What Are AI Agents Actually Doing in Advertising? 4 More Questions for Launching AI Agents in Your Media Workflow

1. How Can AI Agents (and Multi-Agent Systems) Be Used in Advertising and Media?

So, what can these agents actually do for advertising? Quite a lot – they’re already tackling tasks across the campaign lifecycle that used to eat up countless hours. Here are some of the top use cases in media and advertising where AI agents are making an impact:

‍

Campaign Strategy, targeting and segmentation

Understanding and micro-segmenting audiences is another domain where agents shine. An AI agent can analyze troves of customer data (web behavior, past purchases, demographics, etc.) and discover niche audience clusters. It might find, for instance, a late-night shopper segment converting on specific products, and then automatically shift targeting to focus on that segment. By learning these patterns, agents help deliver hyper-personalized targeting – serving the right message to the right people at the right time. This goes beyond what manual analysis typically yields, often uncovering non-intuitive insights (like a certain interest group that responds to a creative angle).

‍

Media Planning

Before campaigns even launch, AI agents assist in media planning. They can crunch historical campaign data and market trends to forecast outcomes – predicting, for example, expected conversion volumes at different budget levels. This helps in setting more accurate KPIs and media mix models. Agents can also suggest optimal channel mixes or timing, for example, recommending more budget in weekday evenings based on predicted user activity. In essence, they bring data-driven rigor to campaign planning, functioning like an ever-ready strategy analyst prepping your playbook.

‍

Cross-Channel Budget Allocation

In modern media plans, spreading and adjusting budget across channels is a dynamic challenge. AI agents excel at cross-channel coordination, maintaining a holistic view of all your platforms (search, social, display, etc.) at once. They can automatically shift spend from, say, Facebook to TikTok if data shows better performance on one, ensuring every dollar is placed where it has the highest impact. This kind of continuous re-balancing is something humans find hard to do in real time across many channels, but an agent can handle it effortlessly.

‍

Automating Routine Media Ops

Many nitty-gritty tasks in advertising – pulling performance reports, adjusting keyword bids, checking pacing against budget – are now being offloaded to agents. This automation not only saves time but also reduces errors. An AI agent might automatically generate a morning dashboard with insights and anomalies, so the team immediately knows where to focus. By acting as diligent “assistants,” agents free up human marketers from granular monitoring and fiddling, letting them concentrate on creative strategy and big-picture decisions.

‍

Real-time Campaign Optimization

One of the most powerful uses of AI agents is live optimization of ad campaigns. Agents can monitor performance metrics (impressions, click-through rates, conversions, etc.) as they stream in, and make instant adjustments. If an ad’s CTR drops or cost-per-click spikes, an agent can tweak bids, swap out underperforming creatives, or reallocate budget on the fly without waiting for a human review. While certain platforms offer such a dynamic optimization within the platform itself, agents can make these adjustments in a true cross-channel analysis. This means campaigns continuously self-optimize across the board. Marketers no longer have to play catch-up from yesterday’s data – the agent is tweaking and tuning 24/7 to maximize ROI.

‍

Dynamic Creative Generation and Testing

Agents can also flex their creative muscles. Using generative AI, an agent can create ad variations on the fly – generating different headlines, images, or calls-to-action tailored to audience segments or even individual users. For example, if the weather changes to rain, an AI agent might switch a travel ad’s image from a sunny beach to a cozy cabin to better appeal in that context. Moreover, agents can run multivariate tests at a scale impossible for humans, testing dozens of ad copy versions simultaneously to learn which resonates best. This results in highly personalized and continually optimized creatives.

It’s worth noting that many advertising platforms already incorporate AI agent-like technologies under the hood. The next step is more custom or controllable agents that brands and agencies deploy for their specific needs (think an in-house agent that knows your business rules, working alongside your team). The use cases above are being tried and tested in industry pilots, and early results show agents can drive meaningful uplifts – from improving conversion rates to reducing customer acquisition cost – by responding to data faster and more intelligently than any manual effort could.

