Valentine, a tenured associate professor of management science and engineering at Stanford’s School of Engineering, told the room of finance chiefs that CFOs have a strategic opening to lead on AI if they are willing to quantify the value and be accountable for it. She argued that generative AI is moving out of its experimental phase and into something CFOs know well: systematic measurement. Two years ago, she said, rigorous accountability would have been premature. Today, it’s essential.
That example reinforced a broader point in Valentine’s remarks: the requirements for safe, production-grade AI are fundamentally different from those for everyday employee experimentation. She drew a sharp distinction between two very different forms of AI transformation. One begins at the frontline, where employees use tools such as Gemini or NotebookLM and discover practical applications through experimentation. The other is driven from the top, where production-grade use cases demand robust data infrastructure, engineering rigor, and governance. Both matter. Each requires a distinct operating model.
