When I integrated WordPress, Sanity, and Slack, I unlocked the ability to effortlessly manage and update content. This integration dramatically improved how customers discover my brand, products, and services through AI Search.
With these native integrations, I’ve streamlined my workflow, enabling me to publish, update, and coordinate tasks more efficiently. This not only enhanced my brand’s visibility but also optimized customer interactions at every touchpoint.
Embracing these tools has revolutionized my content operations, ensuring my digital presence is cohesive and compelling. The ease of use and the seamless syncing of data have allowed me to focus on what truly matters—creating value for my customers.
I recently had the pleasure of speaking with Kirk Williams, a seasoned PPC expert who has been making waves in the industry since 2009. As the founder of Zato, a specialized PPC micro-agency, and author of notable works like Ponderings of a PPC Professional and Stop the Scale, Kirk is a familiar face at global conferences such as BrightonSEO, SMX, and HeroConf.
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One key aspect Kirk emphasized was the importance of taking on clients who are a good fit. His biggest lesson? Avoiding clients that don
Analyzing nearly two million LLM sessions across nine industries throughout 2025 was a fascinating journey for me. I began with the assumption that ChatGPT would dominate and that AI usage patterns would be relatively uniform with minimal impact.
The findings, however, were surprising.
While ChatGPT does indeed control 84.1% of the trackable AI discovery traffic, it’s primarily serving as a broad-market tool. This discovery significantly impacts strategic approaches.
In today’s landscape, relying solely on a single discovery strategy is not viable. A multi-platform approach that aligns with how and where users find productivity is essential.
Brands must now discern which platforms are empowering productivity rather than merely supporting initial discovery phases.
Various LLMs are excelling in different sectors, often with stark differences. The key takeaway for 2026 is more complex than simply focusing on ChatGPT.
Here’s what I’ve discovered from the data.
The Growth Rate Divergence: ChatGPT vs. Competitors
Throughout 2025, major LLM platforms exhibited significant growth discrepancies:
ChatGPT: 3x growth
Copilot: 25x growth
Claude: 13x growth
Perplexity: 1x growth
Gemini: 1x growth
Although ChatGPT grew, Copilot and Claude experienced much more rapid growth. Platforms like Perplexity and Gemini remained steady, reinforcing specific workflows.
These numbers highlight strategic priorities:
Satya Nadella celebrated Copilot reaching 100 million monthly users.
Dario Amodei revealed that Anthropic’s revenue grew from $100 million to $8–10 billion in under two years.
Aravind Srinivas noted significant interest in Perplexity Finance.
The focus on growth is crucial because it signals true user value:
Copilot excels in the Microsoft ecosystem.
Claude appeals to developers.
Perplexity thrives among finance professionals.
Different LLMs are thriving in various industries at markedly different rates.
Pattern 1: Copilot’s Striking Growth
Copilot’s remarkable 25x growth is indicative of its premier position in B2B environments reliant on Microsoft tools.
SaaS
ChatGPT: 2x growth
Copilot: 21x growth
The rapid adoption mirrors modern SaaS practices, embedding LLMs directly into workflows.
Education
ChatGPT: 6x growth
Copilot: 27x growth
Copilot benefits from educational settings fostering knowledge sharing and synthesis.
Finance
ChatGPT: 4.2x growth
Copilot: 23x growth
Finance aligns with Copilot due to automation needs and context dependency.
Copilot’s growth is most pronounced in industries where professionals are deeply integrated with Microsoft tools.
Instruments like Excel transform into data interpretation powerhouses with Copilot, eliminating the need for external searches.
Implications
For work-centric audiences like SaaS, finance, and education specialists, AI discovery is shifting into LLMs embedded in workflows.
Pattern 2: Perplexity Shines in Finance
While Perplexity has flat growth overall, it stands strong in finance with a 24% market share, unlike in other sectors where it has diminished.
SaaS: down to 7.3%
E-commerce: down to 3.4%
Education: down to 5.2%
Publishers: down to 3.6%
Finance demands accuracy; thus, traceable sources make Perplexity vital in this sector.
Partnering with Benzinga, FactSet, and others, Perplexity offers in-depth data vital for financial decisions.
Trust and verifiability are crucial in finance, and that’s where Perplexity excels.
