Every AI adoption conversation I’ve been part of starts in the same place: how do we implement this?
Which tool. Which model. Which vendor. Which team owns it. What’s the rollout plan.
These are reasonable questions. They’re also the wrong ones to start with.
The question that gets skipped
Before how, there’s whether. Whether this problem is actually ready for AI. Whether the underlying process is clean enough to automate. Whether the data exists in a usable form. Whether the organization has the capacity to adopt something new right now. Whether a simpler solution — a rule, a process change, a better spreadsheet — wouldn’t solve it faster and cheaper.
I’ve watched organizations skip this question and spend six figures on AI implementations that failed not because the technology was wrong, but because the problem wasn’t ready for it. The data was a mess. The process had four upstream dependencies that nobody had mapped. The team using the output didn’t trust it and worked around it anyway.
The technology worked fine. The system didn’t.
Why the question gets skipped
Partly it’s pressure. There’s a real organizational cost to being the person who slows down an AI initiative by asking whether it should happen. Leadership has committed. Budget has been allocated. The announcement has been drafted.
Partly it’s the nature of the tools available. Every AI vendor wants to show you what their product can do. Nobody is selling “determine if you’re ready for AI before buying anything.” There’s no commercial incentive to build that.
So I built it myself.
What Wolflow actually does
Wolflow is seven sequential decision gates. Each one has a specific question it’s trying to answer:
- Is the problem well-defined enough to measure?
- Does reliable, sufficient data exist?
- Is the underlying process stable enough to automate?
- Is there genuine ROI, or just an assumption of ROI?
- Does the organization have capacity to adopt and maintain this?
- Is AI actually the right tool, or would something simpler work?
- Is there a responsible deployment path with appropriate oversight?
If the answer to any gate is clearly no, Wolflow stops and tells you what needs to be resolved first. It doesn’t fail the organization — it gives them the specific thing to fix before proceeding.
Most runs don’t make it through all seven gates. That’s the point.
What this has to do with the job I’m trying to do
The AI adoption strategist role — the one I’m positioning for — is fundamentally about this question. Not about implementing AI. About helping organizations understand whether and where AI belongs, and building the conditions for it to actually work.
That’s a different job than being an AI engineer. It requires understanding business processes as much as technology. It requires being willing to recommend not building something, which is a difficult thing to do in an environment where AI is being treated as a default.
I built Wolflow because I needed a way to think through this rigorously. It turned out to be useful to other people. But its real value to me is as a demonstration: this is how I approach the problem. This is what I think the job is.
If that framing resonates with what your organization is trying to do, I’d like to talk.