How I Wrote AI Operations: The Solution to the Enterprise AI Adoption Gap with AI

I wrote AI Operations: The Solution to the Enterprise AI Adoption Gap in just 10 days, but it took 20 months of learning to get to that point. Organizing years of knowledge, refining it, and working alongside AI as a writing partner unlocked a completely new way of thinking. It wasn’t just faster—it was transformative. And what’s even more interesting is that this is the worst AI will ever be. As the technology improves, so will the ability to create at this pace, making the process even more seamless.

The biggest challenge was managing context. AI is brilliant in short bursts, but writing an entire book requires keeping track of ideas across thousands of words. Even with that constraint, it felt like magic.

I used OpenAI’s ChatGPT 4o inside the Projects feature. The first step was building a detailed, three-level-deep outline with a numbering system to keep everything organized. Once the outline was set, I uploaded it into the ChatGPT project, giving the AI a framework to reference. To maintain consistency, I also uploaded a Writing Style & Tone Guide to ensure the book had a unified voice.

Writing wasn’t a matter of asking AI to generate entire chapters. Instead, I tackled one section at a time, refining each piece before moving on. OpenAI’s Canvas feature helped fine-tune length, improve clarity, and sharpen messaging. Once a section was finalized, I copied it into Google Docs to compile chapters. After finishing a chapter, I uploaded it back into the ChatGPT Project, allowing the AI to reference previous sections and maintain continuity across the book.

One of the most valuable aspects of this process was leveraging Deep Research. I compiled long lists of AI statistics and Change Management statistics, which I uploaded into my ChatGPT project. This allowed me to search those files at any point to find relevant data that supported my points. Rather than hunting down stats in real-time, I could pull from curated, verified sources, ensuring the book was backed by real-world insights.

Maintaining continuity across chapters was another key strategy. Periodically, I asked the AI to review prior sections and suggest ways to reference earlier discussions, reinforcing key themes throughout the book. This helped create a more cohesive narrative rather than a collection of loosely related insights.

For visuals, I used a consistent prompt to generate images with a unified style. While the process isn’t perfect, ChatGPT’s new image editing tools helped refine the artwork.

At various points, I was juggling different AI-driven tasks—using voice mode to brainstorm, running multiple Deep Research agents to explore new ideas, and testing different workflows to see what worked best. Writing this book became an experiment in AI-assisted creativity. It wasn’t just about what the book says but how it was made.

If you’re interested in how enterprises can bridge the AI adoption gap and integrate AI effectively at scale, check out AI Operations: The Solution to the Enterprise AI Adoption Gap at aioperationsbook.com.