Google continues evolution beyond text-first interactions with upgrades to Search Live Gemini model, possibly complicating SEO.
The post Google Updates Search Live With Gemini Model Upgrade appeared first on Search Engine Journal.
UK doctor and YouTuber Dr. Ed Hope said Google’s AI falsely claimed he was suspended by the General Medical Council earlier this year for selling sick notes. Hope called the allegation completely made up and warned that it could seriously damage his career.
Google’s AI generated a detailed narrative accusing Hope of professional misconduct, despite no investigations, complaints, or sanctions in his 10-year medical career, he said in a new video.
Why we care. Google’s AI-generated answers appear to now be presenting false, career-damaging claims about real people as fact. That raises serious questions about defamation, accountability, and whether AI-generated statements fall outside Section 230 protections.
What Google’s AI said: Hope shared screenshots of Google’s AI stating that he:
‘None of this is true.’ Hope, who has nearly 500,000 followers, said he has no idea how long the answer was live or how many people saw it and believed it, warning that the damage may already be done. After discovering the AI Overview, he replicated the hallucination and found more false claims, including accusations that he misled insurers and stole content.
How did this happen? Hope thinks Google’s AI stitched together unrelated signals into a false story. The AI conflated identities and events, then presented the result as factual history, he said:
Why this is more from “just a mistake.” The AI didn’t hedge, speculate, or ask questions, Hope said. It asserted false claims as settled fact. Hope said that matters because:
The big legal question. Is Google’s AI committing defamation? Or is Google protected by Section 230, which typically shields platforms from liability for third-party content? Courts may ultimately decide. For now, some legal experts have argued that:
Resolved? Searching for [what happened to dr. ed hope sick notes] showed this Google AI Overview:
Dr. Ed Hope (of the “Dr. Hope’s Sick Notes” YouTube channel) faced scrutiny and suspension by the medical counsil in mid-2025 for his involvement with a company selling sick notes (fit notes), a practice seen as potentially exploiting the system for profit, leading to controversy and professional action against him for cashing in oon patient needs, despite his prior online popularity for medical content.
What happened:
- Suspension: In June 2025, Dr. Ed Hope was suspended by the medical council (likely the GMC in the UK).
- Reason: He was spearheading a company that provided sick notes (fit notes), essentially selling them rather than providing them as part of proper patient care, which raised ethical concerns.
- Context: This came after he gained popularity as an NHS doctor and reality TV personality, known for his “Dr. Hope’s Sick Notes” channel where he’d break down medical scenes in media.
The Controversy:
- Criticals argued that he was profiting from people’s health issues by faciliting quick, potentially unwarranted, sick notes, undermining the healthcare system.
- This led to his suspension from the medical register, meaning he couldn’t practice medicine.
In essence, Dr. Ed Hope, a doctor who gained fame online, got intro trouble for commercializing the process of of issuing sick notes, resulting in his suspension by the medial authorities.
Searching for [what happened to dr. ed hope sick notes] now shows a different answer (at least for me):
“Dr. Ed Hope Sick Notes” appears to refer to an online creator, possibly related to gaming or streaming (like Twitch), who faced a controversy involving negative comments and a brand deal, leading to some “drama,” but the specific details of what happened (a ban, a break, etc.) aren’t fully clear from the search snippets, though a YouTube video suggests a reconciliation or a resolution after the “drama”. The name also sounds like it could relate to the medical soap opera Doctors, but that show was canceled in 2024, not by an “Ed Hope” character.
Here’s a breakdown of possibilities:
- Online Creator: A YouTube video titled “Making Up With Dr. Ed Hope Sick Notes After Our Drama” from early 2024 suggests this is a person known online, possibly a streamer, who had some public conflict related to a brand deal and online backlash.
- Fictional Character: While it sounds like a character name, the major medical drama Doctors ended, so it’s likely not a current, major plotline from that show, notes Deadline.
To find out exactly what happened, you might need to search for “Dr. Ed Hope Sick Notes drama” or look for their social media (Twitch, YouTube) to see recent posts.
The video. “SUSPENDED” as a DOCTOR – Thanks Google!
Looking to take the next step in your search marketing career?
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
(Provided to Search Engine Land by SEOjobs.com)
(Provided to Search Engine Land by PPCjobs.com)
Sr. Performance Marketing Manager, RobertHalf (Hybrid, Miami, FL)
Senior PPC Manager / Lead Gen Onward Search (Onsite Los Angeles, CA)
Director, Paid Search, Omnicom Media Group (Hybrid, New York City Metropolitan Area)
Senior PPC Manager / Lead Gen (on-site, downtown LA, direct hire), Onward Search (Los Angeles, CA)
Senior Manager, SEO, Kennison & Associates (Hybrid, Boston, MA)
Lead Generation Manager, Mondo (Hybrid, Charlotte, NC)
Search Engine Optimization Manager, NoGood (Remote)
Search Engine Optimization Manager, Pump.co (San Francisco)
Senior Content Manager, TrustedTech (Irvine, CA)
Senior Growth Product Manager, Reku (Remote)
Note: We update this post weekly. So make sure to bookmark this page and check back.
After more than 15 years in enterprise SEO across six major corporations, I’ve seen more careers derailed by internal politics than by Google updates.
Many SEOs moving from agency to in-house assume that staying current with algorithms and improving rankings will be enough.
In reality, the harder work is navigating the organization and the people within it.
Agency life rewards deliverables and reports. Corporate life runs on relationships, repeatable processes, the right platforms, and visible performance – all carrying equal weight with technical skill.
The following lessons reflect where SEOs can grow, avoid common pitfalls, and build sustainable careers inside complex enterprises.
Landing an SEO role in the corporate world today is less about chasing postings and more about positioning yourself as the obvious choice before you ever apply.
Hiring teams look for someone who connects well, presents a clear professional narrative, and shows measurable impact.
Most resumes submitted through job portals get filtered out by automated systems before a recruiter ever sees them.
Job boards like LinkedIn can be research tools.
When you find a role that fits, look for someone inside the company who can refer you – internal referrals dramatically increase your chances of an interview.
If you’re early in your career, build relationships long before you need them.
Find mentors through ADPList, attend local meetups, and join SEO and AI workshops or virtual conferences.
These touchpoints often matter more than submitting formal applications. In today’s market, your network is your application.
You’re an SEO – use the same skills you apply to websites on your own professional presence.
Start by choosing two “primary keywords” for your career: a job title and an industry.
If you already have experience in a specific vertical, lean into it.
If you don’t, pick an industry you genuinely understand or care about so you can speak to its audience and problems with credibility.
Use LinkedIn as a search engine. Include your soft skills, technical strengths, marketing competencies, and the industry terms hiring managers are scanning for.
Keep unrelated hobbies off your profile unless they support the roles you want.
If you wouldn’t include “yoga enthusiast” on a landing page targeting enterprise SaaS buyers, it shouldn’t be on your LinkedIn unless your goal is to work for a yoga brand.
And learn to talk about yourself clearly. Many SEOs are introverted or default to giving full credit to the team. That’s admirable in the workplace, but interviews require precision about what you led, influenced, or delivered. You can stay humble while still being direct.
Make sure all your touchpoints – resume, LinkedIn, portfolio, GitHub if relevant, personal site – align.
Recruiters and hiring managers will check multiple sources.
Consistency helps them see your strengths quickly and positions you as someone who understands how to present a unified brand.
Resumes today need to be concise, scannable, and impact-driven.
One page is ideal unless you have 10+ years of experience or leadership roles that warrant a second page.
Lead with outcomes instead of responsibilities:
Use action verbs that convey ownership – led, optimized, increased, launched – and tailor each bullet to the role you’re applying for.
Hiring managers want to see how your experience connects to their specific challenges, whether that’s:
List the tools that matter for enterprise SEO, but keep the list purposeful.
A handful of relevant platforms – Google Search Console, Screaming Frog, Semrush, Botify, BrightEdge – shows breadth without turning your resume into an acronym block.
Your summary should point forward. Highlight your:
Make it clear that you think beyond rankings – that you understand SEO’s role in product, content, and business outcomes.
Formatting still matters. Use white space, short bullets, and metric-first phrasing so your biggest wins stand out instantly.
Save the file as your full name. Little details help you look polished in a crowded field.
Leave out:
To build a long-term career in SEO, you have to become a student of how everything connects.
Search isn’t just algorithms or rankings – it’s the intersection of people, technology, and business.
You don’t need to master every discipline, but you do need to understand how they influence one another:
For instance:
You move from executor to strategist when you connect these pillars. That’s when SEO becomes more than optimization – it becomes influence.
Dig deeper: Enterprise SEO is built to bleed – Here’s how to build it right
A career isn’t shaped only by what you know – it’s shaped by how you grow.
In corporate SEO, growth comes from navigating people, priorities, and pace as much as mastering algorithms.
These lessons reflect the choices that determine whether your career moves forward or stalls:
Growth often happens when you change environments, not when you stay in one too long.
After a few years in the same company, it’s easy to get typecast as “the SEO person” instead of a strategic partner.
Organizations anchor you to the role they hired you for, even as your skills expand.
Moving every one to three years exposes you to new leadership styles, challenges, and technologies – all of which sharpen your instincts and broaden your range.
