Enterprise AI: From Experimentation to Execution

It has never been easier to build an impressive AI prototype.

But a compelling demo is one thing; successfully implementing AI inside a real organization is another.

Once these systems meet messy enterprise data, operational constraints, security scrutiny, and skeptical users, momentum often fades.

The ROI remains unclear, pilots stall, and initiatives remain trapped in perpetual proof-of-concept mode.

Designed for technology leaders, product managers, implementation strategists, and forward-deployed engineers, this course examines how organizations move AI from experimentation to execution.

We will explore why technical performance alone rarely guarantees adoption or business value and how deployment challenges often emerge from workflow integration, incentives, and trust.

Through case studies, hands-on exercises, and guest speakers from OpenAI and Palantir, students will work through enterprise AI implementation from early scoping decisions to rollout and iteration.

Along the way, they will develop a capstone deployment plan to evaluate readiness, navigate implementation challenges, and assess business value.

No coding experience is required, though familiarity with enterprise software environments is helpful.

This course will demonstrate third-party AI tools that are not managed or supported by Stanford.

Students can explore these tools on their own if they like, but use is optional.

Many tools offer free versions or trials; paid subscriptions, if chosen, typically range from $25–$100 per month.

See the syllabus for more details.