The AI Readiness Checklist: 5 Things to Fix Before You Automate Anything

Most companies get the AI conversation backwards.

They start with the tools. Which AI platform should we buy? What can we automate first? How do we deploy agents? These are reasonable questions — but they're the wrong questions to ask first.

In my experience, the companies that get the most out of AI are the ones that fixed their operational foundation before they automated anything. The ones that didn't? They spent five or six figures automating broken processes — and all they got was faster, more expensive chaos.

This checklist isn't about whether your company should use AI. You should. It's about whether your revenue operations are ready for AI to actually work. If you're a revenue leader at a growth-stage B2B company, these are the five foundations to get right first. Be honest with yourself on each one. The gaps you find here are the gaps that will determine whether AI becomes an accelerant or an amplifier of existing problems.

1. Data Integrity

Most companies think their data problem is a tool problem. It's not. It's a governance problem.

You can have the best CRM on the market and still not trust your pipeline data. The issue isn't usually the system — it's what's inside it. Duplicates. Missing fields. Conflicting data across objects. Stale records that nobody knows how to interpret because the person who set them up left two years ago. Fields that require manual entry but nobody fills out consistently because there's no automation doing it for them.

But here's the thing that gets overlooked most often: the real data integrity issue isn't any individual data problem. It's that there's no system or process in place to continuously vet, correct, and flag the data that matters most. No governance cadence for reviewing account data, opportunity data, or quote data. No automated alerts when critical fields are missing or conflicting. The data just degrades by default, and nobody notices until the board deck doesn't add up.

Ask yourself honestly:

• If your CEO asked for a pipeline report right now, would you trust the numbers without manually checking them first?
• Do you have a defined process for identifying and resolving duplicate records, missing fields, and conflicting data across your core objects (accounts, contacts, opportunities, quotes)?
• Is there any automated system that flags data quality issues before they compound — or does your team find errors manually, after the damage is done?
• Could you explain to a new hire how every critical field in your CRM gets populated, by whom, and how often it's validated?

If you answered "no" to two or more of these, your data foundation isn't ready for AI. Any automation you build on top of it will inherit every problem your data already has — and scale those problems faster than your team can fix them.