What are the most common and costly mistakes organisations make in early AI adoption?

Organizations often falter in early AI adoption by neglecting change management and human factors, leading to stalled implementations and delayed ROI. Leaders frequently overestimate employee readiness while underestimating resistance fueled by job anxiety, resulting in rework inefficiencies and unrealized productivity gains as teams ignore or undermine AI tools [1][6]. This people-centric oversight is compounded by heavy investments in technology without aligning processes or organizational dynamics, where the "brutal truth" is that tech is the easiest part, but people and processes demand the real effort [3]. Another costly mistake is skimping on user training and skills development, with executives allocating just 7% of AI budgets to people versus 93% to tech, exacerbating skills gaps and weak oversight that hinder returns—only 4% of businesses achieve ROI [4][12]. Rushing into broad pilots without narrow, measurable use cases or proper budgeting for scaling costs, like token usage, further inflates expenses and prolongs adoption timelines [5][7][10].