90% People, 10% Technology: Why AI Fails Without Change Management
Contrary to popular perception, most AI failures don’t really look like crashes at all. They look like silence; the AI launches, the dashboards load, and the frontline keeps doing what they’ve always done.
Case in point, a manufacturing client of ours wanted a “quick fix” to a sales forecast the last vendor built but no one used. We urged meeting the people who rely on the forecast upfront; the response was “just improve the model.” Tests looked great: better features, cleaner data, lower error, yet adoption didn’t budge. After go‑live, the real issue surfaced: the user interface didn’t fit how revenue leaders prefer to review demand, controls felt foreign, and graphs lagged. The model wasn’t the blocker; lack of focus on the user experience was.
So, as you can see, this isn’t a data or model problem; it’s an organizational readiness problem: the change never took root because the people most affected were never brought into the story, the workflow, or the win. Simple.
Despite billions being poured into AI, the adoption gap persists because leaders over-index on technical milestones and under-invest in human systems: trust, incentives, governance, and feedback, where adoption is actually won or lost.
So, what’s the real unlock? As you’ll discover in this article, we ought to be treating AI as a transformation of decisions, roles, and culture, not as a tool installation, and make change management the first mile, the middle mile, and the last mile of the journey.