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The New Publishing Standard in the AI Era and What It Replaces
The future of marketing will be one where marketers act as super-orchestrators. Marketers will manage a squad of AI agents (rather than spend time on content creation and time-consuming processes) to deliver highly personalized customer experiences. Ibexa's new platform uses autonomous agents to cut marketing execution time, making the best use of human marketers in their new role as orchestrators.
As part of Ibexa’s June 17 Orchestration Day event, Ibexa’s Chief Product Officer Grégory Bécue outlined how the marketing profession must evolve to overcome execution bottlenecks through agentic technology. In his keynote address, "Leveraging AI and Agents to Improve Marketing Performance," Bécue explained the technical distinction between standard language models and autonomous agents, emphasized the need for tools to take a model-agnostic approach and introduced Ibexa’s forthcoming Agentic AI platform, which aims to reduce campaign execution time and free marketers’ time spent on low-level tasks.
Common Bottlenecks? Execution and Tool Fragmentation
Marketing teams are currently overwhelmed by product saturation. They use an average of 40 marketing tools daily, which creates massive fragmentation. While tactical, generative AI co-pilots are highly adopted, they generate text. They don't solve execution, which is the real bottleneck. True marketing performance is about driving concrete business results, rather than simply producing more content. “Despite making massive investments, we still lack time, and that time is primarily an execution problem,” said Bécue.
What Does the Shift from LLMs to AI Agents Look Like?
Bécue emphasized that the future of marketing technology lies in AI agents rather than standalone Large Language Models (LLMs) or chatbots. Consider this analogy. An LLM acts merely as a "brain." It’s capable of thinking and analyzing, but not much else.
On the other hand, an AI agent is equipped with technical "arms and legs." These allow it to connect to external systems, access data and execute real-world tasks. As underlying models inevitably become commoditized, brand's knowledge and customer data become the true differentiator. "The future is truly agents and having a team of agents who can work alongside you. That's what can really bring value on a daily basis," said Bécue.
How Does One Go About Transforming a Simple Marketer into a "Super Orchestrator"?
AI agents aren’t designed to replace marketers. They lack human strategy, creativity or customer empathy. “Customer knowledge and business knowledge are something you possess, which you have to pass on to the agents," said Bécue.
Instead, by delegating tedious execution tasks — such as translating content into 20 languages, generating multichannel email/SMS campaigns or building weekly reports — marketers will transition away from task-centric execution and become super orchestrators. By guiding agents, marketers’ roles will shift from production to strategy and optimization. “The agents will execute. You will decide, of course, but you'll be doing less executing, and spend more time defining the objective, managing the agents, making decisions, validating and optimizing," said Bécue.
Frequently Asked Questions
A standard Large Language Model (LLM) acts solely as a "brain." It’s capable of thinking, analyzing and generating text, but it cannot act on its own. In contrast, an AI agent is equipped with "arms and legs" via tools and Model Context Protocols (MCPs). This enables the agent to actively connect to external systems, access data and execute real-world tasks rather than just answering prompts.
No. AI agents lack human strategy, creativity and customer empathy. Only marketers possess the essential customer and business knowledge that must be passed on to guide these agents. Instead of replacing professionals, agents will take over tedious, task-centric execution.
Marketers’ roles will transition to one of orchestration. Their focus will be on defining objectives, managing agents and optimizing performance.
Execution time is the main bottleneck that current generative AI co-pilots can’t fix. AI agents solve this by directly automating complex, multi-step execution tasks.
Being model-agnostic means that Ibexa's platform does not lock businesses into a single language model or vendor. It allows organizations to connect disparate data from customer and product platforms into a unified ecosystem. This gives them the flexibility to select and test the best underlying models (such as OpenAI, Anthropic or Mistral) for their specific marketing tasks.
How Can AI Help Your Business?
AI can improve productivity by shifting focus from manual tasks to strategic results. Bécue emphasized a model-agnostic approach, allowing businesses to connect disparate data from product and customer platforms into a unified ecosystem.
Ibexa can optimize your marketing performance. Learn more at ibexa.co.
