The AI Expectations Gap: Why Marketing Teams Feel Like They're Drowning
If AI is the Life Raft, Why Are Marketing Teams Still Drowning?
Since 2023, something shifted. Suddenly, executives expected creative teams to deliver more—faster turnarounds, higher volumes, better results. The reason? AI was supposed to make it all possible.
But here's what's actually happening: 85% of creative leaders say executives now expect more because of AI, yet most teams are still figuring out how to make these tools work. The technology exists, but the infrastructure, training, and workflows to use it effectively? Not quite there yet.
"We're creating a new kind of burnout," says Júlio Aymoré, Group Creative Director of Generative AI. "It's a human problem, not a technology problem."
The Real Problem
The issue isn't AI itself—it's the mismatch between what people think AI can do and what it actually delivers in practice.
What people think: AI = instant automation = do more with the same resources
The reality: AI = new tools that need training, vetting, integration, and constant human oversight
Teams are expected to produce at AI-enabled speed, but often lack:
Approved tools that meet legal and security standards
Clear guidelines on when and how to use AI
Time to experiment and learn
Workflows that actually integrate AI effectively
Meanwhile, the pressure to ship more, faster keeps mounting.
Why "More" Doesn't Mean "Better"
It's easier than ever to generate 50 ad variations in an hour. But do they perform? That's the question nobody's asking until it's too late.
Cassandra Gill, Senior Director of Growth, puts it bluntly: "AI has so many applications for marketers, but we still have constraints and limitations. Almost none of us in marketing have full visibility into how different aspects of our work will be impacted."
The trap: using AI to increase output without a strategy for quality, relevance, or effectiveness. You end up with more ads that perform worse—and a team that's exhausted from managing the volume.
What Actually Works
Instead of using AI to do more of everything, use it strategically:
Bring it back to results. When leadership pushes for "more," ask: "More of what? To achieve what outcome?"
Use AI for rapid experimentation. Generate multiple concepts quickly, test them, gather data, then double down on what works.
Automate the repetitive stuff. Grammar checks, brief structuring, resizing assets—these are perfect AI use cases that genuinely free up time.
Set realistic expectations. Be the expert who can explain what AI can and can't do. Most stakeholders don't understand the limitations.
The Performance Marketing Advantage
Here's the good news: if you're in performance marketing, you've been dealing with AI algorithms for years. Google and Meta have used AI to optimize ad delivery for nearly a decade.
"As performance marketers, we know how important it is to iterate quickly based on feedback. AI has accelerated that pressure, but it's nothing new in our field," says Gill.
You already know how to test, measure, and adjust. AI just speeds up the cycle. The key is staying focused on outcomes, not activity.
Bottom Line
AI won't solve your capacity problems by magic. But used strategically—for experimentation, automation of low-value tasks, and data analysis—it can genuinely help. Just don't let anyone tell you it means your team should suddenly produce 3x more with the same resources.
That's not AI. That's just wishful thinking.
