As I explore the latest updates to ChatGPT, I’m excited to share that it now incorporates more images into its answers, bringing a fresh, multimodal approach to search. This enhancement makes images just as vital as text for exploring brands and products.
OpenAI has unveiled this visual upgrade, which pulls images from the web to enrich answers about a variety of topics, such as people, places, and products. It’s a fascinating development that shifts ChatGPT from providing simple text responses to offering a more interactive search experience.
How it works. With this update, ChatGPT becomes more than just a text generator. It now offers a search experience similar to what I’m used to:
Images will appear when they add clarity to the information.
These images, sourced from the web, align with the most relevant text.
If I’m curious about an image, clicking on it expands it to its original size and shows the source.
Where it’s live. The rollout of this update is occurring globally, and I’ve noticed it gradually becoming available across all ChatGPT plans that I access:
I’ve used it on web, iOS, and Android platforms.
It’s important to note that it only works with responses created by GPT 5.1.
Why we care. I realize that search is evolving to be more multimodal, integrating text, images, videos, and audio. Beyond ensuring that my brand is part of AI-driven replies, it’s crucial to consider how our visuals show up when ChatGPT responds to queries.
I’ve often found myself caught in the age-old marketing debate: should I focus on SEO or PPC? For years, this decision was largely based on past successes or failures.
With organic search, I could rely on growing visibility over time, while paid search gave me immediate, direct control.
Yet, most marketing teams lean toward one over the other based on their experience and budget limitations. But as we move into the future, this binary choice is no longer enough.
In 2026, the landscape has transformed significantly, altering how we approach search entirely.
Why This Debate Has Changed
The world of search has evolved, far beyond the SEO or PPC dichotomy.
Our search behavior is not the same. Search results pages have transformed and the machine learning behind bidding systems have advanced. And then there’s AI, the latest player on the scene, shaking things up.
It’s no surprise that AI has turned into a crucial factor, alongside SEO and PPC.
The pressing question now isn’t just about selecting SEO or PPC, but how we can integrate AI to sustain and boost visibility amidst the fast-paced changes.
This challenge also highlights another issue: fragmentation. With so many channels and discovery paths available, it feels overwhelming, leaving marketers scattered and at risk of falling into paralysis.
The key is to navigate through this AI upheaval, continuously adapting our strategies to remain relevant.
The Old Debate: SEO vs. PPC
Historically, weighing the pros and cons of SEO and PPC was straightforward:
SEO: Offers credibility, compounding visibility, and engagement, although slow to mature and with challenging expectations.
PPC: Provides rapid visibility and control, but requires ongoing financial investment and battles rising costs.
In my experience, a combined strategy proves most effective.
SEO fuels demand.
PPC captures it.
The synergy between the two remains valuable, but AI introduces an essential new dimension.
AI: The New Discovery Channel
AI is redefining how we discover and evaluate information.
Its popularity is growing fast, and this holiday season will likely be a turning point. Simple, integrated tools mean AI is embedded in our daily tech use.
Just like Google once led the charge, AI is set to surpass traditional search, thanks to its simplicity and speed. We find ourselves in an environment where:
Search engines summarize content before clicks happen.
Chat tools offer answers without redirecting traffic.
Product exploration starts with AI, moving beyond Google Search.
Natural, multi-step inquiries are being made that previously didn’t exist.
Thus, visibility hinges on AI presence. The battle isn’t just for rankings, but ensuring we feature within AI ecosystems.
Lacking AI visibility means being edged out. While this may not fully manifest today, it will soon dominate the scene.
Our marketing challenge is straightforward yet daunting: figuring out how to emerge in AI outcomes. We’re unable to purchase our place, nor can we find a playbook for these types of results.
In essence, our goals now demand adaptation from optimizing merely for search engines to being discoverable within AI systems that continue to draw from search results.
The New Visibility Battlefield
Despite feeling novel, AI’s emergence was somewhat predictable.
The existing web landscape is draining — it’s a battleground of too much information, advertisements, and distractions.
Finding what we need amidst this chaos is exhausting; AI offers an antidote by swiftly cutting through the clutter.
It’s undoubtedly refreshing. Yet, we must ponder the potential downsides.
