Building a durable GTM data foundation: lessons from the field

Go-to-market success doesn’t begin with technology—it starts with clarity. In a recent LinkedIn Live, McKenzie Jerman, Bombora’s Senior Director, Partner Management joined Karan Chaudhry, Co-founder of Sprouts.ai, to highlight a common challenge facing business leaders: believing simply adding more data will fix GTM problems. In reality, growth comes from making the right data sources — whether Intent, CRM, or engagement signals — work together in a way that is consistent, trusted, and actionable.


Here are four lessons every revenue leader should keep in mind when building a durable, data-driven GTM strategy.

1. Gain alignment before you define models

Internal misalignment is one of the most underestimated risks in data strategy. Sales may define the ideal customer profile (ICP) one way, while marketing interprets it differently. Without shared definitions for ICP, MQL, or SAL, even the best scoring models will fall apart.

Clarity begins with alignment. Agreeing on roles, responsibilities, and definitions ensures that Intent data—and every other signal—can be acted on with confidence.

2. Treat “data health” as an ongoing responsibility

Dirty, siloed, or outdated CRM data is one of the biggest obstacles to GTM execution. The problem is especially acute in fast-moving industries where job changes, layoffs, and restructurings quickly make records obsolete.

Healthy data requires both a single source of truth and clear ownership. Whether that responsibility sits with RevOps, sales ops, or a dedicated data lead, someone must be accountable for maintaining trust in the system. Without it, the downstream impact is predictable: wasted outreach, low conversion, and declining confidence in the process.

3. Use Intent to validate your ICP

As organizations scale across geographies, products, and industries, the ICP definition becomes more complex. Intent data provides an important reality check. By comparing closed-won opportunities with historical research activity, teams can see where Intent consistently shows up and where it doesn’t.

Instead of treating ICPs as static assumptions, test them against the signals the market is sending. If you’re seeing little to no Intent in a vertical, despite a few wins, it may be time to redirect resources toward markets where demand is clearly evident.

4. Apply AI as an enhancer, not a replacement

AI promises significant efficiency gains, but its value is only as strong as the quality of its inputs. “Garbage in, garbage out” applies just as much to GTM models as it does to generative AI.

Use AI to extend your team, not replace it. Automate enrichment, streamline data cleansing, and connect disparate sources while keeping humans in the loop. Strategic judgment, context, and trust still come from people and the unique experiences they bring.

Building for clarity, not complexity

The throughline across these lessons is clear: success isn’t about data volume or finding one perfect, silver-bullet source to solve all your problems, but data clarity. Alignment gives signals meaning. Healthy systems build trust. Market trends validate focus. AI extends what’s possible—but only when the foundation is solid.

The goal for GTM leaders isn’t to capture every possible data point. It’s to ensure the data you do use is reliable and usable across workflows and supported by people and processes that bring it to life.

That’s how organizations move from being data-rich to genuinely insight-driven and build a foundation strong enough to support growth in any market conditions.

Partnerships like Bombora and Sprouts.ai make that possible by combining high-quality Intent data with AI-driven enrichment and activation. That’s the difference between being busy with data and driving outcomes with it. To learn more about making sure your signals are trusted, actionable, and measuring real Intent, reach out to us.