Untapped Value of AI in Performance Marketing: Three Areas
What do AI-powered marketing tools and gym memberships have in common? They both have a ton of potential, but those gains often go unrealized.
The heart of the issue is that we’ve all become used to automation-based technologies. They’re our frame of reference for software that can help make us more efficient. We know how to create set rules, workflows, and turn it on. They deliver speed, taking the actions for us, based on rules we’d already defined.
So no wonder many still see AI as a simple switch they can flip to stretch their budgets or draft ad copy. But those use cases are just scratching the tip of the iceberg. Marketers ready to explore further will find an incredible depth of value, rich with visibility, efficiency, and performance gains.
How Most Marketers Use AI Right Now
According to Salesforce's Generative AI Snapshot Research Series, most marketers use AI the most for basic content creation (76%) and writing copy (76%).
Maybe because those are the low-hanging fruit opportunities for AI developers. Last year, 2,324 AI tools represented 77% of all growth in the marketing technology landscape. Most were developed for content creation, with five of the top ten use cases linked to content-related tasks.
That tells us two things:
- AI is already a core part of marketing tech stacks
- But many tools are still geared toward surface-level tasks
These are strong launchpads for adoption, but that’s just the tip of the iceberg of what AI can accomplish:
- Targeting: Identify, expand, and convert your target audience across platforms.
- Creative: Generate engaging contextual visual and static assets that increase engagement and conversions.
- Experimentation: Continuously adapt and optimize campaigns by analyzing trends, attribution data, and real-time signals.
- Optimization: Automate and optimize bids and budgets autonomously to improve performance over time.
And the best part? AI provides marketers with visibility into why things are working, bringing sunlight to areas we couldn't see in the walled gardens of platforms like Meta and Google.
The Unexplored Value of AI for Performance Marketers
Far-Reaching, Nuanced Visibility
Performance marketers rely on platforms like Google and Meta, not just because of their reach and ubiquity, but because they’ve invested heavily in AI features for advertisers. These platforms remain powerful, but they’re walled gardens.
You put money in, conversions come out, but what happens in between is anyone’s guess. Sometimes marketers wonder if their ads could’ve performed even better, or feel the need to perform incrementality testing. That uncertainty can be challenging in a world where every marketing dollar is under a microscope. You need answers and clarity that these walled-gardens simply can’t provide.
But your own AI can bring those insights to the surface.
My customers tend to be most excited about how AI allows us to shine a light on areas they couldn’t see before. With advanced data analysis, marketers can get clarity on the true drivers of performance beyond even what some attribution tools can deliver.
For example, an e-commerce brand might uncover that a specific region responds best to ads featuring certain backgrounds or product SKUs. Meanwhile, a SaaS company could use AI to identify how engagement varies by industry or to spot bundles and upsell opportunities based on usage patterns.
Experimentation
Performance marketing will always be a game of momentum. You capture attention, Sales nurtures the relationship, and if all goes well, you win their business.
But that momentum doesn’t happen overnight—it takes time to build and tune the machine that delivers that momentum.
The challenge? You don’t have the luxury of time anymore. That’s why traditional A/B testing and experimentation strategies fall short; they’re too slow. In fact, marketers spend roughly 6.3 days each month on A/B testing.
(Heads up: this next paragraph is just a little technical. It’s worth it.)
That time spent A/B testing is why I’d like to tell you about multi-arm bandit models. They’re a type of machine learning that uses reinforcement learning. Basically, an AI is given directions, takes an action, and is rewarded when the outcome is favorable. It can do this endlessly in a self-reinforcing loop, getting better each time it tries something new. We use these models to optimize performance, so you can experiment at scale and dynamically shift budget to top-performing ads, audiences, or creatives in real time.
So, instead of waiting months or quarters for results to trickle in, the AI can act on insights faster and make proactive, data-backed decisions that maximize their return on ad spend (ROAS)—just like Betabrand did with Pixis' AI, which quickly identified high-performing keyword clustering cohorts on Google.
That said, don’t try to do everything at once. Just like when humans run A/B tests, if you test multiple variables simultaneously, you won’t get clear results because you won’t know what impacted the outcome.