Implications
In finance, selection of platforms that integrate with licensed data and credible sources is critical. Success hinges on being part of these authoritative ecosystems.
Pattern 3: Claude’s Dominance in Analysis
With just a 0.6% share, Claude might appear to be an underdog, but it thrives in specialist sectors like publishing and finance.
Publishers: 49x growth
Education: 25x growth
Finance: 38x growth
SaaS: 10.3x growth
Claude’s strength lies in standalone, strategic thinking rather than integrated tools like Copilot.
Publishing professionals and financial analysts use Claude for its substantial context window, enabling complex and strategic queries.
Implications
Target audiences that require in-depth analysis should focus on creating structured and detailed content. Claude’s user base is smaller but highly influential.
Pattern 4: Challenges in Tracking Gemini
The data concerning Gemini is puzzling, showing both growth and declines. This could be attributed to issues with attribution rather than an actual decline in users.
Education: −67% tracked traffic
SaaS: +1.4x growth
Finance: +1.3x growth
E-commerce: +2.7x growth
Gemini’s interaction model keeps users within its ecosystem, making measurement challenging.
The reality is that usage might still be robust, but the tracking systems need to catch up with user behaviors.
Implications
As AI-assisted conversions increasingly occur, traditional last-click attribution models need reconsideration.
Monitor brand search performance and invest in broader visibility strategies.
Strategizing Your LLM Approach
AI discovery is diversifying rather than converging. Tailoring strategies based on your audience’s preferences and behaviors is crucial.
Enterprise Audiences: Focus on Copilot integration for SaaS and B2B environments.
High-Stakes Decisions: Consider Perplexity’s reliability in providing traceable data.
I recently discovered that Google’s testing a fascinating new feature in Search ads. They’re incorporating third-party endorsements, complete with publisher logos and quotes, to offer a layer of external validation for paid results.
This experiment places brief endorsements from external publishers right under the ad description, showcasing the third party’s name, logo, and favicon.
What’s showing up. I first spotted this test when Sarah Blocksidge, Marketing Director at Sixth City Marketing, shared a screenshot on Mastodon. In that example, a Search ad included the line “Best for Frequent Travelers,” attributed to PCMag, along with the publication’s favicon.
The endorsement is positioned directly beneath the ad copy, making it visually distinct from the standard text written by advertisers.
Why we care. If this feature is expanded, it could transform Search ads to mirror product reviews more closely, potentially granting advertisers with substantial third-party validation an edge in highly competitive auctions.
What Google says. A spokesperson from Google Ads confirmed that this is a “small experiment” being conducted:
“This is a small experiment we are currently running that explores placing third-party endorsement content on Search ads.”
However, Google hasn’t revealed any specific details regarding eligibility, the content sourcing process, or how endorsements are chosen.
What we don’t know yet. It’s not yet clear if advertisers will be able to opt into this feature, request specific endorsements, or influence which third-party sources are displayed. Google hasn’t clarified whether this test is linked to existing review extensions, publisher partnerships, or other trust and safety initiatives.
What to watch. Should Google decide to broaden this experiment, the prominence of third-party credibility could significantly impact ad performance, shifting focus from advertiser claims to external validation at the search stage.
For the moment, this intriguing test is limited, but it offers a glimpse into how Google might continue to merge ads, trust signals, and editorial-style context within search results.
I’ve discovered how custom GPTs can revolutionize how we handle SEO, transforming repetitive tasks into efficient workflows. By leveraging AI, we can speed up our processes, from planning and analysis to reporting and technical work.
If you don’t have access to paid ChatGPT, don’t worry. You can still utilize these prompts by saving them as standalone references in your notes. Remember, they’re just starting points, so modify them to fit your team’s requirements.
Working with AI requires trial and error. My advice is to start with small tasks to practice writing prompts. Iterate on them and take notes on what produces good outputs.
AI can sometimes be verbose, so it’s helpful to set strict formatting guidelines and clear context. Upload resources and articles to guide AI results, and always define the role and audience upfront.
Let’s dive into seven prompts that I’ve found incredibly useful for developing custom GPTs dedicated to planning, analysis, and ongoing SEO tasks:
1. Project plan GPT
By analyzing previous project plans, I can create a GPT that assists in drafting this year’s focus areas.
How to set it up
Input project plans from previous years.
