Why 90% of “AI-Readiness” Spend Is Corporate Procrastination

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:

1. Clear business pain

2. Clear data input

3. 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:

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

1. Identify a real business pain

2. Audit what data you already have

3. Write a 1-page product brief

4. Ship a low-code prototype in 4 weeks

5. Measure value

6. 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.