Something shifted for me about six months ago. I was watching one of my team's analysts spend the better part of a week pulling performance data, formatting slides, and writing commentary that — frankly — said roughly the same thing every month. Not because she wasn't sharp. She is. But the process was designed for humans to do robot work, and it showed.
That was the week I started building our first agent pipeline. And it changed how I think about everything we do.
We've been using AI like a calculator
Most owned media teams I talk to are using AI the way we used spreadsheets in 2005 — as a slightly faster way to do the thing we were already doing. Generate a meta description. Suggest some keywords. Draft a paragraph we'll rewrite anyway. It's useful, sure. But it's fundamentally passive. You poke it, it responds.
Agentic AI is different. You don't give it a task — you give it a goal. "Improve organic visibility for our butter category pages." And then it figures out the steps: audit what's there, find the gaps, draft content, monitor what happens, adjust. Not once, but continuously.
I've started describing it to stakeholders as "a junior team member with infinite patience and perfect memory." That framing lands every time.
Three things this changes right now
The boring stuff disappears first. Monthly reporting, content audits, competitive monitoring — this is where agents earn their keep immediately. That reporting pipeline I mentioned? Seven agents, talking to each other, turning raw Excel exports into branded PowerPoint decks with written commentary. What used to take a week of analyst time now takes minutes. And honestly, the commentary is better than what we were producing manually, because the agents don't get tired on slide 47.
You have to start thinking about AI as an audience. This one's sneaking up on people. If LLMs are reading your content to answer user queries — and they are — then you're not just optimising for Google anymore. You're optimising for comprehension by machines. Schema markup, semantic structure, clean content hierarchies... these aren't nice-to-haves. They're how you stay visible in a world where the answer box replaces the click.
Your team changes shape, not size. I don't think agentic AI means smaller teams. I think it means differently skilled teams. The people who can define sharp objectives, evaluate whether an output is actually good, and course-correct when something drifts — they become the bottleneck. Execution gets cheaper. Judgment gets more expensive.
How I'd start if I were starting today
Honestly, the biggest mistake I see is teams trying to go straight to "AI-generated content at scale." That's the deep end, and the water's murky.
Start boring. Pick a process that's repetitive, low-risk, and easy to validate — reporting is perfect. Build an agent that does it. Learn how agents fail (they will). Learn what good guardrails look like.
Then work outward: content operations, localisation, personalisation. Each step teaches you something about how to define objectives that agents can actually execute against. By the time you get to the ambitious stuff, you'll have the muscle memory.
The teams that move first on this won't just be more efficient. They'll be operating differently — thinking in systems instead of tasks, defining outcomes instead of writing briefs. The constraint stops being capacity and starts being imagination.
And that's a much better problem to have.