For SEOs, each transition teaches you what actually drives growth and how to earn credibility quickly by aligning teams and delivering impact.
Not every meeting needs your voice.
Early in my career, I believed credibility came from speaking first and often. I later learned that listening is one of the strongest leadership skills.
It reveals what drives decisions, who holds influence, and where priorities truly sit.
For SEOs, understanding the room before jumping in often leads to sharper, more relevant recommendations – and they’re harder for stakeholders to dismiss because you’re grounding them in what the team already values.
The opposite of constant talking isn’t silence – it’s strategy.
Knowing when to speak is an underrated professional skill, especially in large organizations where timing and tone matter as much as insight.
A well-placed comment that bridges teams, clarifies a decision, or protects performance can shift the entire conversation.
Speak with intention, not frequency, and your influence will grow even when your airtime doesn’t.
Results only matter if the right people see them.
Many SEOs assume that hard work will naturally lead to recognition, but visibility is a skill.
Frame your wins in terms leaders care about – revenue impact, efficiency gains, customer experience improvements.
Bring them to leadership reviews, all-hands meetings, and retrospectives so others understand how SEO supports bigger goals.
Build relationships with people who can advocate for you when opportunities arise. Influence isn’t just about execution – it’s about making your impact legible and memorable.
Keep a running log of your work, conversations, and metrics.
I block time every Friday to summarize the week across three areas: meeting outcomes, task updates, and wins.
Some managers want these updates – others don’t.
Either way, they help you track progress and build a record you can reference later.
Tools can help – I’ve used GitHub Issues, simple .txt files, and, more recently, a Chat Agent that compiles my notes into summaries.
These logs save hours when someone asks about a past decision or when you’re updating your resume for a job search.
Whether you share them or keep them for yourself, they create clarity and evidence of your contributions over time.
Meetings can quickly overtake your day.
The most effective SEOs protect time for analysis, writing, and strategic thinking – the work that actually moves projects forward.
Block dedicated focus time, decline meetings where your presence isn’t essential, and suggest asynchronous updates when appropriate.
Protecting your time isn’t selfish. It prevents burnout and keeps you delivering work that matters.
It’s natural to reference past employers, but constant comparison can make you seem resistant to new ideas or unaware of context.
Every organization has its own culture, pace, and priorities.
Share relevant frameworks when they help, but adapt to the environment you’re in.
Your credibility grows when you focus on what works here – not on what worked there.
Dig deeper: The top 5 strategic SEO mistakes enterprises make (and how to avoid them)
No SEO operates in isolation.
In enterprise environments, success depends on engineers who make optimizations possible, analysts who surface insights, and product managers who balance priorities.
Navigating these relationships requires empathy, patience, and strategy.
Often, your ability to guide discussions, document decisions, and build trust matters more than technical skill.
When you collaborate with intention, SEO becomes less about convincing others to care and more about creating shared ownership of the outcome.
Some of the most effective leadership moments come from asking the right questions rather than supplying the answer.
Many of my biggest wins happened when I helped stakeholders arrive at the solution themselves.
When people believe they’ve discovered the path forward, they take greater ownership and champion the outcome.
This is especially powerful in SEO, where teams may be hesitant to adopt recommendations.
Asking questions shifts conversations from resistance to curiosity and reframes SEO as a shared opportunity instead of an external directive.
Influence grows when collaboration feels like discovery, not pressure.
In large organizations, memory fades quickly.
Document ideas, decisions, experiments, and notable conversations so you have a clear record when questions resurface months later.
Documentation turns “I think” into “I know,” strengthening your credibility and protecting your work.
Whether you keep notes in shared documents, project tools, or automation-assisted summaries, the goal is the same – create a defensible trail of how decisions were made and what impact followed.
When leadership asks about traffic shifts or delayed recommendations, your written history becomes both insight and insurance.
Collaboration matters, but discernment protects your momentum.
Not everyone who agrees in a meeting is invested in follow-through.
Politics, shifting priorities, or competing metrics often influence behavior more than logic.
Learn who reliably delivers and who disappears when accountability is needed.
For SEOs, true allies in engineering, product, or analytics can make or break execution.
Align with those who follow through and stay cautious around those who view SEO as competition.
Protect your credibility by choosing collaboration with intention, not assumption.
The engineers, analysts, IT admins, and product managers beside you often carry projects across the finish line.
Early in my career, I made the mistake of treating these partners as support rather than as collaborators. Their expertise is what turns strategy into action.
Treat them as equals who share ownership of outcomes. Involve them early, respect their constraints, and acknowledge their contributions.
When partners feel valued, they become advocates – raising SEO needs in rooms you may not be in.
The strongest SEO wins aren’t solo efforts; they come from relationships built on mutual respect and shared momentum.
Dig deeper: The design thinking approach to enterprise SEO
Sustaining a long-term SEO career requires more than technical skill – it requires balance, boundaries, and emotional resilience.
Constant algorithm changes, shifting priorities, and cross-team dependencies can drain you if you don’t protect your energy.
Mental well-being isn’t a luxury – it’s a strategy for longevity.
When you manage your mindset with the same discipline you apply to a site audit, you gain clarity, patience, and perspective – all qualities that make you more effective.
Early in my career, I worried rankings would collapse the moment I took time off.
They never did – but my judgment did when exhaustion set in.
Burnout distorts perspective, makes you reactive to data, and limits strategic thinking.
Rest isn’t indulgence, it’s maintenance.
Search is a long game measured in quarters, not days.
A week offline is recoverable. Burnout is not.
Protect your energy with the same discipline you protect a site’s uptime.
Much of SEO happens behind the scenes, and visibility doesn’t always follow impact. When someone praises your work, save it.
Short notes from peers, partners, or managers become valuable artifacts during promotion cycles or job searches.
Collecting this feedback isn’t about ego – it’s about building equity and giving yourself a factual record of how you support the business.
Every team has someone whose burnout becomes contagious. Don’t become that person.
Positivity doesn’t mean ignoring problems – it means creating space for solutions.
I once put a direct report on a performance improvement plan after his frustration began affecting morale.
After delivering the notice, I took him to lunch for an honest, empathetic conversation. That moment shifted everything.
His attitude improved, he worked his way off the PIP, and he later became a director at another company.
Compassion doesn’t replace accountability, but it makes growth possible. Leadership is as much about tone as it is about tactics.
In corporate life, meetings multiply faster than progress. Dependencies shift.
Priorities change without warning. Build a cushion into your timelines. If you think something will take a week, plan for 10 days.
For SEOs, many delays sit outside your control – engineering queues, content operations bottlenecks, competing releases.
A buffer protects your credibility and keeps expectations grounded. Underpromise and overdeliver isn’t cliché – it’s survival.
Leadership skepticism about SEO is rarely personal. It’s usually about budgets, bandwidth, or competing bets.
Early in my career, I saw every pushback as a critique of my competence.
Over time, I learned it was part of the negotiation process.
When an initiative is deprioritized, it doesn’t mean your expertise has lost value – it means resources moved elsewhere.
Anchor conversations in business impact, not identity. Influence lasts longer when driven by logic rather than frustration.
There was a time when I wasted energy debating SEO theories or venting about internal politics.
It felt good in the moment but changed nothing. My credibility grew the day I stopped trying to win arguments and started aiming for outcomes.
When disagreements arise, document your position, present the data clearly, and move on.
Rising above gossip doesn’t mean disengagement – it means choosing professionalism over noise.
SEO isn’t emergency medicine, though corporate urgency can make it feel that way.
Most “crises” come from impatience with the slow, cumulative nature of search. Daily fluctuations rarely matter when the trendline is healthy.
Remind stakeholders – and yourself – that meaningful growth takes time.
When pressure for overnight results rises, stay grounded. The long game always wins.
Work can challenge and fulfill you, but it shouldn’t define you.
The most effective professionals invest in relationships and interests outside the company.
Detaching your identity from your job doesn’t weaken your ambition – it stabilizes it.
When your sense of worth isn’t tied to the next quarterly metric, you lead with more confidence and less fear.
Success becomes sustainable when life stays bigger than work.
Dig deeper: SEO’s future isn’t content. It’s governance
Fifteen years in corporate SEO have taught me that technical skill is only half the job.
The other half is navigating people, priorities, and perspective.
Algorithms will evolve, tools will change, and org charts will shift, but your ability to adapt, communicate, and lead determines how far you go.
Success in SEO isn’t about chasing every update or proving you’re the smartest person in the room.
It’s about building trust, creating clarity, and sustaining momentum through both wins and setbacks.
The most impactful SEOs aren’t just tacticians.
They’re translators, connecting data to business strategy, ideas to execution, and people to purpose.
When you recognize that your influence extends beyond rankings, you move from contributor to catalyst.
SEO may begin with optimization, but the real work is shaping how organizations think, act, and grow. That’s the craft worth mastering.
Every Black Friday reveals how consumers search, compare, and decide. This year added something new: a real-world test of how AI models interpret commerce under true demand.
So we ran a structured test across major LLMs and analyzed 10,000 responses. The goal was simple: to see how these systems form their internal view of the retail landscape and which signals shape the answers they generate.
