When Predictable Revenue came out in 2011, the model worked so well that software companies began reorganizing their sales teams around it.
Instead of asking expensive account executives to prospect and close their own business, companies specialized the work. SDRs attacked large lists, qualified interest and booked meetings, while AEs sat downstream and focused on turning those meetings into revenue. Marketing fed the machine with content, clicks and MQLs, and as long as enough prospects responded, adding more people and activity could produce more pipeline.
The trouble was that the model didn't stop working all at once.
It just became a little less effective every year. Companies compensated with more SDRs, sequences, automation and marketing activity, leaving many with bloated GTM organizations built around response rates they could no longer produce.
Meanwhile, AEs who had spent years receiving qualified meetings either forgot how to prospect for themselves or had never really learned.
It was a better model, and the fundamental idea behind it still makes sense today.
One of ABM’s biggest wins was getting sales and marketing working together instead of marketing generating leads and tossing them over the fence to sales.
Both teams could agree on the accounts they actually wanted to win and coordinate their efforts around them. Intent data added another layer by helping them identify which of those accounts might be moving into a buying window, while tools like 6sense and Demandbase helped marketing surround those accounts with advertising and content as sales worked to identify the people involved in the decision.
That was a meaningful improvement over the old model, and it still is.
The problem came as the same intent data became widely available. If you and five competitors are all watching the same account, seeing the same surge and trying to identify the same buying committee, the signal may still be useful, but it is no longer much of a competitive advantage.
At that point, ABM can turn into a much more coordinated version of the same race everyone was trying to escape: identify the account, find the players, get there first and start pushing for engagement.
That’s where the next question becomes more interesting:
What if you could identify the problem before the buyer ever showed conventional intent?
The problem came as the same intent data became widely available. If you and five competitors are all watching the same account, seeing the same surge and trying to identify the same buying committee, the signal may still be useful, but it is no longer much of a competitive advantage.
Worse, the buyer experience starts to look familiar. One vendor launches ads, another starts a sequence, another has an SDR call, and pretty soon the same people are getting hammered from every direction because several companies have all concluded that the account is “in market.”
In other words, ABM may have narrowed the universe, but once everyone had access to the same intent signals, the execution could start to resemble the Predictable Revenue model all over again: more messages, more outreach and more pressure on the buyer to respond.
And buyers reacted the same way they eventually did to that model. They tuned it out.
During a recent Fireside Chat hosted by Austin Lawrence Group, Sangram Vajre, co-founder and CEO of GTM Partners, explained why getting ahead of that crowd matters: “when you are first to market, your win rate dramatically increases.”
But if everybody receives roughly the same signal at roughly the same time, you're not really first. You're one of several vendors discovering the opportunity after the buyer has already started researching the problem and shaping what it thinks it needs.
Which suggests that the bigger opportunity may be to get there before there's any conventional intent at all.
Most ABM programs begin by defining the ICP, identifying target accounts and looking for evidence that those accounts are in market.
But what if you started with a different question:
Why did our customers buy from us in the first place?
Not their industry, revenue or employee count. What was happening inside their business when continuing to do what they were already doing became difficult enough that they needed to make progress?
In Demand-Side Sales 101, Bob Moesta and Greg Engle call this the struggling moment: the point when the buyer realizes the status quo is no longer good enough and begins looking for a way to make progress.
If you understand those moments, you can begin looking for the conditions that precede them. Perhaps a new CRO inherited a pipeline problem. An acquisition introduced systems complexity. A regulatory change created an operational burden. A company dramatically expanded its sales team without increasing its ability to generate demand.
Now you're no longer simply asking who looks like your customer or who is researching your category.
You're asking who appears to be entering the kind of situation that caused your customers to need you in the first place?
And that may happen months before anyone searches for a product like yours.
Finding the problem before the buyer does is only useful if you can help them see it.
That’s the idea at the heart of The Challenger Sale. A trigger gives you a hypothesis that a problem may be developing. The next step is to establish problem agreement, show why the status quo may no longer be sufficient, and reframe the issue in a way the buyer hadn't considered.
That’s where getting there before conventional intent becomes valuable. Once a buyer is researching solutions, talking to vendors and defining requirements, you're already competing inside a buying process they helped define.
Reach them earlier with an insight that helps them recognize a problem they didn't know they had, and you have an opportunity to shape how they think about it before your competitors even know there’s a deal.
That may lengthen the sales cycle because you're engaging before the buyer has decided to buy anything. But if they do decide the problem is worth solving, you're no longer just another vendor trying to win against criteria someone else helped establish. You helped them understand the problem and shape what a solution needs to accomplish.
Amos Bar-Joseph, CEO of Swan AI, described the objective as becoming “relevant to what is happening at the business at this single moment in time.”
That's considerably more powerful than simply being first to call.
This is where AI can take ABM considerably further.
Amos calls them “alpha” signals: observable conditions that aren't necessarily sitting in the same commercial database your competitors use.
He gave the example of a Swan customer selling software into real estate that monitors municipal records for increases in evictions because it has learned that rising eviction volume creates an operational problem its software can address.
The company doesn't have to wait for the property manager to start researching software. It can recognize evidence that the problem itself is developing.
Once you understand the struggling moments that cause your own customers to buy, you can do the same thing. AI agents can monitor thousands of websites, job postings, public records, regulatory filings, executive changes and industry-specific sources looking for evidence that those conditions are emerging.
That's the real opportunity AI creates for ABM. It can do the kind of account research no team of SDRs could possibly do at scale, looking for specific evidence that a company may be developing a problem you know how to solve.
Of course, that only works if you know what you're looking for. The AI agent needs to be built around a real understanding of why your customers buy and the conditions that tend to appear before they do.
Finding that signal still doesn't mean immediately firing off a sequence. It means you've found an ICP account where your hypothesis may be worth testing.
Research the account and get the right problem-oriented content in front of the people likely to care. That might mean advertising built around your Challenger narrative, an article that helps them recognize the problem, research that challenges the status quo or an invitation to a discussion with peers facing the same issue.
The point is to see whether they agree with the problem. Do they recognize it in their own business? Does the content help them see why the status quo may no longer be sufficient? Does the reframe give them a different way to think about what’s happening?
If they begin engaging with that content, you’re creating first-party intent around the problem, rather than waiting for third-party intent to tell you they may already be shopping for a solution.
That's also a much more useful point for sales to enter. Instead of marketing handing them:
“Acme Corp is surging.”
They can say:
“Acme appears to be entering the same situation that caused several of our best customers to buy. Here's what changed, here's the problem we think it may create, and here's how people inside the account are engaging with our point of view.”
The bigger advantage may not be spotting intent before everyone else. It may be recognizing the problem that will eventually create the intent, getting the right ideas in front of the buyer to help them recognize it, and developing first-party evidence that they care before your competitors even know there's a deal to compete for.
If you're trying to figure out what those signals might look like in your market, that's part of what we work through in our complimentary GTM Analysis. We look at why your customers actually buy, the struggling moments that precede the decision and the Challenger narrative around the problem, then identify custom triggers AI could monitor and ways to test for problem agreement before sales starts selling.
Take us up on a complimentary GTM Analysis →