Over the past year I’ve been co-writing a book about AI-powered growth and organizational maturity. The working title is AI-Powered Growth. (Pretty obvious what it's about). A big part of the book focuses on helping organizations understand where they actually are in their AI journey.
Not where they think they are.
Where they really are.
Most companies experimenting with AI fall somewhere along a maturity curve. Some are experimenting with prompts and tools. Others are building internal systems. A smaller number are integrating AI into operational workflows.
The challenge is that most of the frameworks used to evaluate AI maturity are static.
They live in:
- consulting decks
- whitepapers
- strategy documents
- maturity model diagrams
They describe stages of capability, but they rarely help someone diagnose their current state in a practical way.
While writing the book, it became obvious that many of the concepts we were describing naturally lent themselves to structured assessments.
The Problem With Static Frameworks
Many maturity frameworks look something like this:
Level 1 – Exploration
Level 2 – Experimentation
Level 3 – Operationalization
Level 4 – Strategic Integration
These models are helpful conceptually, but they leave people with an obvious question:
How do we actually know where we fall on this spectrum?
That question is rarely answered.
Organizations end up having informal discussions that sound like this:
- “We are probably somewhere between Level 2 and Level 3.”
- “We have a few pilots running.”
- “We’re experimenting with ChatGPT internally.”
Those conversations are subjective.
What we needed instead were diagnostic questions that forced concrete answers.
For example:
- Do you measure AI output quality or accuracy?
- Are AI workflows integrated into operational systems?
- Do you have governance around model usage?
- Are teams trained to evaluate AI outputs?
Once you start asking questions like these, the maturity discussion becomes much more grounded.
Why Assessments Work Better Than Frameworks
Frameworks explain ideas.
Assessments expose reality.
Assessments do three things extremely well:
- They force specific answers
- They reveal capability gaps
- They produce a measurable score or maturity level
This is why diagnostics work well in many disciplines:
- leadership assessments
- technical skill evaluations
- operational maturity models
Instead of simply describing maturity levels, you ask questions that reveal them.
As we continued writing the book, we realized that many of the frameworks we were describing already contained the raw material for assessments.
They included:
- diagnostic prompts
- capability checklists
- evaluation criteria
- operational questions
Those elements are naturally suited for quiz-style evaluation.
The Idea
Instead of burying these assessments inside a book, we decided to build something simple that would allow readers to actually run the diagnostics themselves.
The concept was straightforward.
Take the frameworks from the book and convert them into interactive assessments that allow someone to:
- answer structured questions
- receive a maturity score
- identify capability gaps
- understand where improvement is needed
That became the foundation for a small tool we built called LevelUpQuiz.
The platform acts as a landing zone for the assessment frameworks described in the book.
Rather than simply reading about AI maturity models, people can interact with them directly.
Using the Book as a Corpus
The book itself serves as the conceptual foundation.
It contains the frameworks, diagnostic questions, and evaluation logic used to design the assessments.
From a design perspective this works well because the book provides:
- conceptual context
- explanation of each capability area
- guidance on what maturity looks like
The assessments then provide the practical evaluation layer.
Readers can explore the ideas in the book and then run assessments to see how their organization compares to the maturity concepts described.
Why Quizzes Work Surprisingly Well
When people hear the word quiz they often think of something trivial.
But quizzes are actually extremely effective diagnostic tools.
A well designed assessment forces someone to answer structured questions that expose real operational practices.
Instead of broad discussions like:
“Are we good at AI?”
You get concrete evaluation questions such as:
- Are AI outputs reviewed before being used in production workflows?
- Do you track prompt or model performance over time?
- Are AI systems integrated with operational data?
- Do teams have guidance for evaluating hallucinations or errors?
These kinds of questions quickly reveal whether AI usage is experimental or operational.
That clarity is incredibly useful for teams trying to move beyond experimentation.
A Tool for Self Diagnosis
The goal of the platform is not to declare that an organization has “passed” or “failed” at AI adoption.
Instead, it provides a structured way to answer the question:
Where are we today?
Once that question is answered, the next question becomes easier:
What capabilities do we need to develop next?
Organizations pursuing AI maturity often discover that the biggest gaps are not technical. They are operational.
Things like:
- governance
- workflow integration
- evaluation practices
- organizational alignment
Assessments help surface those gaps much earlier.
From Framework to Practical Tool
Building the platform was ultimately a way to make the book more practical.
Frameworks are useful for thinking.
Assessments are useful for action.
Combining the two creates a more effective way for people to engage with the ideas.
If you are curious about the assessment platform that grew out of the book, you can explore it here:
levelupquiz.ai
The goal is simple.
Help people understand where they are in their AI journey and provide tools that make it easier to move forward.
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