Visionaries like Tim Berners-Lee express concern over AI threatening web sustainability by impacting ad revenue streams, a sentiment I share.
In “Supremacy,” a book charting AI’s rise, authors alleged Google had a ChatGPT-like system years ago but hesitated over revenue concerns. Their claim seems plausible to me.
AI’s efficiency is undeniable. It’s cleaner, faster — and hence will dominate. It stands as a true advancement.
The world of digital marketing has devolved into a war of endurance. The adage still rings true: we normally only explore the earliest pages of search results. We need no longer hide on these pages, as AI scours deep and wide.
Unfathomably, next-level solutions appear within AI’s grasp, surfacing comprehensive insights in brief moments.
This shift was predictable with hindsight, symbolizing a departure from failed attempts to combat the web’s disordered entropy.
AI signifies a fresh paradigm, rising from the modern web’s tumult.
Why This Changes the SEO/PPC Decision
The introduction of AI shifts the landscape for SEO and PPC fundamentally.
1. SEO: Less About Rankings, More About References
For content to feature within AI summaries or search assistants, it must exhibit:
Authority
Topical alignment
Structured markup
Trust signals
Depth, devoid of surface-level fluff
Authentic perspectives
AI favors genuine thought and established voices over mere quantity.
2. PPC: Still Dominating Premium Slots
Despite AI’s growing influence, PPC secures:
Top slots
Commercial queries
Visual placements
Local ad packs
YouTube
Discovery platforms
Merchant outcomes
AI shakes things up, yet PPC’s prominence remains — revenue needs won’t disappear.
3. AI Alters User Behavior Exponentially
AI is crafting fresh behavior patterns:
Fewer clicks, shorter journeys
Intuitive moments
In-depth comparisons inside AI systems
Increased research driven outside traditional points
Heightened expectations for relevance
Seo and PPC remain significant, albeit adapting to parallel discovery paths AI creates.
Is SEO vs. PPC vs. AI Even the Right Question?
Marketers often see SEO, PPC, and AI as competitors. Truthfully, they’re three intertwined visibility layers.
SEO fosters presence, providing foundational visibility.
PPC amplifies position, stimulating awareness.
AI frames discovery, offering context and relevance.
Each component complements the others:
SEO supplies content AI distills.
PPC fosters initial visibility, attracting early engagement.
AI delves into extensive analysis, shaping your market presence.
I embarked on this article seeking an answer to the age-old question: which reigns supreme — SEO, PPC, or AI?
Mid-journey, clarity emerged: this outdated question will no longer suffice by 2026.
General counsel proves challenging, given unique circumstances.
For example, a local plumbing business may have started with PPC while growing through local SEO and referrals.
Eventually, reducing PPC reliance might have been tested unless leads dwindled.
Contrarily, a college with complex site structures, coupled with strong authority, could transition from ads — assuming proper planning and site optimization.
Now, a third ingredient has emerged: AI, with SEO, PPC, and AI forming a unified strategy.
Separating AI from SEO is no longer feasible. The disciplines of AEO, GEO, and related labels are increasingly married.
Understanding AI and SEO’s connections in retrieval-focused generation contexts becomes crucial.
While PPC’s link to AI isn’t as prominent, early integration is already in motion, evidenced by Google incorporating ads into AI summaries.
Optimizing AI echoes optimizing SEO’s practices.
While early, the need to optimize for AI is evident, demanding attention from SEOs and GEOs in the near term.
Inaction is costly; we lack a complete guide, yet actionable insights remain available.
How to Build Visibility Across SEO, PPC, and AI
By 2026, success isn’t mere “ranking,” but “being referenced.”
Staying afloat requires optimizing for machine-led content evaluation.
1. Adopt GEO
Format your content for AI retrieval.
Two to three short, concise sentences followed by layered context appeals to LLMs.
Utilize bullet points, clear logic, and data tables for AI to parse easily.
2. Feed the Knowledge Graph with Entity SEO
AI confirms facts using entities like people, brands, and ideas.
Your About page, schema markup, and author bios must be impeccable.
Without Google’s understanding of your identity, authority citations become unlikely.
3. Target Citation Gaps
AI systems link to trusted sources, favoring niche gurus and major outlets.
