Do These 9 Non-AI Steps First

Alex Lieberman

@businessbarista·Jun 8

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.

Step 1: Identify the problem

- find the manual process worth automating. turn your brain off autopilot & 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.

Step 2: 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.

- Least sexy part, but generally where the people driving transformation (FDE, GTM engineer, etc) should spend the majority of their time.

- Before you reimagine a process you have to become an expert in that process. Which means you either need to have the business context yourself or absorb it through osmosis (See what @DBredvick did at @vercel)

Step 3: Collect the data

- Gather sample inputs, documents, edge cases

- Example: for my content machine ai workflow, I gathered past slack messages/notion transcripts to test automated ideation & I pulled past X/linkedin posts to build .md files of my content voice

Step 4: Build the prototype [The AI Part]

- Whether its 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, just worry about proving there's a there there.

Step 5: Test & iterate

- Validate with real users and edge cases

- 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 & feedback + edge cases as possible. Turning every process into a self-improving loop before scaling is key.

Step 6: Integrate with systems

- Point-in-time data is good for testing the workflow, but live data is necessary before going into production.

- Example: for my content machine, i'm hooked up to notion/gmail/slack for content ideation & i'm hooked up to X & Linkedin to post content once it's ready to go.

Step 7: Roll out & train

- Whether the new process lives on a live link, on GitHub or an internal library, next step is hand-holding your peers/users through the onboarding process of your new workflow/product.

Step 8: Drive adoption

- It's actually pretty simple (just not easy). Introduce a new workflow that saves someone a lot of time and integrates with their already existing behavior so they don't have to deal with re-education.

- Embed the workflow in your culture where adoption is tracked, ideas & feedback are celebrated, and new/creative use cases become social currency in your business.

Step 9: Empower contribution

- Treat your new process like an opensource project. Allow users to become contributors. Whether they are literally pushing code or are simply empowered to add ideas/feedback to a kanban board that gets serviced by engineers, make everyone feel like a builder.

Step 10: Measure & capture value

- Everyone is ROI obsessed atm. If you're in the experimental phase of AI adoption in your company, fuck ROI. The goal is to empower people to throw a lot of shit at the wall & see what's worth focusing on. You don't need to be scientific during this process. Intuition is more than enough in gauging what's working vs. not working.

- 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/sales cycle speed.

Everyone wants the magical AI button. But if you skip the boring groundwork, that button does nothing. These 9 non-AI steps are where the real transformation happens, and they work even if you never ship a single model. Use this as a pre-flight checklist before you burn budget on “AI projects” that quietly die in a shared folder.

Turning The 9 Steps Into A Playbook

Run each new AI idea through this sequence before you open an LLM playground. First, identify one painful, repetitive process and sketch how it actually works today, step by step. Collect real inputs and edge cases from the people doing the work, then ship a scrappy prototype and beat it up with those same users. Once it’s useful for you as customer zero, wire it into live systems, train people in how it changes their day, and make usage visible so wins are celebrated. Finally, give your team permission to tweak, break, and improve the workflow so it keeps evolving instead of becoming yet another abandoned internal tool.

The Psychology Behind Doing Non-AI Work First

Clarity beats cleverness: mapping processes forces you to see where the real friction is, not where the shiny tools are.
Ownership creates momentum: when operators help design workflows, they’ll actually use and defend them.
Progress without models: just documenting, tightening, and integrating workflows often delivers ROI before AI enters the chat.
Behavior > features: adoption happens when new workflows fit existing habits, not when you bolt on another “AI tool”.
Contribution compounds: treating your workflow like open source turns every user into a sensor for new edge cases and ideas.

Real-World Style Plays Using These 9 Non-AI Steps

Vercel documented how sales and go-to-market engineers actually worked deals, collected real call notes and edge-case requests, and only then layered in AI to draft technical responses that mirrored top performers.

Morning Brew built an internal content workflow by first mapping idea-to-publish steps, gathering past posts and Slack threads, and then integrating AI into a system that pushes drafts straight into their existing Notion and social channels.