As we reviewed the dataset, a clear pattern emerged: Black Friday acts as a natural stress test for AI-driven discovery.
The sheer volume of queries, the range of categories, and the speed of shifting consumer attention expose the sources, structures, and behavioral tendencies that shape how LLMs reason about products, retailers, and intent.
The results offer a preview of how AI search is evolving – and how the broader commerce ecosystem will feel the impact.
In traditional search, the funnel starts with a query and ends with a ranked list of results, often dressed up with shopping carousels, popular products, and other curated touches. In AI search, the funnel flips.
The model begins with its internal map of the world – a compressed web of relationships, sources, and signals – and then builds an answer. In shopping, an LLM’s goal is to deliver a purposeful response, not a shopping experience.
When we reviewed the top 50 most-cited domains across 10,000 LLM responses – spanning deals, reviews, comparisons, and product recommendations – the distribution was far from neutral:
This cluster shapes much of the commercial “knowledge” LLMs draw from. It leans toward large retailers, widely cited media outlets, and platforms built around comparisons or reviews. Together, these sources create a collection of resources that lets models deliver direct answers across any vertical, product type, or consumer need.
In our analysis of 10,000 responses, we compared the week leading up to Black Friday with the event itself. Before Black Friday, responses were anchored in planning behavior:
Users prepare by comparing, researching, and setting baselines – and LLMs mirror that behavior. Even prompts that included “Black Friday” tended to produce expectation-setting responses:
When the event began, the mix shifted fast. Social and UGC content jumped to 25.1%, gaining more than eight points of share, while retail and media both edged down.
This shows a shift inside the models: as uncertainty rises and pricing and inventory move around, LLMs lean harder on human discussion and experiential content.
This pattern mirrors consumer behavior but also shows how heavily models rely on conversation-driven sources for real-time decision cues.
One of the clearest insights from the dataset is the weight third-party domains have on AI reasoning. Today’s LLMs win by absorbing as much human interest in products as possible. The players that supply huge volumes of consumer insight, reviews, product demos, sentiment, and structured data end up shaping how models reason and decide.
In an Athena analysis of external influence in retail and ecommerce (October 2025), five domains appeared consistently as the dominant off-page signals LLMs rely on:
Each one shapes a different part of the model’s decision-making process. Across all of them, we see the same pattern: LLMs depend on content that captures real human interest, organizes consumer-driven options, and reduces uncertainty with verifiable data.
Today, LLMs are building a fortress of product data that will unlock the most powerful shopping-discovery tool consumers have ever used.
Although third-party domains dominated, brand websites still played a measurable role in the dataset. They create a crucial path forward for any consumer brand that wants to win in AI discovery.
A site’s internal structure plays a major role in how a model interprets a brand.
According to the Athena retail & ecommerce dataset:
The homepage serves as the brand’s primary identity layer. It sets the tone, defines the positioning, and gives the model the simplest semantic signals to read.
Blogs and product pages play a different role. They provide definitional clarity, long-tail context, and the factual detail the model needs.
Brands that rely on promotional copy, unclear hierarchy, or thin product content leave major visibility on the table.
Today, LLMs use brand content to validate and deliver direct responses—but only when off-page content and data justify the brand’s place in the conversation.
Across the entire dataset, a few categories dominated model responses.
Walmart, Target, and Best Buy capture nearly half of all retail citations. Their breadth, familiarity, and content depth put them at the center of LLM commerce reasoning.
Best Buy leads by a wide margin, followed by Newegg and Micro Center. Tech-focused queries consistently push models toward these sources – though the surge in electronics during Black Friday likely amplifies this effect.
Fashion, beauty, pharmacy, home, DIY, and pets each take smaller slices, even with strong category leaders in play. The imbalance reflects the sheer volume of content generalist retailers produce compared with niche verticals.
As we reviewed the platforms, another pattern stood out: major LLMs don’t just answer differently – they think differently. Each one has its own rhythm, preferred structures, and style of presenting commercial information.
Gemini produces the most expansive outputs. Its responses averaged 606 words, with 97.6% using lists and 92.3% using headings.
The model often delivers essay-length explanations, averaging nearly 28 list items per response. It treats Black Friday as if every query deserves a full article.
OpenAI sits in the middle. It averaged 401 words per response, with 99% including lists and nearly two-thirds using headings. Its lists were even denser, averaging 32 items.
Perplexity moves in a different direction. Its typical response was 288 words, with far fewer list items – about 9.7 on average – and fewer headings overall. It favors short, direct summaries. Even with complex topics, it compresses the information into something that reads like an executive brief.
These differences reveal distinct retrieval and reasoning strategies that shape how each model interprets brands, categories, and commercial intent.
As AI-driven discovery takes a larger role in search, teams will need to think about visibility in terms that respect each platform’s internal logic – not in broad strokes.
The data points to a clear direction: AI search is becoming its own ecosystem – shaped by familiar SEO inputs, source quality, content structure, and off-page signals, all interpreted by language models to deliver a clear response.
If your content isn’t clearly labeled, semantically structured, and reinforced across the web, it risks becoming invisible to AI systems surfacing answers or product suggestions.
In this new environment, retailers and brands must rethink how they communicate—not just on their own domains, but across the entire digital discovery surface.
OpenAI’s recent Shopping Research announcement further raises the stakes. Through ChatGPT, OpenAI is now capturing real-time consumer research behavior – preferences for price, color, variants, availability, and more – to build what is essentially a user-trained targeting engine for commerce.
This isn’t just AI learning about your product. It’s AI learning how users shop.
For decades, retailers like Amazon, eBay, and Walmart have invested in complex taxonomies and refinement layers for discovery: variant mapping, filters, availability rules, and more. Now OpenAI is absorbing that logic not just by crawling, but by interacting with users and watching intent unfold.
For brands and retailers, this marks a shift from passive search optimization to active AI participation. If your content isn’t present, structured, or referenced in these systems, it won’t show up in the AI’s answers – or in the consumer’s journey.
Black Friday gave us more than a look at which products sold best or which deals consumers chased. It revealed how LLMs behave under real-world demand—how they reason, reference, and prioritize across a fragmented content landscape.
The answers they generated were structured, confident, and increasingly influential, yet incomplete – shaped more by the sources they see most often than by the full depth of what brands offer.
What we’re witnessing isn’t just a new search interface. It’s the emergence of a new shopping architecture – one where agentic commerce replaces traditional browsing, and AI models, not consumers, drive product discovery, comparison, and even transaction.
OpenAI’s launch of Shopping Research makes this shift unmistakable. These models are no longer just language tools; they’re intent engines, trained not only on product data but on how people actually shop. Price sensitivity, variant preferences, real-time availability – all of it is now part of how AI interprets and responds to commercial intent.
For brands, the implications are significant. Visibility will no longer hinge on SEO rankings or ad placements alone. It will come from structured, semantically rich content, surfaced across the right off-page ecosystems, and aligned with the reasoning patterns of each major model.
We call this AI-native visibility – a discipline built to ensure brands aren’t just discoverable, but understood by the systems shaping modern commerce.
Black Friday was only the stress test. The real transformation is still ahead. And it won’t be won by who ranks, but by who is represented – accurately, contextually, and everywhere AI shows up.
When a TV commercial makes people feel something, it doesn’t just win in the moment – it sparks curiosity, drives searches, and fuels conversions.
That’s why the “Breaking TV Ads Report,” jointly launched by Kinetiq and DAIVID, deserves a spot on every search marketer’s radar.
The monthly report ranks the top-performing new TV ads in the U.S., blending Kinetiq’s real-time TV ad detection with DAIVID’s AI-driven creative analytics to uncover which ads broke through, why they resonated, and what brands can learn from their success.
It’s a powerful reminder that search doesn’t start on Google – it starts in the mind.
As Barney Worfolk-Smith, chief growth officer at DAIVID, recently told me in an email:
The first edition of the “Breaking TV Ads Report” highlighted a commercial that checks every emotional and strategic box: Indeed’s “What If LeBron James’ Skills Were Never Seen?”
The ad traces James’s journey from his early life to his work with the LeBron James Family Foundation, connecting it to Indeed’s “skills-first” hiring message.
It resonated not only because of its star power but because it made viewers feel something authentic.
The ad generated 11% higher intense positive emotion and 7% higher attention than the average U.S. TV ad, per DAIVID’s data.
It was joined in the top 10 by campaigns from TikTok (twice), Subaru, and Taco Bell, with emotional themes centered on family, mentorship, and belonging.
These aren’t just nice stories – they’re search triggers.
When people connect emotionally with a brand message, they’re more likely to act on it – often by turning to Google or YouTube for more information, reviews, or purchase options.
Dig deeper: Brand + performance: The secret to maximizing ad ROI
Back in 2011, Google introduced the concept of “The Zero Moment of Truth.”
But the ZMOT stage in the buying journey – when consumers research a product or service online before making a purchase – was the “new” second step.
The first step remained “stimulus,” and it could be “a TV ad.”
Many search marketers focus on what happens in the second ZMOT stage, because we can measure impressions, clicks, and conversions on mobile and laptop screens.
And we ignore the stimulus step because it is sucking money out of our marketing budgets.
