Building is cheap now. The trouble is that cheap to build has quietly turned into cheap to own, and those are two different things. Plenty of teams can list the agents they've shipped. Far fewer can say who fixes one at two in the morning when a model drifts or a Salesforce admin repurposes a field and the output turns to gibberish.

In this episode of Revenue Architects, John Lloyd is joined by Mollie Bodensteiner, VP of Revenue Operations at ZoomInfo, to take on the build versus buy call. Her reframe: most teams aren't building, they're assembling. Wiring together models, data sources, and triggers, then claiming engineering-grade confidence over someone else's model with some logic glued on top. The test she uses is simple. Who notices when it breaks, and how fast? If you can't answer that, you composed it.

Then the calls themselves. ZoomInfo could build a dialer, but shouldn't, once you price in the regulation and the cost of maintaining it in house. Post-sales went the other way: proprietary enough to build, but with a product manager, a roadmap, and a release schedule behind it, not a go-to-market engineer vibing it up. John and Mollie also get into why the signal is the easy part, why surface area is the penthouse and entity resolution is the parking garage, and why ownership is a name with a backup, not a department.

MCP comes up in every other GTM conversation. Plenty of people can define it. Far fewer have connected one. When 50 senior GTM leaders were asked which AI use cases are working and which are overhyped, agents querying GTM data via MCP split them more than anything else in the survey: 52% were undecided, and only 16% said it was genuinely working.

In this episode of Revenue Architects, Florin Tatulea and John Lloyd define what MCP actually is and where it breaks. Think of it as a USB-C port for AI: one standard interface instead of a brittle custom integration for every platform. An API runs on fixed endpoints set in advance. MCP lets the agent decide what to call and grounds it in live data rather than whatever was last written to the CRM. Which is where the trust problem sits, because the protocol is only as good as the layer underneath it.

Then the play. Churn risk prevention, built as five prompts: pull the book of business filtered to accounts inside their renewal window, layer in pain points from recent call recordings, add competitive intent and review-site activity, weight it into a tiered risk score, then draft outreach to whatever lands in critical. Churn rarely announces itself in one system, so the query has to reach across four.

Florin and John also cover what makes it possible: resolved identity, so a fused risk score isn't reading one account as four separate records, a grounding test for whether an agent can trace its output back to a real source, and the part nobody assigns, someone owning recalibration.

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.

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.

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 agents plugged in via MCP sat at the bottom of the list.

Florin and John break down why this is not really a budget problem, why a unified data layer is hard to buy as a single project, and why the biggest blocker is often ownership rather than technology. They also unpack the gap between where information lives and where decisions actually happen.

The play: run a lost-deal trace. Take one real deal that slipped, walk it backwards, find the signal or context that existed somewhere in the stack but never reached the right person or workflow, and build that connection first. Don’t try to build the whole data layer in one go. Find the first brick, prove its value, and start there.

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.

When you ask GTM leaders about AI, you expect a wish list of capabilities back. Smarter models, better content, sharper agents. But that's not what we saw on our survey.

In this episode of The Context Layer Tapes, Florin Tatulea and John Lloyd pick up where episode one left off, digging into what GTM leaders actually want AI to do and why they still can't. Nearly 40% described the same ambition in their own words, and it wasn't more AI. It was AI that connects the data they already have and acts on it instantly.

Florin and John break down why leaders aren't asking for capability but writing the build spec, why the signals (and the people who own them) keep bouncing off each other instead of connecting, and the automated signal-to-action play that turns scattered intent into a tailored offer in the right rep's hands the same day.

Same ambition, same AI. The only thing standing between "we wish we could do this" and "we do this every day" is the layer underneath. Stop wishing AI could connect what you have. Build the layer that connects 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.

In this first episode of The Context Layer Tapes, Florin Tatulea and John Lloyd unpack fresh research from 50 senior GTM leaders at US enterprise companies. The top three things blocking them from getting value out of AI aren't three problems. They're one problem, showing up three times. And no, it's not the algorithms.

Florin and John break down why the AI conversation has been pointed at the wrong layer for years, why "nearly right" AI is the silent killer of time savings, and the play GTM teams need to run if they want AI to actually deliver.

AI thrives on context. Give it foundational context and watch it do wonders.