Redirect digital PR efforts toward “mentions” on sites AI deems authoritative.
4. Invest in Freshness and Data
LLMs lean towards recent data. Regularly update facts, timestamps, and comparisons.
Static content may falter against continually refreshed material.
5. Embrace Redundancy: The Hybrid Approach
No channel stands alone. Execute PPC for instant visibility, nurture SEO for long-term authority, and set AI-ready data structures simultaneously.
6. Build a Content Engine
Leverage “They Ask, You Answer” frameworks to tailor content that addresses audience needs.
When I reflect on the evolution of SEO and SEM, I realize just how much these fields have transformed alongside search technologies. As Gary Illyes from Google once pointed out, embracing change is vital, even when it’s hard to accept.
Gary Illyes reacted to a Microsoft Bing article by Fabrice Canel and Krishna Madhavan about AI Search and its impact on conversion measurement. He made a strong statement about the future of search, something I deeply resonate with.
Coevolve. On LinkedIn, Gary emphasized, “SEM and SEO will need to coevolve with search, just like it has for the past 30 years.” It’s a clear reminder that adaptation is a constant necessity in our field.
I’ve witnessed many SEOs and SEMs adapt to these shifts, much like the path SEO has taken since its inception as a service. The most successful professionals continue to evolve.
SEO is not dead. The notion that SEO is fading away is not new. I’ve heard it countless times, yet SEO remains a critical component of digital marketing, continuously evolving with technological advancements.
The challenge is real. As search features change, it’s vital to embrace this evolution to ensure continued success. Those ready to accept and adapt to these changes will find new opportunities.
Why we care. I encourage others to engage with the new search features. Understand them, learn how they can draw users to your content, and figure out how to turn these interests into conversions.
Change isn’t easy or comfortable, but it’s an inevitable part of the future that we must prepare for.
In my experience, the open web often feels like the Wild West, especially in recent times. Many creators, myself included, have watched as our hard work is scraped and fed into large language models without any hint of permission.
This situation has become a free-for-all, leaving website owners with almost no means to opt out or safeguard their creative endeavors. There have been attempts to address this, such as Jeremy Howard’s llms.txt initiative. Much like robots.txt helps us manage site crawlers, llms.txt aims to provide guidelines for AI companies’ crawling bots.
However, a promising new protocol is on the horizon, potentially granting site owners like myself more control over how AI firms utilize our content. It looks like this might become part of robots.txt, allowing us to set definitive rules around AI system access and usage.
IETF AI Preferences Working Group
In response to this issue, the Internet Engineering Task Force (IETF) began the AI Preferences Working Group earlier this year in January. Their mission is to craft standardized, machine-readable rules to empower site owners to articulate AI usage preferences for their content.
Since its inception in 1986, the IETF has established core Internet protocols like TCP/IP, HTTP, DNS, and TLS. Now, they’re laying down foundations for the open web’s AI era. Leading this group are co-chairs Mark Nottingham and Suresh Krishnan, joined by figures from Google, Microsoft, Meta, and more.
Of particular interest is Google’s involvement via Gary Illyes, who is part of this working group.
“The AI Preferences Working Group will standardize building blocks that allow for expressing preferences about how content is collected and processed for Artificial Intelligence (AI) model development, deployment, and use.”
What the AI Preferences Group is Proposing
This group aims to deliver new standards that empower site owners to determine how LLM-powered systems can utilize their open web content.
A standard track document detailing a vocabulary to express AI-related preferences, independent of content association methods.
Standard track document(s) that explain how to associate these preferences with content using IETF-defined protocols and formats, for example, Well-Known URIs and HTTP response headers.
A standard approach for reconciling multiple preference expressions.
At the time of writing, nothing is set in stone yet. Early documents, however, provide a sneak peek into potential standards.
This working group published two crucial documents in August.
These documents propose significant updates to the Robots Exclusion Protocol (RFC 9309), suggesting new rules and definitions enabling site owners to specify AI content usage permissions.
How It Might Work
AI systems on the web are categorized and assigned standard labels. Whether a directory will exist for site owners to identify system labels remains unclear.
