The fundamentals of SEO haven’t changed. You still need technical access, content clarity, and external credibility.
But the requirements inside those pillars are evolving fast. AI-driven discovery systems are now shaping how your brand is surfaced, trusted, and recommended.
And for many enterprise teams, the response has been:
A content brainstorm.
A wait-and-see approach.
Or nothing at all.
That’s not a strategy gap. It’s an execution problem in the making.
Is your company preparing for what’s coming after ‘SEO’ fades away?
Your SEO team may be running efficiently – protecting rankings, publishing content, and salvaging what they can from Google traffic.
But has the SEO team focused on AI visibility?
Is your brand ready for Google Gemini to start being the new SERPs page?Have you prepared your brand for visibility in ChatGPT, Perplexity, Claude, and others to become entry points for discovery?
When SEO fades away, it will morph into AI optimization. Your teams need to adapt to this.
What’s the timeline? Sooner than you think
We don’t know when AI usage will surpass SEO usage, but it’s likely sooner than you expect and hope for.
There are plenty of studies on the topic, all speculation.
Here’s what executives need to know.
At any moment, Google could make its AI mode the default view.
Then, boom, users would be using AI instead of a “search engine.”
Think that’s not happening soon?
Google is testing it now.
Notice the screenshot below that shows AI Mode as the first tab in the search results, which is traditionally the default view users see
Most companies haven’t answered fundamental AI optimization questions
Most companies are not prepared. And that’s a real risk
AI-driven discovery is accelerating, and a single platform shift could render your traditional high organic rankings obsolete overnight.
Few companies have conducted a thorough AI visibility audit to answer foundational questions like:
Which templates, modules, or content blocks are unreadable to AI crawlers, even if Google can read their content.
Which internal links are invisible to simplified crawlers and bots.
What AI systems are currently saying about your brand, products, and services.
What AI systems are not saying – and whether those omissions are costing you visibility.
Which personas AI associates with your offering.
How you’re positioned against competitors in synthesized comparisons.
What misinformation or hallucinations are surfacing about your company.
Fewer companies have started operationalizing AI optimization
Across organizations, AI optimization has not been accounted for in day-to-day workflows. Common execution gaps include:
PR and SEO teams haven’t collaborated to align on the citations and external signals needed to inform LLMs about your brand, products, and services.
SEO teams haven’t begun structured entity research or gap analysis – a critical distinction between traditional SEO and AI optimization.
No AI-specific technical training for dev, QA, or product teams to understand which JavaScript patterns break visibility for non-Google bots.
No content updates for specificity, where marketing and content teams are still optimizing for message rather than machine-parsable clarity.
Developers, product managers, QA testers, and related roles haven’t been trained on lowest-common-denominator, bot-friendly coding practices. The JavaScript that works fine for Google and Bing is often unreadable to less sophisticated AI crawlers, but your teams likely aren’t aware of the discrepancy, nor are they working to address it.
Marketing managers and content stakeholders haven’t been briefed on how much more specificity and technical detail is required in content to support AI understanding, beyond traditional messaging frameworks.
Every team – from SEO to product – has yet to treat LLMs like the brand advocates they’ve quietly become. Your sales team is trained on product intricacies, specs, personas, and real-world applications. LLMs, by contrast, are often trained on surface-level marketing language. For many companies, that means AI may not know enough to represent your brand accurately – or at all. I see this mostly for high-end products, particularly in the B2B sector, where you want the sales team to lead the sale.
This is about to become a widespread execution problem.
Yes, there’s a strategic gap – but the real risk is operational.
Most organizations haven’t made the necessary adjustments for AI optimization, and they’re already behind.
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Thinking you can coast because you’re a big brand?
Domain authority, scale, and strong backlink profiles always helped enterprise visibility in Google SERPs, but that’s no longer guaranteed.
For some markets, the AI marketplace resembles the earliest days of SEO.
In those days, big brands were losing out to small, agile competitors who optimized faster and with more precision. That’s happening again.
AI systems don’t inherently favor big brands. They favor:
Clarity.
Structure.
Comprehensiveness.
Citations.
Smaller, more focused players are already surfacing more reliably in AI responses because their content is:
Highly specific.
Entity-rich.
Reinforced by third-party citations.
Easier to crawl and synthesize.
Large brands, on the other hand, tend to rely on legacy authority – assuming their visibility will carry over.
But AI doesn’t reward assumptions. It rewards structured knowledge and trustable signals.
This is the executive reality check.
If your brand hasn’t defined itself clearly, and if no one has tested what AI systems actually “know” about you, your scale may not be enough to keep you in the conversation.
