Stop Picking the Tool Before You Understand the Problem
 
A vendor demo is not a business case. Too many AI projects start with a tool someone got excited about, and only later go looking for a problem it might solve. Budgets get spent. Nobody can point to what actually improved.
 
The AI Decision Atlas, from Articsledge, reverses that order. It starts with the problem, tests whether AI genuinely fits using the Articsledge Gate Method, checks what a simpler option would cost, and only then talks about tools. It is not an anti-AI book, and it is not a pro-AI book. It is a method for finding out, problem by problem, where AI actually earns its place — and where it doesn't.
 
This guide won't tell you AI is always the answer. 24% of the problems inside are rated "weak fit" or "do not use AI." That is what a consistent, honest test turns up across a broad set of real business problems — not the easy-win highlight reel a sales pitch would choose.
 
What's inside
The Articsledge Gate Method: an eight-gate decision framework testing problem clarity, outcome clarity, your current baseline, data readiness, verifiability, accountability, full operating cost, and simpler alternatives, in that order, before AI enters the conversation
 
Seven plain-language solution patterns — including Find and Ground, Extract and Structure, Classify and Route, and Detect and Alert — that sort any AI use case into a shape you can reason about, so you can judge a new problem by analogy to one you already understand
 
A complete index of 100 real, distinct problems across more than twenty sectors, each rated Strong fit, Conditional fit, Weak fit, or Do not use AI
 
Twenty-four fully worked problem entries, each covering the desired outcome, the main risk, the strongest non-AI alternative, a small test design, success measures, and clear stop conditions
 
A major "When Not to Use AI" section: fifteen specific reasons to say no, sorted into five practical groups, each pointing toward a cheaper, more accountable option
 
Four real, independently documented case studies, named and sourced: Mayo Clinic's ECG screening tool, which delivered a measured clinical benefit; a costly, mixed-outcome project at MD Anderson Cancer Center; and two rejected or disputed deployments, at the Dutch tax authority and Air Canada, each serious enough to trigger legal or regulatory consequences
 
Six reusable worksheets — including a suitability scorecard, a problem-definition worksheet, a data-readiness checklist, and a stop-condition template — plain text and simple tables that need no software and print fine in black and white
 
A working table of contents, cross-references between chapters, a glossary of plain-English terms, and color-coded suitability ratings throughout, built for both screen reading and printing
 
Who it's for
This book is written for people who make real operational decisions, not people who write code. Small-business owners, team leads, operations and product managers, consultants, educators, procurement teams, and anyone evaluating an AI purchase will find it accessible. No background in machine learning or data science is assumed; unavoidable technical terms are defined in plain English as they appear, so a reader can pick it up cold and still follow every gate and every rating.
 
What this edition includes
This is a first edition, and its scope is stated plainly rather than oversold. The atlas indexes all 100 problems with a sector, a solution pattern, and a suitability rating for each one. Twenty-four of those problems receive the complete worked treatment described above. The remaining 76 are classified and rated in the same index, ready for a reader to reason through using the same method, and are planned for full treatment in a future edition. Four real case studies are included, spanning the full range from useful to rejected, with a wider set also planned ahead.
 
Why it's worth reading before you spend anything
Every statistic and case study in this book is sourced and dated. Every case study names the real organization involved, with outcomes reported honestly, including the ones that did not go well. This book makes no promise about your return on investment — that outcome turns on your own data, workflow, and rollout, not on any book. What it offers instead is a repeatable method: define the problem, gather the evidence, test it small, and know in advance exactly what would make you stop. Read it before the budget gets spent, not after.