Currently, the defined labels include:
search: for indexing/discoverability
train-ai: for general AI training
train-genai: for generative AI model training
bots: for all types of automated processing, such as crawling and scraping
For each label, you can set two values:
y to allow
n to disallow.
I found it interesting that these rules can be applied at the folder level and customized for different bots. In robots.txt, they’re implemented using a new Content-Usage field, akin to existing Allow and Disallow fields.
Here’s an example robots.txt that the working group shared in their document:
Explanation Content-Usage: train-ai=n indicates that no content on this domain may be used for training any LLM model, whereas Content-Usage: /ai-ok/ train-ai=y permits model training using content within the /ai-ok/ folder.
Why Does This Matter?
There’s significant buzz about llms.txt within the SEO community and its use alongside robots.txt. Yet, no AI company has confirmed adherence to these guidelines, and Google disregards llms.txt.
Website owners, including myself, crave more explicit control over how AI companies leverage our content—be it for training models or RAG-based responses.
I feel that the IETF’s new standards signify positive progress. With Illyes as a contributing author, I remain optimistic that once finalized, companies like Google will embrace these standards, respecting new robots.txt rules during content scraping.
I’ve been deeply involved in the compelling discussions around AI, especially the intriguing intersection of ‘AI hype meets AI reality.’ Tools like Semrush One and its Enterprise AIO tool have taken center stage, offering invaluable insights into what’s happening inside LLMs. The big questions I often ponder are: How many citations are we capturing and just how many mentions are our brands accumulating?
When this data first emerged, it felt revolutionary. However, it quickly prompted other questions, like ‘What’s the ROI here?’ and ‘How can I integrate this data into my team’s marketing strategy?’ Ensuring that this valuable and fascinating data translates into actionable insights is a challenge I enjoy tackling.
It’s no secret that the data these tools provide is incredibly valuable. But, what steps do I take next? Let’s uncover this journey together.
The Fundamental Challenges of Tracking LLMs
Tracking LLMs can be more challenging than traditional metrics like Google rankings. Google rankings may show where I stand, but ranking doesn’t always correlate with traffic or revenue. Even if I rank highly, an AI Overview could dominate the search, reducing my traffic for a given keyword. I need to ask myself, is this the right traffic for my business goals?
The big difference between traditional SEO rankings and LLM visibility is the straightforward correlation between strong rankings and increased revenue, which is more complex with LLMs. I can easily track user behavior after they land on my site from organic search, but it’s not so clear-cut with LLMs.
SEO effectively drives traffic to my site, allowing me to evaluate the success of my conversion rate optimization (CRO) strategies. However, LLMs operate differently, leaving me with the task of creatively connecting the dots.
The Problem with Methodology
As I dive deeper into using LLM-related data, I realize this approach requires me to step out of my comfort zone as a performance marketer. My usual reliance on direct attribution and data points is shifted toward constructing a narrative that ties LLM visibility to larger brand storytelling.
This method isn’t novel, however. Brand marketers have dealt with indirect metrics since the days of billboard advertising. Still, the shift requires me to create insights from what might seem like fragmented LLM data.
Metrics and Approach to LLM Impact Measurement
Uncovering the true value brought by LLM visibility metrics is a layered and comprehensive process. To do this accurately, I need to understand the wider ecosystem of my organization’s promotional efforts. This understanding allows me to determine the root cause of site traffic or branded searches effectively.
For instance, if a TV ad campaign runs concurrently with optimizing for LLM mentions, analyzing their impact becomes essential. Only with complete awareness of such activities can I identify true causality or correlation.
From here, I find that LLM visibility data is usually just the starting point. It’s unlike traditional SEO insights, which might be more apparent and direct. My task is to delve deeper, probing these data points to uncover richer insights.
The Branded Search of It All
I’ve noticed that brand search provides exceptional insights into LLM performance, offering a rich vein of marketing intelligence. The comparison between two competing chicken wing chains, Buffalo Wild Wings and Wingstop, brightened this understanding for me. While their LLM citations differ, their brand awareness through social media presence offers a clearer picture of market positioning.
Simply examining the branded search traffic showed me how both brands performed similarly on Google, despite their different social media followings. Here lies the heart of utilizing search data creatively to find LLM visibility data strategies.
