Article

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

Best Practices: AI-Ready Data

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

Table of Content

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.

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

AI needs order. Models thrive on rows and fields – not messy free-text chaos spread across systems.


Action: Audit your data sources – CRM, marketing automation platforms (MAP), spreadsheets, intent tools. Then invest in a customer data platform (CDP) or centralized data layer to normalize these inputs. Without this foundation, your AI initiatives will stay stuck in neutral.

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

Duplicates aren’t just a data nuisance – they confuse models, fragment buyer profiles, and throw off your metrics.


Action: Use identity resolution tools and enrichment providers to unify records across systems. Establish a deduplication protocol and run regular audits to keep your CRM clean and AI-ready.

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

AI doesn’t close deals – people do. And most B2B decisions involve 6–10 stakeholders.


Action: Build account-level views that include roles, titles, departments, and influence levels. Don’t stop at the lead form – use enrichment and behavioral data to map out full buying teams.

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

Your AI is only as smart as the signals you feed it. Without context, it’s flying blind.


Action: Track signals like content views, email clicks, website visits, event attendance, and third-party research. Standardize how these are tagged and mapped across platforms for consistent input.

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

Bad lead-to-account (L2A) matching breaks everything: routing, scoring, engagement, pipeline visibility.


Action: Invest inprecise L2A matching. It’s essential for clean analytics, effective scoring, and accurate attribution. The better your match, the better your AI.

How often is our data refreshed and updated?

AI can’t reason with stale data. Outdated firmographics or job titles lead to misfires.


Action: Set regular refresh cadences for contact, account, intent, and technographic data. Automate updates with trusted enrichment partners – avoid one-and-done uploads that quickly expire.

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

The buyer journey often begins in stealth mode. Don’t let those signals go to waste.


Action: Use tools like reverse IP lookup, form fills, and intent matching to associate anonymous engagement with known personas or accounts. Feed this into your models to complete the picture.

What level of data governance do we have in place?

Without ownership and governance, AI becomes everyone’s problem – and no one’s responsibility.


Action: Clearly define data ownership across Marketing Ops, RevOps, and Sales Ops. Document standards and governance processes around hygiene, privacy, and compliance to keep AI initiatives on track.

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

Disconnected systems mean disconnected insights. AI can’t help if it doesn’t see the whole picture.


Action: Integrate your CRM, MAP, enrichment providers, and intent platforms. Break down data silos to ensure smooth, real-time flow across your GTM stack.

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

Not all AI is created equal. Scoring leads, predicting churn, automating chat – each needs different inputs.


Action: Pick one high-impact use case – likeAI-driven lead scoring – and work backward. Identify the data it needs, where that data lives, and how to make it usable. Avoid trying to boil the ocean.

Conclusion

AI doesn’t just need data – it needs the right data. Clean, current, connected, and contextualized. If your GTM systems are disjointed or your CRM is littered with half-formed records, even the smartest AI won’t deliver meaningful results.


The good news? You don’t need to overhaul everything at once. Start with one question. Fix one gap. Build momentum. With the right foundation, AI becomes a force multiplier – helping your teams focus, personalize, and convert faster than ever.


Your data is either your greatest AI advantage – or your biggest blocker. The difference is what you do next.


Stay tuned for next week’s blog where we will discuss the goal you need to set your sights on – the definition of AI-readiness as it pertains to B2B customer data.

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Leadspace Buying Team Intelligence

B2B deals don’t close because one contact engages, they close when an entire buying committee aligns. Yet most GTM systems still operate at the individual record level, leaving revenue teams blind to the relationships, roles, and signals that actually drive decisions.


Leadspace’s Buying Team Intelligence makes buying groups visible, measurable, and actionable by connecting people to roles, accounts, hierarchies, and real-time buying signals in a unified, living data graph.


The result: sales, marketing, and RevOps teams can identify who truly influences and approves decisions, prioritize accounts showing coordinated buying activity, and orchestrate multithreaded engagement based on how buyers actually buy rather than on how CRM records are structured.

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Leadspace Buying Team Intelligence

B2B deals don’t close because one contact engages, they close when an entire buying committee aligns. Yet most GTM systems still operate at the individual record level, leaving revenue teams blind to the relationships, roles, and signals that actually drive decisions.


Leadspace’s Buying Team Intelligence makes buying groups visible, measurable, and actionable by connecting people to roles, accounts, hierarchies, and real-time buying signals in a unified, living data graph.


The result: sales, marketing, and RevOps teams can identify who truly influences and approves decisions, prioritize accounts showing coordinated buying activity, and orchestrate multithreaded engagement based on how buyers actually buy rather than on how CRM records are structured.

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Product sheet

Leadspace Buying Team Intelligence

B2B deals don’t close because one contact engages, they close when an entire buying committee aligns. Yet most GTM systems still operate at the individual record level, leaving revenue teams blind to the relationships, roles, and signals that actually drive decisions.


Leadspace’s Buying Team Intelligence makes buying groups visible, measurable, and actionable by connecting people to roles, accounts, hierarchies, and real-time buying signals in a unified, living data graph.


The result: sales, marketing, and RevOps teams can identify who truly influences and approves decisions, prioritize accounts showing coordinated buying activity, and orchestrate multithreaded engagement based on how buyers actually buy rather than on how CRM records are structured.

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Why Waterfall Logic Matters in B2B Data Aggregation

Modern go-to-market teams are swimming in data – firmographics, technographics, intent signals, engagement scores, and countless enrichment sources.


But here’s the truth: more data doesn’t automatically make your business smarter. It often just makes it messier.


When multiple data vendors, enrichment tools, and APIs are all trying to update the same record, the result is chaos – inconsistent fields, conflicting values, duplicates, and manual clean-up that never ends.


That’s where waterfall logic becomes a game-changer.

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Article

Why Waterfall Logic Matters in B2B Data Aggregation

Modern go-to-market teams are swimming in data – firmographics, technographics, intent signals, engagement scores, and countless enrichment sources.


But here’s the truth: more data doesn’t automatically make your business smarter. It often just makes it messier.


When multiple data vendors, enrichment tools, and APIs are all trying to update the same record, the result is chaos – inconsistent fields, conflicting values, duplicates, and manual clean-up that never ends.


That’s where waterfall logic becomes a game-changer.

Waterfall Logic

Article

Why Waterfall Logic Matters in B2B Data Aggregation

Modern go-to-market teams are swimming in data – firmographics, technographics, intent signals, engagement scores, and countless enrichment sources.


But here’s the truth: more data doesn’t automatically make your business smarter. It often just makes it messier.


When multiple data vendors, enrichment tools, and APIs are all trying to update the same record, the result is chaos – inconsistent fields, conflicting values, duplicates, and manual clean-up that never ends.


That’s where waterfall logic becomes a game-changer.

Sales and Marketing Success

Article

Why Keeping B2B Profiles Up-to-Date Boosts Sales & Marketing Success

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.


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. 


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.


Let’s look at 10 common strategies for ensuring that your buyer profiles are current and accurate, then consider a more realistic solution for automating the entire process.

Sales and Marketing Success

Article

Why Keeping B2B Profiles Up-to-Date Boosts Sales & Marketing Success

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.


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. 


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.


Let’s look at 10 common strategies for ensuring that your buyer profiles are current and accurate, then consider a more realistic solution for automating the entire process.

Sales and Marketing Success

Article

Why Keeping B2B Profiles Up-to-Date Boosts Sales & Marketing Success

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.


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. 


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.


Let’s look at 10 common strategies for ensuring that your buyer profiles are current and accurate, then consider a more realistic solution for automating the entire process.