‍

2. Who Is Already Using AI Agents or Multi-Agent Systems in Advertising?

AI agents in advertising might sound cutting-edge, but several companies and platforms are already on board – either openly or behind the scenes:

‍

Major Ad Platforms

Google and Meta heavily leverage AI automation in their ad products. Google’s Performance Max campaigns, for instance, use AI to determine where and how to serve ads across all Google inventory. Meta’s Advantage+ shopping campaigns automatically test creative combinations and optimize targeting using AI. These are essentially built-in agent systems that marketers are using (perhaps without thinking of them as “agents”). They’ve been credited with improving campaign performance by taking over tedious optimization tasks.

‍

Advertising Resource Management Companies

Emerging SaaS platforms like MINT are at the forefront of creating ad hoc systems tailored specifically for the media and advertising workflows. Rather than offering generic AI assistants, these companies are building MAS that reflect the real structure and dynamics of media teams—agents that function as strategists, planners, optimizers, and advertising professionals, each designed to handle a distinct role in the campaign lifecycle.

‍

Enterprise Marketing Suites and CRM Leaders

Salesforce (the big CRM company) has been very vocal about AI agents. They announced an “Agentforce” platform that lets businesses deploy trusted autonomous agents within their workflows. In marketing terms, that could mean an agent handling lead nurturing or automating parts of the customer journey. HubSpot and other marketing cloud providers are also exploring agent-like features to go beyond simple chatbots – for example, HubSpot’s ChatSpot and similar copilots are evolving toward more agentic capabilities (like performing multi-step marketing tasks when commanded).

‍

Media Agencies and Ad Tech Firms

Big advertising agency networks are not sitting idle. WPP’s media arm Xaxis has for years used an AI-powered optimization engine (aptly named Copilot) to tweak programmatic bids and budget allocations in real time. While they may not have called it an “AI agent,” it essentially functions as one for improving campaign ROI. Other agencies are incubating their own AI tools for campaign management. We’re also seeing independent ad tech startups embed multi-agent logic – for example, platforms that orchestrate an “intelligent media buying team”: one agent might scan market trends, another adjusts bids, another writes ad variations.

‍

Top Brands experimenting Agents in Creative Strategy

Some brands are experimenting with agent-like AI to assist in creative generation (e.g., Coca-Cola’s recent AI contest for ad visuals was supported by generative AI tools, if not full agents). Additionally, multi-agent approaches are used in areas like real-time personalization – e.g., one agent monitors user behavior on a site, and another agent decides which content or offer to show next (a form of one-to-one dynamic marketing). While not all these are public case studies yet, industry conferences are rife with examples of pilot programs where a “swarm” of AI agents works on an advertising problem and often outperforms traditional methods.

The adoption is still in early stages, but momentum is building. Some of your peers are already testing or using AI agents in parts of their advertising workflow – from global CPG brands to digital-native e-commerce players – and it’s likely to become far more common in the next 1-2 years.

‍

3. What Should You Consider Before Adopting AI Agents for Advertising?

For marketing and media leaders eyeing these technologies, it’s wise to proceed with a strategic game plan. Here are key considerations and tips for adopting AI agents in advertising workflows:

‍

Start with Clear Goals & Use Cases

Don’t deploy an agent just because it’s trendy. Identify specific pain points or opportunities in your media workflow. Is your team spending too much time manually optimizing bids? Do you have more data than your analysts can parse for insights? Those could be ripe use cases for an AI agent. Clearly define what success looks like (e.g., reduce CPA by 20%, or free up 10 hours/week of team time). A focused use case will guide the design of your agent and make it easier to measure impact.

‍

Pilot and Scale Gradually

Treat your first AI agent like a pilot program. Integrate it gradually rather than flipping the switch on full multi-agent system from day one. For example, you might initially use an agent just to make budget recommendations, with humans still approving changes. As trust and performance grow, you can give other agents more autonomy. This phased approach helps your team adapt and lets you catch any issues on a small scale before they grow. It also builds organizational buy-in as you can demonstrate quick wins.

