Mario, a go-to-market practitioner at Airbyte, explains that real progress requires two foundations: product-marketing fit with clear personas and competitive insight, plus RevOps with clean data and reliable reporting. Without them, companies blast the wrong people with mass outreach and AI just amplifies bad signals.
We're gonna get into trying to separate the hype versus reality now.
Yeah, Mario, again, you've been at the, you're at the front row at Airbyte. You've been advising also like a bunch of other companies on the side, so you're super deep in the space. Where do you think from your point of view go-to-market engineering feels like an actual step change and where is actually being oversold?
That's an interesting question. I think it's a step change when you have a couple of things. It's a step change when you have your sort of like PMM hat on done right. So you have product market fit, you know, your personas, you know, your playbooks, you know what works, what doesn't work. That's really, that can really be a catalyst. I think what you can do more damage than good is when you're starting to send thousands of emails to the wrong persona with the wrong messaging and then you sort of like don't realize, sorry, you sort of like think, okay, like why is this not working? Like everybody's doing it. You know, you go on LinkedIn today and the average CEO gets probably like 10, 20, 30 messages a week from clay agencies promising these insane outbound workflows. And I'm like, yes, but you have to do the foundation. You have to have the foundation right. There are two foundations that I think are very important for go-to-market engineers to be able to do their jobs. As I said before, one of them is the product marketing foundation. You need to know your persona, your competitor, your market, and so on and so forth. And the second is your RevOps. I think as companies grow, bad data equals bad signal equals bad bad results. Like, you know, at Airbyte Data Integration Platform, we always say garbage in, garbage out. And I think when AI is in the mix, that's a multiplier effect. And so like, if your data is bad, if your assumptions on your market are bad, you're just going to do more damages. And I think that that's where it's oversold. These companies that are pre-product market fit, the things that they can just hack their go-to-market by using AI. And I think it's not always the case.
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