership effectiveness. Companies that thrived weren’t those with the best pre-2020 strategic plans—they were those whose leaders could abandon those plans fastest.
Eric Yuan at Zoom demonstrated this approach. When COVID hit and usage exploded 30x in three months, Yuan didn’t convene strategy committees or commission studies. He made fifteen major product decisions in ninety days, many without complete data. Because of how rapidly it adapted, Zoom’s valuation grew from $16 billion to $158 billion in twelve months.
The lesson isn’t about pandemic response—it’s about decision-making velocity under uncertainty. Yuan succeeded because he optimized for learning speed over planning depth.
Why Your Best People Are Your Biggest Problem
Here’s what I’ve observed repeatedly: the most experienced executives often struggle most with AI implementation. Their deep domain expertise becomes a trap, making them over-analyze decisions that require rapid experimentation.
Healthcare provides a telling example. Research shows that physician resistance to AI diagnostic tools often stems from their extensive medical training and experience. Swedish healthcare leaders noted that their deep understanding of existing clinical protocols made them overly cautious about AI implementation, seeking comprehensive validation that delayed adoption by years. Meanwhile, healthcare systems in developing countries with less established infrastructure deployed basic AI diagnostic tools faster and began improving patient outcomes sooner.
The paradox: expertise can reduce learning velocity when the environment is changing faster than expertise can adapt.
Three Characteristics of AI-Ready Leadership
The executives succeeding with AI share distinct approaches that separate them from traditional high performers:
1. Temporal flexibility over temporal control.
Instead of managing to fixed timelines, AI-ready leaders adjust timeframes based on learning velocity. If an AI pilot reveals unexpected insights in week three, they accelerate decisions rather than waiting for the planned eight-week evaluation period.
2. Outcome consistency over process consistency.
AI-ready leaders maintain clear goals while varying approaches radically. If customer satisfaction is the target, they’ll switch from rule-based systems to machine learning to human-AI collaboration based on results, not preferences.
3. Ecosystem thinking over organizational thinking.
AI-ready leaders recognize that AI advantage comes from external partnerships, open-source tools, and vendor relationships rather than internal development alone. Building everything in-house isn’t control—it’s competitive suicide.
The Singapore Airlines Model: Systematic Experimentation
Singapore Airlines CEO Goh Choon Phong demonstrates how traditional industries can adapt without abandoning operational excellence. Instead of transforming everything simultaneously, SIA runs systematic AI experiments across different customer touchpoints.
They test AI-powered personalization on specific routes, automated customer service for particular request types, and predictive maintenance on selected aircraft. Each experiment has clear success metrics and predetermined expansion criteria. The key insight: systematic experimentation isn’t less rigorous than comprehensive planning—it’s more rigorous because it generates real data rather than theoretical projections.
Your AI-Leadership Stress Test
If you’re ready to evaluate your own AI readiness, ask yourself these questions:
- When did you last make a significant decision with less than complete information? If the answer is “never” or “rarely,” you may be optimizing for the wrong skills.
- How often do you change course based on new data that contradicts your initial assumptions? Leaders comfortable with AI pivot regularly without seeing it as failure.
- What percentage of your strategic initiatives produce unexpected outcomes? If everything goes according to plan, you may not be experimenting enough for an AI environment.
The Choice Every Executive Faces Now
The AI-leadership skills that built your career may not be the ones that sustain it. AI demands a fundamental shift from controlling outcomes to optimizing learning velocity.
This isn’t about abandoning rigor—it’s about applying rigor to different priorities. Instead of perfecting plans, perfect your ability to learn and adapt faster than uncertainty can accumulate.
Executives who make this transition will define the next decade of business advantage. Those who don’t will spend it explaining why their perfect strategies didn’t work in an imperfect world.