Collaboration
The AI wave might be exciting, but it’s also scary sometimes. A 2024 study found that nearly 60% of marketers fear AI could replace them, up from just 35% in 2023.
It’s a valid concern, but I humbly believe that most of the fears about people losing their jobs to AI are unfounded. Especially for knowledge workers like you and me. We’re more likely to lose our jobs to other people using AI than the AI itself.
Our RVP Customer Success in LATAM, Alvaro Martinez, uses an analogy I like:
“AI marketing tools are co-pilots. They’re not running rogue. Once marketers understand that they’re still behind the wheel, the sooner they can gain peace, clarity, and confidence.”
I’ve found that confidence continues to grow when they understand the guardrails many AI tools have in place.
Each AI-powered system differs. Ours is a combination of machine learning, rule-based logic, and AI-driven decision-making. This setup offers peace of mind because marketers can switch to manual control at any moment, disable AI completely, and even review the reasons behind each decision.
How Performance Marketers Can Get More From AI
Most marketers probably feel like they’re on their own and navigating uncharted territory without a map or compass. While there may not be a complete map, we can start to see the path forward.
It can be helpful to adopt an open mindset when thinking about AI—one that embraces new opportunities and builds on potential AI use cases or ideas rather than disregarding them. The reality is that AI isn’t just a path to efficiency and speed, it’s also a path to quality and depth if you’re open to it.
So where should you start? Exactly where other marketing strategies start: your data.
Evaluate the Quality of Your Data
AI is only as good as the data you feed it. While you may need some volume of data to give an AI enough to work with, It's all about data quality at the end of the day. Just like humans work better with more context and direction, better input means better output for AI models and machine learning.
That’s why the first question you should ask isn’t, “Which tool should I use?” but rather:
“Do I have complete, current, and contextual data to guide the AI in the right direction?”
Without the right data, AI can make its own decisions, which could mean prioritizing ad spend in ways that don’t align with your goals. Ensuring your AI has the best data available means better recommendations, which sets your campaign up for success from the jump.
Set Clear Goals and Expectations for AI
Like any marketing strategy or tool, AI is only as effective as the strategy behind it. That’s why clarity matters, especially at this stage of the game.
My advice? Focus on what you can impact right away and what moves the needle for the business.
At Pixis, we typically see marketers turn to us for three key reasons:
- To scale without adding headcount
- To reduce customer acquisition costs (CAC)
- To do both, which is by far the most complex
And if you’re not sure where to start, start with your time.
Look back at the past few weeks. Where are you spending the most time? Are you logging in on the weekends? Doing things on holidays you’d rather avoid? The AI can handle some of these tasks for you, even while you’re sleeping.
Understand AI’s Role in Your Performance Marketing Engine
AI needs to really fit in as part of the way you and your team work. It’s best not to use it in isolation. It must operate across creative, targeting, bid, and budget management.
In other words, AI can’t be a single touchpoint or tactic; it needs to be woven thoughtfully into your entire marketing engine.
But how do you know where to use it? And what would it even look like to weave it in fully?
Start by laying your campaigns and all related workflows on the table. Literally. Mapping your workflows visually can help you ideate where AI could be a strategic copilot.
Make an explicit note of where you’re using AI today, and where you are not.
Then, ask yourself: Where can I use AI to drive even more performance and efficiency?
For example, instead of just using AI to write ad copy, use it to analyze previous creative to understand which formats have performed best. Or, use it to identify audience segments you can use to build lookalike audiences that lead to more efficient campaigns down the road.
The Real Impact of AI Lies Below the Surface
If you placed AI on a timeline of digital advertising, it would barely register—a big reason why most marketers are still trying to figure it out.
The same was true with Facebook ads in late 2007. Marketers didn’t know what to think at first. Some were hesitant, skeptical, and unsure how social media would fit into their strategies. Obviously, Facebook ads have become a staple since then, but it took time, patience, and education.
We’re at a similar inflection point with AI. The tools are here, and the upside is staring us in the face. What we all need to do now is dive in, trust the process, and let the confidence grow—because what’s beneath the surface is worth it.
Ready to dive below the surface and explore the real value of AI? Request a demo today.
