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When it comes to AI, there’s a lot of noise out there.
Constant big announcements from major companies, new tools and updates every month, sweeping predictions about the next 5 or 10 years…
But if you’re in Marketing Ops, you’re not thinking 10 years out. You’re probably thinking about this quarter, this year, and what you should realistically be preparing for.
That’s the lens we wanted to take here. We asked our leadership team to share what MOPs professionals should be paying attention to in 2026.
Not abstract trends, but practical shifts: what’s changing, what needs to be in place, and where the pitfalls are.
Many of their ideas overlapped, so rather than present four separate viewpoints, we’ve combined their takes into one cohesive picture.
Let’s get into it!
AI agents have been a hot topic in the marketing world (and the entire world) since late last year, but 2026 is the year they actually start delivering value inside companies. Especially towards the second half of this year.
We’re moving past the experimentation phase, where individual teams played with isolated tools. The shift now is from solo agents to orchestrated systems. Think of it as going from a solo musician to a full orchestra playing together.
What does that look like in practice?
Imagine one agent monitoring your CRM for missing or incorrect data. Then, a second agent picks up those flags and enriches or corrects records. A third updates scoring and routing. A fourth analyzes engagement and recommends next best actions. All running quietly in the background with minimal human involvement.
Eventually, this will led agent-to-agent interactions on both sides of the conversation. For example, when your AI emails a lead, it might not be a human reading it. It could be the lead’s AI agent reading, prioritizing, and summarizing it for them. This will fundamentally change how we think about content strategy and communication.
The uncomfortable truth is that the biggest shift in 2026 isn’t AI itself; it’s building the data foundation that makes AI actually work.
Too many GTM teams have been moving fast on top of weak data. Their foundation is full of Inconsistent account data, messy lead sources, duplicates everywhere, and half-manual lifecycle models. AI can’t fix this for you. It will simply automate the chaos.
The mindset shift required to combat seems counterintuitive at first: We need to slow down to speed up.
This means standardizing fields, aligning definitions, fixing routing rules, and cleaning up years of messy inputs. With strong, clean data in place, AI can accelerate teams in meaningful ways. This is what separates high-performing GTM teams from everyone else.
It’s also important to remember that data also can’t stay siloed. For so many teams, data is currently managed as “marketing data”, “sales data”, or “customer data,” but it’s the same person moving through your funnel. Those silos create discrepancies that break AI and hurt downstream results. Data governance needs to become an organizational function, not a departmental one.
And governance extends to AI itself. “Human in the loop” isn’t a sufficient guardrail anymore. When AI is scoring leads or generating personalized emails at scale, we can’t assume that anyone will be reviewing every single output. Instead, MOPs teams will need to build real systemic guardrails to ensure quality and brand alignment.
To reach the orchestrated AI future we’re talking about, you need a robust architecture supporting it. CRM, MAP, CDP, and product systems connected together, with AI agents layered on top. And none of this works if teams are working in silos instead of collaborating.
We expect AI adoption to centralize into pre-approved platforms.
It’s similar to what happened years ago with websites. Marketing got tired of waiting weeks for IT to make changes, so hosted landing pages emerged. Now, tools like HubSpot, Marketo, and Salesforce that are already approved can slip past InfoSec and let teams implement AI features without the overhead.
The tradeoff is that decentralized experimentation tends to produce better results. So this centralization may lead to some disappointment. Instead of finely tailored agents, teams may end up with limited out-of-the-box features.
As for headcount, we don’t believe the hype that AI will replace your team. We’re not at a stage where AI can fully replace staff. The real gains are coming from AI acceleration with the right team. We view AI (and AI Agents) as a wingman, not a full-on replacement.
In fact, companies that already cut teams may start hiring again, because automation often creates scale that requires more capacity to service. So we expect hiring and team expansion in strategic areas.
That said, if you’re in the market for a new role, being “AI-activated” is now career-critical. You need to know how to work with AI, build AI into your workflows, and manage AI within your systems. If you don’t, job insecurity becomes more and more real.
We want to end things with a brief note about metrics.
Remember when vanity metrics were things like website visits and email open rates? If they didn’t convert to revenue, they didn’t matter.
Well, AI is creating a whole new generation of vanity metrics.
Statements like, “We handled X conversations with our chat agent,” sound impressive at face value. But if there’s no conversion and no revenue at the end, you’re just paying for a tool that talks to people without actually driving results.
Dig deeper into these flashy metrics and get to the bottom of how (and if) they represent value in a meaningful way.
If you want to learn more about how your team can drive real results with AI in Marketing Ops, book a free 30-min call with one of our senior consultants here.