In a not-so-far future, your ad campaigns will proactively adapt to market shifts and user behavior without your intervention. Your job will be to teach your AI agent how to optimize the campaign: your strategy, campaign goals, desired audience, brand guidelines, and all the context that gives direction to agentic AI. The agent will be a member of your team, acting on your guidance, not replacing your judgment.
This is the agentic AI future of ad optimization. And while we're fully there yet, we’re actively laying the foundation for it today.
Let’s explore how you can use AI for ad optimization now to prepare for a future where agentic AI is the norm, with practical tips on how to get started and where to look for meaningful tools. First, let’s talk about why advertisers who rely only on manual optimization are falling behind.
Ad optimization today is fragmented. Advertisers juggle countless levers, from audience targeting to creative testing to budget pacing, all of which require ongoing manual refinement. The responsibility falls on the advertiser to analyze results, identify what’s working, and make changes accordingly.
Machine learning has helped in specific areas for years. Take AdRoll’s BidIQ, which automatically adjusts bids based on intent signals and performance goals. But bidding is only one part of the equation. It doesn’t account for the strategic decisions behind audience design, creative testing, and overall campaign setup.
Even with this level of support, advertisers are still translating business goals into media strategies, reviewing performance metrics, and manually adjusting campaigns. It’s like spinning a dozen plates at once. As soon as one falters, your attention shifts, and another could fall.
This process also struggles to scale. Dynamic creative optimization (DCO) helps automate variation, but often feels templated or less-than premium. Newer AI models, especially LLMs, are starting to unlock more flexible, adaptive creative possibilities, getting us closer to one-to-one communication, but we’re not fully there yet.
Live footage of an advertiser flawlessly juggling multiple campaigns…but for how long?
Now imagine logging into your DSP and seeing an AI-generated summary of campaign updates. Your AI agent shifted more budget toward a channel driving stronger CPA, tested multiple versions of creative, and identified an underperforming audience to pause.
Your role is to review its decisions, refine its training, and align its actions to your brand and goals. You’re still steering the ship. AI is helping it move faster and more efficiently.
In this future, AI agents will:
Autonomously optimize toward your goals by managing budget, audiences, and creatives with minimal input
Adapt in real time to seasonality, behavioral shifts, creative fatigue, and performance trends
Enable deeper personalization by tailoring messages at an individual level in ways no human team could manage alone
This future is not about removing the human. It is about elevating the human. Marketers become the coaches and strategists while AI handles execution.
While we’re not yet living in a fully agentic reality, many tools available today are paving the way. Here’s how advertisers are already using AI to optimize their campaigns and build the muscle for what’s next.
AI systems can detect unusual performance patterns or identify missed opportunities, then surface tailored suggestions.
With conversational interfaces, you can even ask AI questions about your campaigns and get actionable insights in plain language.
Take, for example, our AI assistant. You see a hint about our next section in its conversation flow:
You can give AI your campaign goals and let it generate draft campaigns, recommend strategies, or suggest initial budget allocations.
Some platforms are experimenting with higher levels of automation, but these systems often operate without clear visibility into how decisions are made. For now, the most reliable tools are the ones that keep you in control while helping you move faster.
Machine learning has long been used to optimize bids in real time. AdRoll’s BidIQ, for example, evaluates intent signals and applies the best bid to each impression, keeping your campaigns efficient and goal-aligned.
AI helps uncover new, high-performing audience segments by analyzing behavioral trends and contextual data.
It also supports privacy-forward targeting by relying less on user-level identifiers and more on aggregated signals and contextual relevance.
Tools like AdRoll’s AI Ad Builder help create ad copy, image variations, and CTA suggestions to speed up and scale production.
DCO takes it further by assembling the best combinations of creative for each impression. While still evolving, these tools offer a preview of how AI can help tailor messaging at scale and reveal connections between content and performance that might otherwise go unnoticed.
As AI-powered tools become more sophisticated, some solutions promise complete automation. But full autonomy without transparency comes with risk.
Black-box AI systems often don’t explain how decisions are made. This makes it difficult to audit performance, ensure brand safety, or refine strategy. Advertisers should feel confident understanding and guiding the AI, not guessing what it’s doing behind the scenes.
That’s why we believe the best AI is explainable, collaborative, and built to support your expertise rather than replace it.
To prepare for an agentic future, you need to get comfortable with AI now. This is how you can:
Start by exploring AI features already available within your ad platforms — these are the campaign and creation tools, DCO capabilities, and audience targeting suggestions we mentioned above.
Experimenting with these features will build trust and familiarity, preparing you for more advanced AI capabilities down the line. There’s not much better advice we can give you than to learn by doing!
Instead of micromanaging every aspect of your campaign setup, set clear goals and allow platform AI to handle some of the optimization. The next time an AI pop-up asks how it can help, indulge it and see if its capabilities are up to the task.
This enables you to experiment with AI in a safe environment — on many platforms, AI is coming to you asking how it can help. Give it your goals and see how it performs against them.
Use AI insights as strategic signals
Many DSPs surface AI-driven suggestions, like how to increase audience reach or how to improve campaign performance.
Use these insights to guide broader strategic decisions. When patterns emerge, consider what they reveal about your customers and how your messaging is landing.
AI creative is getting a bad rap for its ethical concerns and dubious quality, but it’s improving rapidly. Specifically, AI creative is stronger at working with existing creative to improve it (think Adobe’s AI image editing as opposed to DALL-E’s image generation).
Try ad creation tools and DCO capabilities periodically to see how they handle your existing brand creative and improve it. Offer the AI system diverse versions of your creative. Let the AI optimize and test these variations.
Agentic AI isn’t a distant vision. It’s where the industry is headed. But even now, the tools available can help you move faster, personalize better, and optimize smarter.
Using these capabilities today isn’t just about improving performance. It’s how you build the foundation for a more collaborative future — one where AI supports your decisions, not replaces them.
Last updated on July 10th, 2025.