Rather than merely counting traffic, I am now compelled to consider the number of branded keywords involved, providing a sometimes surprising view on brand awareness and diversity. This approach provides a richer understanding of LLM visibility’s impact.
Direct Traffic: My Trusted LLM Data Companion
I’ve come to see direct traffic as an essential part of my LLM data narrative. Far from being a black hole, direct traffic can often indicate brand awareness and affinity, especially when correlated with LLM visibility metrics. Understanding these correlations allows me to paint a clearer picture of AI’s practical impact on consumer behavior.
For instance, if I compare LG and TCL, LG’s superior direct traffic and increasing momentum in LLM visibility suggest a tangible AI-driven influence, a possibility I must explore through multi-metric analysis.
Considering various metrics together and identifying shared trends offer insight into how LLM visibility might be affecting my brand’s overall recognition and engagement.
Not Just One Metric: Stitching Together LLM Data Stories
Ultimately, it’s about developing a comprehensive data story from LLM visibility insights. This story goes beyond direct KPIs, utilizing various data sources, such as bounce rates and organic traffic, to add depth and relevance to the narrative. Every piece of performance-focused data stands as testimony to the expertise we can bring to LLM visibility.
Total LLM visibility data, when creatively amalgamated with performance data, can transform insights into actionable strategies that align with pragmatic business objectives, showcasing our value in the AI-driven landscape.
When people ask me how to assess the ROI of their marketing campaigns, I always suggest starting with the customer acquisition cost (CAC). CAC, alongside Customer Lifetime Value (LTV or CLV), is vital in navigating the realm of B2B marketing.
By examining your CAC, you can identify which marketing channels deserve more attention and which aspects of your marketing strategy could use improvement. Benchmarking your CAC against industry standards is key.
The aim of this article is to guide you in recognizing what qualifies as a good CAC in your industry and to encourage you to even explore how your CAC fares compared to related industries.
Calculating Your Customer Acquisition Cost
To calculate your CAC, simply divide your total marketing and sales expenditures by the number of new customers acquired, using the formula below:
Make sure to perform this calculation annually or on a rolling basis to accommodate seasonal customer behavior changes. If your B2B business enjoys consistent year-round sales, consider quarterly CAC analysis to gauge the impact of new initiatives.
Additionally, calculating CAC per channel allows you to compare different marketing strategies effectively.
This report emphasizes B2B CACs. For B2C data, see our B2C Edition.
After determining your CACs, you can measure them against the industry averages shared below.
Average Customer Acquisition Cost (CAC) By Industry
The table below presents average CACs across 29 B2B industries, gathered from client data spanning January 2022 to August 2025. Consider these dataset limitations:
Within each industry, we categorize CAC as Organic or Inorganic. Organic CAC includes mainly SEO and Organic Social, while Inorganic CAC covers PPC / SEM and Paid Social.
Email marketing, events, and other channels are excluded due to insufficient data.
Data from client analytics is anonymous. Organic data leans towards SEO and Inorganic towards PPC / SEM, given our B2B clientele and service focus.
Below are the analysis results:
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Average Customer Acquisition Cost (CAC) for SaaS Companies
Our team also reviewed average customer acquisition costs across 22 SaaS industries to determine each industry’s B2B CAC.
SaaS Industry
CAC
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How Your CAC Relates to Customer Lifetime Value
While CAC reflects acquisition costs, Customer Lifetime Value (LTV) reveals the average profit per customer. Calculate LTV by dividing your profit over a chosen period by the number of unique customers, and multiply by their average purchase frequency. Aim for an LTV to CAC ratio of at least 3:1 for optimal financial health.
Keep in mind historical trends and competitor data. A 2:1 LTV to CAC ratio isn’t necessarily negative if you’re seeing improvement over time.
Particularly during new campaigns or long-term strategies, your ratios may fluctuate. For example, if you’ve launched an SEO campaign, results typically appear after 4-6 months.
How to Lower Your CACs
Organic CAC often triumphs over inorganic due to its longevity and skill-based approach. Investing in organic channels yields sustainable results without ongoing cash infusion.
If you’re curious about organic marketing to reduce your CAC, feel free to contact us. Our firm, with multiple U.S. locations, has helped various B2B sectors achieve superior ROI with SEO strategies.
