As generative AI mainstreamed in late 2023, it rapidly began to transform how companies went to market. At that time, Amer Deeba, CEO and co-founder of data security startup Normalyze (later acquired by Proofpoint), was busy building his go-to-market (GTM) team and GenAI was just starting to rewrite the GTM playbook, and the startup’s entire messaging, demo strategy, and field strategy had to be rebuilt in months. And as Deeba explains, the team’s skills in place at the start weren’t the same set of skills that could navigate the changes. “If they were stuck in old ways, and they didn’t get what was happening and what we needed, then they wouldn’t be able to adjust,” Deeba says.

Retraining wasn’t a nice-to-have. It was necessary to stay relevant and execute successfully.

That pressure to retool GTM staff quickly or fall behind the market is now a standard feature of go-to-market strategy. The combination of AI-literate buyers and AI-capable tooling is narrowing the range of GTM work that benefits from the traditional go-to-market profile. And what’s opening in its place is quite specific: technically deeper, more systems-aware, and built to work alongside agent infrastructure rather than around it.

Chris Tilton, a marketing executive running his own practice in Amsterdam, stopped recruiting traditional outbound business development representatives (BDRs) several years ago. The old model of high-volume, scripted qualification calls and a low technical floor broke down as buyers arrived at first contact already armed with AI-assisted research. What Tilton built instead was a hybrid role: part technical support, part customer success, part sales. “CISOs do not want to talk to some BDR that knows nothing about their products, has no technical expertise, and can’t solve a problem for them,” he says. His solution was to recruit from Apple Genius Bar desks. These are people who had spent years in college blending technical troubleshooting with customer-facing sales judgment, and who could engage an informed buyer without reaching for a script.

Priya Gil, CMO at AI customer engagement platform provider Iterable, sees go-to market teams tightening. Rather than five generalist marketers, she envisions two or three specialists with genuine domain depth, such as product marketing, brand, and paid, with each managing a team of AI agents that handle the execution layer: campaign assembly, segmentation, competitive monitoring, and first-draft copy. “I do see teams getting smaller,” Gil said. “They’re going to start moving towards a model where you’re hiring specialists with deep expertise in particular areas who are well-versed in AI and can handle managing a team of agents.” The generalist, the person who covered three functions adequately but none deeply, loses ground in that reality. The specialist who can coach an agent, catch when its output is wrong, and push it toward something better becomes in demand.

Mary Shea, founder of Meerkat and a former Forrester principal analyst, connects this to a longer-running shift in what buyers truly need from a sales conversation. As buyers arrive having already completed the bulk of their research, the traditional account executive skill set, such as pitching, relationship-building, and managing the qualification process, becomes less differentiating. What they want on arrival is technical depth. Shea has predicted that the traditional seven-to-one account executive-to-sales-engineer ratio will invert toward six sales engineers for every closing AE, precisely because educated buyers want a technical conversation, not a pitch, she says. The sales engineer who is credible on the product and capable of engaging with the buyer’s specific environment is the profile that survives contact and closes more deals with an informed buyer.

All of this will take time for CMOs and their marketing teams to adjust. Deeba’s experience at Normalyze warns how retraining under market pressure is necessary, and companies that move too slowly will lose ground. The talent calculus has changed. The go-to-market leaders who are navigating it most directly are betting on technical depth over volume, specialists over generalists, and people who can work with AI tools rather than those who need to be protected from them.