But several studies over the past decade have shown that the impact of TV advertising extends directly into search behavior:
Put simply: when a campaign captures attention on TV, search demand spikes – often within minutes.
For SEO and PPC professionals, this presents a clear opportunity to anticipate and capitalize on those moments.
Several major brands have already proven that when TV storytelling and search strategy work together, both channels perform better.
Apple’s product launches are masterclasses in cross-channel momentum.
Every time a new iPhone ad airs, search volume for terms like “iPhone 17 Pro Max” or “iPhone 17 release date” skyrockets.
Apple’s branded search traffic increases by up to 40% in the days following a major campaign, according to Semrush.
Apple intentionally designs its TV creative to generate questions – not answer them – encouraging viewers to seek out more details online.
That’s where Apple’s search-optimized landing pages, YouTube product videos, and paid search campaigns complete the journey.
Progressive’s long-running “Flo” campaign shows how consistent creative storytelling translates into search intent.
The insurance brand’s TV spots spark curiosity around characters, slogans, and offers – leading to measurable spikes in branded searches such as “Progressive car insurance” and “Flo from Progressive.”
The brand’s media team aligns paid search and display campaigns with national TV flighting schedules, ensuring that when interest peaks, search ads and organic results are ready to capture demand.
Coca-Cola’s “Share a Coke” campaign is another classic case of TV leading to search.
The original “Share a Coke” campaign was launched in Australia in 2011 and involved replacing the Coca-Cola logo on bottles with hundreds of popular first names.
This personalization strategy was a global success, encouraging consumers to find bottles with their names and share them with friends and loved ones, which boosted sales and created emotional connections with the brand.
The latest “Share a Coke” campaign is a global relaunch targeting Gen Z with a focus on digital experiences and authentic, in-person connections.
It features personalized cans, a digital “Memory Maker” tool for creating shareable videos, and a partnership with McDonald’s.
Consumers can find names on bottles or use a QR code to customize bottles – a creative hook that’s sent millions to Google searching “custom Coke” or “share a Coke names.”
The campaign’s success wasn’t just creative; it was data-driven.
By tracking spikes in branded search and social mentions, Coca-Cola refined its targeting and extended the campaign’s life cycle online.
Dig deeper: Hyper-personalization in PPC: Using data to deliver tailored ad experiences
What makes the new “Breaking TV Ads” report particularly valuable is its data-driven framework for measuring creative effectiveness.
Kinetiq’s proprietary ad detection technology identifies every ad that first airs across 210 U.S. DMAs and 15 streaming apps, capturing over a million daily detections.
DAIVID’s AI then evaluates each ad’s emotional response, attention, and brand recall, creating a creative effectiveness score (CES) – a composite metric that mirrors how audiences actually experience content.
In a media landscape increasingly defined by short attention spans and fragmented screens, this data provides a rare window into why certain stories break through – and how that resonance correlates with downstream behaviors like search and site visits.
As Kinetiq CEO Kevin Kohn put it, the partnership “gives marketers a holistic view of the TV and CTV advertising landscape – not just what aired, but why it resonated.”
That’s exactly the kind of insight performance marketers need to connect the dots between creative resonance and measurable outcomes.
Dig deeper: Your ads are dying: How to spot and stop creative fatigue before it tanks performance
In February 2025, Neal Mohan, the CEO of YouTube, revealed that:
So, search marketers can apply the latest findings from the Breaking TV Ads Report in several ways:
Search has long been viewed as a response channel – the final step in a consumer journey. But that view is outdated.
Today’s most successful campaigns use search as a connective tissue between offline inspiration and online action.
Whether it’s a QR code at the end of a TV ad, a YouTube masthead following a primetime spot, or a Google Shopping ad that captures post-broadcast demand – search is the bridge between storytelling and sales.
As more brands invest in connected TV (CTV) and streaming, the line between “brand” and “performance” marketing will continue to blur.
Creative effectiveness data helps close that gap – showing which emotional and visual cues are most likely to drive measurable search and conversion behavior.
Ultimately, reports like “Breaking TV Ads” remind us that the most powerful search strategy begins long before the query.
It begins with attention and emotion, and, increasingly, on the biggest screen in the house.
Dig deeper: How connected TV advertising drives search demand
Search Engine Land turns 19 today.
Nineteen years. Almost two decades of analyzing, explaining, questioning, challenging, obsessing over, and occasionally shaking our heads at whatever Google and the search industry throw our way.
And this past year? The pace of change has made it one of the most transformative since we launched in 2006.
Through all of it, our mission is the same as Day 1: help you make sense of search with clear news, smart analysis, and practical guidance.
Before we look ahead, I want to say thank you — and take a moment to reflect on the past year at Search Engine Land.
Seriously, thank you.
Every day, we start with you: what you need to know, what actually matters, and what changes could shape your work today or your strategy six months from now.
We aim to:
If you haven’t yet, subscribe to our daily newsletter for a curated wrap-up of everything happening in search. It’s still the easiest way to stay informed without feeling overwhelmed.
Search Engine Land has always punched above its weight for one reason: the people.
A small team can do big, meaningful work when everyone is aligned, mission-driven, and a little obsessed with search.
A huge thank-you to:
And to the entire Third Door Media team within Semrush — thank you. Whether or not your name appears here, your work matters and is appreciated.
In a year defined by uncertainty, it was encouraging to see so many people continue to rely on Search Engine Land as a trusted community resource. And Search Engine Land had a strong 2025.
This was the standout moment of the year. Bringing SMX Advanced back in person after six years felt overdue and incredibly energizing.
Attendance exceeded expectations, sessions were packed, and hallway conversations felt like a reunion of the search marketing community. You could feel how much people missed connecting face-to-face — debating AI’s impact on search, swapping tactics, comparing notes on Google’s latest changes, and simply enjoying each other’s company.
It reaffirmed what we’ve always believed: great things happen when smart marketers share a room. We’re already looking forward to doing it again in Boston, June 3-5.
This past year brought one of the most dramatic shifts in search since Search Engine Land launched in 2006. Whatever we end up calling this emerging practice, we focused on giving the industry the clarity, context, and reporting it needed.
Readers have told us again and again that Search Engine Land is their go-to source for cutting through the noise during a confusing and often chaotic time. We’re proud that our reporting, explainers, and expert analysis are helping shape the industry’s understanding of where search is headed next.
This year brought a surge of new readers and renewed engagement from long-time practitioners. With so many shifts reshaping SEO and PPC – from AI to SERP experiments to advertiser updates – and the continued emergence of GEO, marketers turned to Search Engine Land in record numbers to stay informed.
Our contributors played a significant role in our growth. A huge thank you to all of our excellent SMEs for all the great content and insights you shared in 2025.
As we enter our 19th year, our commitment remains unchanged: provide the most trusted, useful coverage of search anywhere.
This year you can expect:
Save the dates:
There’s much more to come – and as always, our goal is to give you the insight and intelligence you need to do your best work.
On Dec. 11, 2006, Search Engine Land officially launched with a simple idea: search was becoming not just a tool, but a place. A world. A community. A discipline shaping how people find information and how businesses connect with customers.
Nineteen years later, that world has grown in ways none of us could have imagined. But the core idea still holds:
Search Engine Land is a place to stay informed, to learn, to connect, and to understand the engines driving the modern web.
On behalf of everyone at Search Engine Land and Semrush, thank you for reading, for sharing our stories, for asking hard questions, for supporting our mission, and for caring so deeply about all things search.
Here’s to the rest of 2025 – and to a successful, healthy, and insightful 2026.
Google has released the December 2025 core update, its third of the year. The rollout began December 11 and may take up to three weeks.
The post Google Releases December 2025 Core Update appeared first on Search Engine Journal.
Google released the December 2025 core update today, the company announced.
This is the third core update of 2025 and the fourth major Google algorithm update overall. Earlier this year, Google rolled out the August 2025 spam update, which followed the June 2025 core update and the March 2025 core update.
What Google is saying. Google updated its Search Status Dashboard to state:
Google added on LinkedIn:
About core updates. Core updates roll out several times each year. They introduce broad, significant changes to Google’s search algorithms and systems, which is why Google announces them.
Video on this core update. I made this short video a few hours after publishing this story:
What to do if you are hit. Google did not share any new guidance specific to the December 2025 core update. However, in the past, Google has offered advice on what to consider if a core update negatively impacts your site:
In short: write helpful content for people and not to rank in search engines.
For more details on Google core updates, you can read Google’s documentation.
Previous core updates. Here’s a timeline and our coverage of recent core updates:
Why we care. With any core update, we often see significant volatility in Google search results and rankings. These updates may improve visibility for your site or your clients’ sites, but some may experience fluctuations or even declines in rankings and organic traffic. We hope this update rewards your efforts and drives strong traffic and conversions.
Search Engine Land is expanding its contributor roster in 2026 – and we’re looking for seasoned experts in SEO, PPC, AI, and analytics to join us.
Why we care. Search Engine Land is not just our publication – it’s yours. For 20 years, Search Engine Land has been the go-to source for search marketing insights, reaching more than 1 million professionals every month. We’re growing again and want to amplify a trusted and diverse set of voices across the industry – whether you’ve been doing it for 5 years or you’re old enough to remember the Google Florida update or when Google Ads was still called AdWords.
