The Provenance of the AI Innovation Prompts I'm Giving You

And why Claude Sonnet 4.5 changes everything

Nov 17, 2025

Hi! I’m West Stringfellow.

This work is the culmination of a 7-year, multi-million-dollar quest to fix the $2 Trillion per year failure problem in American business by helping you use AI to accelerate innovation in your career and company.

I love building businesses:

founded a company that Target acquired
launched new businesses at Amazon, PayPal, Visa, Rosetta Stone, and startups
led turnarounds as Chief Product Officer at public and pre-IPO companies
built and led a Techstars startup accelerator

I’ve been where you are. I learned on the job for more than 25 years and study my disciplines daily.

To accelerate your growth, I’m sharing what I’ve learned so you can use AI to get the job done the right way the first time.

The $2 Trillion per Year Failure Problem

American businesses are stuck in failure mode:

95% of new products fail (Harvard)
95% of AI investments have no return on investment (MIT)
75% of venture-backed startups never return capital to investors (Harvard)
73% of change initiatives fail (McKinsey)
50% of small businesses fail within 5 years (BLS)

That adds up to more than $2 trillion spent on failure every year in the U.S.

That’s trillions of dollars in capital that could be creating jobs, lowering prices, paying living wages, and growing the American economy.

Having spent my career helping companies innovate, I saw this failure firsthand and I had the opportunity to fix it inside startups and Fortune 50s.

Based on this experience, in 2015, I set out to build an AI that could automate innovation. Think of it as ChatGPT, but hyper-specialized on innovation.

To help the model learn, I created training data through years of research. Obviously, ChatGPT (and others) won, so I’ve translated the research I originally created to train AI into a structured set of AI prompts that I will be sharing with you in future posts.

In this first post, I want to start by showing you where this dataset came from and why I believe it is a legitimate body of research you can use to grow your business.

How This Helps You

Right now, companies are pouring billions into AI. They want faster decisions, lower costs, and products that win. Most workers are anxious about their jobs.

But the real divide is simpler:

Most people will use AI to generate content.
A small number will use AI to generate revenue.

The people who generate revenue growth are the ones companies promote, fund, and trust with the hard problems.

Those who win the next decade of AI transformation will know how to:

Make high-quality, data-driven, customer-obsessed decisions
Analyze customers like a top product manager
Use AI to accelerate research, insight, and execution
Uncover insights and spot opportunities before competitors
Design solutions people will pay for
Build teams and systems that compound growth and efficiency
Apply AI as a force multiplier at every step

That’s what this curriculum teaches: how to grow a business using AI where it is today while preparing for tomorrow.

For you, the goal is simple: to make yourself the “human in the loop” with AI:

the person your company relies on
the person who creates momentum
the person who turns AI into meaningful business outcomes.

Businesses need more people who orchestrate innovation by combining human judgment and creativity with structured business processes and the speed of AI.

» This is how you become valuable in the era of AI. «

In 2015: Building an AI Trained on World-Class Business Research

Here was the problem I saw: the knowledge that drives growth at companies like Amazon isn’t accessible. It’s locked in consultants’ expertise or behind expensive educational institutions that require both pedigree and wealth to access.

I first discovered the power of machine learning (ML) in 2005 at Amazon, leading marketplace fraud in Europe. We used ML models to fight fraud, completing in nanoseconds what would have taken us weeks manually. By automating these processes, we saved significant money. Amazon then reinvested those savings into growth initiatives.

That’s the growth cycle:

Make operations efficient
Capture the savings
Reinvest in growth

That flywheel is what made Amazon’s growth unstoppable.

But there’s a barrier: the training isn’t democratized.

Few workers know how to deeply understand customers, reduce costs, and reinvest intelligently.
Most organizations don’t have the clean data or documented processes needed for efficient innovation - let alone automation with AI.

I want every company to have access to this same growth cycle. For me, this is about expanding access to the American dream.

» For you, this is a massive opportunity. «

The fastest way to make more money is to help businesses make more money. These prompts and processes give you the tools to do exactly that.

But then, I had an insight:

Build a machine learning model that gives away the basics of efficient innovation for free - creating a viral hook - and monetize the specialized application to each company’s proprietary data and processes.

The mission was clear. And in 2015, I founded Potintia, Inc. ( https://potintia.com ).

My first step was straightforward: build the dataset to train the machine.

Expensive Lessons

My first principle was simple: creators of knowledge should be paid.

I designed my machine learning system to track provenance - where every insight came from and how often it was used - so authors could receive revenue when their ideas powered the model.

