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When a global financial services firm sought Sam\u2019s guidance, the problem seemed familiar. The firm had deployed AI tools across its business. Adoption was uneven, and the gap between teams was growing.<\/p>\n<\/div>\n
In some corners of the organization, people were already using AI to draft client materials, summarize research, and speed up analysis. In others, they avoided it entirely: unsure what was permitted, worried about quality, or skeptical that leadership really meant it. Managers were fielding questions they weren\u2019t equipped to answer. If my team uses AI, what changes in our standards? What happens to accountability?<\/p>\n
The leadership team quickly realized the problem wasn\u2019t the technology. It was the people around it. The evidence is clear. BCG\u2019s 2024 research<\/a> finds top AI-performing companies invest 70% of their transformation resources in people and processes, not technology. Mercer\u2019s Global Talent Trends 2026<\/a> finds that employee concern about AI-driven job loss has surged from 28% to 40% in two years\u2014anxiety that impedes value creation unless leaders address it directly. The World Economic Forum\u2019s Future of Jobs Report 2025<\/a> projects 39% of core workforce skills will change by 2030. AI has not made human development less important. It has made it the primary lever for competitive advantage.<\/p>\n Based on our work with senior executives\u2014Jenny<\/a> as an executive coach and leadership development expert, Sam<\/a> as a global transformation leader who helps organizations redesign how they develop and deploy talent\u2014we have identified four strategies for building the learning culture that makes AI investments work.<\/p>\n<\/div>\n The first capability is cultural, not technical. Mercer\u2019s research<\/a> finds that for innovation to succeed, employees must feel safe to experiment, ideate, and face potential failure. McKinsey\u2019s research on psychological safety<\/a> finds that a positive team climate is the single most critical driver of willingness to experiment. Yet McKinsey\u2019s research found fewer than half of employees report one. That gap is where most AI adoption efforts quietly die.<\/p>\n \u201cMichael,\u201d a senior marketing and sales leader Jenny worked with at a global consumer packaged goods company, worked with his team to define what good experimentation looked like, named the behaviors that signaled progress,\u00a0 and made clear that early mistakes were expected, not penalized. Within six months, voluntary AI tool usage across his team had increased by more than 40 percent, and managers who had previously avoided AI began openly sharing what they were testing in team meetings\u2014modeling the curiosity the culture needed. \u201cWe can buy the best AI on the market,\u201d he told Jenny. \u201cBut if our managers don\u2019t know how to lead differently, the tools are just expensive noise.\u201d\u00a0<\/p>\n Pro tip:<\/strong> Run a \u201cpsychological safety audit\u201d before your AI rollout. Ask managers: Do your team members feel safe admitting they don\u2019t know how to use a new tool? If the honest answer is no, address the culture first. No training or tooling will overcome a team that\u2019s afraid to try.<\/p>\n<\/div>\n1. Make It Safe to Try<\/h2>\n
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