Executives rarely doubt AI’s potential anymore, but many are quietly unsure of their organization’s ability to make it pay off. If you’ve poured time and money into AI pilots and yet the bottom line barely moves, you’re not alone. In the new HBR article “Overcoming the Organizational Barriers to AI Adoption,” Jin Li, Feng Zhu—head of the Platform Lab at the Digital Data Design Institute at Harvard (D^3), and Pascal Hua argue that the problem isn’t the technology itself, but what happens when AI collides across three dimensions—people, processes, and politics—and show what it takes to turn AI from a side experiment into a real performance engine.
Key Insight: People Need Safety and Status
“Fear of status loss can be even more powerful than fear of job loss.” [1]
The authors argue that AI adoption doesn’t stall because employees are irrationally anti-tech. It stalls because, from their point of view, the risks are personal and immediate, while the benefits are abstract and uncertain. The article identifies three intertwined people problems: uncertainty about what AI will actually do, fear of being replaced, and fear of looking less competent. The last of the three, which the authors call ‘the self-image problem’, explains why some professionals secretly use AI but hide it from their colleagues, worried that it makes them look lazy or less skilled.
Key Insight: AI Only Works When You Redesign the Work Around It
“AI adoption often falters when organizations treat it as a simple overlay on existing processes.” [2]
The authors frame this challenge at three levels: nodes (individual workflows), edges (interactions between teams), and the broader network (end-to-end systems). For one example at the edge level, a Japanese cosmetics company used generative AI to turn store-level anecdotes into structured, credible intelligence, enabling headquarters to run faster campaigns and iterate in real time.
Key Insight: AI Rewrites Internal Power
“AI unsettles the traditional hierarchy built on two pillars: experience and headcount.” [3]
Hierarchy disruption can appear when junior employees using AI outperform their seniors, undermining tenure-based status. The authors report that some firms have responded by explicitly baking AI mastery into competency models and speeding up promotion cycles, so learning new tools pays off quickly. At the same time, managers whose influence is tied to headcount may quietly block automation that would shrink their teams. If AI changes who has information, who has leverage, and who gets credit (or blame), then adoption operates at the intersection of politics and culture.
Why This Matters
For business leaders and executives, capturing ROI from AI won’t be a function of model sophistication alone. The takeaway is that AI strategy is organization design strategy. You must confront employee fears, rebuild workflows from the ground up, and actively manage the power shifts AI introduces.
If these insights sparked your curiosity, the full article offers a deeper, research-driven look at the people, processes, and politics that determine whether AI actually delivers value. It’s packed with data, case studies, and practical examples that go further into the organizational realities, and opportunities, of AI adoption.
References
[1] Li, Jin, Feng Zhu, and Pascal Hua, “Overcoming the Organizational Barriers to AI Adoption,” Harvard Business Review, November 11, 2025, https://hbr.org/2025/11/overcoming-the-organizational-barriers-to-ai-adoption.
[2] Li et al., “Overcoming the Organizational Barriers to AI Adoption.”
[3] Li et al., “Overcoming the Organizational Barriers to AI Adoption.”
Jin Li is Zhang Yonghong Professor in Economics and Strategy, Director of the Centre for AI, Management and Organization (CAMOM), and Area Head of Management and Strategy at Hong Kong University Business School.
Feng Zhu is the MBA Class of 1958 Professor of Business Administration at Harvard Business School, lead of the D^3 Platform Lab, and co-chair of the Harvard Business Analytics Program (HBAP). Professor Zhu is an expert on platform strategy, digital innovation and transformation, competitive strategy, and business model innovation.
Pascal Hua is National Managing Partner of Technology and Transformation at Deloitte China.