Because I wasn’t using hyperscale LLMs, attribution was still possible. And it honestly never crossed my mind that you could legally scrape the entire internet, train on it, and then sell the output back to the people who created the content, the way foundation models do today.

To find the best best business insights, I turned to the most traditional source of business intelligence: books.

Attempt #1: OCR the World’s Best Business Books

I bought hundreds of the top business books, cut off the spines, and scanned them page by page. The plan:

Use Optical Character Recognition (OCR) to read the text
Have the machine learn from it
If a customer later used knowledge from a specific author, that author would receive a revenue share

In theory, it was clean and ethical. In practice, it fell apart.

High-quality OCR was slow and expensive.
Fast OCR was cheap but inaccurate.
And when OCR did work, the machine still struggled to extract meaningful business insights from raw text.

That approach failed.

Attempt #2: Humans Read, Extract, and Synthesize

I hired a team to manually read books. For each title:

Two people read the book independently
Each wrote notes on key concepts and frameworks
Then they compared notes and produced a synthesized book summary

Please click on titles to read the book summaries on Google Drive:

This produced genuinely valuable insights, early innovation patterns, and repeatable processes.

But it was slow, expensive, and completely unscalable.

Attempt #3: Professional Structured Research

Next, I hired a team of management consultants, lawyers, product managers, researchers, and graduate students to manually curate thousands of data points from books, blogs, journals, news articles, and analyst reports. They researched topics including:

Please click on titles to view the research on Google Drive:

What took weeks and cost tens of thousands of dollars you can now do yourself in five minutes with Claude. I’ll show you how in the coming posts.

The research output was slightly useful. For some topics, the machine could now identify business topics.

But it still couldn’t understand them, or explain how to do anything with them. And it was still very expensive and slow.

This approach didn’t work.

I needed a way to get this level of research done at scale, for free.

The Mentorship Platform

The idea came to me while I was building the Techstars accelerator at Target. At the time, I was juggling three roles: running my own startup, serving as VP of Innovation managing $77 billion in revenue, and building a startup accelerator.

Across all three, I kept doing the same work:

understanding customers
mapping competitors
analyzing markets
figuring out investor dynamics
building new products and businesses that found the intersection of these

Whether the investor was Wall Street, Target, or a VC, the fundamental / atomic units of analysis were nearly identical.

And the expensive part was always the upfront research. I’ve spent millions gathering the data needed to justify a $30 million product launch or a large-scale technology rollout. Before you invest that kind of money in one direction, you need confidence in the direction.

While brainstorming and iterating with potential customers and friends, I had an idea:

create a platform where experts share their knowledge, use that knowledge to train the machine, and pay contributors when their insights are used.

I’d already seen the power of creators and influencers while building a social media marketplace at Target, my team and I patented social commerce years before Facebook and Instagram launched their versions. I knew the model worked.

In 2018, I brainstormed and documented Potintia’s first platform. This document took weeks to get right. Today with Claude Sonnet 4.5, I can generate the same level of detail and quality in well under an hour. I’ll show you how I do that in coming posts.

Then I flew around the world meeting development agencies across the US, Europe, Eastern Europe, and Asia. I ultimately hired Agile Engine and spent a few weeks in Kyiv with their team designing the platform.

The platform specs are here:

The cost: $130,000 for an MVP without ML, more than $800,000 with ML.

To demonstrate the business model, I needed the ML. And before investing nearly $1M, I needed stronger customer validation.

At the same time, something unexpected was working.

The Eight Cs

I’d written a short document synthesizing lessons from my career and the research we’d done so far. I called it The Eight Cs: eight fundamentals every business must understand:

C1–C4: Customer, Competition, Capabilities, Context
C5: Core analysis
C6: Corollaries (learning from similar solutions)
C7: Capital needs
C8: Cadence of continuous learning

» Click Here to Access the Eight Cs PDF on Google Drive «

The Eight Cs resonated immediately in Silicon Valley. Product managers loved it. My friends were emailing the PDF to one another. It was organically gaining traction.

I was invited to lecture on it at Carnegie Mellon’s Silicon Valley campus to master’s students in AI and business. Afterward, the Distinguished Professor who ran the program told me the students said it was the best guest lecture of the year.

The Decision: Validate Before Building

I had a choice:

Spend nearly $1M building the platform, or
Confirm customer demand first

The traction from The Eight Cs gave me my answer.

So I decided to open-source the Eight Cs and test the demand directly. That’s when I created HowDo ( https://howdo.com ).

