Punit Bhatia

expert panel

Artificial intelligence has become the fastest-moving investment category in the corporate world. Boards are asking about it, investors expect it and competitors are announcing new initiatives seemingly every week. For many Fortune 500 CEOs, however, the challenge isn't deciding whether to invest in AI—it's deciding where to place the first major bet.The stakes are high because the wrong investment can consume millions of dollars while delivering little business value. Organizations across industries are launching AI labs, experimenting with custom models and deploying new tools at scale, yet many still struggle to achieve measurable returns.That reality raises an important question: If you were making your first significant AI investment today, where would you focus—and what would you avoid?To find out, we asked members of the Senior Executive AI Think Tank, a community of leaders and practitioners specializing in machine learning, generative AI and enterprise transformation. Their answers reveal a striking consensus about where AI creates value, why so many organizations get their priorities wrong and the foundational investments that should come before any large-scale AI deployment.

expert panel

For many organizations, AI training has become synonymous with productivity. Employees learn how to write better prompts, automate routine tasks and generate content faster than ever before. But as AI becomes embedded in everyday business decisions, a more important question is emerging: Are organizations teaching people how to use AI, or how to use it responsibly?AI can generate recommendations, summarize information and accelerate workflows, but it cannot assume accountability for outcomes. That responsibility still belongs to people. Yet many training programs spend far more time on tools than on judgment, ethics, governance and critical thinking.This concern is reflected in Deloitte's “The State of Generative AI in the Enterprise” research, which found that regulatory compliance concerns, risk management challenges and the lack of governance models rank among the leading barriers to scaling AI initiatives. As organizations move beyond experimentation, the challenge is no longer simply getting employees to use AI—it is ensuring they can use it responsibly.To explore what modern AI fluency should look like, we turned to members of the Senior Executive AI Think Tank, a curated community of experts in machine learning, generative AI and enterprise transformation. Their perspectives offer a roadmap for moving beyond AI tool proficiency and building the judgment, oversight and responsible-use practices that enable organizations to create lasting value from AI.