Foundation advantage: why AI is only as good as
the data underneath

Would you trust a custom GPT’s output and hand it to your manager without knowing which files it was trained on? Most professionals would say they won’t. But that’s essentially what’s happening across many go-to-market teams right now.

AI is moving into the systems go-to-market (GTM) leaders use to prioritize accounts, score sales opportunities, and decide where budget goes. The recommendations arrive fast and look confident. What could go wrong? The challenge is: what if the data—often invisible—is of poor quality?

Weak data doesn’t stay contained. It compounds. 

One flawed signal becomes a misscored account, which becomes a misallocated campaign, which becomes an input into the next bad decision. Bad data doesn’t move through AI in a straight line. It’s a flywheel; it has the power to become amplified.

Turing Award winner and MIT adjunct professor Michael Stonebraker said it plainly: “Without clean data, or clean enough data, your data science is worthless.”

Recently, brands and agencies have invested a lot of time thinking about AI in their tech stacks and tools; but have they been thinking enough about the relationship between AI and their data stacks?

You need data that’s explainable, differentiated, and grounded in real business behavior—not modeled, inferred, opaque, or generic.

AI-powered tools are only as strong as their inputs

Writing for IBM Think, Alexandra Jonker and Judith Aquino point to poor data quality as one of the most common reasons AI initiatives fail, regardless of how sophisticated the architecture is. As the saying goes: “garbage in, garbage out.”

Snowflake CMO Denise Persson framed the challenge clearly in a recent interview with The Current: “Data is truly the foundation for AI today… You cannot have an AI strategy if you don’t have a data strategy first.”

In GTM, this isn’t a one-time training problem. These systems ingest data continuously. Every week of weak signal is another week of flawed outputs, and the degradation carries downstream. When an AI-powered solution ingests incomplete, stale, generic, or opaque data, you get flawed scores, flawed recommendations, and a pipeline full of accounts that were never really in-market.

Learn how to evaluate AI-powered GTM vendors: what to ask about data, consent, and risk.

Data sourcing matters for effective GTM

Not all data sources hold up. Durable means the source has been in place long enough to establish a real pattern. Reliable means it’s editorially authoritative and you can trace exactly where it came from. 

For successful GTM campaigns, data rights, proper sourcing, and transparency aren’t checkboxes—they are fundamental guardrails.

When a vendor can’t say what their data actually represents, you get “black box” recommendations that you can’t validate or trust. This problem gets worse when you combine that data with everything else. 

Building a view of your market means stitching different signals together. Intent data comes from one source. Firmographics from another. Engagement data from across your own systems. 

If you don’t know what each input represents or where it came from, you can’t tell what the combined picture is actually saying. Are three sources corroborating each other, or are they reselling you the same underlying data three times? What looks like consensus might be one signal counted three ways. 

That’s the risk with opaque data. It might be perfectly good, but you have no way to check, and no way to know what you’re double-counting. Without reliable foundational data, AI doesn’t give you an edge; it just scales guesswork.

It’s part of why Bombora’s Data Co-op is structured the way it is and is such a powerful source.

Bombora’s Data Co-op is a durable data source

Bombora’s Data Co-op is the underlying data asset that powers our entire suite of B2B go-to-market solutions, including Company Surge® Intent data, Visitor Insights, and B2B Digital Audiences.

It’s notable that 86% of the data in our Co-op is shared exclusively with Bombora for the purposes of deriving Intent; no other Intent data provider has access to these highly relevant signals. We collect our activity data with proprietary first-party tags placed on thousands of participating sites across the B2B web. The direct tag is the only way to measure true engagement (time on page, scroll depth). Our Data Co-op is governed by persistent privacy-first, consent-driven data collection protocols.

When you layer Bombora Intent data into your view of the market, you know what you’re adding, and you know it’s explainable, differentiated, and grounded in real business behavior.

Bombora has been recognized by Forrester as “the gold standard for account-level intent data feeds.” Learn more.

