The Innoba Books

Building MVPs with AI

Build the smallest useful evidence before you build more

Use AI-assisted creation to test a meaningful assumption with focused scope, a credible experience, and a clear decision at the end.

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Inside this Book

Depth you can build on

Develop a connected understanding of Building MVPs with AI, with the depth to revisit important ideas and apply them to your own work.

Pages: 391
Parts: 5
Chapters: 18

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Why this Book

Understanding that carries into practice

This Book develops MVP judgment in a time when AI can produce software quickly. It teaches you how to decide what the first version must prove and what can be omitted without losing the learning objective.

The professional problem

Faster building can encourage larger scope and premature confidence. An MVP may become a rushed product with too many features or a demonstration that cannot test the important uncertainty.

What you learn

Build understanding you can use

How to define the question the MVP should answer.
How to identify the riskiest assumptions.
How to select the smallest coherent user journey.
How to decide what to build, simulate, do manually, or postpone.
How to use coding agents without losing scope control.
How to define evidence and feedback.
How to decide what the result means.

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Explore the contents

A closer look inside the Book

Follow the ideas from their foundations into practice. Each part of Building MVPs with AI develops a connected set of skills and decisions you can bring to your work.

Part 01

Foundations of AI MVP Building

Chapter 1: What an MVP Really Is
Chapter 2: Problems, Users, and Value
Chapter 3: Validating Before You Build

Part 02

Designing the MVP Before Building

Chapter 4: From Idea to MVP Hypothesis
Chapter 5: Scoping the Smallest Useful Version
Chapter 6: Designing the First User Journey

Part 03

Building with AI Tools and Clear Constraints

Chapter 7: Writing an AI-Ready Product Brief
Chapter 8: Choosing the Right Build Path
Chapter 9: Building the First Prototype with AI
Chapter 10: Testing Functionality and User Experience

Part 04

Feedback, Measurement, and Iteration

Chapter 11: Getting Real User Feedback
Chapter 12: Measuring What Matters
Chapter 13: Iteration, Pivoting, and Scope Decisions
Chapter 14: Quality, Trust, and Responsible MVPs

Part 05

From MVP Learning to the Next Product Decision

Chapter 15: Preparing for a Controlled Beta
Chapter 16: Working with Constraints
Chapter 17: From MVP to Product Roadmap
Chapter 18: Your AI MVP Builder System

Core ideas

Principles that stay with you

An MVP is a learning system

Small scope should protect a meaningful test.

Risk guides scope

Build around the assumption that most needs evidence.

Manual work can be valid

Not every future capability belongs in the first version.

Usability affects evidence

A confusing experience can invalidate conclusions.

The result must change a decision

Learning should determine what happens next.

Your capabilities

Turn knowledge into professional judgment

MVP hypothesis definition.
Scope reduction.
AI-assisted prototype planning.
Evidence and feedback design.
Iteration and stop/change/proceed judgment.
Communication of limitations.

Who this Book is for

Bring your context to the learning

Product professionals, founders, consultants, designers, internal innovators, and non-programmers preparing to build a first application or validate a solution.

Professional applications

Put it to work

Scope a first AI application.
Create a client proof of concept.
Run an internal pilot.
Test an AI-enabled service.
Reduce a broad idea to one coherent journey.

Connected Learning Paths

One foundation. Several ways forward

Connect this Book with complementary capabilities and work toward a specific professional outcome.

Product Thinking

turns assumptions into evidence.

MVP Design

forms the core.

Validate an AI Opportunity

provides a focused validation artifact.

Build Your First AI Application

controls implementation scope.

From Idea to Working Product

connects learning with a product.

Launch Your First AI Solution

prepares a focused real-world test.

Go-to-Market Strategy

aligns market activity with learning.

Career connection

Part of a larger professional capability

MVP discipline joins strategy and execution. It enables building to test thinking rather than replace it.

Your transformation

Build only what helps you learn

You should be able to define an MVP question, reduce scope without losing coherence, plan an AI-assisted build, identify evidence, and use the result to make a better decision.

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Our Methodology

A clear progression from learning individual skills (books) to building complex abilities (learning paths and academies) and an entire career around them.

A group of books builds learning paths; a group of learning paths builds Academies; and a group of Academies builds the AI Strategist & Builder Career.

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