Specify a format for consistency.
Determine the number of items or sections to include.
Include specific details unique to your team.
Optionally, integrate team feedback and retrospectives.
Example prompt
Based on last year’s project plan, outline this year’s focus. List three critical items for each quarter, ensuring at least one covers link building.
Include a one-sentence summary for each recommended item and at least two KPIs to measure success.
[Insert last year’s plan.]
Now critique the plan. Offer three reasons against focusing on these items, providing sources for your notes.
By connecting performance dashboards or custom GA reports to ChatGPT, it can handle initial issue identification. This allows me to focus on investigating critical trends.
How to set it up
Hook up reporting tools or upload data directly.
Direct AI on specific aspects to investigate.
Set frequency for data review, such as daily or weekly.
Provide examples of pages or categories to analyze.
Example prompt
Here’s the weekly site report. Analyze this week’s performance against last week’s data, summarizing sessions, conversions, and engagement.
Highlight three successes and three areas needing improvement, color-coded by significance.
[Insert report doc.]
3. Competitor analysis GPT
I’ve found it invaluable to scrutinize what works on competitor sites. This often involves tools like Semrush or Ahrefs.
How to set it up
Integrate Ahrefs, Semrush, or upload relevant reports.
Select competitors and identify top-performing pages.
List key metrics for evaluation.
Create unique prompts for various levels of analysis.
Now, more than ever, custom GPTs are making a significant impact alongside existing SEO tools and workflows. They’re not about replacing the tools we use, but about making initial tasks smoother so that we can focus on insightful and strategic actions. By integrating them into our everyday processes, from planning to technical checks, we can really enhance our productivity.
I’ve come to realize that SEO now serves as both a brand and performance channel. The traditional traffic model has been disrupted by AI Overviews and zero-click SERPs, making brand strength crucial for SEO ROI.
For years, SEO was straightforward: rank higher, get more traffic, then boost the sales pipeline. However, this simple equation is rapidly evolving, much to the frustration of marketing leaders.
With AI Overviews and users getting answers directly from LLMs, the idea of “rank and receive traffic and leads” is less effective now. Even top keyword positions don’t guarantee the clicks they once did.
This shift has sparked challenging discussions in boardrooms. Executives often question, “If traffic is down, how can we measure SEO success?”
It’s obvious now: the traffic model has changed, yet the demand for ROI remains. We must treat SEO as a brand-dependent performance channel, not just a traffic provider.
Why traffic and pipeline are no longer in lockstep
Linear attribution has never fully reflected the dynamic nature of organic search. While ChatGPT isn’t replacing Google, it’s augmenting it.
Users now verify information across platforms due to skepticism of search and LLM results. Where research once happened solely within Google’s ecosystem, it has become more scattered.
Today’s organic search is akin to a pinball machine, with buyers bouncing across channels unpredictably. This introduces complexity that traditional attribution software struggles to follow.
Such complexity has broken the linearity executives crave. Traffic and pipeline charts, once aligned, now often diverge.
Across B2B SaaS portfolios, a common pattern emerges: organic sessions may be flat or declining, yet rankings for high-intent terms stay stable, and the pipeline from organic search grows.
This mismatch doesn’t indicate SEO failure. Rather, it shows that traffic is no longer a reliable business impact measure.
The traffic lost to zero-click searches often consists of informational, low-intent content. What remains is higher-intent traffic, closer to conversion.
We’re seeing the “atomization” of search demand. Short-head, broad keywords are declining, while specific, long-tail queries with higher intent are rising.
Many leaders mistakenly react to dropping sessions by pushing for quantity, aiming to regain the lost numbers through top-of-funnel content. This often inflates vanity metrics without delivering qualified leads.
SEO ROI is now the downstream outcome of brand traction
For years, SEO was viewed as a pure performance channel. We believed optimizing some keywords would suffice.
In reality, SEO has always depended on brand strength. The rise of AI-driven engines highlights this, expecting reputations, not just keywords.
If your brand lacks authority, technical optimizations alone won’t elevate your status. Brand strength determines organic performance limits. Search engines seek web-wide consensus, and weak associations hinder results.
Brand strength for LLMs means owning topical authority, aligning with customer queries, being validated by trusted sources, and having clear positioning.
