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The AI Leadership Mandate

To thrive, organizations must become human centered.

Corporate leaders are facing a convergence of forces exposing serious weaknesses in their artificial-intelligence strategy. Employees are losing trust in leadership, returns on AI investment are uncertain, and succession pipelines are fragile. There’s also mounting pressure to transform organizations faster than many workers are prepared to adapt to the advancing technology.

So perhaps it’s not surprising that nearly 30 percent of employees actively sabotage their employer’s AI strategy, according to the AI company WRITER. Their survey with research firm Workplace Intelligence of 1,200 C-suite executives and 1,200 employees reveals that among Gen Z workers, that number rises to 44 percent. The findings suggest that the resistance may stem less from AI itself than from employees’ concerns about how their organizations are implementing the technology and the implications for their work.

This is not a worker attitude problem. It is an urgent crisis, and addressing it requires a transformation in leadership, one that uses AI to upskill human capability, is empathetic and human centered, and strengthens trust, engagement, innovation, and collaboration. Leadership systems need to evolve fast enough to implement this transformation responsibly and sustainably, but they need to do so while driving long-term performance.

A matter of trust

According to Gallup’s 2026 State of the Global Workplace report, global worker engagement has fallen to a 10-year low even as organizations accelerate AI adoption. AI is already delivering meaningful gains in efficiency, accessibility, research, and decision support in certain industries, companies, and individual task-based use cases. Yet a study by Boston Consulting Group suggests that upwards of 60 percent of companies had seen minimal financial gains from AI investments as of last year, while only 5 percent had seen substantial value creation. A McKinsey report indicates that many workers feel they’ve had insufficient AI training and that 86 percent of leaders feel that their organizations are unprepared for AI integration.

The result is a growing deficit in which only a fifth of people trust their leadership (according to Gallup), yet CEOs don’t see employee engagement as a top priority (per EY). That disconnect is difficult to ignore. Without engaged employees, it becomes harder to sustain productivity, retain customers, innovate, grow, and even cut costs.

Nevertheless, organizations continue pushing AI deeper into operations, strategy, and workforce structures.

History shows that large transformations—such as a reengineering or major tech rollout—often fail due to leadership gaps. Poor communication, weak change management, and underinvestment in people undermine results. When leaders focus on short-term gains, silence dissent, and ignore long-term consequences, quick wins rarely last.

The leadership perception gap

A survey finds that leaders consistently gave themselves higher marks than their direct reports gave them. This disconnect could make AI integration more difficult.

Leaders are operating under extraordinary competitive pressure. They’re balancing fiduciary responsibilities with investor demands, global competition, margin requirements, and expectations from boards, shareholders, and markets to adopt new conditions quickly or risk falling behind. In some ways, the pattern is strikingly similar to what we saw in the technology boom of the 1990s and during the 2007–09 financial crisis. Now, the biggest issue for leaders is not simply how fast they can implement AI but how to evolve their organization, culture, and leadership.

The toll of ‘quiet cracking’

AI is now embedded across nearly every dimension of work and daily life. Just three years ago, about 20 percent of companies had adopted enterprise AI. Today, that figure exceeds 90 percent.

Yet many human systems—the environments in which people are working—are deteriorating. Employees are experiencing a gradual, often invisible erosion of engagement, performance, and trust. They continue to show up for work, but their motivation and purpose are increasingly absent. The Gallup report points to rising disengagement, emotional withdrawal, and frustration, and a Harris poll from May finds that 60 percent of US workers reported having a toxic boss. Moreover, a different Gallup survey indicates that the gap between how leaders rated themselves and how their employees perceived them was significant.

Broader indicators suggest growing strain. The suicide rate among working-age adults has increased significantly over the past two decades. Financial stress and disengagement place significant burdens on individuals but also on organizations, driving lost productivity and higher turnover.

These conditions are contributing to what has been described as “quiet cracking,” coined by Frank Giampietro, the chief wellbeing officer at EY Americas. This is different than “quiet quitting,” which describes a deliberate choice by an employee to scale back their effort, usually based on frustration or disengagement. Quiet cracking, by contrast, refers to employees who feel trapped in jobs in which they aren’t engaged yet keep powering through despite stress, anxiety, and exhaustion. This creates deep “cracks” under the surface.

The leadership systems problem

The rush to implement AI is happening before many organizations have built the workforce readiness and leadership capability required to use the technology responsibly and effectively.

As disengagement deepens, trust erodes and tensions grow. For example, former Google CEO Eric Schmidt faced intense backlash in May at a University of Arizona commencement address for suggesting that AI could eventually replace swathes of programming and engineering workers. The reaction exposed anxiety about how organizations are pursuing AI without adequately addressing human consequences.

