Marketing to Builders: Why the Next GTM Audience Doesn’t Read Your Homepage – MarTech Cube

Marketing to Builders explores why the Next GTM Audience Doesn’t Read Your Homepage and how AI agents are reshaping B2B marketing.I was in a meeting last year when someone asked, “When do we start marketing to bots?” I didn’t have a clean answer then. I do now, because we’ve already started, and I think every CMO needs to be in on this conversation. The window to win this game is wide open right now, and the teams that move first are going to compound an unfair advantage.Product-led growth is evolving into agent-led growth, and the buying journey is moving fast into the hands of builders and the AI agents they trust to evaluate vendors. Some of that activity is happening completely out of sight. No homepage visit you can track. No demo request form. Just a quick function call to your API docs, a scan of your schema, a filter decision, and boom. They’re done.That is the new first impression. But the exciting part: most marketing teams are still optimizing for a classic buyer who no longer shows up first, which means there’s enormous upside for the teams ready to meet this moment.The builder is the buyer nowBuilders are now at the center of how products get discovered, evaluated, and adopted. In the GTM space, they’re the RevOps engineers, GTM engineers, and growth operators who design systems, automate workflows, and stitch tools into the pipelines that drive revenue.Bloomberry’s analysis of more than 1,000 GTM engineering job postings shows the role grew 205% year over year in 2025, climbing from roughly 1,400 listings in mid-2025 to more than 3,000 by January 2026. The broader RevOps category expanded from around 5,800 professionals in early 2022 to over 150,000 by 2024. And the trajectory is accelerating.Forrester’s State of Business Buying, 2026 report shows that more than 90% of B2B buyers are already using or planning to use generative AI to support their purchasing decisions, and most of those decisions are now shaped well before any seller enters the conversation.We’ve spent the last 12 months studying these builders, getting deep into their workflows, and talking to them directly to understand exactly how they think. They bring a fundamentally different mindset to the evaluation process.They start and end with functionality, reliability, and raw performance. They live in the tools, study APIs, scan documentation, scrutinize match rates, and probe data freshness. They want to see exactly how a system behaves once it lives inside a real, live workflow. And increasingly, they’re handing the heavy lifting to AI tools before they ever talk to a human.Because we listened to them, we completely re-architected how we deliver our product. We cater directly to how they actually operate: making our data instantly accessible, machine-readable, and ready to plug straight into automation pipelines via APIs and MCP, wherever they work. We deliver the data exactly how they need it, at the speed their workflows demand.But if they are, you become the obvious choice in a market where most competitors are still asleep at the wheel. Marketing has to evolve to meet builders and agents where they are, and the upside for the teams that do is massive.You can’t charm an LLM with a smiling stock photoMost CMOs are already thinking about generative engine optimization. How do we get cited in ChatGPT, Gemini, Claude, etc.? How do we show up when an AI recommends a vendor?But there’s a more interesting question underneath it: what happens when the buyer IS the AI?This shift is happening fast. Gartner projects that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% today, and IDC expects more than 1 billion autonomous agents running inside enterprise environments by 2029, executing 217 billion actions a day.When a growth team deploys one of those agents to evaluate data vendors, that agent isn’t browsing for inspiration. It’s executing a task. It verifies capabilities, checks latency, reads schema documentation, and filters out anything that reads like marketing copy.Soft narrative doesn’t survive this filter. But clean documentation, verifiable specs, and structured data that a machine can use to make a decision do. I’ve started thinking about this as two communication layers that have to coexist beautifully:If your data is fragmented or your docs are unmapped, an agent filters you out before any human executive ever sees a shortlist. Your creativity never gets its chance to matter.This is what I mean when I say technical surfaces are your brand assets now. API docs, dataThe sea of sameness problem, and the opportunity inside itContrary to every LinkedIn post claiming “I replaced my marketing team with AI agents,” the most valuable thing I can do is find better people to build with the tools. Replacing is the wrong path. Multiplying is the right one.Because if every brand uses the same base models with the same generic prompts, brand distinction collapses. The CMOs who navigate this beautifully are the ones building brand-specific AI strategies fueled by proprietary data, voice, and perspective.Right now in most companies, agents run and humans supervise. The mental model of marketing as a relay race between specialized teams is being outrun by organizations thatoperate like a control room overseeing agentic workflows. And that’s a thrilling shift to be part of.The opportunity is on the table. Your brand’s AI presence is becoming as important as your first-party brand. AEO, GEO, whatever you want to call it: the brands that win will be the ones that structure their content and data so that when an agent queries for the best solution, the answer is obvious.The companies that get this right are the ones rethinking what their data layer is for. Verified contacts, real-time signals, and deep integrations into the tools where builders actually live are no longer back-office concerns. They are becoming the brand itself. The B2B data intelligence layer is shifting from a sales utility into the foundation that agents reason against, query, and act on. And the bar is rising fast: match rates, compliance, freshness, and global coverage now decide whether your stack shows up in an agent’s shortlist or gets filtered out before a human ever sees it.That shift changes how revenue teams think about their stack. The strongest enrichment waterfalls today are built on data that is continuously verified, multi-sourced, and structured for machine consumption. The teams winning in 2026 are the ones treating data quality as a brand asset.Build for the machine so your people can focus on the humans, and the future of marketing opens up in front of you.Yael Abukasis, CMO and Head of Revenue Growth at LushaYael Abukasis is the CMO and Head of Revenue Growth at Lusha. With over 16 years of experience spanning advertising agencies like McCann, and tech companies such as Google and Natural Intelligence, she expertly balances the “two hats” of marketing: bold creative intuition and data-driven conversions.Yael is a passionate advocate for “builder-first” cultures, transforming traditional marketing teams into high-velocity systems architects who work with AI to scale human  taste. Previous Post Next Post source