My goal was simple: teach people how to innovate on their own without hiring expensive consultants. By open-sourcing, I could gather data from real conversations with the people interacting with my online content and use these as a free source for the content needed to train the machine.

HowDo.com and Real Conversations

I put everything I knew onto a website, hired a small team, and started sharing openly on HowDo.com.

In the first year, more than 100 people reached out - founders, product managers, operators - looking for guidance. They were stuck on a startup idea, a product problem, or a new business they were trying to build.

The content was helping, but there was a problem:

It wasn’t structured enough.
People didn’t know where to start, how to progress, or how to apply the ideas outside of tech.

The concepts resonated, but the path wasn’t clear.

That’s when I realized:

If I structured everything as discrete, repeatable processes - the same steps every company must take to innovate - that could be the foundation I needed to train the machine.

Life Lessons

As I started shaping these processes, I realized I needed to learn how to communicate them well and that the engagement on my website would translate to social media with the right content.

I tried hiring actors and social media influencers and supplying them with scripts and/or presentations. That was surprisingly non-linear, difficult, expensive, and unproductive.

So then I tried filming myself for social media, but I kept having full-scale panic attacks. I used to have serious stage fright.

But then I went to The Second City in Chicago (where most SNL cast members train) where I studied improv, stand-up, and writing for late night for multiple levels. It helped me break through the fear and communicate with far more confidence and clarity.

And then, just as I was finally ready to start filming, life hit hard.

Over a short period of time:

A tree collapsed on my house, ripping off half the roof and requiring me to move all my work into the basement.
The basement then flooded, destroying years of notes, books, and hard drives.
A close family member overdosed; I helped them through a coma and home.
Two months later, my dad was killed in a car accident on the way to his wedding, which I was officiating.
Weeks later, COVID hit.
Weeks after that, my partner’s mom was diagnosed with terminal brain cancer, and we quarantined to care for her.
During quarantine, my partner had a mastectomy and two emergency surgeries.
And then my grandmother, who I spoke with several times a week, started dying of cancer.

It was an overwhelming stretch of loss and responsibility. It slowed the work, but it did not stop it.

During that time, I read Mindset by Carol Dweck. The book became so important to me that when the basement flooded, the first thing I tried to find was that book. I still have the water damaged copy, and several additional copies. I’ve given a copy of Mindset to at least fifty people because I believe it is one of the most important books I have ever read.

It helped me adopt a growth mindset.

Even when life feels like a waking nightmare, I had to keep moving forward. Through the ambiguity and instability. I had to find the opportunity inside the pain. I had to get stronger. To improve. To rebuild.

Life is filled with painful lessons. Just like innovation.

These experiences were the crucible that forged my conviction: mindset is not a soft skill, but a critical foundation for business innovation.

This is when I began deeply studying mindsets and added it to the training data.

My growth mindset has helped immensely as I practice and learn AI. We’ll deep dive into how it will help you in a future post.

The Structured Process

To accelerate progress, I hired Sagence, a management consulting firm I had worked with for more than a decade. They had helped me raise over $130 million across Fortune 500s and startups. (Sagence was recently acquired by PwC - Congratulations again!)

Sagence took all the research we had gathered and helped me organize it into a cohesive system. The result was a 400-slide presentation that became the backbone of the course.

We also analyzed analyzed how the highest-growth companies operate, innovate, and scale. We analyzed the products and services offered by Alphabet (Google), Amazon, Apple, Meta, and Microsoft (Individual product teardowns are linked from each company’s name.) and created a strategy document to summarize the insights.

I then hired MIT professors, management consultants, product management leaders, and investment bankers to conduct deep research across dozens of topics and fill the gaps where our earlier work had fallen short.

I combined all of that research with the structured presentation and turned it into courses. Through seven rounds of iterative testing with customers, I organized everything into four core areas that every business must master to innovate and grow.

Mindset: Develop the mindsets that drive business innovation and growth.

Plan: Grow your business by designing solutions that customers need.

Tools: Use the tools that drive growth at Fortune 500s and startups.

Team: Build innovation capabilities that accelerate every initiative.

This structured system became the foundation for everything I would test later with AI.

Now I needed to test how this iteration performed for our customers. We put the courses on HowDo.com and the internet took over.

Traffic grew quickly and in just over a year we achieved over 50,000 monthly visitors on organic traffic alone. We iterated constantly. I ran dozens of live tests, hired more than forty people to take the full course and provide feedback, talked to several hundred HowDo.com customers, and continuously reworked the website and its contents to get the structure right.