Further Reading
For deeper insights into CAC and its relation to LTV, browse the following resources:
Have you ever wondered how to make your products stand out in Google AI Shopping and its AI Mode? I’ve discovered that optimizing feeds, utilizing schema, improving imagery, and crafting conversational Product Detail Page (PDP) content are key strategies to enhance visibility.
I attended a fascinating talk yesterday at the Simply Business headquarters in London, where Jonathon Heard, the Industry Head, Insurance at Google, shared some groundbreaking insights. He revealed that Google Search is gearing up to direct complex queries straight to AI Mode, effectively bypassing the traditional search process.
Heard also hinted at future enhancements in Google Search Console, aiming to provide separate reports for AI Mode and AI Overviews.
Bypassing Google Search. According to Heard, with the advent of Gemini 3, complex queries will be automatically channeled through AI mode, a feature currently being tested in the US.
One curious attendee asked about the implications of these changes. Heard confirmed that any query entered in the standard Google search could indeed be redirected to AI Mode. This revelation sparked a lively discussion, as the audience realized the significant shift this represents.
Although Google previously hinted that AI Mode might become the default search experience, they later retracted those statements. Robby Stein from Google downplayed the speculation, emphasizing the company’s focus on easy access to AI Mode for interested users.
AI Mode & AI Overview Search Console data. During the panel discussion, Simon Schnieders, Founder of Blue Array, inquired about the potential for separate AI Mode and AI Overview data within Search Console. Currently, these data points are lumped together, making it challenging to assess their individual performance.
Heard responded that Google is actively exploring this possibility, acknowledging the need for new data structures as search interfaces evolve. Schnieders welcomed this openness, noting it was the first time a Google representative had mentioned it.
Heard further elaborated, highlighting the rapid pace of change and the necessity to adapt reporting structures to keep up. He mentioned that although nothing is publicly announced yet, the transformation in reporting is a constant conversation within Google.
Here is the video of the event:
Why we care. I’ve reached out to Google to confirm the accuracy of Jonathan Heard’s statements. If Google transitions to an AI-centric approach bypassing traditional search, it will dramatically alter how users discover websites, content, and services.
Additionally, Google’s reticence to discuss AI Mode and Overview data in Search Console since the SGE demo could signal substantial upcoming changes. We will update this story as soon as we receive new information.
From search engines to generative engines, I’ve been part of the journey where the essence of SEO is deeply rooted in empathy. These days, it goes beyond mere optimization, demanding a bigger role in orchestrating clarity throughout the enterprise.
Headlines claiming another “AI winter” seem to circulate more frequently, and the statistics seem to support this skepticism. According to MIT’s research, although 80% of organizations have piloted GenAI and 40% have deployed it, only a mere 5% have scaled it. Further, seven of nine sectors have shown no structural change. Similarly, McKinsey reports reveal a disconnect where 36% of executives report no revenue impact, and only 19% have seen revenue grow over 5%, with 87% expecting growth to take years. Implementation is common, but impact is scant.
Yet, these headlines and figures overlook the real-time transformations within enterprises. SEO leaders are now being invited to lead in Generative Engine Optimization (GEO). It’s not because we’re AI specialists or understand every intricate detail of large language models—we often don’t. It’s because SEO is fundamentally about empathy, which is crucial now more than ever.
SEO has never solely been about keywords or search rankings. It’s driven by empathy on two primary fronts: understanding search engines—where Google aims not just for quality content, but to increase queries and ad revenue—and understanding users—ensuring they encounter the least friction in finding what they seek despite platform constraints.
Now, a third form of empathy comes into play—not for machines, which have no wants, but for the growth-driven giants building them. Their goals are straightforward: maximize adoption, engagement, and usage. Like Google, they’re eager to sacrifice accuracy for these metrics.
As SEO professionals, we often hesitate to acknowledge this, but the adage “just create good content” was never entirely true. Google favored backlinks and its own preferred content. An algorithm based on patterns can’t differentiate between quality and mediocrity—and AI providers will likely follow suit. Ignoring this reality is naive.
