Article
Taking Action: 1-Step Closer to AI-Ready B2B Data
Best Practices: AI-Ready Data

By now, you’re well aware that AI is changing how B2B go-to-market (GTM) teams engage buyers, qualify leads, and drive pipeline. As you prepare for this shift towards AI, it’s critical that you don’t lose sight of the fact that AI isn’t plug-and-play – it’s data-dependent. If your CRM is cluttered, your intent signals are inconsistent, or your lead-to-account mapping is broken, your AI strategy will underperform before it even begins.
To unlock real results from AI – faster routing, better scoring, smarter engagement – you need a rock-solid data foundation. That starts by asking the right questions.
In recent blogs, we explored the reasons GTM teams feel obligated to get their data AI-ready and the top questions they have as they embark on their journey to AI-readiness. In this blog, let’s dive into the actions you can take today to start driving impact.
How do we identify and resolve duplicate or incomplete records in our CRM?
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How to audit and fix duplicate CRM records in 2026
Your CRM is supposed to be the system of record for your entire go-to-market operation. In practice, it often becomes a graveyard of duplicate contacts, mismatched accounts, and stale fields that no one trusts. When that happens, every downstream system that depends on CRM data starts making bad decisions.
Scoring models weight the wrong signals. Routing sends leads to the wrong reps. Segmentation breaks. Campaigns reach the same buyer five times across three different records. The problem is not that your team is careless. The problem is that CRM data quality issues compound fast, especially when you are pulling data from multiple sources and running enrichment at scale.
This guide walks through how to find the root causes of duplicate records, build governance rules that hold, and maintain data validation and cleansing as an ongoing operation rather than a quarterly fire drill.

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Measuring TAM coverage, not just TAM size
You already know your TAM number. That number looks useful in planning decks and board slides. It tells you how many accounts fit your ICP and how much revenue sits in the market.
It does not tell you whether your team has enough territory coverage to work that market well.
That gap matters. In outbound TAM development, territory performance depends on coverage over volume. If you assign a large market without measuring who you can reach, who you can route, and who you can engage across the buying group, you create blind spots inside your territory model.
This is whereEnterprise Data Management becomes operational. It gives you a way to measure TAM coverage at the account, contact, and buying group level. You stop asking how big the market is. You start asking how much of it your team is equipped to work right now.

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Turning account engagement into buying momentum with Custom Audiences
Account engagement rarely fails from lack of activity. It fails when you see activity at the account level but miss who is driving it, how interest is spreading, and when to act. That gap slows follow-up, weakens targeting, and leaves pipeline exposed.
If you want stronger account engagement, you need more than account coverage. You need Custom Audiences built from buying-team signals. That gives you a way to move from broad account reach to coordinated influence across the people who shape a deal.
That shift matters because B2B buying is already group-driven. According to 6sense research, 92% of B2B purchases involve groups of three or more people. A lead-centric model misses that reality. Your targeting should reflect the full buying team.


