Everyone wants AI in their marketing stack. Most of them aren't ready.
Let’s be honest: AI is the shiny object of the moment in Marketing Operations. Every vendor is “AI-powered,” every deck mentions machine learning, and every CMO wants to know why you’re not using AI to do something clever with lead scoring or content.
But here’s the problem: most companies are trying to implement AI before they’ve done the basics.
They haven’t mapped their processes. Their data is messy. No one knows who owns what. And yet… they’re plugging in AI like it’s going to fix everything.
That’s not how it works. And in most cases, it just makes things worse.
You can’t automate a mess
One of the first things we ask when someone says they want to “bring AI into marketing ops” is: what process are you trying to automate or improve? Half the time, there’s no clear answer. Or worse, the answer is something vague like “personalisation at scale.”
If you don’t know your own workflows - what they are, who owns them, what the pain points are - you’re not ready for AI. You’re barely ready for automation.
AI should come after you’ve done the hard thinking:
What tasks are repeatable but resource-heavy?
Where are humans adding minimal value?
Where does speed or scale matter most?
Otherwise, all you’re doing is building a Rube Goldberg machine that outputs bad decisions faster.
Your MarTech stack is probably already bloated
Most B2B companies are already drowning in tools. ESPs, CRMs, MAPs, CDPs, attribution platforms, analytics tools, and now - AI this, AI that, and AI in things that don’t need AI.
Adding another tool (especially one branded as “smart”) without reviewing what’s already in place is a fast way to waste money and confuse everyone.
So before you chase that new AI plug-in or “intelligent assistant,” ask:
Do we already have a tool that does this?
Will this add clarity or complexity?
Who’s going to maintain it, own the output, and improve it?
If you don’t have a solid answer to those, AI is just another acronym in a sales deck.
Bad data in = garbage AI out
There’s a painful truth most vendors won’t tell you: AI doesn’t magically clean your data.
If your segmentation is dodgy, your scoring models are misaligned, or your CRM has a dozen fields for “Job Title” - AI will still give you answers. But they’ll be based on garbage. Fast garbage. Polished garbage. But still garbage.
In fact, AI often hides the mess - by making things look smarter than they are. That’s dangerous.
Before adding AI, clean house:
Fix your taxonomy and tagging.
Audit your fields and data sources.
Standardize naming conventions.
Eliminate duplicates and dead records.
The smartest thing you can do with AI is feed it well.
AI isn’t a strategy. It’s a tactic.
You wouldn’t tell your CFO, “Our strategy this year is Excel.” So don’t say your marketing strategy is “AI.” It’s a tool. It’s a layer. It’s a way of executing faster or smarter - but only after you know what you’re doing and why.
Too many AI projects fail because nobody asked: what’s the actual goal here?
Is it reducing manual campaign builds? Improving conversion through smarter segmentation? Forecasting pipeline more accurately?
Start with the outcome. Then find the best way to get there. AI may or may not be part of the answer.
The teams getting it right? They’re doing their homework.
There are companies using AI well. They’re automating repetitive tasks, surfacing insights, scoring leads more intelligently, and spotting performance patterns before a human would. But they all have something in common:
They mapped their workflows.
They cleaned their data.
They aligned teams on goals.
They knew what they wanted AI to do, not just what they wanted it to sound like on a slide.
In other words, they did the unsexy stuff before plugging anything in.
🚫 TL;DR:
If you wouldn’t let an intern rewire your entire MarTech stack, don’t let AI do it either - unless you’ve done the research, defined the problems, and prepared the ground.
Want AI to make a difference in your Marketing Operations? Step one is strategy. Not software.
