Firstly, let’s get something out of the way:
Not everything that claims to be AI is AI.
In fact, if you removed the letters “A” and “I” from the average MarTech vendor's current slide deck, you'd be left with a handful of vague promises and a chatbot that panics when you type in anything unexpected.
And yet, here we are. Boards are demanding “AI integration.” CMOs are parroting vendor hype. MarTech budgets are shifting, and everyone is suddenly fluent in buzzwords. But here’s the rub: much of what’s being sold as artificial intelligence is really just advanced automation, glorified statistics, or, frankly, smoke and mirrors.
Spoiler: it’s not all doom and gloom - some of it’s genuinely revolutionary.
But the first step is knowing the difference.
What is AI - really?
Let’s define terms before anyone gets too excited.
Artificial Intelligence in a business context is software that can perceive, decide, and act - often in a way that adapts over time. If your MarTech tool isn’t doing at least two of those three things with a feedback loop, it’s not AI. It’s just good engineering.
The main AI categories:
Symbolic AI (Old-school rules engines)
Think: “If X, then Y”
No learning, just a decision tree with a superiority complex
Statistical AI (Modern ML)
Think: predictive lead scoring, anomaly detection
Finds patterns using models and data
Generative AI (ChatGPT, etc.)
Think: net-new content from training data
Humanlike surface abilities, still stats underneath
Spotlight: Agentic AI (the real future of Marketing Ops)
Agentic AI refers to systems that don’t just respond - they take initiative.
What makes it different?
Goal-oriented: You set a target; it figures out how to reach it
Autonomous: It makes decisions, not just predictions
Self-improving: Learns from its own wins and fails
In Marketing Ops, this could look like:
Launching a campaign based on observed buyer journey breakdowns
Rebuilding workflows without asking
Flagging data model drift and rewriting attribution rules
If your current stack needs babysitting and prompting? That’s not agentic - it’s needy.
The “fake AI” problem
Most vendors using the AI label are actually delivering:
Conditional logic
Scripted flows
Rule-based automation
Useful? Sometimes. Artificially intelligent? Nope.
Fake AI giveaways:
Behaves the same every time
Doesn’t improve without a developer
Avoids technical explanations
No feedback loop or adaptation
Offenders in the wild:
“AI” lead routers using ZIP codes
Chatbots with three scripted paths
Subject line tools with 200 pre-written options
If the tech can’t learn, adapt, or do more than it did on day one, it’s not AI. It's a fancy spreadsheet with ambition.
The grey area - borderline AI
Some tech walks the line. It’s not fake, but it’s not truly intelligent either.
These are tools that often include:
Predictive modeling
Some adaptation
Limited autonomy
Examples:
Lead scoring: ML-trained, but only retrains if a human says so
Send-time optimization: Might update weekly, might not
Web personalization: Uses rules, not learning
Litmus test:
Can it change its behaviour without you telling it to?
If yes: it’s on the AI spectrum.
If no: it’s still just automation.
Real AI in the wild
Now for the good news. These are tools that do cross the line into true AI:
Dynamic content generation
Writes emails, scripts, pages based on real-time data
Adaptive journey orchestration
Changes campaign flows based on audience behavior
Conversational intelligence
Summarizes calls, flags deal risks, suggests next steps
Auto-optimizing media spend
Real-time bid shifts without a media buyer watching
Campaign-building copilots
You describe the goal; it builds and adjusts the strategy
These tools evolve. They learn. They actually earn the “intelligent” label.
Why the confusion persists
1. Vendor inflation
AI gets funding, so everyone says they use it - even if it’s duct tape under the hood.
2. Board-level FOMO
C-suites want “AI adoption” but often don’t know what it means.
3. Procurement theater
Buyers tick the “has AI” box without verifying.
4. Zero standards
There’s no governing body for what qualifies as AI in MarTech.
5. Media hype feedback loop
No one wants to admit the emperor’s new chatbot isn’t actually learning.
What Marketing Ops leaders should do
✅ Demand technical transparency
Ask your vendors how their AI works. If they can’t explain it, don’t buy it.
✅ Define the use case first
AI is a tool, not a strategy. Start with the problem.
✅ Build AI literacy internally
Your team should be able to sniff out hype.
✅ Pilot, don’t commit
Test for real adaptation before a full rollout.
✅ Stay ahead on agentic tech
The shift from reactive to proactive systems will define the next era of ops.
Final thoughts: AI is not magic - it’s maths with ambition
True AI doesn’t just automate - it decides, adapts, and evolves. But right now, most marketing tech stacks are drowning in faux-AI tools that talk big and deliver little.
The danger isn’t that AI will replace you.
The danger is that someone who knows how to use it properly will outpace you - fast.
So ask yourself (and your vendors):
What does your “AI” do when no one is watching?
If the answer is “nothing,” you’ve got work to do.
Want to see what real AI could look like in your stack?
Let’s talk. No hype. Just reality.
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