Why the Rush to AI in SEO May Be Overhyped, and What Matters Right Now
AI has captured the collective imagination of the digital marketing world, especially in SEO, where the promise of automation, scale, and smarter search experiences is deeply compelling. But as interest skyrockets, so does the pressure: “AI is the future.” “If you’re not optimizing for LLMs, you’re already behind.” “Prompting is the new SEO.”
While there’s no doubt that AI will play a growing role in how we approach content, search engines, and visibility, the current urgency, often positioned as a survival imperative, deserves a closer look.
From my perspective, having led SEO for global brands and worked alongside enterprise teams for over 16 years, I’d argue that many organizations are being encouraged to sprint before they’ve learned to walk. This doesn’t mean ignoring innovation. It means pursuing AI in a way that’s aligned with your business goals, technical maturity, and audience needs, not the hype cycle.
Pressure to Adopt AI Quickly
Many SEO professionals are feeling a push to “be AI-ready,” often based on external influence rather than internal performance data. Teams are encouraged to experiment with content generation tools, add AI-driven optimization layers, or reorient strategies to be “LLM visible”, often without a clear connection to ROI.
This pressure is understandable. Agencies, platforms, and consultants are excited about the possibilities and eager to guide brands into the next search phase. But, in the process, the fundamentals of effective search visibility, technical integrity, high-quality content, and user-centric UX can get deprioritized.
Most Brands Still Win by Nailing the Basics
At the enterprise level, many organizations still struggle with foundational SEO elements:
Poor site structure and internal linking
Limited or inconsistent schema markup
Thin or outdated content for core commercial themes
Fragmented keyword targeting across owned domains
AI can’t solve these problems; it can only accelerate whatever strategy is already in place. If the core is broken or under-optimized, layering AI on top often leads to more noise, not more results.
As an example, one of the most impactful SEO improvements I’ve overseen recently involved consolidating redundant URLs, fixing broken internal links, and updating metadata and content hierarchy across a set of high-traffic product pages. The result? A sustained lift in rankings and conversion rate. No LLM integration required.
Showing Up in LLMs Isn’t a Different Game, It’s the Same Game, Done Better
There’s growing interest in how to “rank” or “appear” in large language model (LLM) responses like those in ChatGPT, Gemini, or Perplexity. But the underlying mechanism—how LLMs determine trustworthy, useful, and authoritative content—is still rooted in the same principles we’ve long optimized for:
Structured, crawlable content
In-depth, helpful articles that answer specific questions
Signals of trust like reviews, citations, and earned media
Semantic clarity through schema and topical authority
In other words, the best way to earn visibility in LLMs is to continue doing what works in traditional search, just more consistently and with sharper execution.
A Look at the Data: Is LLM Referral Traffic Worth the Shift?
Here’s a question I always encourage teams to ask: What do your analytics say?
At the global company I currently support, LLM referral traffic (from platforms like Perplexity or ChatGPT) accounts for less than 1% of total traffic for this channel. That doesn’t mean it won’t grow, but it does mean we’re not restructuring our strategy around it yet. Before investing heavily in optimization for these channels, brands should:
Benchmark their current visibility in LLM tools
Monitor referral traffic from emerging AI search engines
Track how users engage with that traffic (e.g., bounce, conversion, repeat visit)
If the data supports a focused investment, great. But apart from that, a measured, test-and-learn approach is often more sustainable than a full-scale pivot.
Where AI Does Provide Value
All that said, there are absolutely areas where AI tools can enhance SEO workflows when applied thoughtfully:
Topic clustering and content planning: Using LLMs to identify related subtopics and gaps
First-draft outlines or rewrites: Accelerating ideation for editorial content
Competitive analysis: Parsing SERPs faster with AI-aided summaries
Scalable Meta Creation: Generating optimized titles and descriptions programmatically to support large-scale content updates and improve CTR
Keyword classification: Grouping search terms by intent or funnel stage
The key is using AI to supplement strategic thinking, not replace it.
A Constructive Path Forward
Rather than reacting to hype, I recommend brands focus on these five priorities when considering AI in SEO:
Audit your fundamentals: Is your site technically sound and crawlable?
Invest in content quality and depth: Are you meaningfully answering what users are searching for?
Strengthen your off-site signals: Are you earning citations from trusted, third-party domains?
Evaluate performance data before shifting focus: Where is your traffic coming from now, and what converts?
Pilot AI tools where they solve actual problems: Don’t chase novelty; solve bottlenecks.
Final Thought
AI will reshape how we work in SEO, but it’s not a race to see who adopts it fastest. The real winners will be the teams who stay grounded in their strategy, data, and audience needs while integrating AI in practical, purposeful ways.
So, before overhauling your roadmap to chase LLM visibility, ask yourself: Is this solving the problem we have, or just responding to the loudest conversation in the room?
Article by
Julian Connors
Julian Connors is the Head of Global SEO at Bose Corporation, where he leads global organic search strategy and optimization across digital platforms. With over 17 years of experience, Julian brings deep expertise in technical SEO, content architecture, and search experience design. His work bridges enterprise content systems, including Adobe Experience Manager and Salesforce Personalization, with performance-focused SEO initiatives.
A trusted voice in the industry, Julian has been published in outlets such as Search Engine Journal, Search Engine Land, SEMrush, Advanced Web Ranking, and Adweek. He is known for applying data-driven strategies, A/B testing frameworks, and search intent modeling to elevate both brand visibility and user engagement. His thought leadership spans search engine algorithm adaptation, SEO tool development, and integrated content performance optimization.
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