Article

The Top 10 Questions B2B GTM Teams Ask About Getting Customer Data Ready for AI

AI-Readiness Best Practices

Metro Intent Data
Metro Intent Data
Metro Intent Data
Metro Intent Data

Table of Content

AI is no longer a future concept – it’s already reshaping how modern Go-to-Market (GTM) teams prioritize leads, personalize outreach, forecast revenue, and identify closeable business. But before any of that magic happens, there’s a critical prerequisite: your customer data needs to be AI-ready!


GTM leaders are quickly realizing that messy, incomplete, or disconnected data renders even the smartest AI models completely useless. 


So how do you get your customer data in shape for AI?


Here are the top 10 questions GTM teams are asking as they take on this challenge:

Is our data structured and centralized enough to support AI use cases?

AI models thrive on consistent, structured data. If your customer data lives in disconnected silos – CRM, MAP, intent platforms, spreadsheets – it’s time to centralize and standardize it before layering on AI.

How do we identify and resolve duplicate or incomplete records in our CRM?

Duplicates and partial records confuse AI and lead to inaccurate scoring or prioritization. Identity resolution and data enrichment are critical steps to create a clean, unified view of each customer and buying team.

Do we have a complete picture of the buying committee at each account?

AI models need visibility into all stakeholders involved in a deal – not just the lead who filled out a form. That means enriching profiles and building full buying teams with roles, titles, and influence levels.

Are we capturing and tagging the right signals (intent, engagement, channel activity)?

Your AI is only as smart as the signals it receives. GTM teams are asking: are we tracking email opens, content views, meeting attendance, website visits, third-party intent, and product usage in a consistent, usable way?

What is our lead-to-account matching process – and is it accurate?

One of the most foundational steps in making customer data AI-ready is ensuring leads are reliably matched to the correct account. Poor L2A matching leads to broken workflows, inaccurate scoring, and missed opportunities.

How often is our data refreshed and updated?

Outdated data is a silent killer. GTM teams want to know: are we refreshing our firmographics, technographics, intent, and contact details regularly enough to keep AI models from making stale or misinformed predictions?

How do we handle anonymous signals and convert them into actionable insights?

Not all buying behavior is tied to a known lead. Website visitors, ad engagements, and content views often start anonymous. Smart GTM teams are asking how to de-anonymize those signals and tie them back to accounts or personas.

What level of data governance do we have in place?

Bad data hygiene leads to bad decisions. Before deploying AI, GTM teams need to define ownership, set standards, and create processes for how data is collected, stored, maintained, and audited.

Are our systems integrated to ensure smooth data flow across the stack?

Even the best data is useless if it’s trapped in one tool. To make customer data AI-ready, teams need to ensure tight integration between CRM, marketing automation, sales engagement, intent platforms, and enrichment tools.

What is the specific AI use case we’re preparing our data for?

Not all AI needs the same data! Whether you’re building an AI-based scoring model, chatbot, account prioritization engine, or pipeline forecast, knowing your use case helps determine which data needs to be cleaned, enriched, and modeled first.

Conclusion

AI isn’t plug-and-play. At least, not if you want it to drive real results. Getting your customer data ready is step one, and it’s where most teams either build a strong foundation or get stuck in the mud.

The good news? You don’t have to tackle everything at once.

Start with the highest-impact use case for your team. Focus on data that affects it directly. Build strong processes for identity resolution, lead-to-account matching, enrichment, and signal capture. Then layer on AI with confidence. When your data is clean, complete, and connected, AI doesn’t just work. It wins.

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It takes a lot of buying signals to build the buyer profiles that our sales and marketing teams need to win business. Firmographics, demographics, technographics, intent, 1st-party data, predictive and engagement. Generally speaking, we can’t get all of those signals from one place. Instead, we’re forced to buy numerous sets of static data from several vendors, then blend it all together. Buying multiple sets of data from multiple sources is inherently expensive, and manually combining that data with our first-party data is time-consuming, cumbersome and error-prone. We might jump through those hurdles at first, but the problem arises when we need to update it. 


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Not updating your buyer profiles is a huge mistake in a world of data-driven decision making, where all of the decisions you make depend on having accurate, complete and up-to-date data. Even the best predictive AI models will point you in the wrong direction if the data being analyzed isn’t correct. Simply put, if you want your sales and marketing teams to have the tools they need to reach out and close business, you need to give them buyer profiles that are dynamically updated.


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Keeping B2B buyer profiles up-to-date is essential for maintaining effective sales and marketing strategies. Unfortunately, keeping profiles up-to-date is tedious and often neglected. Failing to update our customer or buyer profiles does your sales and marketing teams a tremendous disservice. Your teams could spend thousands of dollars and weeks of focus trying to get in touch with a great lead from last year, completely unaware that the person changed jobs three months ago – all because their profile within your CRM and marketing automation systems wasn’t up-to-date. People change jobs and companies go throughM&A all the time – and your sales and marketing teams need to stay informed. That’s just one of the many ways your teams will lose with outdated buyer profiles. Having accurate, up-to-date data seems like a no-brainer, right? Let’s consider why many sales and marketing teams don’t always have the data they need.


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A static data set is just a snapshot in time, and there’s no way to tell when data has changed to the point where you need a new snapshot. This means that in order to ensure our buyer profiles are up-to-date, we need to regularly buy all of our data over and over again, continuously blending it all together with the old data. That’s going to be very expensive, and it’s going to require a lot of time and effort dedicated to data unification. In many cases, by the time you’ve enriched your new data with the old data, that new data has already become old data. Naturally, many companies will decide that their slightly old data is good enough because they can’t justify the cost of constant data purchases or the time spent unifying it.


Not updating your buyer profiles is a huge mistake in a world of data-driven decision making, where all of the decisions you make depend on having accurate, complete and up-to-date data. Even the best predictive AI models will point you in the wrong direction if the data being analyzed isn’t correct. Simply put, if you want your sales and marketing teams to have the tools they need to reach out and close business, you need to give them buyer profiles that are dynamically updated.


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