Revenue Architects: The GTM.AI Podcast

Most AI in GTM is failing, not because the models are wrong, but because the layer underneath them isn't ready. Revenue Architects is the bi-weekly podcast for GTM operators responsible for the infrastructure AI actually runs on. The data, the signals, the systems that decide whether AI in GTM works or fails. Hosted by John Lloyd, Principal Business Consultant at ZoomInfo, every episode starts with original data, lands a real take from operators in the trenches, and ends with a workflow you can actually build. No hot takes or vendor pitches. Just the data, the opinion, and the play. If you're a GTM or RevOps leader responsible for making AI work in your motion, this show is built for you. New episodes every two weeks.

Revenue Architects: The GTM.AI Podcast is a business podcast hosted by Unknown Host, with 9 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Revenue Architects: The GTM.AI Podcast, a business show with 9 episodes published.

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  Episode #9

  #9: How to structure a GTM team that can actually run AI, with Mallory Lee

  Sep 17, 202627 minS1

  Job titles have moved faster than the jobs underneath them. RevOps, GTM engineer, growth engineer, ops partner. Some of it is real change in the work. Some of it is relabelling. And underneath the titles sits a question many teams haven't answered: AI is running somewhere in the workflow already, so who actually owns it, who gets to ship it, and who is accountable when it gets something wrong? In this episode of Revenue Architects, John Lloyd is joined by Mallory Lee, VP of Revenue Operations at Zipline, on how to structure a GTM team that can actually run AI. Her answer isn't one owner, it's points of entry. Someone dedicated to CS and sales operations. Marketing ops as her night job alongside demand gen. A systems administrator on her team who gets everything connected from a plumbing standpoint and takes each new MCP through IT and internal security. The stakeholders sit outside all of it, getting pitched tools all day and pushing requests in. Her job in the middle is deciding which tool gets used for what, because there are now unlimited options and people early in their careers are frightened of picking wrong. Then the hiring. She isn't screening for a long list of tools someone has used for five years that haven't existed for five years. She wants people who learn systems fast. She's also cancelled a planned analyst hire, because she now does that analysis herself and RevOps teams are getting smaller because of it. John and Mallory also get into why the silo is almost always the customer success group, why standardising a few things beats everyone using AI their own way, and why workflows fail on the edge case nobody built for rather than the tooling.

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  Episode #8

  #8: The non-negotiables in an AI-ready tech stack, with Brendan Powers

  Sep 3, 202627 minS1

  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, in the prompting or in the data sitting underneath it. An agent that needs an account's industry can't do much with the half of your accounts where that field is empty. In this episode of Revenue Architects, John Lloyd is joined by Brendan Powers, Principal Go-to-Market Operations and Engineering Manager at ZoomInfo, on what a stack has to look like before an agent can run on it without a human checking the output. His answer starts underneath the agent: enrichment data, CRM data, and product usage in the warehouse, because the play for a customer who barely logs in isn't the play for one about to blow through their credits. Then the working rule. Make as much of the agent deterministic as you can, and hand it data instead of asking it to decide. When it breaks, you want to know which layer failed, and a heavy prompt doing five jobs will never tell you. Then Salesforce. Rep time in the CRM is trending down and to the right, and Brendan is building custom apps in his terminal without being an engineer, but it's still the source of truth and still the trigger for almost every agent they run. Klarna is the cautionary tale for anyone in a hurry. John and Brendan also get into why shadow AI is usually an enablement gap rather than a discipline problem, why the account notes rotting in someone's Evernote are suddenly worth something, and why the job is wide guardrails, not gatekeeping.

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  Episode #5

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

  Jul 23, 202628 minS1

  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 on them long after they've gone cold. When 50 senior GTM leaders were asked what needs fixing in their setup, 56% pointed at the same gap: their AI tools don't have access to real-time signals. In this episode of The Context Layer Tapes, Florin Tatulea and John Lloyd get into why speed, not just data, is where teams fall down. Act on a website visit within 20 minutes and you can lift conversion 2 to 3x. Wait days on a social signal, or months on a job change, and you've lost the buyer's trust before you've said a word. Two clocks are always ticking, the signal decaying and the buying window closing, and the deal is lost when they fall out of sync. Then the plays. The setup: a connected account plan that meshes first-party and third-party signals across the whole book of business. The trigger: an in-market score built across intent, website and social engagement, firing on the spike rather than any single signal, which took one team's call connect rate from 3% to 12% in a week. To close, Florin and John cover what makes it possible: identity resolution and first-and-third-party unification, and the part nobody budgets for, owning the process so the signal reaches the rep in time.

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  Episode #4

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

  Jul 9, 202623 minS1

  <p>Most teams are still wondering which AI tool they should buy next. But when 50 senior GTM leaders were given a blank cheque and asked what they’d build in their data stack, the answer wasn’t more agents, better prompts, or a flashier model. It was the foundation underneath.</p><p>In this episode of The Context Layer Tapes, Florin Tatulea and John Lloyd dig into why 62% of senior GTM leaders said their next investment would be a unified data layer connecting all of their GTM tools. Continuous enrichment, intent feeds, and real-time signal engines followed close behind, while AI a...

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  Episode #3

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

  Jun 25, 202624 minS1

  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. In this episode of The Context Layer Tapes, Florin Tatulea and John Lloyd go a layer deeper than data. Drawing on research from 50 senior GTM leaders at US enterprises, they dig into why nearly three-quarters say the real blocker is how data moves between their tools and their CRM, not the data sitting inside any one of them. Florin and John break down why this is an infrastructure problem and not a data problem, why the CRM was never built to be a live hub for autonomous agents, and the multi-agent handoff (watcher, researcher, actor, executor) that turns a single intent signal into tailored outreach without a human copying between tools. You can have good data in every tool and still fail. The teams that win build the intelligence once and deploy it everywhere. Stop wiring AI into one tool at a time. Build the layer that connects them.

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