Insight
7/4/25
Why 90% of “AI-Readiness” Spend Is Corporate Procrastination
Table of Contents
Most AI-readiness efforts burn money and ship nothing. Here’s the framework we use to go from chaos to working AI in 4–6 weeks.
We’ve seen it again and again:
“Let’s get ready for AI.”
Exec signs off. Teams gear up.
12 months later:
Another lakehouse license
A prompt-engineering retainer
An LLM pilot no one uses
$800K spent
Zero models in prod
Sound familiar?
The problem isn’t the tools.
It’s the lack of clarity.
90% of AI-readiness spend is just noise
Look at this chart of 2,000+ “AI tools”:
Everyone’s buying something.
But no one’s actually shipping.
Most orgs are skipping the only three things that matter:
Clear business pain
Clear data input
Clear path to usable value
Until those are locked, everything else is distraction.
Here’s What the Best Teams Do Instead
1. Define a Real Business Problem
Talk to end users. Not managers. Not consultants.
Find the bottlenecks, frustrations, delays.
Good places to look:
Customer Success (manual churn prevention)
Sales (no visibility on deal health)
Ops (copy-pasting between tools)
Your AI use case must remove pain, not “explore potential.”
2. Audit the Data You Already Have
Don’t start by buying tools. Start by understanding:
What data exists?
Where is it?
Is it accurate?
What’s missing?
Use this audit to rank ideas by ROI vs complexity.
Avoid projects that are:
High risk, high complexity, low business upside
Prioritize ones that are:
Low effort, high trust, immediate user value
3. Write a Product Brief
Treat it like a product, not a research experiment.
Example Brief:
Question | Answer |
|---|---|
Who is this for? | CS team |
Current state? | No visibility into at-risk customers |
Result? | Churn, low retention, poor upsell |
Solution? | Flag risky accounts + highlight upsell plays |
Complexity? | Moderate: requires metric alignment and feature store |
This aligns teams and sets clear expectations.
4. Ship a 4-Week Prototype
No GPUs.
No Kubernetes.
No multi-year roadmap.
One input → One output → One user interaction
This could be:
A Streamlit app
A Slack bot
A RAG assistant in your support docs
A Google Sheet with smart recommendations
Fast feedback = fast wins.
5. Measure the Delta
Track 3 things:
Time saved
Revenue unlocked
Risk reduced
If you’re not tracking these, your AI isn’t productized.
It’s just a science project.
6. Only Then: Scale Infrastructure
Scale AFTER value, not before.
Once people are using the prototype:
Add observability
Lock schemas
Harden pipelines
Move to prod
Until then, every dollar spent is a bet without a model.
TL;DR — The Anti-Procrastination AI Framework
Identify a real business pain
Audit what data you already have
Write a 1-page product brief
Ship a low-code prototype in 4 weeks
Measure value
Scale only after it works
This is how companies actually ship AI—and how they stay 12 months ahead of the ones still “getting ready.”
Want help getting from chaos to clarity?
Align their teams
Fix their data layer
Implement AI roadmap aligning lowest lowest-hanging business objectives.
Complexity vs RO Matrix
Launch usable genAI / LLM products fast
👉 Book a 30-minute AI-readiness teardown
We’ll walk through your stack, use case, common proven industry use cases and blockers, and hand back a 3-step action plan in 1 call.
