For the better part of the last decade, B2B marketers have been told that serious account-based marketing requires an expensive intent-data engine telling you which accounts are supposedly in market.
And for a while, those platforms were pretty slick...
If the software could spot that a target account was suddenly researching your category and suggest there might be a buying process forming, that felt like some Minority Report stuff.
Marketing could then create digital “surround sound” around the account by increasing ad exposure to the people inside it, while sales started reaching out with the assumption that something might be brewing.
Done well, the whole thing gave both teams a smarter way to decide where to spend their time and budget.
That advantage depended heavily on the information being scarce, however, and scarcity has a nasty habit of disappearing once enough vendors figure out how to sell the same thing.
Jonathan Spier, CEO of GetRev.ai, described what has happened to syndicated intent data pretty plainly on a recent episode of the SaaS Backwards Podcast: “Everyone has access now to the same information, and therefore everyone's going after the exact same buyers with the same information.”
So when five competitors get essentially the same alert at roughly the same time, whatever advantage the signal once gave you starts to disappear.
Now you’re fighting to stand out while the prospect gets barraged with emails, phone calls and ads from everyone else who received the same intelligence, and by the time all of that starts happening, there’s a good chance you’re already late to the party.
Intent data still has value because it can help you prioritize accounts that are actively researching your category, even if several of your competitors are seeing the same thing.
The trouble starts when companies build too much of their ABM strategy around that small population of visible buyers and keep paying premium prices to identify them.
Jonathan told us that GetRev has seen intent contribute no more than about 5% of pipeline for many companies, then asked the obvious question: “Where's the other 95% going to come from?”
The exact percentage will vary, but the larger problem is difficult to escape. There are only so many buyers actively researching your category today, and a growing number of vendors are chasing the same ones.
Meanwhile, many future buyers are dealing with changing circumstances that may be creating a problem worth solving, even though they could still be months away from actively researching solutions or appearing in third-party intent data.
Traditional intent platforms tend to become most useful after the buyer has already started behaving like a buyer, while AI gives us a practical way to identify the conditions that may lead to a buying process much earlier.
Long before a company starts actively researching solutions, you can often see changes inside the business that may create a need for what you sell.
A company enters a new geography, acquires another business, wins a huge customer, replaces an executive, starts hiring aggressively, loses key people, changes technology or begins wrestling with some other operational headache. Those events can create the problem your product eventually gets hired to solve.
A salesperson cannot realistically monitor those changes across 1,000 or 5,000 accounts, which is one reason specialized data platforms became so attractive in the first place.
AI changes the economics because it can continuously examine company announcements, job postings, filings, earnings calls, websites, LinkedIn activity and other public or semi-public information, then look for the specific conditions that matter to your business.
Jonathan described the opportunity as finding “a way to get to companies before they even know they're in market.”
That capability matters because many companies already have most of the infrastructure required to act on what AI finds.
LinkedIn can put ads in front of people inside those accounts. And your CRM already contains the sales outcomes you can use to test whether certain signals actually correlate with better opportunities and customers.
Once AI can handle much of the account research, the burden shifts to the ABM platform to prove it is still worth the extra cost.
We recently ran into this exact question with a B2B company selling into a relatively narrow enterprise market. They already had HubSpot, including buyer-intent and job-change signals, and were considering adding a platform like 6sense.
Our recommendation was to hold off because they already had most of the pieces they needed: a finite universe of target accounts, useful first-party data in HubSpot and enough existing signal coverage to begin testing what actually predicts opportunity.
Rather than adding another expensive platform, we recommended tightening the named-account universe and layering AI-assisted research and what we call Alpha signals over it. In their case, those signals include geographic expansion, major customer wins, mergers, rapid growth paired with service-operations hiring, and changes in operations leadership.
HubSpot can then combine those signals with first-party engagement and actual sales outcomes, allowing the company to prove whether the model produces better opportunities before deciding whether another intent or ABM platform adds enough incremental value to justify the cost.
These are the kinds of conversations we expect a lot more companies to have over the next couple of years.
Before renewing an expensive ABM platform, pull apart what you actually use it for and ask which of those jobs still require specialized software.
And if it is going to justify a large renewal, the value has to show up in measurable sales outcomes.
One of the recurring complaints about systems like these is that marketing may find the account-level signal useful and can surround that company with advertising, while sales is still left trying to figure out which people inside the account actually matter, who is experiencing the problem and what context will make the outreach relevant.
That becomes even more frustrating when the system still depends on somebody raising a hand before sales has enough information to act.
The real test is whether the platform helps identify better opportunities, gives sales useful contact-level context and buying-committee coverage, and produces more qualified conversations, opportunities and pipeline than you would have generated without it.
If most of the perceived value comes from identifying which known accounts appear to be active, scoring those accounts and surrounding them with advertising, I would compare that against what you can already accomplish with your CRM, first-party engagement data, direct advertising and AI-assisted account research.
Then ask what additional value the platform is providing: whether it helps identify the right people inside the buying committee, gives sales enough context to start a relevant conversation, and ultimately produces more qualified conversations, opportunities and pipeline than you would generate without it.
The real opportunity with AI is its ability to research thousands of companies for the changes and conditions that tend to precede a need for what you sell, then use those signals to help marketing and sales decide where to concentrate their effort.
That gives you a way to identify potential opportunities earlier and bring more context into the conversation than a conventional account-level intent signal provides.
So before you sign that renewal, make the platform prove what it is adding beyond what you can now accomplish with your CRM, first-party data, direct advertising and AI-assisted account research.
More importantly, determine whether that added value is helping you identify better opportunities, reach the right people with enough context to start a relevant conversation, and ultimately create more qualified conversations, opportunities and pipeline.