The organizations most likely to succeed combine technological fluency with emotional intelligence.

Meanwhile, following AI-driven restructuring and layoffs at Atlassian, one of the software giant’s former engineers publicly documented the highly specialized infrastructure systems he had spent years building. The act revealed the fragile nature of the relationship between company and employee and the dependency of Atlassian (and likely many other organizations) on human intellectual capability that is often undervalued during periods of rapid change.

The debate over AI is not confined to companies. The State of Florida sued OpenAI and its CEO Sam Altman in June, alleging that its chatbot ChatGPT poses significant public harms. Whether that claim ultimately succeeds is less important than what they represent—concern about the consequences of deploying powerful technologies faster than institutions are prepared to govern them.

While human capital is the largest operating expense at many companies, most generate remarkably low returns on their investment in people. Decades of research on organizational performance suggest that employees contribute only a fraction of their creativity, judgment, insight, and problem-solving capacity inside traditional hierarchical systems.

This may be one of the most expensive forms of institutional waste, especially in the era of AI. One executive told McKinsey in 2024 that organizations seeking meaningful AI returns may need to spend as much as $5 on people (through training and organizational adaptation) for every $1 invested in technology. The leadership lesson is clear: Sustainable AI transformation depends less on technology alone than on the ability to maximize human capability alongside it.

The organizations generating the greatest long-term advantage will be those maximizing return on brainpower in partnership with AI and creating environments where judgment, creativity, adaptability, learning, and collaboration thrive alongside technological capability.

A new leadership playbook

Organizations must build environments where people, principles, purpose, and performance matter. Here’s how to create leadership that encourages this outcome:

Upskill human capability alongside AI

Technical AI training is important, but employees also need the ability to work with AI while strengthening the uniquely human capabilities technology cannot replace, including judgment, communication, ethics, creativity, systems thinking, empathy, and interdisciplinary collaboration.

Maximize returns on brainpower

High-performing institutions maximize returns on brainpower through the environments they create. To do the same, hire smart people, and build systems where they can learn and contribute. Give them autonomy and flexibility while also maintaining accountability. Leaders must lean into their own brainpower too. Employees lose trust when leaders use AI carelessly or without transparency. In the AI era, credibility depends not just on adopting AI early, but on visibly demonstrating informed, disciplined, human-centered use.

Normalize recognition, experimentation, and learning

Innovative organizations have a foundation of trust. Leaders should publicly recognize excellence, encourage experimentation, reward thoughtful risk-taking, and create environments where learning matters more than image management.

Create purpose-driven accountability

The best goals stretch people without overwhelming them, creating focus, energy, meaning, and shared ownership. Purpose-driven accountability creates commitment rather than compliance.

Lead with vulnerability and intellectual humility

High-trust leaders acknowledge uncertainty, ask for help, communicate honestly, and welcome dissenting perspectives rather than punishing them. They don’t pretend to have all the answers.

Nvidia offers a model for this. Under CEO Jensen Huang, the tech company has paired aggressive AI innovation with a culture that emphasizes the skills that only humans bring to the table. No company is perfect, but Nvidia is leading the way in developing its capacity to learn and evolve amid change.

Will companies view not only creativity but also other distinctly human capabilities as expendable infrastructure or as the foundation of sustainable innovation? In legacy systems, employees often learn that survival means staying quiet, avoiding risk, managing optics, and fitting in, but the limitations of that model are becoming hard to ignore.

Across leadership forecasts, organizational research, executive trend reports, and workforce studies, a pattern emerges. The organizations most likely to succeed combine technological fluency with emotional intelligence. Many of the capabilities most associated with long-term company resilience remain profoundly human, including empathy, authenticity, imagination, collaboration, and the ability to work effectively across generations. Co-intelligence—where humans and AI work together—will become a defining characteristic of high-performing organizations.

If implemented wisely, AI has the potential to reorganize low-value work, accelerate learning, expand human creativity, and allow people to focus more deeply on the uniquely human dimensions of leadership and innovation.

The next generation of leadership has both an opportunity and a responsibility. We can create organizations where innovation and well-being coexist, trust and accountability reinforce one another, and long-term stewardship matters as much as short-term performance. The future of work will not be won by organizations that automate the fastest. It will be won by organizations that humanize the best.

Susan Lucia Annunzio is president and CEO of the Center for High Performance, a leadership consulting firm. She is also adjunct associate professor of executive education at Chicago Booth. Nicole Yelsey is a cofounder of KindWorks.AI and a strategic advisor to companies. Tim Seabrook is cofounder of KindWorks.AI and CTO of a stealth AI startup. They all coteach High Performance Leadership, an executive education course at Booth.

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