This phase of the work took over a year and cost several hundred thousand dollars. Today, you can generate the same level of research in about one minute with Claude. The pace of change is unprecedented and its impact transformational. In coming posts, I will show you how to do this.

Technology was accelerating linearly. Amazon kept rolling out new web services, and machine learning improved steadily. My audience was consistently growing. HowDo’s process was getting better. And new clients were calling me asking for help.

» Then ChatGPT launched. «

**Oh f*ck…**

Hyperscale LLMs could suddenly do almost everything I had been trying to build. It looked like, with a bit more scale, LLMs might be the winning technical thesis.

Obviously, I dropped everything and spent nearly every waking hour testing my innovation process inside ChatGPT and trying to figure out LLMs. I stopped investing in new platforms or training my models until I understood what was happening.

For months, I tested my training data inside ChatGPT. ChatGPT 3.5 was still so nascent that none of the results made sense.

Then Microsoft invested $10 billion in OpenAI. That changed everything for two reasons:

I was building an ethical, attributed data set designed to train a model that pays creators for their knowledge; but Wall Street and one of the largest tech companies on Earth had just endorsed a strategy built on:
1. scraping the internet
2. stealing everyone’s content
3. training the model on their content
4. selling the content back to the people who created it via the model
Anyone looking to compete would need tens of billions of dollars and/or a revolutionary technical breakthrough.

My technical thesis and business model appeared dead. That forced me to rethink everything from first principles.

The Final Pivot: Humans + AI

Given that LLMs are a black box, I had never considered them a solution for business intelligence and automation.

LLMs are not deterministic, and they are not truly intelligent. They simply predict the next token. They are powerful, but not precise. They don’t understand process, sequence, or consequence. I still believe we will need entirely new technologies to become 99.999% accurate when using “AI” to make automated million-dollar business decisions.

AI will create Accelerated Innovation and Augmented Innovation long before it becomes Automated Innovation or anything close to true Artificial Intelligence.

And that’s when I had my biggest breakthrough:

I had been obsessed with teaching machines to innovate, but machines will not replace all human decision making in business for at least a decade. The real opportunity is teaching humans and AI to innovate together.

The humans who win the battle for jobs in business will be irreplaceable orchestrators: people who can manage multiple AI agents with unique disciplines (e.g. product, engineering, design, finance, and operations), align their output with industry best practice and process, and exercise excellent executive judgment and creativity.

To help these people succeed, I’m open sourcing my prompts and processes.

Two Years of Testing Prompts

Over the last several years, I have run these innovation processes through every AI model I could. I benchmarked all of them for innovation and product work.

One model kept rising to the top in my testing: Anthropic’s Claude.

Claude consistently outperformed the other major models I tested for product management and innovation work. In the future, that may change. I am always excited for new model launches.

My friends joke that I don’t have a social life because I spend so much time with Claude. They are only partially wrong. Working with Claude is learning how to communicate my ideas and grow my businesses. It’s all practice for working with humans.

Until recently, most AI responses were too immature or inconsistent to rely on. They required all of my experience, research, and domain knowledge just to steer them in the right direction and produce a reliable result, let alone a viable work product.

But performance has improved with the latest generation of Claude Sonnet 4.5 and ChatGPT.

Results are becoming much more stable
Simple prompts are producing relatively consistent results
In Claude: Math and presentation skills significantly improved

It’s not McKinsey, but it’s dramatically faster. And even when it’s wrong, you can iterate so quickly that the overall process becomes incredibly efficient.

It finally feels possible to share my prompts with you and trust they will work.

So here we go…

Why I’m Sharing Now

For the first time, the technology is finally capable of consistently applying the proven processes.

It is now possible to take the innovation processes I spent years refining and put them directly into people’s hands.

But let’s be clear, this isn’t a handoff. This is the moment to start the conversation. This is how we learn, improve, and grow the economy: together.

But before we dive into all that, I wanted you to understand the provenance behind this work. It represents millions of dollars of my own money and seven years of my life. I’m sharing it because I believe it can genuinely help you.

In the next posts, I’ll walk you step-by-step through the full process so you can learn how to innovate in your own career and company:

How to use AI to understand customers
How to structure your thinking and mindset to thrive with AI
How to turn vague ideas into product specs in a handful of prompts
How to turn AI into measurable growth, not just a content and prototype generator

As we go through this together, I look forward to hearing from you, learning what you need, and refining this so it works for you.

Best, West

PS: I still believe we’ll need technologies beyond current LLMs to achieve fully autonomous AI. So I’m still (slowly) working to build the platform. If you want to help, please reach out.

Business Evolution by West Stringfellow

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