Hi 👋🏻
Over the past few weeks I've had a lot of conversations about AI agents. Most of what gets called "the agent era" is still hype — demos that don't survive contact with a real workflow, tools that look impressive in a screenshot and break the moment you try to run them at scale.
But underneath the noise, a few directions are starting to feel real. Three in particular keep coming up. They're not predictions so much as bets — places where I think the next 12-18 months will actually move.
Here they are.
AI search chatbots might become obsolete sooner than most people realise
The current race is to rank inside ChatGPT, Perplexity, Claude, Gemini. Get cited. Show up in the answer. Win the AI search game.
That race is real — for now. But there's a scenario where the destination itself disappears.
Once everyone has their own agent — for creative, marketing, research, content planning, performance tracking, even personal stuff like shopping — your agent is the one doing the searching. The chatbot interface starts to look like a transitional technology. Why would a buyer open ChatGPT and type a question when their agent already knows what they need and goes and gets it?
When that happens, "ranking in AI search" stops being the goal. Those platforms might be a thing of the past entirely. The new game is showing up inside your buyer's agent journey — being the source their agent trusts, the brand their agent surfaces, the data their agent pulls from.
It's a different optimization problem. And the teams treating AI search as the endgame might be optimizing for a layer that's about to dissolve.
The takeaway: Don't just optimize for AI search. Start thinking about how your brand shows up inside agents — the ones your buyers will be using, and the ones doing work on their behalf.
More on this in my discussion with Beatrice on the Semantic Agent podcast: From Automation to Agents: Rethinking Workflows, Data and SEO ✨
Marketing is getting more interesting and harder at the same time
The good news: marketing has never been more personalized, more contextual, more sophisticated. The tools are extraordinary.
The bad news: there are more data points to stay on top of than any human team can realistically track, and a growing black box around how your brand actually shows up across AI-driven surfaces. Social feeds curated by algorithms. Search results generated by models. Creative variants spun up dynamically. Paid placements optimized by systems you don't control.
You can no longer manually audit how your brand is being presented. There's too much of it, moving too fast, across too many surfaces.
The upside — and this is the part that gets buried — is that AI itself becomes the solution. The same technology creating the complexity can absorb it. A data ingestion and analysis machine, when it's wired up right and trusted to run on its own.
The marketers who win the next phase aren't the ones working harder to keep up. They're the ones who let AI do the watching, the analyzing, and the flagging — so the humans can stay focused on the calls that actually require judgment.
The takeaway: Stop trying to monitor everything yourself. Build the system that monitors it for you.
Team performance will stay flat until agents fully kick in
This is the one I think about the most.
Most marketing teams right now are stuck in a weird middle ground. They're using AI at random — a ChatGPT tab here, a Jasper draft there, a Midjourney image when someone remembers. It feels productive. But team-level output hasn't really moved.
The reason is simple: scattered AI usage doesn't compound. You need the work itself to change shape.
The productivity shift will happen when teams turn their work into repeatable processes — workflows perfected on the tedious tasks, run by agents, with humans kept in the loop only where it matters: strategy, creative direction, brand voice, taste, brand safety.
That's the version of AI adoption that actually moves numbers. Not "we use AI." But "our agents run these specific workflows against these specific metrics, and our humans focus on the work only humans can do."
Most teams aren't there yet. The ones that get there first will pull away.
The takeaway: Audit your team's work. The repeatable parts should be running on autopilot. The judgment parts should have your best people on them. The middle — where most teams sit today — is the trap.
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industry news and updates you might have missed
Sharing some interesting studies on AI Search and SEO Automation from recent weeks:
Community discussion 🌟
I recently asked the MLforSEO community about their approach to structured data in for traditional and AI search, and there was a great discussion, with many dismissing it moves the needle for AI Search, while others doubling-down on semantic entity structures and knowledge graphs (which is in line with what Beatrice Gamba from Wordlift recommends as part of her MLforSEO Academy Course AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands).
A couple of notable recent studies and analyses to read on the topic:
Here are some approaches shared:
This week's discussion topic is all about agentic workflows and platforms, what works and where to invest time - share your thoughts in our community.
(If you haven't already...) Join 750+ AI/ML-interested marketers on our Slack community to stay up to date with discussions on AI/ML automation in SEO and marketing.
Happy learning! ✨
Lazarina
