Revenue
Architects

How to get the human and AI split right

with Greg Symons, Founder, President and COO, Asymbl

Asymbl has already run the experiment many GTM leaders are still making predictions about. Roughly 120 employees work alongside 200 digital workers — and every one of those agents reports to a named manager, the same way a new hire would.

How to structure a GTM team that can actually run AI

with Mallory Lee, VP of Revenue Operations, Zipline

Job titles have moved faster than the jobs underneath them. AI is already running somewhere in the workflow — so who owns it, who gets to ship it, and who is accountable when it gets something wrong?

The non-negotiables in an AI-ready tech stack

with Brendan Powers, Principal Go-to-Market Operations and Engineering Manager, ZoomInfo

When an agent gets something wrong, the model is the first thing anyone blames. Bumping it up a tier is the cheapest available fix, which is why it happens so often and why it so rarely works. The fault is almost always upstream.

Build or buy? The GTM decision that's hard to decipher

with Mollie Bodensteiner, VP of Revenue Operations, ZoomInfo

Building is cheap now. Cheap to build has quietly turned into cheap to own, and those are two different things. Mollie's reframe: most teams aren't building, they're assembling.

How to use an MCP to act on live GTM data (and the play that shows how)

MCP comes up in every other GTM conversation. Plenty of people can define it; far fewer have connected one. Asked which AI use cases are working, 52% of leaders were undecided on MCP and only 16% said it was genuinely working.

Why your best signals go cold before anyone acts (and what to implement)

Every buying signal has a half-life. A pricing-page visit, a job change, an intent spike — they all decay, and most GTM teams act long after they've gone cold. 56% of leaders said their AI tools have no access to real-time signals.

Why your best investment isn't more AI (and what to do instead)

Given a blank cheque, 50 senior GTM leaders didn't ask for more agents, better prompts, or a flashier model. 62% said their next investment would be a unified data layer connecting all of their GTM tools.

Why AI can't serve your GTM stack (and how to fix it)

Most teams plugged an AI agent into their CRM and expected it to just work. The data was there. The model was smart enough. It still came up short.

What GTM leaders want from AI (and how to build it)

Ask GTM leaders about AI and you expect a wish list of capabilities back. That's not what the survey showed. Nearly 40% described the same ambition in their own words — and it wasn't more AI.

Why AI in GTM isn't working (and what's underneath it)

Every GTM team that's invested in AI in the last 18 months expected leverage. What they got was a sophisticated form of double-checking.