Foundation First
Not uploaded. Not emailed. Put on a truck.
That's the data infrastructure underneath a lot of companies running AI initiatives right now. Most don't have an AI problem. They have a data problem they're hoping AI will solve. Foundation First is the practice of fixing that before you build anything on top of it.
Decisions get made on calls and leave no record. Institutional knowledge walks out when people do. The operational data supposed to feed an AI initiative is sitting in someone's inbox, in PDF format. A year into digital transformation at a legacy logistics company, that's the actual starting point: mapping those decisions, tracing what data each one needs, building capture points that never existed.
Nobody puts that work in the press release. It's not interesting until it works. But without it, AI doesn't give you better answers. It gives you wrong answers you can't argue with, generated faster.
Five of the Eight Domains
Domain 01
AI automates a specific job. Name it first.
Leadership wants AI, but there's no target workflow. Just a mandate to "explore."
One named workflow, measured today, that AI would clearly speed up or replace.
Domain 02
AI can only use what's actually captured.
Your team pulls spreadsheets before every meeting because the systems don't talk to each other.
The data needed for most decisions is captured automatically. No exports required.
Domain 03
Available isn't the same as trustworthy.
Someone always has to "clean it up first." That someone is usually the same person.
Data moves clean and consistent from source to decision. The team trusts it without checking.
Domain 04
If decisions happen in meetings, AI has nowhere to plug in.
Key operational calls happen in someone's head or a hallway conversation, not in a system.
Decisions are driven by defined inputs and documented logic. AI can augment that. It can't replace a hallway.
Domain 05
Tools don't fail. Adoption does.
New systems get used for three weeks, then quietly abandoned. Change management is a recurring post-mortem item.
The team iterates on tools because they solve real problems. Adoption isn't a project. It's a reflex.
Score Yours
Five questions, two minutes, five of the eight domains. Auto-reply tells you what's blocking you and what to fix first.
Take the Assessment →The Foundation-First Sequence
Stage 01
Map every key operational decision against the eight domains. Find what data it needs, what's missing, and what's never been captured at all.
Stage 02
Design the data flows, capture points, and decision logic that have to exist before anything automated is layered on top.
Stage 03
Build in production, not a prototype. The infrastructure that actually runs the decision, with a named internal owner accountable for it.
Stage 04
Run it without outside support. The test isn't whether it works. It's whether it keeps working when the external help is gone.
Foundation-First Assessment
Five questions, five of the eight domains. Auto-reply delivers your score bracket with a plain-language interpretation and what to fix first. The full assessment covers all eight.
Foundation Gaps
The infrastructure isn't ready. The auto-reply covers what to fix first, and why skipping this stage is how AI initiatives fail.
Partial Foundation
Some pieces in place, specific gaps to close. Usually one domain is bottlenecking everything downstream.
Strong on these five
Solid across these five. The three this doesn't cover (infrastructure, security, ownership) often decide whether a build ships. The full assessment covers all eight.
One question per domain, five of the eight. Scored 1–3. Takes two minutes.
Free. No account needed. Results by email.
About
In 1992, I found the internet before most people knew it existed: Gopher, FTP servers, raw text. When browsers arrived, I taught myself HTML. The next year I heard about a startup called AudioNet, putting sports and radio on the internet. Got a meeting with the COO, showed up with a webpage sketched on a notepad, got hired on the spot.
AudioNet became broadcast.com. Yahoo bought it for $5.7 billion. Twenty years at Yahoo and Verizon Media followed, shipping the industry's first rich media ad units and eventually running a global team at $500M in annual revenue. Then VP of Engineering at 84.51°, Kroger's data company, running a 150-person org building advertising infrastructure on data and AI.
Now VP of Product Solutions at a legacy logistics company, leading AI and digital transformation from the inside.
What I actually build is the foundation that makes AI work: capture points for decisions that currently happen on calls and leave no record, formats that work across systems instead of inside one person's spreadsheet, clean inputs before anyone touches a model. The gap between AI that changes how a company operates and AI that lives in a slide deck is almost always in the infrastructure, not the model. I've built that foundation before. I'm building it again now.
I still write code, because it's the fastest way to find out what I don't actually understand yet.