What Our Own AI Journey Taught Us About Trust, Data, and Tools That Don’t Talk to Each Other

Every product has an origin story. Ours started with a gap we couldn’t ignore: the AI conversation happening across our industry was miles ahead of what was actually happening inside most marketing teams. As the marketing leader here at Validity, I spent the past couple of years watching that gap up close—in our research, in our own team’s experiments with AI, and in the hard questions our customers kept asking about trust, brand risk, and whether AI could actually be relied on. That’s the thinking that led us to build Validity Engage, and it’s the story I want to walk through here. 

The AI gap we encountered 

For the past couple of years, we’ve watched a strange split play out across our industry. Everyone is talking about AI all day, in every room, at every conference. But when we asked marketers how far AI had actually gotten into their day-to-day operations, the honest answer was not far. In our own research, barely one in 10 marketers said AI was truly embedded into their workflows. Everyone was experimenting. Almost no one had reached the point where AI was a structural part of how their team ran.  

We saw two reasons for that gap, which helped guide us on our Validity Engage journey. First, a huge share of marketers told us their data wasn’t ready to support AI at any real depth—not messy in the ordinary way marketing data is always a little messy, but not trustworthy enough to hand over to an automated system. Second, almost nobody had a dedicated budget for AI. Teams were cobbling it together: borrowing from other line items, stretching experimental dollars, layering AI on top of tools that were never designed to support it. Point solutions bolted onto an already fragmented stack.  

That combination—unreliable data plus a patchwork of disconnected tools—is exactly the wall most marketing teams hit. And it’s exactly the wall we wanted Engage to remove.  

What our own team’s AI journey taught us  

Some of the clearest lessons came from watching our own use of AI evolve. Years ago, we added live chat to our website so a rep could talk with a prospect in real time. As the underlying technology matured, AI started handling more of those conversations directly. Internally, there was real hesitation at first—would people push back? Would it feel impersonal?  

Instead, people responded well. They got answers immediately. They could book a meeting on the spot instead of waiting for a callback. What we learned from that shift stuck with us: people don’t resist AI on principle. They resist AI that isn’t useful. The moment it saves them real time or gives them something they actually need, the hesitation disappears.  

We saw the same pattern internally as our own marketing team leaned harder into AI—for research, for messaging frameworks, for first and second drafts of content. It got noticeably better at understanding our brand voice the more context we gave it, and it freed up real time for higher-value work. But we kept hitting a ceiling: every tool we used sat in its own silo, working from its own slice of data, with no shared thread connecting insight to action across the whole program. We could get efficiency in pieces. We couldn’t get it as a system.  

Why a platform, not another point solution  

That’s the problem Engage was built to solve. Rather than marketers adding one more standalone AI feature to an already crowded stack, we wanted to bring our existing capabilities together—email creation and testing, deliverability monitoring, contact data quality—under one platform, with AI operating across all of it instead of being isolated inside each piece.  

The data question mattered just as much as the architecture. If nearly half of marketers don’t trust their data enough to let AI make real decisions with it, then better AI alone was never going to be enough. Validity Engage is built on the world’s largest email data network —signals from hundreds of ISPs, direct mailbox provider partnerships, and billions of behavioral data points processed every day. That depth is what lets the AI inside Engage make recommendations worth trusting, instead of guesses dressed up as insight.  

Guardrails, not blind automation  

None of this meant loosening our own standards for how we use AI in marketing. If anything, building Validity Engage sharpened them. A human still reviews everything before it goes out the door. Brand standards get fed to the AI explicitly rather than left for it to infer over time. And we’ve drawn a clear line based on stakes: let AI take the lead on lower-stakes, high-volume touchpoints, and keep human judgment front and center for the moments that carry real relationship risk.

Transparency runs through the same logic. Our chatbot works because people know it’s a chatbot. As AI moves deeper into email and other channels, we’ve held onto that same principle: be upfront that AI is involved, and make sure people understand that the value they’re getting is because of it, not despite it.  

Where that leaves us  

Validity Engage exists because we kept seeing the same story from every angle—our own team, our own customers, and our own research. Marketers want AI to be structural, not experimental. They can’t get there without data they can trust and tools that talk to each other. And none of it works unless the people on the receiving end feel like AI is making things better for them, not just faster for us.  

That’s the thinking behind Engage, and it’s the same thinking behind our webinar series: the AI Executive Briefing. The next webinar episode in the series features our CTO, Matt Gore. He joined to dig into what it actually takes to get an AI initiative off the ground—including the data-readiness questions most teams aren’t asking yet but should be.  

Want to see how Engage puts these ideas into practice? Take a look today!