Capitalizing on shifting incentives within the enterprise’s workflow has been eye-opening. A short while ago, my PR team hesitated about digital outreach proposals. Yet, when I introduced a GEO pilot—using identical product descriptions across various platforms to better interpret our offerings—their attitude changed completely. That illustrates how reframing from SEO to GEO transformed their reception from resistance to enthusiasm.
The focus isn’t solely on visibility. When visitors arrive at our site, it’s not just about keyword optimization; it’s about optimizing their entire journey. Do they encounter the right message and next steps with minimal friction? Previously, we might have called this conversion rate optimization. Is it SEO now? Honestly, I’m unsure what SEO entails. What I do know is that to drive value, we must evolve. It’s about aligning with outcomes, not protecting a label.
This isn’t just theoretical. Here’s how I’ve been orchestrating at Adobe. Instead of optimizing for small traffic gains, I collaborate across teams to focus on what truly matters:
With Product Marketing, utilizing visuals to convey our message effectively.
With Comms and Client Success, leveraging case studies that resonate with buyer needs.
With PR, maintaining consistency across third-party sites to avoid GEO fragmentation.
With Account Executives, analyzing account discussions—identifying key contacts, uncovering objections, understanding why prospects select us over competitors. This vital intelligence feeds back into our content strategy and positioning.
This is just the surface level. The next horizon is data—curating our own ontology to standardize how the enterprise describes itself, ensuring consistent communication across teams and systems.
Enterprise teams are reaching out to us for guidance. Departments like Product, PR, Analytics, and Compliance are in pursuit of clarity. The tough truth is that if we remain complacent, GEO will be tackled by other areas in fragmented ways. Product will focus on features, PR on reputation, and analytics will get lost in metrics, leading to disjointed strategies.
As SEO specialists, we’re ideally positioned to lead GEO efforts due to our core skill of empathy, which enables us to balance platform incentives with user needs, transforming ambiguity into alignment. This is exactly what’s needed for GEO to succeed, preventing noise and activity without tangible outcomes.
Ultimately, SEO isn’t dead; it’s evolving into something unrecognizable and demanding leadership. Leadership means acknowledging our limited LLM knowledge but understanding how to assemble and align the right people.
If your reports still focus exclusively on traffic, rankings, or visibility dashboards, you’ve fallen behind. Enterprises require orchestration, not more metrics.
Whatever we choose to call this discipline, it’s shifted from merely optimizing to orchestrating clarity—across platforms, teams, and user journeys. That’s our mandate. Without our leadership, SEO, and its new form stretches beyond recognition, will lack an owner. So I ask, is SEO dead, or has it evolved into something far greater?
I’ve delved deep into the world of SaaS SEO agencies, reviewing 63 firms to bring you the best in the business. My ratings are based on crucial elements such as industry experience and the caliber of their leadership, among other key aspects:
Notable Clients (30%): The firms’ track record with leading SaaS companies is a primary indicator of their SEO campaign success.
Leadership Experience (20%): I scored each agency based on the SaaS marketing expertise of its executive team.
Median Employee Tenure (15%): Long-tenured employees suggest the firm invests in skill development crucial for sophisticated campaigns.
Average Review Score (10%): Customer satisfaction as reflected in reviews is a strong marker of the agency’s effectiveness.
GEO Offering (10%): Agencies advancing into AI platform rankings beyond Google get extra points.
Year Established (5%): A history of effective strategy adaptation over years signals reliability.
Founder Status (5%): Agencies still led by their founders showcase stability and vision.
Media References (5%): Frequency of citation in authoritative sources underlines their thought leadership.
For each agency, I also highlight their Main Focus, reflecting their specific SEO approaches. Here’s my research distilled into a ranked list:
Highlighting First Page Sage: This top-rated agency in the US, serving giants like Salesforce and Verisign, offers a personalized approach, emphasizing content excellence for long-term ROI. For SaaS companies aiming to prioritize lead generation, their expertise is unmatched.
Next up, I introduce other trailblazers like REQ—known for its seamless blend of traditional advertising and SEO—and Clay Agency, which excels at optimizing user experience to boost conversions. No stone is left unturned in our evaluation!
Explore the sense of collaboration and strategic depth brought by each agency, helping you identify the right partner to elevate your SaaS brand in 2025.