The playing field has changed. Visibility is no longer inherited.
It’s earned through precision, reinforcement, and cross-functional alignment – the kind that makes it inevitable your teams deliver what AI crawlers and LLMs need to recognize, trust, and recommend your brand.
Now, let’s look at how AI is forcing teams across your entire organization to morph.
The new demands on the same SEO pillars
Let’s be clear: This isn’t about abandoning what works in traditional SEO.It’s about recognizing that AI-powered visibility introduces new criteria for being findable, relevant, and referenced – criteria that most enterprise teams still struggle to operationalize for SEO.
Let’s look at a few traditional SEO pillars for success:
Content SEO: Optimized with keywords and semantic signals.
Technical SEO: Coded so that Google and Bing crawlers can access all links and content.
External links: Earned from third parties that mention and link to your brand.
Conversions: Balancing visibility with business impact.
Each still matters. But AI changes what execution within each pillar now requires.
Content SEO changes: From keywords to entity clarity and coverage
Traditional content SEO focused on targeting keywords and including semantically related phrases to signal topical relevance. But AI systems don’t evaluate relevance the same way search engines do.
AI systems synthesize answers based on how well they understand entities – people, products, companies, categories, and associated attributes – and how those entities relate to one another.
To be visible in an AI-generated response, your content must:
Define your products, services, and brand entities clearly and consistently.
Explain who each offering is for and why it fits their needs.
Reinforce attributes like features, specs, use cases, benefits, and differentiators.
Cover these entities completely across multiple pages and content types, and ideally in third-party citations.
Most SEO teams have not created an entity map tied to business priorities.
They have also not audited content to see whether key entities are:
Missing.
Under-defined.
Inconsistently reinforced.
In a nutshell, while they understand the keyword content gap analysis, they don’t understand the entity content gap analysis.
Because of this, in most organs, content briefs still optimize for keyword clusters, not for building the structured, detailed entity coverage that LLMs depend on.
Technical SEO changes: JavaScript rendering to old-school technical SEO
Most enterprise teams believe they’ve already handled technical SEO.
Pages render. Links crawl. Templates pass Core Web Vitals.
But here’s the blind spot: AI systems don’t crawl like Google.
Furthermore, most companies haven’t tested what’s actually exposed – or what’s hidden – when these systems attempt to interpret your site.
AI crawlers crawl less than Google
This means there is little to no margin of error.
Vercel reported that AI crawler activity is significantly lower than Googlebot activity, meaning every missed opportunity counts (or costs).
Source: Vercel
Most AI crawlers do not render JavaScript
Most AI crawlers do not render JavaScript, Vercel’s study also found.
Crawlers are fetching a high volume of 404 pages
This isn’t theoretical. It’s what I’m seeing with a current enterprise audit.
LLMs crawl and source URLs that return 404s, indicating they may lack the logic to remove URLs returning a 404 status from their knowledge bases.
Vercel’s study found that AI crawlers are fetching a lot of 404 pages.
This matches and audit I’m doing for a client, where they’re sourcing URLs that return 404 errors.
This shows they’re not all sophisticated enough to pull the 404 status URL from the recommendation knowledge base.
These are just a few examples of why we need to revisit bot-friendly simplicity.
We must actively examine how AI crawlers interact with our sites and resolve the issues they encounter.
These are verifiable gaps – but only if you audit for them.
If you haven’t taken meaningful steps toward AI optimization, it’s likely that LLMs know far less about your site, brand, products, and services than Google/Gemini does.
This is where technical SEO must pivot.
SEO teams need to go back to the fundamentals and run an AI-specific technical audit.
Identifying crawl inefficiencies and surface-level rendering failures that affect AI systems, even if everything appears fine in Google Search Console.
Miss this step, and you’re effectively putting a sales rep into the field without proper training.
LLMs are now your front-line brand advocates, and right now, they may be operating with critical knowledge gaps.
Every team across the organization, including PMs, developers, and QA testers, needs to consider this when making coding and testing decisions.
External links: From backlinks to machine-readable trust
In traditional SEO, backlinks were the currency of credibility. But AI systems don’t rely on link equity.
LLMs evaluate what’s said about your brand, and by whom.
These systems synthesize answers from multiple sources, looking for consistent, detailed, and structured references to:
Your brand.
Your products.
Your use cases.
Your differentiators.
That means:
A single backlink may carry little weight if the surrounding content says very little, even if it’s from a highly authoritative website.
If third-party sources that don’t explain what you do (or worse, explain it incorrectly) will result in lower visibility, or worse, inaccurate knowledge.
