Until recently, a friend of mine worked as a scrum master in a large, traditionally structured organization.

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In his department, Marketing, the agile setup worked remarkably well. The team was younger, more project-oriented, and comfortable with speed, iteration, and close collaboration. He flourished in that environment.

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In other parts of the same company, the picture was far more complex.

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Teams that had built their work around long-standing customer relationships, clearly defined responsibilities, and a more deliberate pace struggled to translate the new model into their daily practice. What was experienced as empowerment in one area created ambiguity and friction in another. The issue was not resistance in any simplistic sense. It was a question of fit.

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I often find myself thinking about that experience when I look at the current wave of AI, because we have seen a similar pattern before.

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Roughly a decade ago, agility and scrum began to move beyond their original context in software development. Initially, these approaches had emerged as a response to the limitations of waterfall project management methodologies, offering a more iterative, client-centered, and adaptive way of working. Within that context, they proved highly effective.

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Their success, however, did not remain confined to IT. Organizations across a wide range of industries such as telecommunications, pharmaceuticals, financial services, manufacturing, and beyond, began to adopt agile principles at a broader organizational level. Traditional functional structures were, in some cases, dismantled or significantly reshaped in favor of cross-functional teams, flatter hierarchies, and project-based collaboration.

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Some of these transformations were successful and created real value but many proved more difficult to sustain than initially anticipated.

 

McKinsey research suggests that only a relatively small proportion of organizations succeeded in achieving enterprise-wide agility, even as the concept itself gained widespread popularity. Later studies indicate that while highly successful agile transformations can deliver significant improvements in speed, customer satisfaction, and engagement, they represent a minority rather than the norm.

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In practice, organizations frequently encountered a more complicated reality: mixed operating systems, cultural friction between departments, unclear reporting lines, and increasing frustration around expectations of speed and output.

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The latest State of Agile report reflects similar challenges, pointing to issues such as siloed teams, inconsistent adoption across the organization, difficulty measuring business value, and tensions between different ways of working.

 

What I observe now, in a number of organizations, is not a dramatic abandonment of agile, but a quieter form of recalibration.

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Many are moving toward hybrid models, in which some functions remain agile while others operate within more traditional structures. In some cases, elements of hierarchy and clarity are being reintroduced. This is not necessarily a sign of failure. It is often a recognition that the original transformation did not fully align with the organization’s culture, history, or operational needs.

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This is where the comparison to AI becomes particularly relevant.

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The current wave of AI adoption is driven by a similar combination of promise, pressure, and momentum. The potential is significant, and in many areas, already visible. At the same time, leading research points to a more nuanced reality.

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Harvard Business Review highlights that many organizations are operating in a kind of “liminal space,” caught between high expectations and still-emerging results, and explicitly advises leaders to focus on the trends that are most relevant to their specific context rather than attempting to pursue all of them at once.

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Deloitte, in its Global Human Capital Trends report, emphasizes that competitive advantage is increasingly linked not to technology alone, but to how effectively organizations integrate human judgment, creativity, and collaboration with technological capabilities.

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McKinsey adds that while AI adoption is advancing rapidly, a relatively small share of organizations report meaningful bottom-line impact, and many leaders acknowledge that their organizations are not yet fully prepared for widespread implementation.

 

Taken together, these insights point to a familiar tension. High expectations, uneven outcomes, and organizations navigating change at different speeds and with different levels of readiness.

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Which raises a question that feels increasingly relevant:

 

Are we, once again, at risk of confusing aspiration with applicability?

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Not every industry requires the same level of technological integration. Not every function benefits from the same operating model. And not every organization should move at the same pace.

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The challenge is not whether to change, it is how to ensure that change fits the organization it is meant to serve—its culture, its structure, its people, and its reality.

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And sometimes, that includes the willingness to recalibrate.

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Not as a step backward, but as a step toward alignment.

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— Chris Newman

Newman Seminars