Product-Led Validation: A New GTM Motion for AI-Driven Software
AI-first software embeds deep in the stack. That means engineering now has a veto in every deal. PLV is how you get through the gate.
If you’re following the series on pricing models in the AI age, despair not! Part 3 will be out on Monday. In the meantime, this is something I’ve spent the week thinking about.
TL;DR: AI-era tools don’t sit on the edge - they run inside the business. Engineering doesn’t buy them, but it blocks what it doesn’t trust. So stop pitching and start building for quiet technical approval.
AI-Driven software is fundamentally different from legacy SaaS
B2B software used to live (mostly) on the edges of core technology in a business. Most SaaS tools used across functions were discrete, modular, and easily replaceable.
Take the endless Martech universe. You might plug in a lead scoring tool, a webinar platform, or an analytics dashboard. They ingested data via an API, ran some logic, and pushed it back into your CRM. If something failed, you lost a report or misrouted a few leads—but the business didn’t stop. You could rip it out and try another vendor without too much hassle.
AI-driven software doesn’t work that way. These products don’t sit adjacent to the stack—they operate inside it. They ingest sensitive data, influence decisions, automate actions, and interact with other systems in real time. If they fail, the consequences cascade: broken workflows, exposed data, corrupted outcomes. You can’t plug and play.
Whether it’s monetization infrastructure, AI copilots for sales, or LLM-driven agents, these tools are deeply entangled with how a company actually runs. They’re not apps. They’re infrastructure. And that changes how they get bought—and who gets to say no.
Engineering is now a mandatory checkpoint in every serious B2B deal
When a product reaches into core systems, the risk calculus shifts. This isn’t about feature gaps or pricing objections—it’s about operational fragility. Data leakage, latency issues, architectural misfit—these are not edge cases. They’re frontline risks. And evaluating them falls squarely to engineering.
In AI-era buying, engineering isn’t optional. They aren’t brought in to rubber-stamp a decision. They’re a gate. Not just influential—decisive. But here’s the tension: engineers typically aren’t the buyer. They’re not the user. They often don’t even feel the pain. But they can kill the deal—by refusing to integrate, raising security concerns, or simply deciding it’s not worth the hassle. They hold unilateral veto power.
Worse, they don’t need a good reason. “We’ll build it ourselves” is always on the table, even when it’s obviously cost-inefficient. Saying no costs them nothing. This dynamic creates an asymmetry. You can’t sell to engineering. But you absolutely have to get through them.
They don’t want a sales call. They won’t convert through onboarding. Their concern is simple: will this break something I’m responsible for? If the answer isn’t obvious—and credible—they’ll withdraw support. Often silently. Either way, the deal stalls.
This is the go-to-market reality most AI-driven companies now face. And existing GTM models are really not build for it.
Why SLG and PLG both fall short on their own
Sales-led growth tries to solve the engineering bottleneck by throwing people at it. Pre-sales, custom demos, architecture workshops, “GTM engineers.” And yes—if your ASP is high enough, this can work. But only if you’re willing to burn margin and stretch your team. It’s slow, bloated, and misaligned with what engineers want. At best, they tolerate it. At worst, they disengage. No one ever earned a developer’s trust with a 90-slide ROI deck.
Product-led growth fares worse. Self-serve onboarding, freemium plans, usage-based models—these work when the buyer is also the user and the implementer. In AI-first B2B, that’s rarely true. The buyer wants diligence. The user wants UX. The engineer wants to see the code. And none of them want to swipe a credit card and deploy an unknown tool that touches core systems. It’s too risky.
So SLG is too expensive and slow. PLG is too lightweight and fragile. But the core issue isn’t the models themselves—it’s that both ignore the permission dynamics of modern software buying.
Product-Led Validation: what it is and why it matters
Product-led validation (PLV) isn’t a growth model—it’s a permission layer that runs in parallel to your growth motion. It doesn’t replace PLG or SLG, and it’s not a compromise. It reflects how software gets bought in the AI era: cross-functional, risk-sensitive, and engineer-gated.
PLV reduces friction without reverting to full sales orchestration. It’s not about conversion. It’s about clearance. You’re not trying to win engineering’s heart—you’re trying to remove their veto.
That means building a parallel path to validation, distinct from the commercial cycle but essential to it. One where engineers can test, inspect, and simulate the product on their own time, with minimal friction, and no interaction with sales. Not to be delighted. Not to be onboarded. But to be reassured. In practice, this includes:
A sandboxed environment with dummy or safe BYO data
Clean, inspectable APIs with architecture visibility
Clear deployment models (e.g. self-hosted, hybrid)
Excellent documentation—especially for security and integrationMinimal or no contact with sales
Control over data ingress/egress, auth, and permissions
The goal isn’t to “convert” engineering. It’s to give them just enough to validate the product without inviting resistance.
PLV is the missing motion in modern GTM
Most enterprise sales teams already have a notion of “technical validation”: a POC, a trial, maybe a technical workshop. But these are scoped within the sales process—as a stage to pass through. They treat engineering as a checkpoint.
That’s a mistake. In AI-native software, engineering isn’t a box to tick. It’s the immune system. If it sees risk, it blocks the organism. PLV makes this dynamic explicit. If you don’t design your GTM to earn engineering comfort—not just buyer excitement—you’ll lose deals you should have won.
This means treating validation as a continuous layer, not a discrete event. You need to market to engineering even when they’re not in the room. You need to build technical experiences—sandbox environments, clean docs, testable APIs, deployable demos—that let them evaluate on their terms. You need to preempt the questions they’ll never ask aloud: How much work is this? What happens if it breaks? Do I trust how it’s built?
Ignore that layer, and you won’t get through the gate—even if the rest of the room is cheering you on. As AI-first software becomes more embedded in the enterprise, PLV won’t be optional. It will be standard operating procedure.
Felix
P.S.
This thinking is part of the Market-Led Growth (MLG) framework. MLG is a way to align business models with market reality—not internal habit. More at marketledgrowth.com