Effective change management and cultural transformation are essential for successful enterprise-wide AI adoption; technical issues are secondary. At Sanofi, a CEO discovered meetings where employees preferred to pre-screen and shape data narratives before sharing raw data, revealing underestimated change-management scale and leadership learning curve. Building trust in AI requires involving employees, showing AI augments human judgment, and integrating tools into workflows such as live financial reporting or AI-generated meeting summaries. Resistance often stems from familiarity and perceived personal stakes, similar to preferring known routes over navigation advice. Leading adoption demands personal engagement, breaking tradition, and empowering respected change agents to pilot innovations with technology teams.
I had made it a habit to drop in on meetings to learn as much as I could about the organization. In one such meeting I was told, quite directly, they were discussing how to avoid giving me raw data without first thoroughly reviewing it and deciding on the "narrative." That moment was a wake-up call. I realized I had underestimated not only the scale of change management required, but also my own learning curve in leading such a transformation.
Building trust in AI requires more than clear communication about its benefits and limitations; it demands a commitment to involving employees in the journey, demonstrating that AI is here to augment-not replace-human judgment. Over time, as AI tools became more integrated into our daily workflows-think live financial reporting or even inviting AI agents to meetings (remember AI doesn't have a career at stake) to provide unbiased summaries-employee trust grew, and skepticism diminished.
Don't delegate the AI revolution Leading an AI revolution requires more than issuing directives from the corner office. It takes personal engagement and a willingness to break with tradition. In 2021, we launched an "AI Fight Club," selecting 12 respected change agents from across different departments-none of whom were AI specialists-to innovate within their own functions in collaboration with technology experts.
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