01
Symptoms are usually too small
Teams often name the loudest complaint and then optimize around it. The book pushes them to inspect the wider issue stack.
New Book / 新书
Diagnosing AI Projects
The Issue Space Field Guide for Managers, Consultants, and AI Teams
Most AI projects do not fail because teams refuse to act. They fail because teams act on a shallow diagnosis.
This page gives a faster public-facing entry into the book: what the method does, what the visuals show, and where to open the reading sample or the regional Amazon links.
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Method Core
6 Issue Space dimensionsFrom domain fit and technical base to leadership, relationships, and wider impacts. -
Case Logic
2 Cases that show how diagnosis changes actionOne broad organizational case and one platform-facing institutional case. -
Reader Promise
1 Practical field guide before teams buy the wrong fixA short, diagnosis-first guide for managers, consultants, and AI teams under pressure.
In One Paragraph
What Diagnosing AI Projects is about
Diagnosing AI Projects is a practical book about AI project diagnosis. It introduces the Issue Space method so managers, consultants, and AI teams can test a complaint across six dimensions before they buy another tool, hire in the wrong place, or mistake symptoms for causes.
Why This Book
It slows the team down just enough to prevent the expensive mistake.
Instead of treating "data", "talent", or "trust" as separate labels, the book reframes AI trouble as a compound problem that needs a structured diagnostic pass first.
02
Diagnosis changes the action mix
The right response may be governance, sequencing, reframing, training, or coordination, not necessarily another tool purchase.
03
Issue Space makes complexity discussable
It gives teams a shared way to map domain fit, technical foundations, managerial leadership, organizational intelligence, relationships, and wider impacts.
Loop View
From complaint to proportionate action
This is a cleaner public-facing version of the Chapter 3 method logic: start with the live complaint, move through diagnosis in sequence, then review and re-enter instead of freezing the first answer.
Example Trigger Issue
"We need more AI talent." The method does not reject this sentence. It uses it as an entry point, then tests whether the real blockage is talent, use-case definition, ownership, training, or something else entirely.Step 1
Name the trigger issue
Capture the live sentence people are already using before anyone hides the problem inside a polished solution.
Step 2
Collect visible symptoms
List the rework, drift, mistrust, delays, and ownership friction before interpretation takes over.
Step 3
Test across Issue Space
Run the complaint across the six dimensions so diagnosis expands before action narrows again.
- Domain fit
- Technical base
- Leadership
- Org intelligence
- Relationships
- Wider impacts
Step 4
Connect the pressure points
See which dimensions reinforce each other and where the real blockage actually sits.
Step 5
Choose the smallest fitting action
Pick the next move that matches the diagnosis, not the loudest internal narrative or the easiest purchase.
Step 6
Review and re-enter
Watch what changed, keep what clarified the issue, and reopen the loop if the complaint was only partly solved.
Why Step 1 Matters
Start with the live sentence
If the trigger issue already contains the solution, the rest of the method becomes biased from the first minute.
Why Step 3 Matters
Symptoms are not yet explanations
The same complaint can lead to very different action paths once it is tested across multiple dimensions.
Why Step 5 Matters
Action should get smaller and sharper
The method earns its value when the next move becomes more proportionate, more defensible, and less wasteful.
Visual Excerpts
Three figures that actually carry the core book logic
These visuals are pulled from the current book export and shown here without hard cropping, so the full diagram stays readable.
Next Move
Read the sample, buy the book, or start a diagnosis conversation.
The shortest route is still the same: open the sample first. But if the book already maps a real organizational problem you are facing, you do not need to stop at the PDF or the Amazon listing.
- Use the sample to test whether the framing fits your issue.
- Use the full book if you want the field guide in one place.
- Contact me directly if you want to discuss diagnosis, workshop use, or speaking.
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Quick Answers
Fast facts for search engines, AI assistants, and human readers
This section is intentionally direct: it gives short, sourceable answers to the most likely book questions.
What does diagnosing AI projects mean?
It means diagnosing an AI complaint across domain fit, technical foundations, leadership, organizational intelligence, relationships, and wider impacts before choosing the response.
What is Issue Space?
Issue Space is a six-dimension diagnostic method for unpacking messy AI project complaints before teams commit to the wrong response.
Who is this book for?
The book is written for managers, consultants, transformation leads, and AI teams who need clearer diagnosis before scaling tools, hiring, or governance changes.
Where can I read a sample?
A public reading sample is available at Bookreadingsample.pdf.