SEO captures pre-existing demand validated by your brand, not creating it from nothing.
The new defensibility metrics for SEO
As traffic no longer headlines KPIs, new defensibility metrics are necessary. Successful teams focus on revenue and reputation impact, not just volume.
Metrics proving business impact include stable top-10 rankings for commercial keywords, increased Ahrefs traffic value, stable solution page traffic, growing homepage traffic, and developing LLM referral traffic.
When pipeline per organic visitor rises, even with falling sessions, the dialogue shifts from “SEO is broken” to recognizing SEO’s evolution.
Modern SEO is moving from acquisition to influence
Successful SEO isn’t about recovering traffic but influencing buyer decisions and enhancing organic visibility. In an AI-first context, zero-click doesn’t imply zero-value.
SEO remains key in building market readiness, positioning brands as authorities even before buyers enter the funnel.
When I first stumbled upon the concept of query fan-out, I realized how misunderstood it often is in the world of AEO and SEO. It’s fascinating how AI searches can take a single prompt and transform it into numerous sub-queries, expanding the scope of search in unimaginable ways.
Understanding this process opened my eyes to the hidden potential these sub-queries hold. By leveraging the data generated from them, I discovered new strategies to enhance SEO effectiveness, making my digital marketing efforts more robust.
I recently discovered that Google is considering ways to allow websites to opt out of its AI-generated search features, such as AI Mode and AI Overviews. Naturally, I was curious about how the SEO community felt about it, so I conducted a poll on X to see if site owners would actually opt out.
The results were intriguing. Out of over 350 respondents, the majority mentioned they wouldn’t opt out. However, around one-third indicated they would prefer to block or opt out of these features. Here’s how the responses broke down:
Question: Would you block Google from using your content for AI Overviews and AI Mode?
33.2% – Yes, I’d block Google
41.9% – No, I wouldn’t block
24.9% – I am not sure yet.
Here’s the actual poll for reference:
Would you block Google from using your content for AI Overviews and AI Mode – Google may be giving us more controls – take my poll below. https://t.co/60M3Vt0YlN
But how do you opt out? Right now, that remains a mystery. Google has only mentioned it is exploring possibilities, without providing a clear mechanism. Frankly, the ease or difficulty of opting out could significantly influence decisions. If it’s straightforward, more sites might choose to opt out; if not, fewer will do so.
So why does this matter? We won’t truly know how many sites will opt out until Google officially offers a way to do so. Rest assured, once they do, there will be extensive reporting on the number of sites that decide to opt out.
To give you an idea, The Press Gazette recently reported that around 79% of nearly 100 top news websites in the UK and US are already blocking at least one AI training crawler, including OpenAI’s GPTBot, ClaudeBot, and others.
My advice is simple: Once Google makes this opt-out feature available, give it a test. See firsthand what the impact of opting in or out could be.
One of the biggest challenges I face in SEO isn’t AI itself—it’s battling the wave of misinformation about it.
SEO isn’t dying — it’s evolving. So, I need to be proactive in understanding these changes and be discerning about the voices I trust in the industry.
I’m not easily surprised, but some of the AEO (or GEO) talks I attended last year were genuinely shocking—even for someone like me who may have had a bit of Botox.
I recall one speaker apologetically addressing a room of marketers, only to promptly suggest outdated tactics as the “secret sauce” for LLM visibility. It was painful to witness.
Thankfully, trusted voices like Lily Ray, Kevin Indig, Steve Toth, and Ross Hudgens came together this week for an enlightening roundtable on the future of search. It was by far the most beneficial AEO session I’ve ever attended, each sharing tactics they’ve successfully used to enhance LLM visibility.
Here’s what they shared and what I’ve learned:
1. Advertorials work
I discovered that LLMs don’t currently differentiate between paid and organic editorial content. Well-placed advertorials on reputable sites can boost a brand’s visibility in AI search, similar to earned coverage. As with traditional PR, the publication’s credibility remains crucial.
2. Syndication can scale visibility
Paid syndication increases reach, but focusing on quality over quantity is essential. I learned to prioritize reputable and relevant publications when employing this tactic.
3. Map pages to every audience and use case you serve
By creating clearly defined pages for each audience, industry, and use case, I can better position my brand as AI search becomes more personalized. This structure assists LLMs in understanding relevance and remains a strong SEO strategy.