Data diversity matters for AI performance

Human health relies on a balanced diet of diverse nutrients. Similarly, AI for GTM needs a variety of data inputs from independent, durable data sources that can be combined to create an accurate view of target accounts and buying behavior.

“No single dataset can fully represent how businesses research, evaluate, and purchase solutions,” says Elena Drozd, Ph.D, SVP, Data Science. “Bombora’s Data Co-op provides a uniquely broad and durable view of B2B research behavior, and when that diverse Intent data is combined responsibly with complementary signals, AI gains the context needed to uncover genuine buying intent rather than reacting to isolated or potentially misleading events.”

Diverse, robust data becomes more valuable as AI tools become more common in GTM workflows. And as modeling tools become commoditized, the raw material you feed them is critical for useful outputs.

Combining multiple transparent, high-quality signals can create a stronger account perspective.

“Data is foundational to AI,” says Senior Data Science Manager Cassie Williams, “but the way we model and apply that data still matters. AI can help curate, enrich, and connect signals at scale, but better models can’t indefinitely compensate for weak, narrow, or noisy data. Combining multiple high-quality, transparent signals gives models a broader view of an account, helping distinguish persistent patterns from isolated activity and produce more reliable outcomes.”

To best capitalize on signals from durable, reliable sources, sales and marketing teams can combine Intent data with data from first-party CRM systems, marketing automation platforms (MAPs), website analytics, and more.

Bombora’s Director of AI/ML Engineering Rohith Bharatha explains, “Every AI system inherits the blind spots of its underlying data. First-party signals are precise, but narrow. Pairing them with Intent data collected across a large publisher network gives models both the depth of your own funnel and the breadth of the market.”

How should B2B companies think about observed vs. inferred GTM data?

To build and execute an efficient GTM strategy in the age of AI, companies need to distinguish between observed actions and assumptions.

Observed data is rooted in actual, verifiable actions taken by a user or an account. This is the gold standard for Intent data because it reduces guesswork.

Bombora relies heavily on natural language processing (NLP) and machine learning models to interpret the semantic meaning, context, and relevance of text across the pages within its Data Co-op, rather than just relying on simple keyword matching. Pairing this with observed contextual content consumption—tracking how and how often professionals from specific companies interact with that content compared to historical baselines—is how Company Surge® intent signals are generated.

When activity on a specific B2B topic spikes above that baseline, that’s a signal that a company is showing high Intent—a statistically significant change in research behavior.

Inferred data takes a piece of known information and uses algorithms, proxies, or simulations to guess a prospect’s intent or profile. Generally speaking, inferred data may not be accurate. 

While AI can turn ordinary touchpoints into predictions, relying entirely on AI-inferred data carries a high risk of generating false positives and hidden biases that are not easily seen or audited.

For example, a keyword-based system might flag an account as being interesting in the keyword “cloud” because it appeared on a page they visited, inferring they want to buy cloud infrastructure. In reality, the article could have been about a weather forecast or a completely unrelated news story.

The observed data advantage—Bombora’s approach:

• Direct monitoring: Content consumption measured through our privacy-first Data Co-op.
• High level of accuracy: Driven by natural language processing (NLP) to understand content context rather than simple keyword matching.
• Actionable timing: Know when an account is actively researching a pain point or solution.
• Reliable, compliant, and transparent: Data is collected under privacy-first, consent-driven protocols and is verifiable back to the source, so you can confidently bring it into your own models, tech stack, and commercial motions.

The real GTM advantage in the AI era

The advantage in the AI era won’t belong to the teams with the most complex algorithms. It’ll belong to those who can vouch for the data that powers them. 

By grounding your AI-driven GTM strategy in observed, transparent, and consent-driven signals rather than opaque inferences, you ensure that your automation scales precision rather than guesswork. 

Ask every data provider you work with, including us: What’s under the hood? What exactly is being measured? How was the data collected? Will the data still be here in three years? 

Whatever you build today inherits the limits of the data underneath it.