Is Your Data Ready for AI?

Introduction

AI doesn’t fix messy data.
Before you build agents, deploy LLMs, or invest in advanced automation, your organization must pass a fundamental checkpoint: data readiness.

AI models are only as good as the data they’re trained or prompted on. Without accessible, clean, and connected data, even the best-designed AI system will deliver poor insights, inaccurate outputs, or fail to act at all.

This article outlines how to evaluate whether your marketing, sales, customer, or operations data is ready for AI—and what to do if it's not.

What “Data Readiness” Actually Means

A company is “data ready” when:

  1. Key data is findable
    Teams can quickly locate customer, financial, and performance data without relying on a specific individual.

  2. Data is accessible
    Information can be pulled from your CRM, analytics, or warehouse via APIs, connectors, or manual exports.

  3. Data is clean
    Fields are standardized (e.g. lead source, timestamps), duplicate records are resolved, and inconsistent naming is fixed.

  4. Data is connected
    You can join data across systems (e.g. ad spend in Meta to MQLs in HubSpot to revenue in Stripe).

  5. Data is timely
    Updates happen regularly—ideally real-time or daily. Stale data creates decision lag.

Why This Matters for AI

If even one dataset is missing or misaligned, the model can fail silently—or worse, confidently give wrong answers.

The 5-Point Data Readiness Audit

1. Inventory Your Tools

Make a list of all tools touching:

  • Marketing (e.g. HubSpot, Meta, LinkedIn, Google Ads)

  • Sales (e.g. Salesforce, Gong)

  • Product (e.g. Segment, Mixpanel, Amplitude)

  • Customer success (e.g. Intercom, Zendesk)

  • Finance (e.g. Stripe, QuickBooks)

Note what data each tool owns and whether it can be exported or accessed via API.

2. Score Each Tool’s Accessibility

Focus first on tools with high business value and high friction.

3. Assess Data Cleanliness

  • Are naming conventions consistent?

  • Do leads, campaigns, or accounts have unique IDs?

  • Are timestamps accurate and in the same format?

  • Are there empty fields, duplicates, or strange entries?

Spot checks often reveal more than audits.

4. Check for Connectedness

Ask:

  • Can you tie ad spend to lead source to sale to revenue?

  • Can you match support tickets to customers to contract value?

  • Do tools share customer IDs or emails for joins?

If the answer is no, you’ll need a data layer or warehouse to connect them (e.g. BigQuery, Snowflake, Hightouch, Census).

5. Test a Pilot Query

Try answering a basic strategic question with your existing data stack:

“What was our ROI on LinkedIn ads for customers who churned in less than 6 months?”

Can you answer it? How long does it take? How many tools are involved? This reveals readiness gaps better than any checklist.

What If You’re Not Ready?

  1. Start with a data pipeline: Use ETL tools like Fivetran, Airbyte, or Make to centralize data.

  2. Define a schema: Create consistent labels and fields across tools.

  3. Add tracking: Use Segment or RudderStack to log customer events.

  4. Hire a data engineer or analyst to build foundational queries.

  5. Audit quarterly: Make data audits a recurring step in your automation roadmap.

Free Template:

Download the Marketing Data Audit Checklist
Covers tool inventory, data access scoring, cleaning checklist, and query test worksheet.

Discovery Question to Ask Teams:

“If you had to guess, how many tools are holding key marketing data across your org—and could they be joined in one report?”

AI AutomationFrancesca Tabor