Skills
About
Executive leader in AI governance, leadership transformation and digital strategy, with 10+ years guiding large-scale innovation across public and private sectors. Founder of HumanLearn, helping leaders bridge technological intelligence with human judgment. Co-Founder of NOVAÉ AI, advancing responsible AI, synthetic creativity and governance for social good. Forbes contributor and AI leader within the Forbes Councils, writing on AI philosophy, governance and leadership. Recognized for thought leadership in AI ethics, organizational agility and the shift from control-based models to co-creation and accountability in AI-driven systems. Areas of focus include AI governance and strategic alignment, executive leadership in the age of AI, organizational transformation, human-centered innovation and AI literacy at the executive level.
Andre Shojaie
Published content
expert panel
Across industries, executives are investing aggressively in artificial intelligence. Yet despite billions spent on experimentation, relatively few organizations have turned AI pilots into scalable platforms that generate repeatable value. According to PwC’s Global CEO Survey, 56% of CEOs report they’ve seen neither revenue nor cost benefits from investments in AI—a signal that experimentation alone is not enough to create enterprise impact. Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in enterprise AI, machine learning and digital transformation—say the problem is rarely technical. Instead, organizations struggle with leadership alignment, operating models, governance and cultural change. Below, their insights reveal a consistent theme: Scaling AI requires redesigning how companies operate—not simply deploying more technology.
expert panel
AI tools are proliferating across enterprises at unprecedented speed. Yet implementation does not guarantee adoption. According to a McKinsey report on generative AI adoption, while organizations are investing heavily, many struggle to translate experimentation into sustained value. The gap is rarely technical—it is behavioral. Members of the Senior Executive AI Think Tank, a curated group of experts in enterprise AI, generative AI and machine learning strategy, agree: whether AI becomes a trusted decision-support system—or a tool employees quietly resist—depends largely on the signals sent by the C-suite. Executives shape consequence structures, model risk tolerance, determine measurement standards and define what success looks like. In short, employees learn how to treat AI by watching how leaders treat it. Below, Think Tank members share what C-suite leaders most often get wrong—and what they must do differently to ensure their organizations gain real, measurable value from AI.