From Greenwashing to Groundwork: Rediscovering Quality in the Age of AI

For three decades, I watched the tech sector wrestle with its soul. As a data leader, I observed firsthand how companies navigated the tension between growth imperatives and human values. The pattern became predictable: Surface-level Corporate Social Responsibility (CSR) initiatives would come and go like seasonal fashion, while HR departments split their identity between compliance enforcers and well-meaning but ultimately performative development programs.

The trends in my generation started with conscious capitalism, corporate social responsibility, sustainability, and DEI. Everyone knew the cycle—these initiatives would fade as soon as the next business strategy demanded attention. They were part of hype cycles. It was the corporate equivalent of fast fashion: trendy, disposable, and ultimately unsustainable.

The Quality Underground

But beneath this churning surface, a different current was flowing. Some companies chose a different path—one Bo Burlingham documented in Small Giants. [He probably doesn't remember me, but he was kind enough to indulge in a phone call that cemented my doctoral thesis on values-driven leaders.] These organizations embraced controlled growth, prioritized quality over scale, and built loyal followings through consistent values rather than flashy marketing campaigns. Speaking with Burlingham confirmed what I'd observed: quality, values-focused leadership never really went out of style; it just went underground.

For a brief moment, it looked like these values might surface in the mainstream. There were international guidelines for integrating metrics into a corporation's financial reporting. In popular culture, the rise of hipster publications like?Kinfolk and products like?TOMs took off. Those who had cashed out of the system?left their jobs?and wrote books like?Can't Not Do?(a great book, by the way), suggesting that a new generation had found a way to synthesize corporate mandates with authentic values. "See?" we could say, "This is how it's supposed to be done."

The Great Disruption

Then, the market transformed—not in a single moment, but through a cascade of crises that fundamentally altered how we think about leadership and success. The 2008 financial crisis first exposed the fragility of our systems. Just as organizations found their footing, the COVID-19 pandemic shattered our assumptions about workplace stability and organizational resilience. Political volatility and administrative changes added layers of uncertainty. Then came the AI revolution, with ChatGPT and its successors forcing us to rethink fundamental assumptions about knowledge work and human capabilities.

These compounding disruptions exposed something significant: the traditional balance between IQ (technical excellence) and EQ (emotional intelligence) we'd carefully cultivated was no longer sufficient. While technical skills remained essential and emotional intelligence crucial, a new capability emerged as paramount: AQ (adaptability quotient)—the ability to not just weather change but to thrive in it.

This shift wasn't just about adding another skill to the leadership toolkit. Rather, it represented a fundamental transformation in how we think about organizational capability:

• IQ (Technical Excellence) became less about knowing everything and more about knowing how to learn continuously
• EQ (Emotional Intelligence) evolved from managing stable relationships to navigating constant uncertainty and anxiety
• AQ (Adaptability Quotient) emerged as the multiplier that makes both technical skills and emotional intelligence effective in rapid change

Working with both leadership groups and data teams during this period of upheaval, I observed how these three quotients played out in real-time. As explored in Driving Self Discovery, organizations that survived and thrived weren't just technically proficient or emotionally aware—they had developed what I call "safe enough" environments that enabled sustainable transformation. The most successful teams balanced all three quotients: the technical rigor to execute, the emotional intelligence to collaborate, and the adaptability to evolve.

As AI accelerated the pace of change, adaptation became the primary currency of business success. But this wasn't the superficial adaptability of following trends—the deep adaptability comes from being grounded in fundamentals and living clear values. The organizations that thrived weren't necessarily those that moved fastest but those that could adapt while maintaining their core purpose and values, a principle that echoes through both Driving Data Projects and Driving Self Discovery.

This new reality demanded a different kind of leadership—one that could balance the stability needed for quality with the flexibility required for survival. This balance brings us back to the wisdom of quality-focused leaders who always understood that sustainable success requires strong foundations?and?the ability to evolve thoughtfully.

The Prophets Return

Yet, in this moment of disruption, something unexpected happened. The voices many had dismissed as out of touch— Gary Marcus with his insistence on AI's fundamental limitations, #EFSchumacher with his human-scale economics—suddenly seemed prophetic. Their frameworks, always valued by those in the quality space, offer blueprints for navigating this new landscape.

Why? Both thinkers understand that technical systems, whether AI algorithms or economic models, must be grounded in human values and community wisdom to be truly effective. Their work provides a foundation for those willing to do the hard work of being misunderstood, working alone or in smaller groups (without the "support" or distraction of the hype), and building systems that serve human needs rather than subordinating humans to system demands.

Conclusion: Digital Pioneers Bridge-Building with Giants

For me, Marcus, Schumacher, and Burlingham's wisdom helps us bridge our increasingly messy human landscape with our complex technological one. Their insights—Marcus's careful skepticism about AI capabilities, Schumacher's insistence on human-scale economics, and Burlingham's documentation of quality-focused leadership—show us how to connect tradition with transformation and technical ability with human capacity.

This heritage of thoughtful critique and practical wisdom guided my mid-career return to graduate school, where I began systematically exploring what makes data initiatives succeed or fail. What emerged, first in Driving Data Projects, was a framework that echoes these pioneers' insights: sustainable solutions require more than technical excellence—they demand the integration of human skills, organizational readiness, and ethical considerations. This work naturally led to Driving Self Discovery, which goes deeper into the human dimension of data transformation, exploring how our personal histories shape our professional responses to change. Understanding how different digital pioneers (or"data archetypes") approach transformation efforts can better bridge the gap between technical possibility and human potential. Just as Schumacher insisted that economics must serve human needs, today's data initiatives must balance technical capability with human capacity and take a broader view than just the bottom line.

The path forward requires bridging all three intelligences these pioneers implicitly advocated:

• The technical rigor (IQ) that Marcus champions
• The human understanding (EQ) that Schumacher emphasized
• The adaptive capacity (AQ) that Burlingham's Small Giants demonstrated (even though he didn't call it that)

We find ourselves in what could be called a "bridge-building moment," when the pressure for rapid AI adoption must be balanced with the enduring need for thoughtful implementation. The skills needed to build these bridges—from understanding AI's real capabilities to assessing organizational readiness to cultivating adaptability—are indeed learnable. But they require the same patience, architectural wisdom, and respect for human scale that our predecessors advocated.

In this light, data transformation becomes an engineering challenge and a continuation of a longer tradition: the careful, quality-focused work of building bridges between systems and human needs while respecting human values. The future belongs not to those who can move fastest but those who can build wisely, creating sustainable connections between fundamental truths and technological change.

CHRISTINE HASKELL, PhD, is a collaborative advisor, educator, research editor, and author with 30 years in technology, driving data-driven innovation and teaching graduate courses in executive MBA programs at Washington State University’s Carson School of Business and is a visiting lecturer at the University of Washington’s iSchool. She lives in Seattle.

ALSO BY CHRISTINE

Driving Your Self-Discovery (2024), Driving Data Projects: A comprehensive guide (2024), and Driving Results Through Others (2021)