Confessions of a Marketing Ops Pro: AI, Data, and more

When we sat down with Alex Long - Senior Marketing Technologist and all-around martech wizard, we knew the conversation would go deep. After all, Alex has spent nearly a decade managing the plumbing behind the scenes of modern marketing: integrating tools, cleaning data, managing platforms, and making sure everything just works.

As CEO of Amoeba AI, I’ve spent the last few years obsessing over how to make data smarter, stories clearer, and AI truly helpful, not just flashy. So, talking to someone like Alex, who lives in the intersection of people, tools, and data every day, felt like having a conversation with the very user we’re building for.

We kicked things off by clarifying roles, because, as it turns out, not all “marketing ops” roles are the same.

“People hear ‘marketing ops’ and think campaign ops,” Alex said. “I’ve never actually launched a campaign in my life. I focus entirely on tech ops; integrations, platform management, ensuring systems talk to each other, and making sure that tech enables the team to do their job, not get in the way of it.”

That distinction is critical. It’s also what gives Alex such a unique view of the evolution of marketing technology over the past decade. She’s seen it all; from the early days of “just Marketo and Salesforce” to today’s sprawling martech stacks filled with personalization engines, chat tools, webinar platforms, and, of course, AI everywhere you look.

But while the tools have exploded in number and capability, the day-to-day challenges haven’t gone away. In fact, in many ways, they’ve multiplied.

“People think having more data is the same as being data-driven,” Alex said. “It’s not. You can have all the dashboards in the world, but if you don’t trust the data or can’t make sense of it, it doesn’t help. I’ve had so many conversations where a stakeholder looks at a report and says, ‘Well, that’s not what I expected,’ and suddenly the data is suspect.”

This is exactly the problem we’re solving at Amoeba: not just surfacing insights, but making those insights understandable—turning them into narratives people can trust and act on. We believe the future isn’t just having access to data, it’s knowing what it’s telling you, and what to do next. Alex agreed.

“What’s exciting about tools like Amoeba is that they bridge the gap between technical data teams and everyone else,” she said. “It’s not just about dashboards anymore. It’s about saying, ‘Hey, here’s the pattern, here’s what it means, and here’s what you might want to try next.’ That kind of clarity is so powerful.”

Of course, none of this matters if people don’t adopt the tools in the first place, and that’s where AI in marketing hits a wall. We talked a lot about the difference between AI tools that truly enhance workflows, and those that just create noise.

“The number one thing people are using AI for right now is copywriting, which honestly makes me a little sad,” Alex laughed. “We’re letting computers write emails, and then using another AI tool to summarize those emails for us. It’s absurd. What I want is AI that helps with the stuff that actually takes time and effort; cleaning data, pulling insights, summarizing calls, automating manual tasks. Basically, I want an AI intern.”

This idea came up again and again... the need for AI to be purposeful. Not trendy, not gimmicky. Just genuinely helpful. For Alex, right now, that includes the AI chatbot her team has running on their website right now.

“It’s been a game-changer. Instead of static decision trees, we have a bot that can understand nuance. It redirects accounting questions to the finance team, routes job inquiries to HR, and only sends quality leads to sales. It saves our reps a ton of time, and it gives visitors a much better experience.”

And when we talked about budget—one of the hardest parts of martech decision-making—Alex had a very practical take. It’s not about shiny demos. It’s about building a business case.

“When I pitch a new tool, I try to make it easy for the person holding the purse strings to say yes. I’ll package it up like, ‘Here’s the problem, here’s how this tool solves it, here’s what I expect the ROI to be.’ Sometimes I wish vendors would just do that part for us.”

(Yes, we’re taking notes on that one. Look out for something along those lines on the Amoeba site soon.)

But even with a perfect use case, Alex says, there’s one more crucial ingredient: someone has to own AI adoption inside the team.

“You can’t treat AI like a side project. If you really want to see ROI, someone needs to be responsible for testing, integrating, and evangelizing it internally. That doesn’t just mean having a technical person onboard, it means having someone who understands the strategy behind it, who knows where it fits into the bigger picture.”

When I asked her where that person should sit inside the org, her answer was immediate: either within a project management office (if you have one), or in marketing/revenue ops. It needs to be close to both the tools and the people using them.

The conversation ended on a philosophical note. We talked about how important it is to know what questions to ask of your data, but also, how powerful it is when a tool can surface questions you hadn’t thought of yet.

That’s what we’re building toward with Amoeba. Not just “AI for marketing,” but AI that acts like a thinking partner—one that can explore side quests in the data, uncover unexpected patterns, and help teams experiment with confidence.

As Alex put it, “The future of marketing ops isn’t just about knowing how to use tools. It’s about knowing when not to. It’s about being strategic, being curious, and helping your team cut through the noise.”

If you’re building your own martech stack and want to get more out of your data without drowning in it, Amoeba might be the partner you’ve been looking for. We’ll bring the AI intern—you just bring the questions.