You're being asked to "do something with AI"-but you don't write code, you're not a data scientist, and you can't afford a public experiment that goes wrong.



This book gives non-technical managers a concrete, operational way to lead AI initiatives without pretending to be machine learning experts. It's a practical playbook you can keep open next to your project plan: checklists, decision matrices, scripts, and templates that turn vague AI ambitions into low-risk, testable projects.



Instead of teaching you algorithms, the book focuses on the decisions you actually make as a product leader:



- What kinds of product problems are a good fit for AI-and which ones almost never pay off?

- How do you compare three competing AI ideas and pick the one with the most realistic ROI?

- What do you need from engineering, legal, security, and vendors so you can sign off with confidence?

- How do you keep pilots small, safe, and measurable instead of open-ended science experiments?



Inside, you'll find plug-and-play tools you can adapt directly to your next initiative:



- Scoping checklists to turn "Let's use AI" into a clear business problem, success criteria, and constraints.

- Plain-language mapping tools that link common product scenarios (routing tickets, ranking content, summarizing feedback, forecasting demand, etc.) to suitable AI approaches-without touching the math.

- A 20-minute ROI quick-calc worksheet that forces realistic assumptions about value, cost, and risk so you can prioritize projects in one meeting.

- Pilot design templates that define hypotheses, guardrails, duration, and sample size before you commit real budget.

- Defensible Go/No-Go decision matrices you can show to leadership, risk, or procurement and actually stand behind.

- Ready-to-use communication scripts for executives, ICs, skeptical stakeholders, and AI-enthusiasts who want to



Alexandra Reed is a product operations and strategy consultant who helps non-technical leaders turn emerging technology ideas into shippable, low-risk initiatives. Over the past decade, she has worked with product, UX, and engineering teams in fast-growing SaaS and platform companies, focusing on how to scope, pilot, and scale data-driven features without burning out teams or budgets. Her work centers on the "operational middle" of organizations-the managers who sit between executive vision and day-to-day delivery. Alexandra has designed and facilitated cross-functional programs on AI experimentation, product discovery, and risk-aware rollout practices for clients ranging from early-stage startups to global tech firms. She writes and speaks regularly about practical