Transformations can be big, messy, high-stakes undertakings. Some leadership teams seem to navigate them with purpose and clarity, while others stall or unravel. Our research aimed to explore one central question:
What differentiates leadership teams that successfully transform their organizations?
For this research, we defined transformation as more than incremental change. It’s a significant change in how the organization’s core business operates and/or a notable shift toward new products or services, business models, or markets.
To explore this question in depth, we used a multi-method approach that combined existing knowledge, lived experience from transformation leaders, large-scale quantitative data, and expert perspectives.
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Extensive literature reviewWe first conducted an extensive review of the prevailing literature on change management, transformation, innovation, and adjacent fields such as adult development theory. This work helped us build a strong theoretical foundation, identify common patterns of success and failure, and refine the questions we would later take into the field. |
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CEO interviewsFrom there, we turned to the leaders who have lived transformation firsthand. We spoke with 24 current and former CEOs who had successfully led their organizations through transformation. These were in-depth, semi-structured conversations designed to uncover how barriers to transformation appear in practice and the interventions that ultimately move change forward. Their stories added nuance to the frameworks we had seen in the literature, grounding them in the realities of executive leadership. |
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Transformation surveyUsing the perspective from our internal experts and extensive lit review, we developed a global survey to test our emerging hypotheses and capture a broad view of transformation in practice. Over 1,000 leaders completed the survey, including CEOs, C-suite leaders, and next-generation leaders (one to two levels below the C-suite). They represented a wide range of industries, geographies, and organizational sizes. |
Participants shared their perspectives on the nature and success of their organization’s transformation efforts, the specific actions taken along the way, and their organizational culture, openness, operating principles, and levels of hierarchy.
To make sense of this rich dataset, we applied two complementary statistical techniques:
First, we ran a factor analysis to uncover the underlying patterns within the data by grouping related variables together. Leaders rated their organization and executive team across 29 dimensions. Through factor analysis, we distilled these into eight distinct “conditions for change,” each representing a meaningful cluster that together form the architecture of successful transformation.
Leaders had been asked to rate the success of their organization’s transformation on a five-point scale, from not at all successful to extremely successful. We ran a regression analysis to determine the relative importance of each of the eight conditions in predicting transformation success. Higher scores indicated greater relative influence, while lower scores do not mean the factor is unimportant—only that its impact was less pronounced in this context. This allowed us to prioritize the conditions for change most critical to driving meaningful transformation.
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Subject matter expertsFinally, we spoke with both internal and external experts throughout the process to test and deepen our findings. Inside Russell Reynolds Associates, we drew on colleagues with deep expertise in leadership assessment, organizational culture, and transformation strategy. We also sought out external specialists in change, innovation, and transformation, whose diverse perspectives helped ensure our work was informed by both research and practice. |
As with any research, there are limitations. Our CEO interviews, while rich in depth, were not designed to be statistically representative. Survey responses were self-reported, meaning they reflect leaders’ perceptions. And while our sample was diverse, certain industries and regions were less represented. We acknowledge these constraints while remaining confident in our findings.
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