Why Most AI Readiness Projects Fail (And How to Fix It)
90% of AI readiness spend is wasted. Learn the 6-step framework that turns AI chaos into working pilots in 4–6 weeks.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
The $800K Science Project
We’ve seen it again and again.
“Let’s get ready for AI.”
Exec signs off. Teams gear up.
Twelve months later:
Another lakehouse license.
A prompt-engineering retainer.
An LLM pilot no one uses.
$800K spent.
Zero models in production.
Sound familiar?
The problem isn’t tools. It’s clarity.
90% of AI-readiness spend is corporate procrastination. Until you lock three things—a real business pain, clear data inputs, and a path to usable value—everything else is noise.
What the Best Teams Do Instead
1. Define a Real Business Problem
Talk to end users. Not managers. Not consultants.
Look for bottlenecks in:
Customer Success: manual churn prevention.
Sales: no visibility on deal health.
Ops: endless copy-pasting between tools.
Your AI project must solve pain, not “explore potential.”
2. Audit the Data You Already Have
Skip the tool shopping. Start with:
What data exists?
Where is it?
Is it accurate?
What’s missing?
Rank projects by ROI vs complexity. Avoid high-risk, high-complexity projects with low upside. Prioritize quick wins.
3. Write a Product Brief
Treat AI like a product, not an R&D experiment.
Example Brief:
Who is this for? CS team.
Current state? No visibility into at-risk customers.
Result? Churn, poor retention, weak upsell.
Solution? Flag risky accounts + highlight upsell plays.
Complexity? Moderate (needs metric alignment + feature store).
This keeps teams aligned and expectations clear.
4. Ship a 4-Week Prototype
No GPUs. No Kubernetes. No multi-year roadmap.
One input → One output → One user interaction.
Examples:
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
If you’re not tracking value, you’re wasting money. Measure:
Time saved.
Revenue unlocked.
Risk reduced.
Without this, your AI isn’t productized—it’s 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 production.
Until then, every dollar spent is a bet without a model.
TL;DR — The Anti-Procrastination AI Framework
Identify a real business pain.
Audit existing data.
Write a 1-page product brief.
Ship a prototype in 4 weeks.
Measure ROI in business terms.
Scale only after it works.
This is how companies actually ship AI—and why they’re 12 months ahead of competitors still “getting ready.”
For more on aligning AI and data strategy, see our AI readiness checklist.
Most AI readiness spend is wasted. If you want to cut through the noise, align your teams, and launch usable AI fast, Book a Data Strategy Assessment.