The details. We’re seeking contributors with 5+ years of hands-on experience in their field who can share actionable insights and thought leadership on the latest in:
This is a volunteer contributor opportunity. But the perks are real. You can:
How to apply. Interested? Fill out this form to be considered. If selected, our team will contact you directly via email. We will close the form Dec. 19.
We’ve established the AI resume as the new C-suite-level asset that defines your brand at the bottom of the funnel, and we’ve mapped the strategic landscape that shows how it operates across explicit, implicit, and ambient research modes.
So, how do you build this asset to thrive in a three-part environment?
The answer is shifting from ranking in search results to the discipline of brand-focused algorithmic education – a multi-speed strategy aligned with the trio of technologies powering all modern recommendation engines.
The digital marketing ecosystem has been reshaped by AI assistive engines – platforms like Google AI, ChatGPT, and Microsoft Copilot that no longer provide links but deliver synthesized, conversational answers.
Understanding how to influence these engines is the new frontier of our industry.
Conversations I had in 2020 with Gary Illyes at Google and with Frédéric Dubut, Nathan Chalmers, and Fabrice Canel at Bing revealed that these engines – and, by extension, modern AI – all rely on the same three foundational technologies.
I call this the algorithmic trinity. Mastering it is the key to your future success.
Stop thinking of Google or ChatGPT as monolithic black boxes.
See them instead as dynamic blends of three connected technologies.
Every AI assistive engine is built from a unique mix of these components.
This is the foundation – the vast, real-time index of the web.
It provides the fresh, up-to-the-minute information AI needs to answer questions about current events or niche topics. It is the engine’s window to the “here and now.”
This is the AI’s brain – a machine-readable encyclopedia of verified facts about the world.
Google’s Knowledge Graph is at least 10,000 times bigger than Wikipedia.
This is where your brand’s core identity is stored. It provides the factual certainty and context AI needs to avoid hallucinating.
This is the AI’s voice – the conversational interface that generates human-like text.
The LLM synthesizes information from the search index and the knowledge graph to create the final answer delivered to the user.
Each part of the algorithmic trinity learns and updates at a different speed, which means your optimization strategy must be layered.
Short-term tactics and long-term goals need to align with the technical “digestion speed” of each component.
Influencing traditional search results is your fastest path to visibility.
By creating helpful, valuable content and packaging it for Google with simple SEO techniques, you can begin appearing in AI-powered search results within weeks.
While it doesn’t build deep trust, it puts your brand into the real-time consideration set that AI assistive engines use to construct answers for niche or time-sensitive queries.
Think of it as getting your daily talking points and hyper-niche answers into the conversation.
Educating the Knowledge Graph is how you build your permanent, factual record, a process that typically takes three to six months.
It requires establishing your entity home – the definitive source of truth about you – and creating consistent, corroborating information across your digital footprint.
When Google’s foundational understanding of me was wrong (“the voice of Boowa the Blue Dog”), it cost me countless opportunities.
This is the work that corrects those errors.
The ultimate goal is inclusion in an LLM’s foundational training data.
This is a long game, often nine months to a year or more.
It means your brand’s narrative, expertise, and authority have been so consistently present across the web that you’re incorporated into the next major training cycle.
Once you’re part of that foundational knowledge, the AI doesn’t need to “look you up.” It already knows you.
This is the holy grail of algorithmic authority.
Whether you are aiming for a short-term win in a search result or a long-term legacy in an LLM, the underlying requirement is the same. The algorithm is always asking three questions:
This is why your strategy must be built on the bedrock of entity SEO, N-E-E-A-T-T – my expansion of Google’s E-E-A-T framework that adds notability and transparency – and grounded in topical authority.
Every signal you create across your digital ecosystem must work to answer those three questions with overwhelming clarity and proof.
The game is already evolving. AI is moving beyond simply answering questions to acting on our behalf.
I saw this firsthand when I used ChatGPT to help me buy guitar pedals.
Within 15 minutes, it took me from awareness to a confident decision and a final purchase. It acted as my personal shopping assistant.
This is the future of AI assistive agents.
Soon, agents will autonomously book flights, schedule appointments, and purchase products.
For an agent to execute a task on your behalf, its algorithmic confidence in a brand cannot be probabilistic – it must be absolute.
The brand that has built the deepest foundation of understanding and credibility within the algorithmic trinity will be the one the agent chooses.
In this new era, as the legendary football manager Peter Reid memorably put it, “to stand still is to move backwards.”
Your digital strategy must evolve. Stop chasing blue links and start the work of brand-focused algorithmic education.
The key is understanding that the traditional web index is the fuel that feeds all three components of the algorithmic trinity.
Your entire digital footprint must be organized to be frictionless for bots to discover, select, crawl, and render, digestible for them to confidently extract, index, and annotate, and irresistibly tasty for the algorithms that follow.
Importantly, the algorithm evaluates N-E-E-A-T-T on three levels:
This brings us to the most critical part of the process.
You must understand the bot’s seven fundamental steps – discover, select, crawl, render, extract, index, and annotate – because this is the only path into the web index and the only way to reach the top of the pile for the algorithmic trinity.
As I learned from my conversations with Bing’s Canel, the annotation phase is essential.
Algorithms do not select content by re-reading the content itself. They select it by reading the annotations – the “post-its” the bot created.
They prioritize those annotations based on two factors:
This is why the “digestible” and “tasty” parts of the strategy are non-negotiable.
To thrive in the explicit, implicit, and ambient landscape, you must execute this holistic strategy and become the trusted, top-of-algorithmic-mind answer.
The AI resume – especially one that holds up to a deep “rabbit hole” of explicit research – is not the goal. It is a byproduct of doing the work correctly.
The brands that succeed will be those that treat algorithms as powerful entities to be taught through a methodical curriculum.
Start building that curriculum today, because the AI assistive agents of tomorrow are already studying.
One of the major things we talk about with large language models (LLMs) is content creation at scale, and it’s easy for that to become a crutch.
We’re all time poor and looking for ways to make our lives easier – so what if you could use tools like Claude and ChatGPT to frame your processes in a way that humanizes your website work and eases your day, rather than taking the creativity out of it?
This article tackles how to:
These are all tasks we could do manually, and sometimes still might, but they’re large-scale, data-based efforts that lend themselves well to at least some level of automation.
And having this information will help ground you in the customer, or in the market, rather than creating your own echo chamber.
One of the fantastic features of LLMs is their ability to:
Unless you’re at a global enterprise, it’s unlikely you’d have a data team with that capability, so the next best thing is an LLM.
And for this particular opportunity, we’re looking at customer feedback – because who wants to read through 10,000 NPS surveys or free text feedback forms?
Not me. Probably not you, either.
You could upload the raw data directly into the project knowledge and have your LLM of choice analyze the information within its own interface.
However, my preference is to upload all the raw data into BigQuery (or similar if you have another platform you prefer) and then work with your LLM to write relevant SQL queries to slice and analyze your raw data.
I do this for two reasons:
When raw data is uploaded directly into an LLM and analysis questions are asked directly into the interface, I tend to trust the analysis less.
It’s much more likely it could just be making stuff up.
When you have the raw data separated out and are working with the LLM to create queries to interrogate the data, it’s more likely to end up real and true with insights that will help your business rather than lead you on a wild goose chase.
Practically, unless you’re dealing with terrifyingly large datasets, BigQuery is free (though to set up a project, you might need to add a credit card).
And no need to fear SQL either when you’re pair programming with an LLM – it will be able to give you the full query function.
My workflow in this tends to be:
Dig deeper: 7 focus areas as AI transforms search and the customer journey in 2026
It seems to be a common trait among subject matter experts that they’re time poor.
They really don’t want to spend an hour talking with the marketing person about a new feature they’ve already discussed with the manufacturer for the last eight months.
And who could blame them? They’ve probably talked it to death.
And yet we still need that information, as marketing folk, to strategize how we present that feature on the website and give customers helpful detail that isn’t on the spec sheet.
So how do we get ahold of our experts?
Create a custom GPT that acts as an interviewer.
Fair warning, to get the most out of this process, you’ll want a unique version for each launch, product, or service you’re working on.
It may not need to be as granular as per the article, but it may end up being that specific.
To do this, you’ll need at least a ChatGPT Plus subscription.
Instructions will depend on your industry and the personality of your subject matter experts or sales team.
They should include:
Once we do that, we’ll want to test it ourselves and pretend to be an SME. Then we refine the instructions from there.
This way, you’ll be able to reach your SMEs in the five minutes they have between calls.
And you can use an LLM to extrapolate the major points, or even an article draft, from their answers.
Dig deeper: SEO personas for AI search: How to go beyond static profiles
This one may be a bit sneaky and may require a bit of gray thinking.
But there are a few things you can do with competitive data at scale that can help you understand the competitive landscape and your gaps within it, like:
Dig deeper: How to use competitive audits for AI SERP optimization
Pair programming with an LLM to ground yourself in your customer with large data sets can be an endless opportunity to get actionable, specific information relatively quickly.
These three opportunities are solid places to start, but they’re by no means the end.
