There is a particular kind of satisfying moment when a document that started as a pile of notes becomes an actual book.

My first ebook is now live on Amazon. It is built around a simple idea: AI can make an analyst faster, but it should not make the analyst optional.

Why I wrote it

A lot of AI advice for analysts starts and ends with prompts. Ask better questions. Add more context. Try another prompt.

That is useful, but incomplete. Real analysis still involves defining the question, understanding the data, checking assumptions, validating outputs, and deciding whether the result is safe to use.

What the book focuses on

• Turning vague business questions into analytical tasks
• Using AI to accelerate SQL, Python, documentation and analysis
• Breaking work into repeatable stages instead of random prompting
• Testing AI-generated answers before accepting them
• Building reusable workflows instead of collecting hundreds of prompts

The principle behind the book

The goal is not to become an analyst who asks AI to do everything. The goal is to become an analyst who knows what to delegate, what to verify, and what still requires human judgment.

If you work with SQL, Python, dashboards, metrics or business analysis, that distinction matters.

Keep exploring

You can also read How to Use AI to Debug SQL Without Losing the Logic and the AI verification checklist for examples of the same approach in practice.