10 Things the Marketing Industry Is Getting Dead Wrong about AI

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 stay ahead of the curve in AI usage and keep pace with very quickly changing user behavior, so 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 own behavior in some of these and understand why it's time to correct course.

TL;DR

• AI visibility in LLMs is not a KPI until attribution improves; treat it as a leading indicator, not a business outcome.
• SEO tactics don't automatically translate to AEO. The formats, signals, and content structures are fundamentally different.
• Backlinks still matter, but brand mentions, trust, and real-world authority signals are gaining ground fast.
• AI readiness is not a one-time project. Models shift, interfaces evolve, and assumptions need to be tested continuously.
• AI-generated content alone will never produce a proprietary point of view. Human expertise is still the differentiator.
• You can't optimize for AI presence without measuring it. Invest in the tools that show you where and how your brand appears.
• Foundational trust, structured data, and clean content architecture matter more than any new AI tool.
• The workforce implications of AI are real and underacknowledged. Junior talent pipelines are at risk.
• AEO is not optional or future-state. It is the new discovery layer, and marketers need to be building for it now.
• The core marketing mission hasn't changed. Understand customer needs, communicate how you meet them, wherever your audience is absorbing information.

Mistake 1. Treating AI visibility like traditional SEO success

A lot of marketers are obsessed with "showing up in AI" and get excited when they see their brand mentioned in response to 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 valuable, but investing heavily in AI visibility without a clear line to business impact is a mistake a lot of teams are making right now.

Mistake 2. Thinking SEO tactics automatically translate to AEO/GEO

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 essential for SEO, which remains a higher-volume play than AEO, but misses the point for AI discovery. Good content helps both, but plenty of content that performs well in search is effectively invisible to AI systems.

Mistake 3. Over-indexing on backlinks instead of brand and experience

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. The initiatives are related, but the weight is shifting, and most strategies haven't caught up.

Mistake 4. Treating "AI readiness" as a one-time project

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 stay close to trusted external perspectives to keep pace with what's actually changing, not what changed six months ago.

Mistake 5. Assuming AI-generated content is "good enough" for brands

There's a dangerous assumption that AI-written content is inherently efficient and effective. In reality, LLMs and Google itself are favoring human-written content more than people realize. AI can absolutely support the content process, and AI-formatted content is, in a somewhat circular way, well-structured for ingestion by AI models, but blindly trusting it with brand voice, nuance, and expertise is a mistake. Relying solely on AI to produce content guarantees you'll never put out an original or proprietary POV. You're just regurgitating what's already out there online, which is not a defensible brand position.

Mistake 6. Underinvesting in tracking and measurement

Everyone wants to optimize for AI, but very few 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 misallocated in one direction or the other.

Mistake 7. Believing tools matter more than trust and data foundations

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 in a way that machines can actually ingest and interpret.

Mistake 8. Ignoring the talent and workforce implications

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 operate without the support they previously relied on. But if junior talent never learns the fundamentals, where does the next generation of senior marketers come from? This is a long-term industry problem that no one has a clear answer to yet, and very few are talking about it seriously.

Mistake 9. Treating AEO as optional or "future state"

Everyone needs to be engaging with AEO at some level today. The last 15 years of marketing playbooks need to be rethought, and that requires aggressively learning new tools, processes, and ways of working. This is the new discovery layer, whether we like it or not. The disruptions ahead are real and significant, but it's a bigger mistake to avoid looking at them than to confront them early.

Mistake 10. Forgetting the core rule of marketing still applies

AEO is 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 none of that changes the core marketing mission: understand customer needs and desires, and communicate how a product or service meets those needs wherever those customers are most apt to absorb that message.

Move quickly, keep pace with AI-driven change, but not at the expense of that foundational function.

This list reflects what I'm seeing and hearing in March 2026. It will change, and I'll revisit it as the landscape shifts. The mistakes that matter most today may look different a year from now.

FAQ

Is AI visibility in LLMs worth investing in right now? Yes, but with the right expectations. Brand presence in AI-generated answers is a meaningful signal that your content is authoritative and well-structured, but it is not yet a revenue KPI. Until attribution between AI visibility and business outcomes improves, treat it as a leading indicator and invest accordingly.

What's the difference between SEO and AEO, and why does it matter? SEO optimizes full pages for search engine rankings. AEO (Answer Engine Optimization) optimizes content to surface as the right answer in AI-generated responses. The formats, content structures, and success signals are different. Good content can serve both, but content built purely for SEO rankings often doesn't translate to AI discovery.

Should brands stop using AI to produce content? No, but they shouldn't rely on it exclusively. AI is a useful tool in the content production process, but it cannot produce original thought, proprietary expertise, or a genuine brand perspective. Those still require human input. Brands that produce nothing but AI-generated content will find themselves unable to differentiate.

What tools should marketers use to track AI brand visibility? Platforms like Profound, Scrunch, and AthenaHQ are purpose-built to track brand presence across AI systems. Without some form of structured monitoring, you have no reliable way to know where your brand is appearing, whether it's being represented accurately, or how that presence is connecting to the pipeline.

How should marketers think about AEO as a priority right now? As a current priority, not a future one. AI is already the default discovery interface for a growing share of users. Marketers who treat AEO as something to address "later" are conceding ground that will become increasingly difficult to recover.

Thinking through your AI search strategy and want a second opinion? Reach out to the JDM team, we're happy to dig into what we're seeing to move the needle in real accounts.