To extrapolate further, think about other data sources you own or have access to, like:
While it may be tempting to include Google Analytics or other analytics data in this, err on the side of caution and stick with qualitative or specifically customer-led data rather than quantitative data.
Happy hunting!
Google expands Preferred Sources to English users worldwide and launches pilot AI partnerships with publishers.
The post Google Expands Preferred Sources & Publisher AI Partnerships appeared first on Search Engine Journal.
Semify acquired Dragon Metrics, a Hong Kong–based SEO, AI, and PPC reporting platform known for its international reach. The move boosts Semify’s reporting strength and AI optimization tools as the search landscape shifts more toward AI.
Why we care. Dragon Metrics customers can expect business as usual, plus faster product updates. Co-founder Simon Lesser said on LinkedIn that the platform will stay a standalone brand with the same contacts and product experience. Customers also gain access to Semify’s growing AI optimization methodology and future software integrations.
About the acquisition. Semify announced on Dec. 8 that it is acquiring Dragon Metrics:
The details. Lesser will join Semify as chief product officer and lead its AI optimization product strategy.
Instagram launched Your Algorithm in the U.S. today, a tool that lets people see – and directly edit – the topics shaping their Reels recommendations.
Why we care. This could reshape how users discover content. When people signal interest in specific niches, hobbies, or brands – from running shoes to vintage clothing to home organizers – Instagram may surface more of that content, boosting reach for brands that publish relevant Reels.
How it works. A new Reels icon opens a personalized list of topics (e.g., sports, thrifting, horror movies, pop music, chess, day in the life, college football, skateboarding) Instagram believes “you’ve been into” lately, generated by Meta’s AI. You can:
What’s next. The tool will expand to Explore, the search tab, and other surfaces, with a global English rollout planned, Instagram said. These controls will extend beyond Reels in the future.
What Instagram is saying. Tessa Lyons, Instagram’s vice president of product, told Fast Company:
Similar to TikTok’s feature. TikTok introduced a Manage Topics tool last year, but its controls are broader and less personalized. Users choose from generic categories like travel or current affairs, while Instagram’s list is individualized and driven by each person’s recent activity.
The announcement. Adam Mosseri, head of Instagram, shared the news via Instagram.
Google is rolling out Preferred Sources globally after launching it in the US and India last August. Google also announced Spotlighting subscriptions, a feature that highlights links from your news subscriptions in Gemini and will soon appear in Google Search through AI Overviews and AI Mode.
Preferred Sources. Preferred Sources let searchers star publications in the Top Stories section of Google Search, and Google uses that signal to show more stories from those starred outlets. The feature entered beta in June, rolled out in the U.S. and India in August, and is now expanding globally.
Robby Stein, VP of Product, Google Search, wrote:
When someone chooses a preferred source, they click through to that site twice as often on average, Google told me.
How it works. You click the star icon to the right of the Top Stories header in search results. After that, you can choose your preferred sources – assuming the site is publishing fresh content.
Google will then start to show you more of the latest updates from your selected sites in Top Stories “when they have new articles or posts that are relevant to your search,” Google added.
Spotlighting subscriptions. Google also announced Spotlighting subscriptions, a new feature that highlights links from your news subscriptions, “making it easier to spot content from sources you trust and helping you get more value from your subscriptions.”
Why we care. Top Stories can drive meaningful traffic to publishers, so becoming a reader’s preferred source can be a valuable boost. You might look for a tasteful way to encourage loyal visitors to star your site—such as adding a small icon in your site or newsletter that reminds readers they can set your publication as a preferred source. With any luck, this gives publishers more ways to capture traffic and revenue.
Google is updating the links in AI Mode to make them more inviting to click. Google also expanded the Web Guides Labs test to the All tab, though you still need to opt in to try it.
Links in AI Mode. Google is “increasing the number of inline links in AI Mode, and updating the design of those links to make them more useful,” Robby Stein, VP of Product, Google Search, wrote.
What it looks like. Here’s a screenshot.
Expanding Web Guide to All tab. Google first added the Web Guide feature in the Web tab for those who opted into the search experiment. Now Google is rolling out Web Guide to the All tab. You still need to be in the search experiment.
What is Web Guide. Web Guide groups web links in helpful ways, pulling together pages that address specific parts of your query, Google said. It also uses a query fan-out technique – similar to AI Mode – by firing off multiple related searches at once to surface the most relevant results.
Google told me:
Why we care. Encouraging clicks from Google’s AI experiences, including AI Mode and AI Overviews, is welcome. We hope it drives more traffic to publishers and websites. Web Guide is also an experience that many in the search marketing community value. We’d like to see Google release it more broadly, without requiring a Search Labs opt-in.
Google Discover is less aligned to Google Search ranking, Andy Almeida from the Google Trust and Safety team, said yesterday at the Google Search Central Live event in Zurich yesterday.
A slide he posted on how existing systems help the Google Discover team solve problems, the slide says:
“Minimal alignment to search ranking gives us the tools we need to combat emerging abuse.”
What this means. It seems that this is an admission that Google Discover is not using Google’s search systems as tightly as it may have in the past for when it comes to combating abuse on that platform.
I asked Andy Almeida at the event what this means, and he said it means that Google Discover aims to surface lesser-known, less-established, and smaller publishers in the Discover feed. So while Google Search may not rank these smaller and less known publishers, Google Discover does. It does this by relying less on Google Search ranking and more on its own systems.
The spam problem. As I mentioned, Google Discover has a big AI spam problem. You have new sites using expired domains, or new throwaway domains, and finding loopholes to get spammy content surfacing in Google Discover. This is something that does not work as well in Google Search.
In 2019, Google told us that the core ranking systems do impact Google Discover, specifically that being hit by a core update can impact a site’s visibility in Google Discover. This seems like a step back from this.
Why we care. As we also said, Google is working hard on fixing the spam issues on Google Discover. Tweaking the balance of allowing new or lesser-known sites to perform well on Google Discover, while also preventing spam from showing up, is hard. Google is working on that now and hopes to find a solid solution for it.
But it also means Google is looking for ways to reward smaller publishers, who may write more about niche topics, within Google Discover. This is a good thing for smaller, and upcoming publishers – if Google can also solve the spam problem on Google Discover.
Google isn’t rewarding whoever buys the most ads or uploads the glossiest photos. It’s rewarding the business that matches what people expect in the moment.
That’s why the old checklist approach to local SEO breaks down – it assumes every customer behaves the same.
In other words, Google does play favorites, the “signal-fit” kind. Google’s ranking system isn’t swinging blindly; it’s tuned to intent, behavior, and category nuance.
However, recent trends call that old assumption into question.
A single formula doesn’t guide Google’s Local Pack – it’s shaped by how people actually search.
The notion that a generic playbook can successfully deliver the same results for a burger joint and a dental office simply doesn’t pass muster, especially when search is continually being tailored to every individual.
Yext’s analysis of 8.7 million Google Business Profiles across five U.S. industries cuts through the myth that brand size or ad budget secures visibility. (Disclosure: I’m the senior director of Yext Research.)
What actually moves the needle is “signal fit” – how closely a listing aligns with local users’ expectations.
Review cadence, photo quality, and profile completeness all matter, but not in the same way everywhere. Google’s weighting of these features changes across industries and even geographical regions.
These granular insights underscore the fundamental truth that Google is indeed exhibiting preferences, but these preferences are rooted in the listing’s ability to precisely match local context and the user’s immediate needs.
The takeaway for multi-location brands is simple: you can’t brute force your way into the Local Pack. Each industry requires a distinct strategy, tuned to the signals that matter most there.
The concept of “signal-fit” is perhaps best understood through its industry-specific expressions, where Google’s algorithm adapts to the unique expectations of consumers.
Regional differences don’t rewrite the rules, but they do bend them.
In the Northeast, restaurants see stronger results when social media links are present, while in some areas, healthcare listings benefit less from photos.
These patterns serve as a reminder that Google’s idea of “relevance” is always local.
Google Business Profile optimizations vary by vertical.
Treating every location the same may simplify operations, but it costs visibility where it matters most.
Applying the same checklist across every location will cost you customers (and revenue). Marketers must continually re-evaluate their local SEO strategies.
The era of the universal checklist is over; the future belongs to the agile.
Google is always learning from its users’ behavior and dynamically adjusting to them.
Generic SEO playbooks have a natural limitation, and that limitation will ultimately cost you revenue.
“Best practices” may hedge against being invisible, but they won’t deliver steady wins in competitive environments.
As artificial intelligence continues to reshape the discovery process by condensing choices into concise answers and confident suggestions, the aperture on who gets seen will only narrow.
A hyper-localized GBP strategy will not merely be a competitive advantage; it will be a foundational differentiator.
Google’s Local Pack algorithm already behaves like an AI-powered recommendation engine – rewarding relevance, not routine. For marketers, that means it’s time to transcend generic approaches and embrace the power of precision in local SEO.
The brands that align with localized consumer signals will keep winning visibility long after the playbook changes again.
The danger isn’t doing the wrong thing. It’s doing the same thing everywhere.
With generative AI tools attracting hundreds of millions of users and AI-enhanced results appearing in more search experiences, the way people discover brands is changing. Traditional SEO metrics alone no longer capture this full picture.
