What’s hype, what’s helpful, and how to actually get started - AI in Marketing
Why Marketing Operations teams need to think differently about AI - and how to do it without the noise, panic, or perfectionism.
The AI buzz has officially reached “louder than a toddler with a drum kit” levels.
Depending on who you ask, it’s either revolutionising marketing or just a shiny distraction. Teams are either racing to build entire AI strategies or hesitating at the starting line, unsure what’s real and what’s just another clever automation dressed up in buzzwords.
Here’s the truth: Most B2B marketing teams are somewhere in the messy middle.
Curious, cautiously optimistic, but quietly overwhelmed.
This article is for them.
First, let’s be honest about what AI really is (and isn’t)
Now we've already written other articles on this - we even have an in depth whitepaper on the topic - but it helps to strip away the smoke and mirrors.
AI, at its core, is software that can perceive, decide, and act - ideally learning and improving as it goes. But that definition gets stretched beyond recognition in marketing circles.
Some systems are barely more than fancy “if this, then that” logic trees - rule-based engines that look clever but never actually learn. Others use machine learning to find patterns and make predictions, which feels smarter… but still has guardrails.
Then there’s generative AI - the kind that writes your subject lines, drafts landing pages, and churns out halfway-decent blog intros. It mimics creativity using statistical patterns from its training data. It sounds human. Sometimes it even fools humans. But don’t confuse mimicry with intelligence.
And finally, there’s the new kid on the block: Agentic AI. This is where things get interesting. Agentic systems aren’t just reactive - they’re proactive. They don’t just wait for prompts; they flag problems, take action, and even course-correct without needing a human to point the way.
If you’re wondering whether something is actually AI or just marketing fluff, here’s the test: Can it change its behaviour without you telling it to?
If yes, it’s probably real AI. If not, well… it might just be a clever trick in a shiny box.
So where does that leave us?
Right now, agentic AI is starting to take real shape in Marketing Operations - and not just in the “labs and keynote slides” kind of way.
Think about your campaign workflows. How much time gets eaten up by briefing, building, QA’ing, adjusting, launching, and monitoring? MOPs teams are often stuck juggling 100 spinning plates, just trying to keep things from falling over.
Now imagine an AI agent that can take a campaign brief, build the campaign itself, monitor performance, and suggest improvements - all while you’re in your 1:1s or trying to eat lunch without another Slack notification.
That’s not science fiction. That’s where this is headed. And the early adopters are already reaping the benefits.
Same goes for analytics.
We’ve all lost days to pulling reports, tidying up data, and trying to extract insights from dashboards that seem designed to hide them.
Agentic AI flips that. Instead of just answering “what happened?”, these systems start to answer “what should I do next?” And they do it while scanning your goals, your targets, and your previous performance - then recommending actions that actually move the needle.
This isn’t about replacing humans. It’s about finally giving humans the breathing room to think, create, and lead instead of constantly cleaning up after the machine.
Meanwhile, the software landscape is shifting under our feet.
One of the most under-reported shifts in AI is that it’s accelerating software development itself.
Thanks to low-code tools, no-code platforms, and AI assistants like ChatGPT or Claude quietly writing software behind the scenes, we’re entering what some are calling the “Hypertail” era.
In short: there are going to be billions - maybe trillions - of tiny apps, agents, and automations, many of them custom-built by non-technical users.
What does this mean for Marketing Ops? It means the gatekeeping around software is crumbling. You no longer need a dev team to build a tool that helps you work smarter. And when AI is added to that mix, the cost and complexity drop even further.
Great. So how do you actually start?
Here’s where most companies get stuck. They want to “do AI,” but haven’t figured out what that actually means in practice. So let’s break it down.
Start by asking:
Why are we doing this? Is it about productivity? Process improvement? Changing how the business works?
Are we racing to build AI, or are we adopting it to achieve outcomes?
What pace are we comfortable with - steady, or accelerated?
How mature is our data environment?
Are we training our teams to use AI confidently?
These sound like basic questions, but skipping them leads to half-baked strategies and a lot of wasted budget.
Then get specific - and small
You don’t need a 12-month roadmap to get started. What you need is a well-framed experiment.
Pick a use case with visible impact but low risk. Write a performance summary with a generative AI tool. Try a prompt-based briefing workflow. Use AI to surface anomalies in your campaign data.
Just don’t try to change the entire engine at once.
Keep it small, move quickly, and document everything. Share wins. Share failures. Share what you learned and how you’d do it differently.
The teams that build this reflex - experiment, learn, repeat - will outpace the ones that try to build the perfect AI plan before touching a single tool.
A final word of caution: AI isn’t just a technology problem
It’s a strategy problem. It’s a people problem. It’s a leadership problem.
The biggest risks aren’t rogue bots or hallucinating models. They’re vague goals, misaligned teams, and the slow erosion of trust when experiments aren’t communicated clearly.
The companies that thrive in this next era won’t be the ones with the most tools. They’ll be the ones with the clearest intent - and the courage to start small, move fast, and keep learning.
In closing: AI won’t save your Marketing Ops. But it might just unlock them.
Used well, AI can free up your people, clean up your processes, and speed up your execution. Used poorly, it’ll just give you another tech headache to manage.
Start where you are. Ask better questions. Run smarter experiments. And don’t wait for perfection.
Your future campaigns will thank you for it.
