Recently, our PR partner sent me a few questions from an Inc.com journalist working on a story about the use of AI in marketing. It was a good range of questions, and I realized as I was sketching out some thoughts that I was focusing on things the marketing industry is getting wrong about AI right now, in March 2026.
That realization didn’t come as a shock – we’ve all been sprinting to set the curve in AI usage and make sure we’re keeping pace with very quickly changing user behavior that it’s only natural we’re making some strategic mistakes.
But that doesn’t mean we need to keep making them.
I have tons of conversations every day with industry peers and leading brand marketers, and these are the mistakes that keep coming up. My hope is that you see your behavior in some of these and understand why it’s time to correct that behavior going forward.
A lot of marketers are obsessed with “showing up in AI” and get super-excited when they see their brand mentioned in the wild for relevant prompts – but they can’t explain what that actually delivers for the business.
Visibility and citations inside AI answers are incredibly hard to tie back to revenue right now. Until attribution improves, AI presence alone is not a KPI you hang your hat on. It’s a leading indicator that you’re putting out good content around a particular topic, which is great – but a lot of marketers are investing heavily in getting visibility and citations without understanding how they’ll impact the business.
There’s a fundamental mindset shift that hasn’t fully happened yet. AI doesn’t return a list of links for full-page content; it surfaces small chunks of content inside longer conversations. Optimizing entire pages for rankings is still great for SEO (which is material, since SEO is still a higher-volume play than AEOO) but misses the point for AEO. Good content does help both SEO and AEO, but plenty of “good SEO” content is useless for AI discovery.
Backlinks still matter, but the industry is clinging to them like a security blanket. AI systems care far more about brand mentions, trust, and real-world signals of authority. Experience, reputation, and being talked about in the right contexts are becoming more important than raw link volume. It’s not a huge difference – the initiatives are related – but it’s an important one.
Too many companies think AI readiness is a checklist you complete and move on from. AI changes faster than Google ever did. Models update, interfaces shift, and behaviors evolve constantly. You need to test your own assumptions, measure the right data, and reference trusted, objective external experts to make sure you’re staying close to trends as they emerge.
There’s a dangerous assumption that AI-written content is inherently efficient and effective. In reality, a lot of LLMs (and Google itself) are favoring human-written content more than people realize. AI can absolutely support the content process – and, in a meta kind of way, AI content is formatted well to be ingested by AI models – but blindly trusting it with brand voice, nuance, and expertise is a mistake. Relying solely on AI to produce content, moreover, guarantees that you’ll never put out an original or proprietary POV; you’re just regurgitating what’s already out there online, which is hardly a good look for a brand.
Everyone wants to optimize for AI, but very few people want to pay for the tools that make optimization possible. Without platforms like Profound, Scrunch, or AthenaHQ, you’re flying blind. If you can’t see how and where your brand is appearing in AI systems, and how that presence is influencing your pipeline and revenue, the resources you’re allocating to AEO are almost certainly under- or over-allocated.
The industry loves shiny new AI tools, but foundational trust, structured data, and clean information architecture are where the real leverage is. Tools won’t save you if your content isn’t credible, consistent, and formatted to be ingested by the machines.
The AI transition should be making more people uncomfortable than it is. A lot of junior-level work is being replaced, and senior marketers can now do more without support. But if junior talent never learns the fundamentals, where does the next generation come from? This is a long-term industry problem that no one has a good answer for yet.
Everyone needs to focus on AEO at some level today. We have to rethink the last 15 years of marketing playbooks and aggressively learn new tools, processes, and ways of working. This is the new discovery layer, whether we like it or not. Yes, it’s intimidating to look far ahead and think about the massive disruptions coming our way – but it’s a mistake not to do it.
AEO is without a doubt a fundamental shift in the marketing ecosystem. Users are behaving differently, the funnel is collapsing, clicks are less reliable than ever, and revenue impact is harder to track. Yet that doesn’t change the marketing mission of understanding customer needs and desires and communicating how a product or service meets those needs and desires wherever those customers are most apt to absorb that message.
Yes, it’s important to move quickly and keep pace with rapid changes introduced by AI, but not at the expense of the core marketing function.
Speaking of rapid changes, this list (the biggest AI-based mistakes marketers are making in the present day) will change over time. I’ll make sure to revisit it periodically to reflect what I’m seeing in the landscape at large. In the meantime, I’m always available for a strategic check-in, so drop me a line if you’d like to chat about what we’re seeing move the needle (for actual business growth) in client accounts.