Over decades of doing organizational design and workforce strategy across organizations of every size, I’ve seen one consistent failure point: building solutions to the wrong problem.
Every leadership team wants to do the right thing; they see a problem, and they want to move to the fix. But the instinct is almost always to reach for the fix that is the most visible and requires the most effort. That is a trap. High effort does not necessarily equate to success or strategic impact, and often the most impactful interventions are the easiest ones.
I’ve watched communication failures trigger leaders to immediately move to structural reorganizations. The dreaded restructure. But structural fixes are only one lever. Sometimes optimal solutions lie in fixing governance failures, skill gaps, or poorly defined strategies.
This is why alignment on the problem before the fix becomes critical. It moves us away from:
And toward a discipline and method that work:
The hard part is assessment. It requires time, commitment, and, if you’re lucky enough to have it, sound data.
Many organizations are learning this the expensive way with AI deployment. Instead of treating AI as a fix for a known root-cause issue, they treat AI as a universal solution to an undefined problem. The result is an expensive fix to the wrong problem with a change-fatigued workforce.
I think 2027 is going to be the year of correction for many organizations: embracing the practice of assessing first to identify root causes, and then determining which solutions make sense, including AI.
The organizations that started methodically, resisting the sprint of early adoption in favor of a marathon pace of caution and structure, will see compounding returns sooner. They will have a workforce that both understands AI’s operational value and actually commits to using it.