The CTO of a mid-size company told me something interesting last month:

"I don't care about your model's F1 score. Tell me how it saves us money."

That conversation changed how I pitch AI projects.

Engineers love talking about architectures and benchmarks. Business leaders want to know: how much time does it save? What's the ROI? When does it pay for itself?

The generative AI use cases that are actually making money right now: code generation (GitHub Copilot reportedly saves 55% developer time), customer support automation, document summarization at scale, and content personalization.

Companies are projected to spend over $100B on GenAI by 2026. But the money goes to people who can connect the technology to business outcomes.

If you can walk into a room and say "this RAG system will reduce our support ticket resolution time by 40%, saving approximately $2M annually" instead of "this RAG system uses a hybrid search approach with cross-encoder re-ranking" — you're not just an engineer anymore. You're the person who gets budget approved.

Learn to speak business. It's the highest-leverage skill nobody teaches in CS programs.