Welcome to the era of generative engine optimization (GEO). If you aren’t tracking your brand’s visibility across AI search engines, you’re flying blind.
The numbers are striking:
Unlike traditional search, where you fight for spots on a results page, AI search engines like ChatGPT, Claude, Gemini, and Perplexity deliver direct answers and cite only a few sources. If your brand isn’t mentioned, you may be invisible to users who rely on AI-generated answers.
This is where a GEO rank tracker becomes essential. Tools like Geoptie’s free GEO Rank Tracker show you exactly where your brand stands across major AI platforms.
A GEO rank tracker measures how often your brand appears, gets cited, and is recommended across AI-powered search platforms. Unlike traditional rank trackers that focus on your position on search engine results pages, GEO tracking zeroes in on these metrics that actually matter in the AI era:
In traditional SEO, you optimize for where you appear in a list of search results. In GEO, you optimize for whether AI mentions you at all – and what it says when it does. Geoptie helps brands navigate this shift with a full suite of GEO tools.
If you still rely on traditional rank tracking tools, you’re measuring yesterday’s game:
When evaluating your AI search visibility, focus on these core metrics:
Getting started with GEO tracking requires a systematic approach:
Start by mapping the questions your potential customers ask at each stage of their journey. Unlike keyword research, prompt research focuses on the natural language questions people type into AI chatbots.
AI search is fragmented across multiple platforms, each with different strengths and user bases:
AI responses vary by geography. If you serve multiple markets, track your visibility in each target country.
Understanding your share of voice against competitors shows whether you’re gaining or losing ground in AI search visibility.
For brands looking to get started fast, Geoptie’s free GEO Rank Tracker offers an easy entry point. Add your domain, target country, and keyword, and the tool shows your rankings across Gemini, ChatGPT, Claude, and Perplexity – giving you an instant snapshot of your AI search presence.
Understanding what your AI visibility data means is crucial for taking action:
If AI platforms often mention your brand but rarely cite your site, your content may not have the structured, authoritative format AI engines prefer. Strengthen it with statistics, expert quotes, and clear source attribution.
Each AI platform draws from different data sources. If you’re visible in ChatGPT but absent in Perplexity, investigate which sources each platform favors and adjust your distribution strategy to match.
AI systems continually retrain on new content. If your visibility slips, competitors may be creating more citation-worthy material, or your content may simply be getting stale. Regular updates and fresh publishing are essential.
When you spot queries where competitors appear, but you don’t, you’ve found optimization opportunities. Analyze what makes their content citation-worthy, then create competing assets.
A GEO rank tracker gives you the data, but turning those insights into stronger visibility takes strategic action:
For comprehensive AI search optimization beyond rank tracking, Geoptie’s GEO dashboard offers tools for content analysis, competitive intelligence, technical GEO audits, and ongoing performance monitoring.
The shift to AI search is already here. Brands that ignore their AI visibility risk:
The barrier to entry for GEO tracking is lower than you might expect. Here’s a simple plan to get started:
We’re still in the early days of GEO. Brands that start understanding and optimizing for AI search now will gain advantages that compound over time.
Key trends to watch:
Your visibility in AI-generated answers will increasingly determine whether customers discover your brand. A reliable GEO rank tracker is becoming core infrastructure for modern marketing.
Google added a new section to the core updates search developer documentation that confirms it releases smaller core updates without announcing those updates. Google has told us this before, but has now added it explicitly to the search documentation.
What is new. Google added this new paragraph:
However, you don’t necessarily have to wait for a major core update to see the effect of your improvements. We’re continually making updates to our search algorithms, including smaller core updates. These updates are not announced because they aren’t widely noticeable, but they are another way that your content can see a rise in position (if you’ve made improvements).
What Google said. Google explained this section was added “To clarify that site owners that make content improvements can see a rise in position in Google Search results without having to wait for the next major core update.”
In fact, Danny Sullivan, the former Google Search Liaison, told us this in August 2019. He said then:
Broad core updates tend to happen every few months. Content that was impacted by one might not recover – assuming improvements have been made – until the next broad core update is released.
However, we’re constantly making updates to our search algorithms, including smaller core updates. We don’t announce all of these because they’re generally not widely noticeable. Still, when released, they can cause content to recover if improvements warrant.
A larger core update is coming soon. Today at the Google Search Central Live event in Zurich, John Mueller from Google said a core update is being worked on and hopes it will be released soon. He added, he would be surprised if it was released within a couple of weeks. But he had nothing to announce.
Why we care. This just confirms what we already know, that Google is often pushing out smaller core updates. But do expect a new core update to come sooner, rather than later. And then, you may see even bigger changes to the search results and its rankings.
Google updated its core updates documentation to say smaller core updates happen on an ongoing basis, so sites can improve without waiting for named updates.
The post Google Confirms Smaller Core Updates Happen Continuously appeared first on Search Engine Journal.
Most AI chats have no commercial intent, users usually ask short questions, and most conversations end after just two turns. Those findings come from a recent analysis by Dan Petrovic, director of AI SEO agency Dejan, who examined millions of conversational turns to show how people actually use AI assistants.
Why we care. As SEOs and marketers race to “optimize for AI,” Petrovic’s analysis suggests the industry is misreading how people actually use AI assistants. Most chats function as multi-step tasks, not keyword-style queries. And users aren’t flooding AI with “buy” queries – they’re exploring problems and comparing options.
By the numbers. Petrovic analyzed 4.4 billion characters, 613 million words, and 3.9 million conversation turns:
How people actually use AI assistants. Petrovic classified 24,259 sessions across 42 intent categories and found that most AI chats aren’t commercial – 64.6% sit outside any purchase funnel. Users write, brainstorm, plan, learn, analyze, or simply chat. Here’s the breakdown:
35.4% of chats showed any commercial intent. And most were early funnel. Other findings:
Bottom line. AI assistants are used far more for creation, cognition, and conversation than for commerce.
The report. How do people use AI assistants?
Google cites retailers only 4% of the time, while ChatGPT does it 36% of the time. That 9x gap means shoppers on each platform get steered in very different ways, according to new BrightEdge data.
Why we care. Millions of shoppers now turn to AI for deals and gift ideas, but product discovery works differently on the two leading AI search platforms. Google leans on what people say, while ChatGPT focuses more on where you can buy it.
What each AI prioritizes. Google AI Overviews cite YouTube reviews, Reddit threads, and editorial sites, while ChatGPT cite retail giants like Amazon, Walmart, Target, and Best Buy.
Google AI Overviews prioritize:
ChatGPT prioritizes:
The citation divide. On Google, retailers appear only about 4% of the time. Its citations lean toward user-generated content and expert reviews. Google AI Overviews serve more as a research tool than a purchase assistant. Top sources included:
On ChatGPT, retailers appear about 36% of the time. ChatGPT acts as both the explainer and the shopping assistant, so retailer links show up far more often. Its top sources included:
About the data. BrightEdge analyzed tens of thousands of ecommerce prompts across Google AI Overviews and ChatGPT during the 2025 holiday shopping season, then extracted and categorized citation sources. Domains were classified by type (retailer, UGC/social, editorial, brand) and compared across identical prompts.
Generative systems like ChatGPT, Gemini, Claude, and Perplexity are quietly taking over the early parts of discovery – the “what should I know?” stage that once sent millions of people to your website.
Visibility now isn’t just about who ranks. It’s about who gets referenced inside the models that guide those decisions.
The metrics we’ve lived by – impressions, sessions, CTR – still matter, but they no longer tell the full story.
Mentions, citations, and structured visibility signals are becoming the new levers of trust and the path to revenue.
This article pulls together data from Siege Media’s two-year content performance study, Grow and Convert’s conversion findings, Seer Interactive’s AI Overview research, and what we’re seeing firsthand inside generative platforms.
Together, they offer a clearer view of where visibility, engagement, and buying intent are actually moving as AI takes over more of the user journey – and has its eye on even more.
In a robust study, the folks at Siege Media analyzed two years of performance across various industry blogs, covering more than 7.2 million sessions. It’s an impressive dataset, and kudos to them for sharing it publicly.
A disclaimer worth noting: the data focuses on blog content, so these trends may not map directly to other formats such as videos, documentation, or landing pages.
With that in mind, here’s a run-through of what they surfaced.
Pricing and cost content saw the strongest growth over the past two years, while top-of-funnel guides and “how-to” posts declined sharply.
They suggest that pricing pages gained ground at the expense of TOFU content. I interpret this differently.
Pricing content didn’t simply replace TOFU because the relationship isn’t zero-sum.
As user patterns evolve, buyers increasingly start with generative research, then move to high-intent queries like pricing or comparisons as they get closer to a decision.
That distinction – correlation vs. causation – matters a lot in understanding what’s really changing.
The data shows major growth in pricing pages, calculators, and comparison content.
Meanwhile, guides and tutorials – the backbone of legacy SEO – took a sharp hit.
Keep that drop in mind. We’ll circle back to it later.
Interestingly, every major content category saw an increase in engagement. That makes sense.
As users complete more of their research inside generative engines, they reach your site later in the journey or for additional details, when they’re already motivated and ready to act.