If LLMs can’t find enough external detail to reinforce what your site says, they either omit you – or make it up.
This is where most companies fall short. No one is monitoring:
Which third-party pages describe your offerings, and what they say.
Whether those descriptions are accurate, up to date, and entity-rich.
How well your brand is represented in the content AI tools are most likely to synthesize.
The facts vs. inaccuracies that LLMs “know” about you.
And importantly, no team owns these citations.
PR isn’t briefed on what LLMs need to fuse them into PR messaging.
SEO doesn’t track citations beyond backlinks.
Likely, no one is correcting inaccurate statements made by third parties.
Likely, there is no governance on allowable vs. unallowable inaccuracies on the Internet to determine which a TBD resource contacts to get corrected.
If third-party sources aren’t echoing the right information, LLMs will either misrepresent you or exclude you altogether.
LLMs use machine-trustable signals – and unless you audit those signals, you won’t know whether you’re being reinforced or forgotten.
Conversions: From persuasion to precision
They need facts.
They need specificity.
They need to understand the structure and substance of what you offer.
This is where most organizations fall short. Their best content (the kind that converts) is often too vague, too polished, or too marketing-driven for an AI system to extract reliable facts.
LLMs don’t summarize intent like search engines. They summarize data.
This is a fundamental disconnect from how most SEO teams approach conversion pages today. CTAs, slogans, and benefit-led messaging may work for humans who come to your site.
But LLM users won’t likely come to your site at first. They’ll learn about you only when the AI system mentions you, and then they ask for more information in follow-up prompts.
LLMs who talk about you need: Clear inputs: product types, features, comparisons, use cases, and specs. Then, they’ll synthesize based on what they clearly know as facts about your brand, not persuasion messaging.
If your website does not include concrete details about what you do, who you serve, and how your offering fits into the broader ecosystem, AI systems can’t confidently surface you.
I foresee this to be a bigger issue with high-end products and services, especially in the B2B space.
I look at my B2B clients’ content and they say very little about what they offer, and LLMs?
They don’t really understand what they do to the extent required to speak on behalf of the brand in prompt responses.
This isn’t about rewriting content to be robotic.
It’s about adding a layer of machine-readable, factual precision so that AI systems can recognize your brand as a credible source, not just a polished one.
Most SEO, UX, content, and marketing teams haven’t accounted for this shift.
They have built pages optimized for conversion, not machine understanding.
If you don’t give LLMs the details of what you offer, they can’t represent you accurately.And if they can’t represent you, you’re left out of the answer.
This is the conversion gap most teams have yet to realize, and it won’t fix itself.
Why this work isn’t getting done
Most SEO teams are already stretched.
Proactive AI visibility work?
It doesn’t have a deadline. It doesn’t come with alerts.
And in most orgs, it hasn’t been assigned with accountabilities.
This isn’t a day or two of work.
It involves extensive research to understand what they’ve been building for years, but your organization has not yet studied it.
You have three major pillars discussed above that require in-depth research and actionable next steps. Then, workflows need refinement, teams need training, and more.
That’s why it slips. It’s a lot of work.
To be successful, AI optimization must be institutionalized across the organization.
And that is a lot of work. Those who try it miss quite a few steps. How do I know? I see what they did with SEO, and the result is other teams creating SEO problems.
This isn’t about reinventing SEO strategy
You don’t need a new AI task force or a platform overhaul.
But you do need a really thorough audit and tweaks to current workflows to make producing the signals AI systems need inevitable – and for it to keep happening, week after week, month after month.
This is operational work:
Testing how your content renders to AI agents.
Defining what entities you must be known for.
Mapping where those concepts live (or don’t) across your site.
Identify which teams influence third-party validation.
Build processes to close the gaps over time – without distracting from revenue-driving SEO.
Training development, product, UX, and QA testing teams on how to account for less sophisticated crawlers in all their tickets, designs, code, and test scripts.
None of this replaces your SEO current strategy – because the SEO channel is still driving millions in revenue that you want to keep and maximize.
However, if you skip the AI optimization steps, you’ll be optimizing for visibility in an ecosystem that’s already shifting away from how it used to work.
Final thoughts
AI didn’t change the pillars of SEO. It changed what those pillars need to deliver.
If no one has audited how your site structure, content, and credibility translate to responses in AI prompts for potential customers, then your visibility is relying on assumptions, not evidence.
In high-stakes environments like AI prompt responses, that’s not acceptable.
AI visibility isn’t just a content problem. It’s an execution problem.
Solve it now – before it shows up in your performance metrics later.