The Vector Method
Strategy Is a Filter, Not a Wish List
Most AI strategies fail before implementation starts. Not because the technology doesn't work—because the strategy was built backwards.
Companies ask "what can AI do?" instead of "where do we lose the most value?"
First question: 47 potential use cases, confused leadership, six months of pilots that never scale.
Second question: 2-3 initiatives that actually matter.
My job isn't to find AI applications. It's to filter ruthlessly—so you invest in what works and ignore what won't."
Here's everything you could do" isn't strategy."
Here's what to do first, and why" is strategy.
What Makes This Work
Most automation fails because it optimizes for the wrong things. Here's what actually matters.
Judgment Over Frameworks
Frameworks are starting points. Every business is different—constraints, data maturity, risk tolerance. I use structured thinking to evaluate, but recommendations come from judgment built through years of strategy and product work. You're paying for someone who knows which questions matter and how to read the answers.
Kill More Than You Start
The kill list is often the most valuable deliverable. Every organization has limited capital, attention, and execution capacity. Wrong initiatives don't just cost money—they cost the opportunity to do the right thing. Better to do two things well than five things poorly.
Assumptions Visible
Every projection depends on assumptions. Most consultants bury them. I state them explicitly. If my analysis assumes cleaner data or more capacity than you have, I want to know before you commit. Transparent reasoning, better decisions.
You Already Know Something Isn't Right
The AI initiatives that keep stalling. The vendor pitches that all sound the same. The internal debates that go in circles. Let's cut through it.