Over the past year, I’ve watched the conversation around AI in marketing change significantly.
At first, almost every discussion centered on content generation. Could AI help teams write copy faster? Create more variations? Produce more content with fewer resources? Those questions haven’t disappeared. But increasingly, I’m hearing a different one from marketing leaders: How can AI help us manage the growing complexity around content?
For enterprise organizations, that complexity is significant. More markets, channels and content come with more workflows, approvals, data, governance requirements, and stakeholders. And that’s where I think one of the biggest opportunities for AI lies. Not simply creating more content but in helping organizations run their content operations more intelligently.
Putting agentic content operations to the test
That idea became the starting point for our latest Storyteq Engineering team Hackathon. Over three days, we challenged eleven teams to build eleven AI agents designed to solve real content marketing problems.
Using our very own Agent ConsoleTM as the orchestration layer, teams explored how agents could interact with content, data, workflows, and organizational knowledge. What stood out wasn’t just what they managed to build in three days. It was where they found value.
I expected plenty of ideas around content generation. Instead, many of the most compelling projects focused on the operational friction surrounding content: finding insights, moving work through workflows, accessing organizational knowledge, and automating repetitive processes. That distinction feels important.
Getting from data to decisions faster
One team tackled a problem almost every large marketing organization will recognize: there is plenty of data available, but getting an answer from it isn’t always easy.
Campaign data lives in one system. User behavior in another. Operational data somewhere else. Something as straightforward as understanding which assets are performing best can mean finding the right dashboard, bringing together several reports or asking an analyst for help.
The team built an Analytics Agent that approached the problem differently. Instead of navigating dashboards, users could ask questions naturally:
• Which assets are driving engagement?
• Which templates are performing best?
• Where is adoption strongest across teams?
The agent brought together data from BigQuery and Mixpanel, interpreted the question, generated the appropriate visualization and returned an answer within seconds.
During the hackathon, it successfully answered more than ten questions across asset, template, and user data with approximately 95% accuracy.
What interested me wasn’t simply that an AI agent could produce a chart. It was the change in how people could interact with the information. The value isn’t faster dashboards. It’s faster decision-making.
AI can remove friction, not just create more output
Another pattern emerged across several projects. The teams weren’t necessarily trying to help marketers produce more. They were trying to help work move.
Enterprise content teams are dealing with more campaigns, markets, channels and personalized experiences than ever. But production rarely slows down because someone has run out of creative ideas. It slows down in everything that happens around the creative work.
Reviews wait for approval. Assets move between teams. Requests sit in queues. People chase stakeholders for information. Processes become harder to manage as organizations scale.
Several teams explored agents that could participate in those workflows: gathering information, triggering actions, routing tasks, scaling content or identifying potential blockers.
I think that’s an important evolution in the conversation around AI. Instead of asking, “How can AI help us create another asset?”, we can start asking: “Where can AI remove the friction preventing our teams from getting great work to market?”
For enterprise organizations in particular, that could mean improving speed without giving up the governance and control they need.
The biggest lesson: context matters more than the model
There was another lesson from the hackathon that stood out to me. The projects I found most interesting weren’t always the most technically sophisticated. They were the ones with the best context.
Most organizations already have a huge amount of knowledge sitting within their content ecosystem. Brand guidelines define what good looks like. Asset libraries contain years of creative work. Metadata provides context and taxonomy. Workflows capture how the organization operates. Analytics reveal what performs.
But that intelligence often sits across separate systems. An AI model on its own doesn’t understand any of that.
Give an agent access to the right content, metadata, workflows, data and business rules, however, and it becomes considerably more useful. That distinction matters as access to AI models becomes increasingly widespread. The model matters. But context determines value.
For content teams, the foundations already exist. The opportunity is increasingly about allowing intelligent agents to work across them.
From AI experimentation to real content operations
The most interesting outcome of the hackathon wasn’t really that eleven teams built eleven working agents in three days, it was how quickly those agents became useful once they could interact with the right information and processes.
That’s why I don’t think the next phase of AI in marketing will be defined purely by who can generate the most content but it will be defined by how effectively organizations apply AI to the real problems their teams face every day.
How do we find insights faster?
How do we remove unnecessary manual work?
How do we make organizational knowledge easier to access?
How do we keep increasingly complex content operations moving?
Several of the prototypes from the hackathon are now moving into further exploration, and many connect closely to what we’re building towards with Agent Console: an environment where organizations can build, manage and orchestrate agents across the content lifecycle.
It’s still early days, but after watching Storyteq teams tackle these problems in three days, I’m increasingly convinced that one of AI’s biggest opportunities in marketing isn’t creating more content, it’s helping people manage the complexity around it. And for me, that’s where agentic AI starts to get really interesting.
