Day 5: The Strategy Day / Chee Ann

Not every day is a code day. Today was a thinking day.

I sat down with GStack's office hours skill to pressure-test how I've been positioning my AI consulting. Went in with one assumption. Came out with a completely different frame.

The assumption I walked in with

"I need to pick fast clients."

That was my answer to the biggest problem in consulting: slow companies waste your time. They want AI but can't make decisions. They sit on proposals. They schedule meetings about meetings. I figured the fix was simple. Just pick clients who already move fast, and skip everyone else.

I had data backing this up. I'd scored my best client on six variables and he hit 6/6. Fast decisions, technical literacy, clear budget. Easy. Just find more of him.

What GStack told me

The skill pushed back. Not on the data, but on the framing.

Instead of "find fast clients," it asked: what if the reason most companies are slow isn't because they're bad at decisions? What if they're slow because they're working in a black box?

And I sat there thinking... that's literally what I've been telling people.

The click

I'd been carrying two ideas separately:

Idea 1: Companies are slow because they're operating in a black box. It's not that they don't ask questions. Of course they ask. But every important question needs dynamic context that lives beyond the dashboard they have. So the CEO asks "why did margins drop?" and the answer is... wait three days for the analyst to repull, aggregate, and reprocess the data. By the time the answer arrives, it raises a new question. Which takes another few days. The feedback loop is so slow that people just... stop asking. That's not a data problem. That's a paralysis machine disguised as a reporting system.

Idea 2: I need to only work with fast clients to protect my time.

They sound unrelated. But the AI connected them. (Rude, honestly. I was very comfortable with my client filter.)

If companies are slow because their data can't answer follow-up questions in real time, then the problem isn't the people. The problem is the infrastructure between the question and the answer. And if that's true, then "pick fast clients" is just avoiding the problem I'm literally built to solve.

I flipped from "I need to pick fast clients" to "I need to give everyone visibility so they can be fast."

That's not a client filter. That's a product. Slightly annoying when your own AI calls you out on your own logic, but here we are.

What I actually shipped

Strategy (9 commits):

Pricing v2 (DISCOVER > DEPLOY > OPERATE)
Ideal client scoring system (6 variables)
Proposal template for AI operations audits
Distribution plan: "Compounding in Public"
Service agreement template v2
Office hours breakthroughs documented (8 frameworks from one session)
Client opportunity map (20 insights ranked by impact)

cheeann.com (1 commit):

New tagline: "Compounding in public, one artifact at a time"

What I'm noticing

Five days in and the pattern is clear. The code days feel productive. The strategy days feel uncomfortable. But the strategy days are the ones that change direction.

I would've kept filtering for fast clients for months if I hadn't sat down and let an AI challenge the premise.

Still processing this one.

Running total

Day 5 of 30
10 commits, 2 repos
1 business model reframe
8 frameworks from one session
0 blockers