The word chutzpa isn’t common parlance these days. But that attitude is what I witnessed Andy Beatman from Microsoft express in a live demo with copilot in front of ~ 100 participants. His informal presentation reminded me of a simple overlooked truth. Our attitudes shape intentions, and subsequently shape actions.

Sometimes seeing is believing. Today, hearing AI is enough.

Pulling his phone from his pocket, Andy Beatman from Microsoft casually opened a conversation with his associate. “Hey Chat, can you help me summarize some handwritten post-it data from the live audience I’m with here in Chicago and tell me the top and bottom five occupations they’ve listed?”

Just like that—no fanfare, no elaborate setup—Andy demonstrated what boldness looks like in the age of AI. His intention was simple but profound: challenge our beliefs about what’s possible when we stop overthinking and start acting.

The Real Fear Behind Executive Paralysis

The financial incentives for E&Y and Microsoft to host these community conversations and nudge the social norm is clear. But merely sharing interesting insights related to their ongoing research studies of predictive work transformations don’t relieve the rising expense of these tools development.

Andy set the table well, giving the audience a simple experience. He let us listen. Hear what copilot can really do, if only you ask.

When we push boundaries or openly challenge assumptions we open our eyes to opportunity, that’s the power of chutzpa. The boundary is a social construct that fits a situation, circumstance, or present social environment norms.

It’s clear that people seem ok seeing AI as an assistant who does help them get a task done faster. But it stops there. Their limited desire suppresses action and also denies results. Andy asked when will our collective experiences trying it, turn us from bearish to bullish?

In Behavioral psychology this reluctant behavior has a name: the desire action gap. It manifests when we feel there are too many steps between what you want to do and actually doing it. It undermines our sense of readiness, and natural resourcefulness making us feel more vulnerable and less resilient.

In the breakout group of financial professionals, I heard the timidity at the enterprise level. Individually, there are small strides being made with AI, but personal productivity is the small stuff. Only one person was leading an AI transformation at the enterprise level, reducing cash flow reconciliation from 30 days to one.

It led me to ask whether the organizational structures, the role and functional boxes we create, make it hard for people to soar, and deliver breakthrough performance. Their responses revealed something far more complex than typical change resistance. These venture capital partners and CFOs finally voiced fears that went beyond surface-level risk aversion.

These weren’t technophobes or laggards. They articulated real fears about financial data and internal performance analysis that contains secrets they cannot afford to have inadvertently exposed. Regulatory risks loom large. Competitive advantages must be preserved. They can’t trust even the most secure “walled gardens” to remain un-breached. To this audience, AI represents that genie in a bottle that won’t return once it’s had its freedom.

But their fear runs deeper than data security. They’re genuinely flummoxed about how to bridge the gap between individual productivity wins and enterprise transformation. They see employees getting small personal gains with AI, but those victories aren’t converting to bottom-line impact.

Here’s the paradox they’re missing: their fear of AI exposure is actually exposing them to competitive risk.

The Readiness Problem Masquerading as Risk Management

You’ve made the investment. According to Ernst & Young’s 2024 research, 88% of senior leaders now dedicate 5% or more of their total budget toward AI investments. This marks a dramatic shift from just three years ago when half spent less than 5%.¹ Microsoft’s Work Trend Index shows similar momentum across industries.²

But here’s what the data also reveals: the productivity paradox exists not because AI doesn’t work, but because organizations aren’t ready for it to work at scale. Data integrity problems plague implementation. Failures of imagination limit scope. The infrastructure and mindset required for AI transformation simply don’t exist in most enterprises.

You’ve bought a racehorse, but you’re trying to race it on a dirt road instead of building the track it needs to run.

The executives who do see success aren’t just investing more. They’re restructuring how work gets done.³ They let AI do the heavy lifting to find budget efficiencies and suggest organizational redesign. They achieve end-to-end digital transformation, not just optimization of existing processes within traditional budget lines.

Evaluating vs. Leading: The Critical Distinction

This is where most executives get stuck. They manage within existing budget lines instead of allowing AI to reveal entirely new ways of structuring work and resources. They evaluate AI’s performance against current workflows instead of leading their organizations toward AI-native operations.

The small wins remain isolated victories. An employee uses ChatGPT to draft emails faster. A team summarizes reports more efficiently. These improvements never scale because leaders proceed incrementally and don’t pursue the transformational. They ask “How can AI make us 10% more efficient?” instead of “How can AI help us reimagine what’s possible?”

According to MIT research, 95% of AI initiatives fail to scale precisely because of this evaluation-versus-leadership gap.⁴ Organizations remain stuck in perpetual pilot mode, never bridging from individual productivity to systemic transformation.

Meanwhile, the successful minority achieves scaled success by making a fundamental shift.⁴ They stop trying to fit AI into their current organizational structure and start designing new structures around AI’s capabilities.

The Desire-Action Gap at Scale

Not many people are standing inside the problem and looking out to find a path to carry them to where they want to go, even if the idea is to escape. Many people inside organizations feel the same reluctance to move. At the enterprise level, those steps include data readiness, organizational change management, and the courage to fundamentally restructure how business operates.

The Theory of Planned Behavior tells us that attitudes shape intentions, which subsequently shape actions. Right now, your attitude toward AI risk combined with organizational unreadiness shapes conservative intentions. This produces minimal actions and minimal results.

Your competitors who embrace systematic transformation, not just AI adoption, are pulling ahead.

The Chutzpah to Transform, Not Just Optimize

True leadership takes chutzpah. It demands the audacity to push beyond “polite” limits when the situation requires bold action. Right now, your situation demands transformation, not optimization.

Your careful approach to AI isn’t protecting you. It’s handicapping you. While you worry about data breaches, your competition gains structural advantages. While you conduct pilots within existing budget lines, they use AI to discover entirely new business models. While you analyze risk, they capture market share through AI-native operations.

The question isn’t whether you can afford to be bolder with AI. The question is whether you can afford to keep evaluating when you should be leading.

Design Your Transformation Lane

Stop waiting for your organization to be “ready.” Start making it ready.

Design internal challenges that don’t just test AI’s capabilities, but reveal new organizational possibilities. Create controlled environments where AI can suggest budget restructuring, workflow redesign, and resource reallocation—not just task optimization.

Let AI do what it does best: find patterns, suggest efficiencies, and reveal opportunities you didn’t know existed. Then have the chutzpah to act on what it discovers. The executives who will lead their industries tomorrow are the ones taking charge today. They’re not waiting for AI to be risk-free; they’re building AI-ready organizations. They’re not hoping for transformation; they’re engineering it systematically.

The transformation lane exists, and the question is whether you have the chutzpah to build the infrastructure it requires and the leadership courage to drive in it.

For further Reference:

¹ Ernst & Young, “Enterprise AI adoption key to corporate growth,” 2024 AI Investment Survey

² Microsoft Work Trend Index, “AI at Work Is Here. Now Comes the Hard Part,” 2024

³ Ernst & Young, “Five AI adoption strategies survey,” 2024 – Organizations with larger AI investments showing “higher rates of positive return across dimensions surveyed”

⁴ MIT research on AI pilot scaling success rates; Microsoft Copilot productivity research and occupation analysis (https://arxiv.org/pdf/2507.07935)

Where are needs apparent in the sequence? The words alone suggest awareness but the behaviors that move us to act are largely unconscious, buried.

Design choices that people make–the red one versus the brown polkadots, are not magic as much as married to trust. Clean lines, polished, modern styles lend an air of credibility, legitimacy.