It's not sexy to say, but most of AI transformation has nothing to do with AI.
There are 10 steps in the sequence of making an internal process or external product AI-native. Only 1 step is AI, and ironically, the other 9 steps are the far harder part.
Identify the Problem
Find the manual process worth automating. Turn your brain off autopilot and turn on your "suck meter."
Funny enough, your company becomes more efficient just by mapping out your processes even if you don't introduce AI.
Understand the Workflow
Map how people actually work today. Grab an 8.5x11 piece of paper or Excalidraw and create a flow chart of the workflow from beginning to end.
This is the least sexy part, but generally where the people driving transformation (FDE, GTM engineer, etc) should spend the majority of their time.
Collect the Data
Gather sample inputs, documents, edge cases.
Example: for my content machine AI workflow, I gathered past Slack messages and Notion transcripts to test automated ideation.
Build the Prototype
This is the AI part. Whether it's engineer-led or SME-led, the goal is to test your hypothesis that there's a better way of doing things for yourself as customer zero. Don't worry about code cleanliness, don't worry about scalability.
If you're looking for inspiration, check out these 42 high ROI AI use cases by category.
Test and Iterate
Before you take the process from single player (only you using it) to multiplayer (many users), you want to beat it up with as many rounds of work and feedback plus edge cases as possible. Turning every process into a self-improving loop before scaling is key.
Integrate with Systems
Point-in-time data is good for testing the workflow, but live data is necessary before going into production.
Roll Out and Train
Whether the new process lives on a live link, on GitHub, or an internal library, the next step is hand-holding your peers and users through the onboarding process of your new workflow or product.
Drive Adoption
Embed the workflow in your culture where adoption is tracked, ideas and feedback are celebrated, and new or creative use cases become social currency in your business.
Empower Contribution
Treat your new process like an open source project. Allow users to become contributors. Whether they are literally pushing code or are simply empowered to add ideas and feedback to a kanban board that gets serviced by engineers, make everyone feel like a builder.
Measure and Capture Value
If you're in the experimental phase of AI adoption in your company, forget ROI. The goal is to empower people to throw a lot of ideas at the wall and see what's worth focusing on. You don't need to be scientific during this process.
If you're in the scale-up phase of AI in your business and you need to realize hard ROI, you need to reskill employees attached to this process, undershoot your approved hiring roadmap, or measurably increase ACV, conversion rate, or sales cycle speed.
Final Thoughts
Here's the bottom line: AI transformation is 10% AI and 90% everything else. The steps that matter most are identifying the right problems, mapping workflows, collecting data, testing relentlessly, driving adoption, and measuring results.
The companies winning at AI aren't the ones with the fanciest models. They're the ones doing the unglamorous work of understanding their processes and empowering their people to contribute. And if you're still frustrated with AI, chances are you're skipping one of these nine non-AI steps.
Start by mapping one manual process this week and see where the opportunities are.