The pace of AI development is astonishing—especially in marketing—where Agentic AI is taking root faster than almost anywhere else. These observations became crystal clear after I attended last week’s HSE Agentic AI Summit, hosted by Julia Nimchinski (shout out for a great conference!). Tech companies are already replacing entire functions with AI agents. These aren’t just tools. They’re early signs of a shift toward Artificial General Intelligence (AGI).
Agentic AI goes beyond generating content or answering questions. It can execute complex tasks based on a simple command. For instance, if you say “run a reactivation campaign,” an agentic system might pull the target audience, draft email flows, test subject lines, send messages, monitor performance, and report results—end-to-end execution without manual handoffs. This is the start of an evolution from chatbots to autonomous agents.
To clarify, this is more of a vision of what the future of marketing can become. There are still several hurdles for most of us to achieve this utopian vision. According to Anis Bennaceur, co-founder & CEO, Attention.com, about 5% of the GTM teams have fully implemented a program that is now generating results. A good start, but more work will be needed for mainstream adoption. For those wondering what can be done to make this vision a reality, here is an overview of what I learned from the conference, followed by three steps you can take to get started.
The Rise of the Autonomous Business OS
There’s growing excitement around scaling organizations with AI agents. The old “growth at all costs” startup playbook is dead. Now it’s about efficient growth—getting more done with fewer people. AI is disrupting the assumption that scale must mean headcount. Some companies are already achieving high revenue per employee thanks to intelligent systems. This is giving rise to what many are calling the “autonomous business operating system.”
A notable quote I heard from one of the presenting CMOs was that all new hire activity will be placed on hold. Hiring managers must assess whether an AI Agent can do the new hire’s responsibilities.
This shift isn’t about replacing people—it’s about empowering them. Agentic AI can dramatically multiply the output of a skilled team member. Instead of hiring ten people, a company can amplify the impact of one great one. Imagine a team of “supermarketers” that generates 10X output. This theme was repeated throughout the event. The focus is on human-AI collaboration, not competition.
We are early in this journey. Most people aren’t using AI agents in their personal lives, and even within companies, adoption is limited to engineers and product managers. Many tools feel intimidating. Asking an agent to run a Google ad campaign, for example, still requires creativity and domain expertise. Agentic AI needs better memory, more goal-oriented task management, and improved collaboration between agents. It’s powerful, but still clunky.
Managing the Hype: Simple Wins Over Speculation
There’s plenty of hype around AI—and it’s easy to get caught up in AGI fantasies. But aiming too high too soon can cause us to miss the real, immediate opportunities. Right now, the biggest value comes from simple, targeted use cases. AI is great at data analysis and content delivery. But creativity, emotional nuance, and strategic insight? Still very much a human game.
Some dismiss Agentic AI as “yesterday’s news” because it seems like glorified task automation. But that misses the point. This isn’t about replacing humans—it’s about building systems that can handle routine work so people can focus on higher-level thinking.
The user experience still has big challenges. Change management is slow. Many companies run on fragmented platforms. Gaining access to all the right data is still difficult. Agents often only work well with specific tools. And when they’re asked to do too much at once, they hallucinate. Unlike simple automations, AI agents are non-deterministic. They require training, not just setup.
I learned a new term last week. Model Context Protocols, or MCPs, help agents interact with apps like HubSpot or Google’s toolset. Think of an MCP as an API for Agents. This avoids expensive custom integrations and allows agents to “speak the language” of different platforms. If this trend continues, we could see agents browsing and acting like humans—querying databases from Slack, for example—opening the door to more widespread adoption.
Let’s hope we don’t have to add another layer to our tech stack dedicated to MCPs!
Making It Work: Strategy, Simplicity, and Structure
Marketing teams are experimenting with Agentic AI across functions—from content production to customer onboarding. Companies are hiring people fluent in AI just to keep up. If you’re not embracing this technology, you risk getting left behind. In some cases, AI is powering autonomous pipelines. Latane Conant, CRO at 6sense, shared that 20% of revenue is now generated autonomously—without human involvement! Pretty impressive.
But all of this depends on data. Quality data is essential for reliable outcomes. And even then, data is useless without a clear hypothesis. Leaders must guide AI on what to look for and why. Expecting AI to “just figure it out” is a fast path to disappointment.
New roles are emerging to manage this shift. Some companies are appointing Chief AI Officers. Others are hiring internal “agent operators” who manage workflows and train teams. There’s also a growing role for “tastemakers”—people who understand what good looks like and can guide AI to deliver it.
To get started with Agentic AI in marketing, focus on three steps:
- Start experimenting today. Don’t wait to feel ready. Use tools like ChatGPT or Gemini to begin testing workflows. Tinkering builds fluency.
- Break tasks into small parts. Instead of handing an agent one big, vague goal, split it into discrete steps. This reduces errors and increases success rates.
- Solve real, simple problems. Don’t aim for science fiction. Find areas where AI can make life easier now—things like content generation, segmentation, or simple automation.
Adopting Agentic AI takes effort. It’s a “slow down to speed up” scenario. Trial and error is part of the process. But when teams are empowered to experiment—through hackathons, show-and-tells, or just time to explore—the results can be transformative.
