Last week we ran a webinar with Lindsay Rothlisberger from Zapier, Lucy Alexander from HubSpot, Davide Grieco from Clay, and Elad Uzan from Lusha.

The goal of the webinar was explore how leading go-to-market teams are moving beyond AI hype and performative workflows to drive real revenue impact.

Rather than broad predictions or abstract thought leadership, the conversation focused on practical examples of how high-performing teams are operating today:

The GTM leaders of some of the fastest growing businesses, together, discussing real solution to real challenges we all face.

They've all rolled out AI and gotten their reps real time back, but ask whether that time turned into pipeline and the answer gets quiet.

That's why I wanted to get them in a room together. To share their learnings.

Every AI vendor leads with time saved per rep. It photographs well in a board deck and tells you almost nothing about revenue.

Zapier built around that number early, then walked it back when the math stopped holding up.

"Right now, we're obsessing over recovered selling capacity. Did the rep get their week back, and did it show up in pipeline generated?"

The distinction Lindsay is drawing matters. Recovered capacity is the input. What a rep does with the freed-up hours is the thing worth tracking.

So she pairs it with win rate on coached deals, a second read that the time is landing on work that closes rather than quietly evaporating into more admin.

Lucy runs the same logic from the marketing side, with pipeline dollars per rep as her North Star. She passed on closed dollars on purpose, even though it's the obvious revenue tie.

Win rates get shoved around by pricing, deal mix, and a separate team she doesn't own, so attributing them back to prospecting is a mess.

Pipeline per rep is something she can pull a clean signal from. Move it, and the work she controls is what moved it.

Davide had the sharpest line of the afternoon, which is funny coming from someone at the company everyone name-drops when they talk tooling.

"When you scroll on LinkedIn, 99% of people are talking about the tools. The tools are not important at all. What's really important is how we design the campaign. That's never done by AI."

His read on AI slop has stuck with me. The slop problem isn't the tools being bad.

It's people handing the thinking to the machine instead of just the execution, then acting surprised when the output reads like everyone else's.

Design the campaign yourself, get specific about what good looks like for your ICP, then point Clay or whatever you run at the parts worth automating.

That reframe, from operator to architect, ran underneath almost everything the panel said next.

Lucy's prioritization work is the detail I'd hand to any marketing ops team still scoring leads off a vendor's static fields.

Her team uses AI to read an account's website the way a person would, asking the fuzzy questions enrichment data can't answer.

Does the company run lead-gen forms? Any sign of active digital marketing? Does the homepage suggest they're at a stage where HubSpot fits? Internally they call it the rep sniff test.

"AI allows us to upgrade from static lead scoring to dynamic prioritization by analyzing factors like lead-gen forms and digital marketing readiness."

The numbers ended the debate. The top 30% of dynamically prioritized accounts generated 95% of revenue.

Reps who worked the prioritized list in order generated about 10% more pipeline per month than those who didn't.

HubSpot's data scientists ran causal analysis, controlling for tenure, prior attainment, and manager, to confirm the tool was moving the number rather than better reps gravitating to it. It was the tool.

Elad shared a Lusha experiment that went sideways in a useful way. They assumed reps wanted fully generated outbound lists: the machine builds it, the rep dials.

Reps turned it down.

"They didn't want the black box. They wanted something they understand the logic of, they understand the reason behind, and the context."

They weren't asking for less AI. They wanted to set what it optimized for, feeding it the signals that matter for their ICP and letting it personalize from there.

The model executes, the rep architects. A tool reps don't trust doesn't get used, no matter how good the demo looked.

The thread tying all four together: the teams pulling ahead aren't the ones with the most AI bolted into the stack.

They're the ones who worked out which context actually matters and built the workflow to put it in front of a rep at the moment it's useful.

Capacity, then context, then a number you can defend.

The hype cycle is over. The workflow problem is what's left, and it's where next year's revenue gets won or lost.

P.S. The full breakdown covers what we couldn't fit here: how Zapier and HubSpot are actually building their context layers, and the metrics frameworks each leader runs day to day.

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From Workflow Chaos to Operational AI : Real RevOps Use Cases
(Thursday, 25th June | 1 PM EST | 10 AM PT)

In this session, we're showcasing AI agents built specifically for the event, deployed against real broken RevOps workflows: lead routing, onboarding, forecasting, handoffs, and resurfacing opportunities before they slip. Live builds grounded in real challenges, not generic hype.

Turning POCs into Revenue: A Framework for Winning Enterprise Evaluations
(Tuesday, June 30 | 1PM EST | 10AM PT)

Everyone's optimizing top-of-funnel. Meanwhile, the deals you've spent 12-18 months trying to close are quietly dying at the POC, the most expensive stage of the enterprise funnel. John Care (author of Mastering Technical Sales) and Gilad Komorov (3X CRO) break down why enterprise POCs fail and the playbook high-win-rate teams use to win them.

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