‍

Ensure Human Oversight and Accountability

Human oversight remains critical, even with the fanciest agents. Assign team members to monitor the agent’s outputs and decisions. Set up dashboards or alerts for unusual behavior (e.g., if the agent suddenly shifts spend heavily into one channel, someone should review why). Having a “human in the loop” for strategic guidance and final say, especially early on, is important to maintain confidence and alignment.

‍

Embed it into your existing tech stack

If you use a certain analytics platform or data warehouse of customer info, connect the agent to it so it has the best data to work with. Similarly, ensure the agent’s actions (like campaign changes) are logged where your team can see them – ideally in your regular campaign management tools. Integration is key for adoption; the agent should feel like a natural extension of your team’s toolkit, not a black box. As an example, MINT’s Multi-Agent System is embedded by default in MINT’s ARM platform for maximum consistency, enabled with media workflow automation and unified media data.

‍

Train Your Team and Adapt Processes

Adopting AI agents isn’t just a technology implementation; it’s a change in how your team works. Invest in training your staff to work alongside the agents – for example, reading agent-generated reports, interpreting its recommendations, and understanding its limitations. You may need to adjust team roles (maybe your media planners become “AI supervisors” who focus on strategy while the agent handles execution). Encourage a culture of collaboration with AI: the agent does the heavy lifting, humans handle the creative and strategic judgment calls. Organizations that combine human creativity and oversight with AI efficiency will get the best results to really unlock the hybrid workforce of the future.

‍

By considering these steps, CMOs and agency leaders can introduce AI agents in a way that amplifies their team’s capabilities rather than disrupting them. Those who’ve done it successfully often start with a small win, maintain transparency about what the agent is doing, and gradually build trust in the AI. The end goal is to let humans and AI each do what they’re best at: AI agents crunch data and automate tweaks at blazing speed, while marketers provide vision, creativity, and critical thinking. When that balance is struck, the results can be truly transformative.

‍

4. Bonus Question: If AI Still Scares People, Why Is Agentic AI So Well-Received?

It’s true, AI can be intimidating. From Hollywood dystopias to job displacement fears, many people hesitate when they hear “artificial intelligence.” But here’s the interesting twist: Agentic AI isn’t triggering that same resistance. In fact, users—especially marketers—seem to be embracing it.

Why? Because Agentic AI is, above all, a user experience.

Unlike traditional AI systems that feel opaque or technical, agentic models show up as intuitive assistants that act like helpful teammates. Marketers don’t need to understand machine learning or write SQL queries to get value. With AI agents embedded in dashboards, campaign tools, or CRM platforms, the interface is as simple as: “Show me which audiences are converting best this week” — and the agent responds with actionable data. It removes friction from insight discovery.

Under the hood, yes, it's complex. There's a lot going on with orchestration, data pipelines, and models. But for the end user, it feels like magic: insights without asking a data analyst, decisions powered by evidence—not just gut feeling. In many ways, Agentic AI fulfills the long-standing promise of “self-service analytics,” only more proactive and more intelligent.

As a result, non-expert marketers are empowered to make faster, smarter decisions—without needing to code or over-rely on technical teams. It’s not just AI for the data scientist anymore; it’s AI for everyone in the room. That accessibility is a huge reason why Agentic AI is gaining traction, not fear.  

‍

From Evaluation to Action: Starting with AI Agents

Successful AI Agents adoption in marketing and advertising comes from a balanced approach. The most forward-thinking CMOs and media leaders will be those who combine the best of both worlds – AI and human. Expect AI agents to become standard members of marketing teams in the next few years, akin to how marketing automation became a staple. Multi-Agent Systems might handle entire campaign lifecycles, from research to execution, seamlessly coordinating like a “well-oiled” digital team. As the technology matures, we’ll likely see even smarter agents that not only follow goals but understand business context deeply. This means now is the time to start learning and experimenting.

Missed Part I of our Q&A session on AI Agents? Read it now at this link.

‍