Is Agentic AI Advertising’s Next Big Disruption

Large Language Models and generative AI have rapidly become widely-adopted tools in the advertising ecosystem. We've experimented with prompt engineering, created synthetic personas, and flirted with automation in content production. But a new frontier is emerging fast-and it's far more transformative than AI that simply completes tasks when asked.

Welcome to the era of agentic AI: systems that can pursue goals autonomously, perceive the world around them, make decisions and take actions across time without persistent human prompts. These aren't just tools. They're collaborators, coordinators, and, in some cases, competitors.

It is early days - and most agentic systems remain somewhat unreliable for more sophisticated processes. They suffer from "hallucinations" and other faults that make current generative AI tools unpredictable. But, over the next 5-10 years, agentic AI seems poised to unlock another wave of transformation for agencies.

What Makes AI "Agentic"?

Agentic AI means different things to different people, and these varied definitions can create confusion. For the purposes of this paper, we'll define AI agents as systems that can accomplish complex, multi-step goals with little to no constant human supervision. They do this by:

• Breaking down complex goals into manageable subtasks
• Analyzing content and context that they have access to
• Using APIs, databases, and tools to act on subtasks
• Making decisions based on feedback or changing conditions
• Iterating independently without requiring constant human input

OpenAI's experiments with Operator, Anthropic's Computer Use, and new entrants like Make and n8nall point toward a shift: from reactive AI to proactive AI.

Some agencies are already experimenting with - and seeing early success from - simple AI agents. One examplewould be agents trained on media processes, platforms, and client-specific goals and KPIs. Once the high-level strategy and budget allocation is approved by clients, then the AI agents login to buying platforms, configure campaigns and monitor performance - all with very little human oversight. Another examplemight be multi-agent teams trained on creative workflows, where a strategy agent is focused on extracting consumer insight and shaping campaign direction, another set of agents handles specific idea development, and still another set of agents develops assets for client pitches and ultimately campaign deployment.

A Hybrid Workforce Is on the Horizon

More than 61% of agencies are already using generative AI as a powerful collaborator, creating a force multiplier effect for their human teams:

• to transcribe meetings and capture action items
• to help write drafts of emails, proposals and presentations
• to assist in brainstorming, exploring idea territories faster
• And more.

But what happens when the AI gains the ability to be proactive, make decisions on its own, and execute routine tasks in pursuit of higher level objectives? This kind of AI can theoretically move up the corporate ladder, so to speak. It grows from intern-level knowledge to junior, entry-level employee capabilities - now able to figure out what needs to be done and take care of it without constant supervision.

If generative AI is the intern you give specific instructions to, agentic AI is the junior strategist or project manager who can figure out what needs to be done-and just gets on with it.

The transformation sparked by generative AI is merely the beginning of several waves of AI-powered disruption. Intelligent agents, which are poised to become deeply integrated parts of a new human-machine hybrid workforce, are about to unleash a new surge of disruption, requiring us (again) to re-examine our workflows, structure and capabilities.

Let's explore where agentic AI might first take root across agency processes:

1. Media & Performance Optimization

Media & analytics were fundamentally changed decades ago with the launch of programmatic buying and machine-leaning-powered algorithms that support real-time decision making. In fact, these systems probably meet some definitions of AI agents.

As the technology advances, though, AI agents will be able to go beyond monitoring campaigns in real time, reallocating budgets across channels, testing new audiences, or pausing underperforming ads - taking on higher-level functions that have, thus far, been the domain of human experts.

Programmatic didn't erase the media department at agencies, nor will agentic AI. It will, however, redefine the value of human media talent. Instead of reacting to data and developing strategies from scratch, they may be setting the guardrails and training the AI agents to act in alignment with the brand and client goals.

2. Creative Operations

Today's advertising production is still largely human-run, despite increasingly pervasive usage of generative AI throughout many creative workflows. But agentic AI may be able to:

• Absorb creative briefs and develop idea territories autonomously
• Analyze which creative variants are resonating
• Request new content for underperforming segments
• Automatically generate and deploy localized assets
• Collaborate with video or design tools to execute on vision

It could turn creative teams into systems designers and creative directors for machines.

3. Account Management

AI agents could streamline account management functions by:

• Drafting client status reports and meeting agendas
• Tracking project timelines and flagging delays
• Following up automatically on outstanding feedback
• Proactively suggesting next steps
• Ongoing competitive tracking, analysis and generation of response recommendations

Account managers will be free to focus on deeper client relationships and strategic advisory, but they'll also be measured against the responsiveness and rigor of AI.

4. Strategy & Intelligence

Already, AI can synthesize insights, analyze campaign history, create synthetic focus groups, and even draft POVs on emerging trends. Agentic AI can go further-autonomously researching, creating decks and briefs, and proposing directions for client initiatives.

This could change strategy from a craft of insight and inspiration to one of curation, refinement, and escalation.

AgentOps: A New Agency Discipline?

As agentic AI becomes more embedded, agencies may need to build a new layer of operational infrastructure: AgentOps.

This could include:

• Designing the rules of engagement: What decisions can AI agents make on their own?
• Training and aligning agents: What tone, voice, values, and preferences must they follow?
• Monitoring and governance: How do you prevent agentic drift (gradually becoming misaligned with objectives) or unintended consequences? AI systems are far from perfect, and we're already seeing examples of AI agents misbehaving- illustrating the importance of a strong governance foundation.

The difficult task for agencies, then, is to become experts in codifying their unique knowledge-from creative quality standards to strategic frameworks-and baking that intelligence into how their AI agents operate.

Marketing to Machines: The Not-So-Hidden Side Effect

As agencies integrate Agentic AI, they must also recognize that this technology will transform consumer behavior and necessitate a new approach to marketing. We're heading toward a future where AI assistants help people make complex purchase decisions-comparing products, managing subscriptions, booking travel, even filtering out marketing that doesn't align with personal values or goals.

That means agencies may soon be marketing to AI agentsas much as to human audiences. It's not science fiction-it's a strategic shift already on the horizon.

A few of the many questions we'll need to ask to prepare for this inevitable future:

• How does our brand get recommended by a personal shopping assistant AI?
• Can our product descriptions be parsed and ranked by a machine?
• What does the machine care about? Human purchase decisions are made on a somewhat unpredictable combination of logic and emotion, but will AI agents eschew all emotion and focus entirely on the rational?
• How do businesses that lean on impulse purchases need to adapt to this future?
• What data do we need to feed AI agents to ensure we're included in the consideration set?

In this future, persuasion meets parsing. Agencies will likely need to get good at both.

So What Should Agencies Do Now?

Here's a quick-start playbook, some of which leans on strategies and principles already outlined in our Generative AI Blueprint for agencies:

1. Audit workflowsfor repeatable tasks and processes that could be "agentified"
2. Experiment with agentic toolsin sandboxed environments
3. Codify your IP-translating your agency's unique expertise and processes into instructions for AI agents
4. Upskill teamsin prompt chaining, decision modeling, and agent supervision
5. Create a task forceon AgentOps to lead governance and standards

• Evaluate the business model - charging for bodies and hours will become more challenging in a world made more even more efficient by machines

And perhaps most importantly: start imagining the new client value proposition. What are you uniquely able to deliver in a world where machines can manage much of the doing?

Final Thought: Be the Architect, Not the Artifact

Agentic AI won't kill the agency model-but it will redraw the value chain. The opportunity is to lead that transition, not follow it.

The future belongs to those who can design the systems, train the agents, and orchestrate the humans and machines into something smarter than either could be alone.

So let's stop thinking about how AI can do our tasks, and start thinking about how it can drive our transformation.