
Julie Averill, former Global CIO at Lululemon, warns that many companies are overstating their AI transformation efforts—a practice she calls 'AI washing'—without doing the foundational work of solving real business problems and building trust with employees. Real value creation, according to Averill, requires honest communication about change, human infrastructure, and business-problem clarity, not just technology adoption.
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Julie Averill, former Global CIO at Lululemon who helped scale the company's revenue to over $10 billion(約1.6兆円), released a new book titled Chief Impact Officer and discussed in a podcast interview how to spot companies performing transformation rather than sustaining it. She cautioned that many firms are engaging in 'AI washing'—claiming efficiency gains from AI layoffs when they are actually rightsizing after pandemic overhiring.
Why it matters
Averill argues that real sustainable value creation depends on solving actual business problems, not chasing technology headlines. When companies focus on technology without building human infrastructure—trust, decision-making clarity, and honest communication with employees—transformation stalls. She notes that 22% of people globally feel AI will not threaten their job, meaning four out of five workers are worried about replacement; leaders who don't acknowledge this openly will struggle to bring teams on the transformation journey.
What to watch
Investors should look beyond AI strategy announcements from boards and instead examine whether individual business leaders (supply chain, operations, etc.) have concrete plans for how AI solves their specific problems. Companies that declare AI strategy without that underlying functional work are performing transformation rather than building it.
Averill's perspective draws on nearly three decades of technology leadership at major retailers, including Nordstrom and REI before Lululemon. Her core argument is that companies today are confusing visibility with execution: a board blessing an AI strategy document is performance art unless the supply chain leader, operations chief, and other business heads have translated that strategy into concrete problems they are solving. This gap between declaration and delivery is what causes transformations to stall, she suggests.
The psychological contract with employees is central to her analysis. When leaders announced return-to-office mandates after COVID without acknowledging the disruption to employees' lives—picking up children, caring for parents—people heard half the truth. Similarly, when leaders declare that AI will augment rather than replace skills while four in five workers silently fear job loss, the trust erodes before the work even begins. Averill argues this is not a technology problem; it is a leadership and culture problem that no amount of AI investment can bypass.
For investors, the implication is that the noisiest AI announcements may actually be a warning sign. Companies that have done the quieter work—aligning functional leaders, building psychological safety, and solving real operational problems—will outperform those that chase headlines. The distinction between performing transformation (declaring it, getting board approval) and sustaining transformation (making it real in daily work) is, Averill suggests, where investment value either grows or evaporates.
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