Marketing Brew Analyzes Agentic AI's Rise in Advertising

I’ve been watching AI’s role in advertising evolve for years, but Marketing Brew’s recent analysis of agentic AI makes one point unmistakable: we’re past simple automation and smarter chatbots. AI agents are starting to plan, execute, optimize, and even transact across the ad ecosystem with far less human involvement than most marketers are used to.

That’s a significant shift.

And if moves from Google, Shopify, PubMatic, agencies, and industry standards groups are any sign, agentic AI is moving from buzzword to operational reality faster than many brands expected.

From Generative AI to Agentic AI

To me, the key distinction is simple: generative AI creates content, while agentic AI takes action.

That difference matters in advertising. Instead of just drafting copy or summarizing reports, agentic systems are being built to handle media planning, campaign activation, bid optimization, product discovery, and the path from search to purchase. In practical terms, workflows that once required teams of buyers, analysts, and platform specialists can increasingly be handled by AI-driven systems working toward defined goals.

Marketing Brew frames this shift as both promising and unsettling, which feels exactly right. The upside is massive efficiency. The tension is governance, transparency, and control.

The Timeline Shows Just How Fast This Is Moving

The pace of change has been striking.

In late 2025, Scope3 and its partners introduced AdCP, an open standard designed to support agentic AI in media buying. It may sound technical, but the significance is straightforward: standards make it easier for autonomous systems to communicate, execute, and scale across fragmented ad environments.

By December 2025, Shopify had launched Agentic Storefronts, giving merchants a way to surface products directly inside AI platforms like ChatGPT and Perplexity. Around the same time, PubMatic and Butler/Till ran what has been described as the industry’s first fully autonomous agentic CTV campaign for Geloso Beverage Group’s Clubtails brand.

The reported outcomes got attention for a reason: more than 5x cost efficiencies, 82% lower buy-side fees, reduced CPMs, and campaign setup times cut by 87% in early tests.

Then January 2026 pushed things further. Google introduced its Universal Commerce Protocol, developed with Shopify and Walmart, to support smoother discovery-to-purchase journeys across AI-powered surfaces. PubMatic formally advanced its AgenticOS vision. And the IAB reported that 66% of agencies were focusing on agentic AI for ad buying and execution, even as 40% said understanding how to use it remained a top challenge.

By spring 2026, the pattern was hard to miss: infrastructure, commerce, ad buying, analytics, and platform support were all moving in the same direction.

Why Google, Shopify, and PubMatic Matter So Much

When I look at this story, I see three especially influential forces shaping the market.

Google is working to define the transaction layer of agentic commerce. With the Universal Commerce Protocol, Business Agent tools, richer Merchant Center data, and direct offers inside AI-led experiences, Google is building a framework where conversational search can lead naturally to purchase.

Shopify is pushing distribution. Its vision is straightforward but powerful: if consumers are discovering products inside AI assistants, merchants need to show up there. That shifts ecommerce from a website-centered model to a networked, AI-surfaced one.

PubMatic is focused on execution. Its agentic tools aim to reduce the friction and inefficiency that have long defined programmatic buying. If those early campaign efficiency claims continue to hold up, agencies and brands will have a hard time ignoring the model.

Together, these companies are shaping three connected layers:

• Discovery
• Commerce
• Media execution

What This Means for Advertisers

For advertisers, the biggest takeaway is that performance marketing is becoming more machine-driven, more data-dependent, and less forgiving of weak inputs.

In an agentic environment, success depends on giving AI systems the right materials to work with. That includes structured product data, accurate attributes, FAQs, inventory details, pricing, promotional logic, and clear brand signals. The old model, where marketers could rely heavily on persuasion-first creative and manually managed workflows, is giving way to something much more operational.

That doesn’t mean creativity disappears. It means creativity has to work alongside structured intelligence.

As conversational queries get longer and more descriptive, brands need content that matches intent with precision. AI agents are pulling from product feeds, reviews, forums, and merchant data. If that information is incomplete or inconsistent, the brand loses visibility at the exact moment an agent is making a recommendation or influencing a purchase.

The Promise Is Real, but So Are the Risks

I understand why so many agencies are excited, and I understand the nerves too.

The promise is obvious: lower costs, faster setup, continuous optimization, and potentially stronger returns. If campaign execution can be streamlined without sacrificing quality, teams have more room to focus on strategy, messaging, audience development, and measurement.

But the risks are just as real.

Governance is a major concern. Who owns the decisions an AI agent makes? How do brands validate accuracy? What guardrails exist for regulated industries like healthcare and finance? How do teams maintain transparency when autonomous systems interact across multiple platforms and standards?

These are not side issues. They will determine whether agentic AI becomes trusted infrastructure or stays a limited-use experiment.

That’s why the agencies taking this seriously are building governance models now, not later. The winners in this next phase won’t just be the fastest adopters. They’ll be the ones that combine speed with accountability.

Advertising Is Becoming More Autonomous

One reason Marketing Brew’s analysis stands out is that it captures the broader industry mood: this is no longer niche.

Agentic AI has entered mainstream advertising discussion because the ecosystem is aligning around it. Standards are emerging. Platforms are integrating. Merchants are syndicating data into AI channels. Agencies are testing autonomous buying. And performance expectations are rising at the same time CPC pressure and competition continue to intensify.

That combination creates a strong incentive to automate intelligently.

I believe we’re heading toward a market where agent-to-agent transactions, AI-mediated product discovery, and autonomous campaign management become a normal part of how advertising works. Not universal overnight, but increasingly common and strategically important.

FAQ

What is agentic AI in advertising?
Agentic AI refers to AI systems that can take action, not just generate content. In advertising, that can include planning campaigns, activating media, optimizing bids, managing product discovery, and supporting transactions.

How is agentic AI different from generative AI?
Generative AI creates outputs such as text, images, or summaries. Agentic AI goes further by making decisions and carrying out tasks based on goals and available data.

Why are companies like Google, Shopify, and PubMatic important here?
They are shaping the infrastructure behind discovery, commerce, and media execution. Their tools and standards could determine how quickly agentic AI becomes part of everyday advertising operations.

What should advertisers do now?
Advertisers should focus on strengthening structured data, improving product and merchant feeds, building governance frameworks, and testing how AI-driven systems fit into campaign workflows.

Conclusion

My view is that agentic AI is more than another advertising trend. It represents a structural shift in how campaigns are planned, products are discovered, and performance is optimized. Brands that adapt early will be in a stronger position to compete in a market where AI agents influence both media buying and purchase behavior. For teams that want clearer visibility into what’s driving revenue and a smarter way to optimize returns as automation accelerates, ROAS Suite is a practical place to start.