4. Homepage clarity
I ensure that my homepage clearly communicates who I serve and what I do. LLMs analyze homepage content more effectively than navigation menus, so relying on the latter alone is a missed opportunity.
5. Optimize your footer
I’ve started optimizing the footer of my site. As Wil Reynolds demonstrated in a compelling case study, LLMs pick up on brand and service signals located there, enhancing visibility.
6. Don’t prioritize llm.txt
Despite ongoing speculation, there’s been no confirmation from significant LLMs about the use of llm.txt files, and Google explicitly states they don’t. I focus my efforts elsewhere for better results.
7. Go multimodal
To improve brand recognition across multiple sources, I repurpose core content in various formats like text, video, audio, and imagery, maximizing the chances for LLMs to pick it up.
8. Actively shape your brand narrative
It’s estimated that 250 documents are needed to meaningfully influence an LLM’s perception of a brand. By consistently publishing and promoting content, I ensure that my brand narrative remains in my control.
9. Freshness carries disproportionate weight
Fresh content generally performs better in AI searches, reflecting LLMs’ preference for recent information. However, purely artificial “refreshing” without meaningful updates is not advisable.
10. Social works fast
Updates on platforms like LinkedIn, including Pulse articles, can appear in AI search within hours, sometimes minutes. Platforms with high trust like Reddit and YouTube display similar rapid visibility.
11. Authority accelerates inclusion
Publishing on respected, niche industry sites can lead to rapid inclusion in LLM responses, sometimes in mere hours.
12. Don’t hide FAQs
FAQs should be accessible and well-detailed, not concealed within accordions. Eight to ten well-addressed questions can effectively signal expertise, intent, and relevance to both users and LLMs.
Is AEO the same as SEO?
John Mueller from Google clarified at Google Search Live that AEO relies on SEO fundamentals: doing tricks may work short-term, but long-term success relies on proven stability.
The correlation is logical when considering modern LLMs like GPT-5, which utilizes Retrieval-Augmented Generation (RAG) to query real-time data. To gain LLM visibility, showing up in search results is essential.
In essence, good AEO practices align with good SEO, though there’s nuance, and while these tactics are effective now, they will evolve as LLMs grow more sophisticated.
The best AI search strategy for 2026
Forget the magic button. Keep testing, remain skeptical about the hype, and be selective about the advisors you trust.
Thanks to Bernard Huang and Clearscope for hosting this insightful panel.
As I delve into Google Ads API v23, I’m excited to share this update marks the beginning of a faster-paced release cycle in 2026. With this update, I’m now able to access improved Performance Max reporting, sophisticated AI-driven audience tools, and more detailed campaign controls.
What’s new:
Performance Max Transparency: I’ve discovered that PMax campaigns now offer ad network type breakdowns, making it easier for me to analyze performance.
More Detailed Invoices: Through InvoiceService, I can retrieve campaign-specific costs, regulatory fees, and adjustments, allowing for more precise financial tracking.
More Precise Scheduling: It’s a game-changer for me to now schedule campaigns using precise start and end date-times instead of limiting to date-only fields.
Local Data Access: I’m now able to access store location details via PerStoreView, which matches the data in the Stores report accurately.
New Audience Dimension: With life-event-based audience building through LIFE_EVENT_USER_INTEREST, my Insights tools are more powerful than ever.
Smarter Demand Gen Planning: The conversion rate forecasts I rely on now vary by surfaces such as Gmail and Shorts, enhancing my strategy planning.
Generative AI Audiences: I can efficiently translate free-text audience descriptions into structured attributes, simplifying audience target creation.
Expanded Shopping Metrics: The inclusion of new competitive and conversion metrics by conversion date helps me improve my shopping ads performance.
Why I care: A quicker update cycle means I can leverage new features faster. With Google’s shift towards automation and AI-driven insights, staying on top of these updates helps me optimize campaigns effectively.
Between the lines: These updates require my team to upgrade client libraries and code, so scheduling development time is crucial to benefit fully from v23.
Bottom line: The Google Ads API v23 is setting the stage for 2026. I’m ready to embrace these improvements that introduce faster releases coupled with enhanced AI insights, refined reporting, and better campaign control for large-scale advertisers.
