In conversations with CEOs about their go-to-market strategies, I increasingly hear the same concern: channels that once produced pipeline with some degree of predictability are no longer performing the way they used to.

One CEO told me his company had not closed a single deal from outbound in a year. Paid search, which once produced at least four to five qualified leads a month, had fallen to zero despite increased spending. The company even offered prospects free steak dinners in cities where it had existing relationships, and almost nobody came.

Our agency has experienced some of the same challenges, as have others we speak with. One agency recently told us it had abandoned outbound altogether and was rebuilding its pipeline around partnerships and events.

The obvious conclusion is that something has changed in the way buyers respond to marketing and sales. Yet many companies continue to address declining performance by doing more of what has stopped working.

What Everyone Still Believes Will Fix It

When outbound slows, the standard response is to send more emails, increase personalization and, increasingly, deploy AI agents with the promise that they can replace much of what SDRs do at a fraction of the cost.

That assumes the problem is human capacity rather than buyer attention. AI can research accounts, generate emails, place calls and execute sequences at enormous scale, but those are the mechanical parts of outbound. It is far less capable of recognizing when a buyer may be struggling to make progress, forming a credible hypothesis about the underlying business problem and navigating the unscripted conversation that follows.

Executives have also become remarkably efficient at filtering unsolicited outreach. Most sales emails I receive know my name, company and title, and some reference something I recently posted online, but very few demonstrate an understanding of what might be happening in my business or give me a reason to reconsider something I already believe.

This is why the future of sales may look more like The Challenger Sale than autonomous prospecting. The opportunity for AI is not to replace the salesperson, but to help a capable salesperson identify a potential struggling moment, understand why it may matter and enter the conversation with a credible problem hypothesis. That is where AI may create far more leverage than simply helping companies produce more outbound.

Your Buyer’s Struggling Moment Changes Everything

Most companies still organize outbound around ideal customer profiles and personas. They identify companies that resemble their best customers, load them into a CRM and begin prospecting. The underlying assumption is that because a company fits the profile, someone there should be interested in what they sell.

The problem is that companies do not buy simply because they fit an ICP. They buy because something changes and the existing way of doing things becomes inadequate.

Bob Moesta describes this as the struggling moment: the point at which circumstances create enough dissatisfaction with the status quo to produce energy for change.

A funding round might create new growth expectations. A new CMO might inherit a pipeline problem. An acquisition could create incompatible systems or competing product narratives. An aggressive sales hiring plan might put pressure on a small marketing organization to generate substantially more demand.

These events are often treated as buying signals, but that is premature. A trigger does not tell us that someone wants to buy; it gives us a reason to investigate whether a struggling moment may be developing.

Consider a company that has just raised a Series A. Rather than automatically enrolling its executives in a sequence, AI can help determine what management has promised investors, where the company intends to grow, what positions it is hiring, whether its positioning has changed and where the increased expectations are likely to create pressure.

Perhaps the company plans to double its sales organization while keeping a three-person marketing team. Perhaps it is moving upmarket while its website still speaks primarily to smaller customers. The funding announcement did not tell us any of that. Research did.

More importantly, the research gives the salesperson something useful to bring to the conversation: a problem hypothesis.

From Buying Signals to Problem Agreement

This is where Jen Allen-Knuth’s approach to outbound becomes particularly useful. Rather than pretending that outside research allows a salesperson to diagnose a company, she advocates what she calls a “specific problem POV, loosely held.” The seller makes an informed assumption about a problem the observable change may be creating, while remaining open to being wrong.

That distinction matters because a signal should not simply become another personalization token. “Congratulations on the funding” is no more relevant than “I saw you went to Ohio State” if neither observation connects to something the executive is actually trying to solve.

Suppose instead that a company has announced eight new sales positions while employing only a handful of marketers. A salesperson might reasonably say: Not sure if adding that many salespeople is putting more pressure on a pretty lean marketing team to create enough demand for all those new reps.

The seller has not claimed to know what is happening inside the company. The seller has used an observable condition to make an informed assumption and invited the buyer to validate or reject it.

That is the beginning of problem agreement, and it is particularly important because the greatest competitor in many B2B purchases is not another vendor but the decision to do nothing. Before buyers need to believe that one solution is better than another, they need to believe there is a problem important enough to solve.

The Bigger Opportunity Is Finding Signals Your Competitors Cannot Buy

Funding announcements, executive changes, acquisitions and hiring patterns can improve outbound, but they remain public signals available to every competitor.

The more interesting opportunity is to identify custom, or “alpha,” signals derived from what a company uniquely knows about why its customers buy.

Start by examining your best customers and reconstructing what was happening before they began looking for a solution. What changed? What made the previous approach inadequate? What did they try first? Which observable conditions appeared three, six or twelve months before the purchase?

Patterns may emerge that conventional intent providers would never consider meaningful. Perhaps customers frequently hire a particular executive before the problem appears, enter a new market, change their website positioning, add a product line or begin hiring specialists after years of relying on generalists.

AI can make those patterns operational. An agent could continuously monitor thousands of ICP companies for combinations of conditions derived from actual customer history. Instead of reporting merely that a company raised funding, it might identify a company that raised funding, announced enterprise expansion, added seven sales positions and still employs only two marketers.

That combination does not prove purchase intent. It provides a considerably better reason to investigate whether a specific problem is emerging.

The resulting model is very different from traditional outbound:

Understand why customers change → identify the conditions that precede the struggling moment → teach AI to find those conditions → research the account → develop a problem hypothesis → test for problem agreement.

AI handles the monitoring, pattern recognition and research at a scale humans cannot. Salespeople provide the judgment necessary to decide whether the signal matters and whether they have something valuable to say.

That may ultimately be AI’s most important contribution to outbound: not helping companies contact more prospects but helping them identify fewer prospects with much better reasons to start a conversation.

Is Your GTM Built to Find Those Opportunities?

This is one of the areas we now examine in our GTM Analysis, along with positioning, sales narrative, demand generation, paid media and outbound.

We look for whether a company understands its customers’ struggling moments well enough to identify proprietary buying signals, whether those signals can be observed in the market, and whether AI could help turn them into actionable account intelligence.

The clues that identify your next customers may already exist in what your current customers have taught you. The opportunity is figuring out what to look for before everyone else does.