Principle One
Problems over Requirements
If you cannot name the problem in one sentence, you do not understand it yet. Start with who feels the pain.
The requirements document becomes sacred. Reality becomes inconvenient.
Where AI & Data Move from Promise to Impact
By JP Schoeffel
Enterprise AI and Data fail at scale because we run them like procurement programs. They succeed when we run them like products.

Every failed AI & Data program shares the same root causes. These four principles address each one.
Principle One
If you cannot name the problem in one sentence, you do not understand it yet. Start with who feels the pain.
The requirements document becomes sacred. Reality becomes inconvenient.
Principle Two
Discovery is how you buy evidence. Use what exists, prove value, earn the right to engineer.
A discovery sprint is not a cost. It's insurance against eighteen months of expensive guessing.
Principle Three
Transformation asks: are we on track? AIDA asks: did the track lead anywhere worth going?
You run hard. You spend a lot. You generate heat. You do not move.
Principle Four
If your AI strategy depends on consultants, it's their strategy, not yours. Build the muscle inside.
You are paying someone else to become good at your business.
AI & Data do not become real through strategy. They become real through ownership, evidence, and impact.
Discover how to run AI and Data like a product, not a procurement project.
The full manifesto is available online with zero signup. Read chapter by chapter, navigate quickly, and jump straight to the sections you need.

Move from principles to execution with practical tools for leadership teams and product owners.
12 diagnostic questions to identify whether your AI and Data program is process-driven or product-led, with a clear score and action path.
Take the AssessmentA guided editor to capture discovery, evidence, outcomes, and the ownership model needed to scale AI and Data as a product.
Start a Case StudyCompanion to The AIDA Manifesto, the operating system for turning AI & Data into impact.
This is not a methodology. It’s a discipline: prove value fast, then scale what works.
More tools are coming for governance, portfolio prioritization, and time-to-value acceleration.
If this manifesto resonated, I'd welcome a conversation.