Signal over noise: how publishers turn Intent data into insights in the age of AI

Every B2B publisher today is sitting on more data than they know what to do with. The hard part isn’t getting that data, but actually figuring out which signals matter and what to do about them.

In a recent webinar hosted by Jacob Donnelly, founder of A Media Operator, industry experts came together to discuss how premium publishers unlock the true value of their data. 

The conversation featured Nina Interlandi Bell, Senior Partner Success Manager at Bombora, and Andrew Hendler, SVP of Brand Partnerships at MIT Technology Review, discussing how publishers use Intent data and artificial intelligence to move up-funnel, build proactive sales strategies, and accelerate monetization.

Below are six of the most useful takeaways for B2B publishers to apply in their GTM strategies:

1. Use proactive pitches in the pre-sale process

“What’s changed in the past year with the combination of Bombora data and AI has been that we’ve been proactive and incorporated it into the pre-sale process—from prospecting to meeting briefs and client research. And the result is that there’s a lot deeper awareness amongst our salespeople of our client businesses and the general marketplace.” —Andrew Hendler, MIT Technology Review

• Why it matters: The opportunity is to use Intent data earlier, before an RFP (request for proposal) even exists, so your team is shaping the conversation instead of responding to a brief someone else already wrote.
• How to model it in your GTM: Stop waiting for RFPs. Hendler’s team tracks research spikes in specific B2B topics across their top 150 accounts. When an organization’s research spikes beyond a certain threshold, the publisher can confidently pitch specific thought leadership programs that align with the client’s real-time business initiatives.

Equip your sales reps with account insights before they initiate contact. When you pitch an advertiser with an explicit, data-backed point of view on their needs, you instantly separate your publication from the noise.

2. Ground your AI in Intent data, not just instructions

“You can give AI very explicit instructions and have it do excellent research, but how do you ultimately get it to figure out that a prospect is worth reaching out to? That’s where context and Bombora Intent data comes in.”  —Andrew Hendler, MIT Technology Review

• Why it matters: AI tools without proper context and without Intent data won’t be able to come up with prospects for your business. When you add that Intent data from Bombora, you get real results with limited hallucinations.
• How to model it in your GTM: On its own, an LLM can’t tell true signals from noise—it will research any account with equal confidence, in-market or not. Layer in Bombora Intent data alongside your own account context, and it can weigh prospects against real buyer behavior instead of treating every company the same way.

3. Help sales teams buy into B2B data

“[For us,] one deal closed, then another deal closed. There’s nothing like deals closing to increase interest in research data and the tool.” —Andrew Hendler, MIT Technology Review

• Why it matters: Introducing automated AI workflows or identity metrics to a senior sales team often triggers natural skepticism. Senior reps have methodologies that have worked for years; you cannot mandate data adoption through a memo.
• How to model it in your GTM: Don’t overwhelm your team with technical data science definitions. Focus on a small pilot group, secure one clear line-item win using Intent signals, and broadcast that success internally. Revenue is the ultimate internal incentive.

4. Improve your outcomes with strong signals from diverse sources

“The benefit of multiple sources [of data] is that when we see overlap, that increases confidence in somebody being a good prospect.” —Andrew Hendler, MIT Technology Review

• Why it matters: Data volume without curation creates operational paralysis. If your tools flag every single-user web action, your sales floor will burn hours chasing casual clicks that represent pure noise.
• How to model it in your GTM: Look for a “Venn diagram” of buyer behavior. Model your targeting around overlapping signals—like an account that’s simultaneously reading an article on your website, researching topics across the Bombora Co-op, and increasing their job postings in a relevant vertical.

5. Keep humans at the core of narrative delivery

“Humans aren’t meant to filter massive amounts of information. That’s what the AI is good for. We’re still good at following a story and trusting the people that are talking about it.” —Nina Interlandi Bell, Bombora

• Why it matters: Conversational AI engines excel at organizing raw numbers at scale, but they can’t build human trust or craft an empathetic narrative. Data points mean nothing if they aren’t woven into a story that addresses a prospect’s strategic corporate pain points.
• How to model it in your GTM: Let automated data tools do the heavy lifting of sorting, filtering, and identifying when buyer intent is surging. Then, have your team apply a qualitative lens to build tailored, account-specific messaging that deepens executive trust.

6. Work better, not just faster, with AI

“The real game is coming from working alongside AI to make the work itself better and not just quicker.” —Andrew Hendler, MIT Technology Review

• Why it matters: Speed is a fantastic operational byproduct of go-to-market tech stacks that use AI to get through more data, recognize patterns, and produce actionable reporting, but true market leadership belongs to the publishers who use data to raise the ceiling of their actual output quality.
• How to model it in your GTM: Build internal environments that foster shared intelligence. Host cross-divisional discovery sessions or monthly demo syncs to unpack what data sources you have and how the data can be used to make the organization stronger, thus making your entire organization more data-literate and strategic.

The businesses pulling ahead aren’t the ones with the most data—they’re the ones who’ve built the discipline to separate real signals from noise. 

They follow Intent signals on the B2B web, use diverse sources of data, earn trust by sharing success stories, and keep humans very much in the loop, delegating specific tasks and analysis to AI where it best serves their business.