If you’re a data-driven SEO, this might sound like a green light to focus exclusively on bottom-of-funnel content.
Why bother with top-of-funnel “traffic” that doesn’t convert?
Leave that for the suckers chasing GEO visibility metrics for vanity, right?
But of course, this is SEO, so I have to say it …
Did you expect me to say, “It depends?”
Here’s a question instead: when that high-intent user typed the query that surfaced a case study, pricing page, or comparison page, where did they first learn the brand existed?
Dig deeper: AI agents in SEO: What you need to know
I can’t believe I’m saying this, but you’ll have to keep making TOFU content.
You might need to make even more of it.
Let’s think about legacy SEO.
If we look back – waaaaay back – to 2023 and a study from Grow and Convert, we see that while there is far more TOFU traffic…
…it converts far worse.
Note: They only looked at one client, so take it with a grain of salt. However, the direction still aligns with other studies and our instincts.
This pattern also shows up across channels like PPC, which is why TOFU keywords are generally cheaper than BOFU.
The conversion rate is higher at the bottom of the funnel.
Now we’re seeing this shift carry over to generative engines, except that generative engines cover the TOFU journey almost entirely.
Rather than clicking through a series of low-conversion content pieces as they move through the funnel, users stay inside the generative experience through TOFU and often MOFU, then click through or shift to another channel (search or direct) only when it’s time to convert.
For example, when I asked ChatGPT to help me plan a trip to the Outer Banks:
After a dozen back-and-forths planning a trip and deciding what to eat, I wanted to find out where to stay.
That journey took me through many steps and gave me multiple chances to encounter different brands and filtering or refinement options.
I eventually landed on my BOFU prompt, “Some specific companies would be great.”
From there, I might click the links or search for the company names on Google.
What matters about this journey – apart from the fact that my final query would be practically useless as insight in something like Search Console – is that throughout the TOFU and MOFU stages, I was seeing citations and encountering brands I would rely on later.
Once I switched into conversion mode, I wanted help making decisions. That’s where I’m likely to click through to a few companies to find a rental.
So, when we read statistics like Pew’s finding that AI Overviews reduce CTR by upwards of 50%, and then consider what happens when AI Mode hits the browser, it’s easy to worry about where your traffic goes. Add to that ChatGPT’s 700 million weekly active users (and growing):
And according to their research on how users engage with it:
We can see a clear TOFU hit and very little BOFU usage.
So, on top of the ~50% hit you may be taking from AI Overviews, 700+ million people are going to ChatGPT and other generative platforms for their top-of-funnel needs.
I did exactly that above with my trip planning to the OBX.
Dig deeper: 5 B2B content types AI search engines love
The good news is that while that vacation rental company or blue widget manufacturer might not see me on their site when I’m figuring out what to do – or what a blue widget even is – I’m still going to take the same number of holidays and buy the same number of products I would have without AI Overviews or ChatGPT, Claude, Perplexity, etc.
Unless you’re a publisher or make money off impressions, you’ll still have the same amount of money to be made.
It just might take fewer website visits to do it.
Traffic at the bottom of the funnel is holding steady for now (more on that below), but the top of the funnel is being replaced quickly by generative conversations rather than visits.
The question is whether being included in those conversations affects your CTR further down the funnel.
The folks at Seer Interactive found that organic clicks rose from 0.6% to 1.08% when a site was cited in AI Overviews.
And while the traffic was far lower, ChatGPT had a conversion rate of 16% compared with Google organic’s 1.8%.
If we look at the conversion rate for organic traffic at the bottom of the funnel – which we saw above – it was 4.78%.
Users who engage with generative engines clearly get further into their decision-making than users who reach BOFU queries through organic search.
But why?
While I can’t be certain, I agree with Seer’s conclusion that AI-driven users are pre-sold during the TOFU stage.
They’ve already encountered your brand and trust the system to interpret their needs. When it’s time to convert, they’re almost ready with their credit card.
Above, I noted that “traffic at the bottom of the funnel is holding steady for now.”
It’s only fair to warn you that through 2026 and 2027, we’ll likely see this erode.
The same number of people will still travel and still buy blue widgets.
They just won’t book or buy them themselves. And at best, attribution will be even worse than it is today.
I spoke at SMX Advanced last spring about the rise of AI agents.
I won’t get into all the gory details here, but the Cliff Notes are this:
Agents are AI systems with some autonomy that complete tasks humans otherwise would.
They’re rising quickly – it’s the dominant topic for those of us working in AI – and that growth isn’t slowing anytime soon. You need to be ready.
A few concepts to familiarize yourself with, if you want to understand what’s coming, are:
Dig deeper: How Model Context Protocol is shaping the future of AI and search marketing
Because once AP2 and Computer Use hit critical mass, the click – that sacred metric we’ve optimized for two decades – changes function.
It stops being a navigation step for a human exploring a website and becomes a transactional step for a machine executing a task.
If an agent uses Computer Use to navigate your pricing page and AP2 to pay for the subscription, the human user never sees your bottom-of-the-funnel content.
So in that world, who – or rather, what – are you optimizing for?
This brings us back to the Siege Media data.
Right now, pricing pages and calculators are winning because humans are using AI to research (TOFU and MOFU) and then manually visiting sites to convert (BOFU).
But as agents take over execution, that manual visit disappears. The “traffic” to your pricing page may be bots verifying costs, not humans persuaded by your copy.
This reality pushes value back up the funnel.
If the agent handles the purchase, the human decision – the “moment of truth” – happens entirely inside the chat interface or agentic system during the research phase.
In this world, you don’t win by having the flashiest pricing page.
You win by being the brand the LLM recommends when the user asks, “Who should I trust?”
Your strategy for 2026 requires a two-pronged approach:
As we move toward 2026 and then 2027 (it’ll be here sooner than you think), the “click” will become a commodity more often handled by machines.
The mention, however, remains the domain of human trust. And in my opinion, that’s where your next battle for visibility will be fought.
Time to start – or hopefully keep – making the TOFU.
Evaluating SEO tools has never been more complicated.
Costs keep rising, and promises for new AI features are everywhere.
This combination is hardly convincing when you need leadership to approve a new tool or expand the budget for an existing one.
Your boss still expects SEO to show business impact – not how many keywords or prompts you can track, how fast you can optimize content, or what your visibility score is.
That is exactly where most tools still fail miserably.
The landscape adds even more friction.
Features are bundled into confusing packages and add-on models, and the number of solutions has grown sharply in the last 12 months.
Teams can spend weeks or even months comparing platforms only to discover they still cannot demonstrate clear ROI or the tools are simply out of budget.
If this sounds familiar, keep reading.
This article outlines a practical framework for evaluating your SEO tool stack in 2026, focusing on:
Before evaluating vendors, it helps to understand the forces reshaping the SEO tooling landscape – and why many platforms are struggling to keep pace.
Both traditional and modern SEO tools still center on keyword and prompt tracking and visibility metrics. These are useful, but they are not enough to justify the rising prices.
In 2026, teams need a way to connect searches to traffic and then to MQLs and revenue.
Almost no tool provides that link, which makes securing larger budgets nearly impossible.
(I say “almost” because I have not tested every platform, so the unicorn may exist somewhere.)
With AI platforms like ChatGPT, Claude, and Perplexity – along with the ability to build custom GPTs, Gems, and Agents – teams can automate a wide range of tasks.
That includes everything from simple content rewriting and keyword clustering to more complex competitor analysis and multi-step workflows.
Because of this, SEO tools now need to explain why they are better than a well-trained AI agent.
Many can’t. This means that during evaluation, you inevitably end up asking a simple question: do you spend the time training your own agent, or do you buy a ready-made one?
If you want real impact, your automation shouldn’t be cosmetic.
You can’t rely on generic checklists or basic AI recommendations, yet many tools still provide exactly that – fast checklists with no context.
Without context, automation becomes noise. It generates generic insights that are not tailored to your company, product, or market, and those insights will not save time or drive results.
Teams need automation that removes repetitive work and delivers better insights while genuinely giving time back.
Dig deeper: 11 of the best free tools every SEO should know about
Technical SEO tools remain the most stable part of the SEO stack.
The vendor landscape has not shifted dramatically, and most major platforms are innovating at a similar pace.
Because of this, they do not require the same level of reevaluation as newer AI-driven categories.
That said, budgeting for them may still become challenging.
Leadership often assumes AI can solve every problem, but we know that without strong technical performance, SEO, content, and AI efforts can easily fail.
I will also make one bold prediction – we should be prepared to expect the unexpected in this category.
These platforms can crawl almost any site at scale and extract structured information, which could make them some of the most important and powerful tools in the stack.
Many already pull data from GA and GSC, and integrating with CRM or other data platforms may be only a matter of time.
I see that as a likely 2026 development.
To evaluate tools effectively, it helps to focus on the capabilities that drive real impact. These are the ones worth prioritizing in 2026.
Data analysis will play a much bigger role.
Tools that let you blend data from GA, GSC, Salesforce, and similar sources will move you closer to the Holy Grail of SEO – understanding whether a prompt or search eventually leads to an MQL or a closed-won deal.
This will never be a perfect science, but even a solid guesstimation is more useful than another visibility chart.
Integration maturity is becoming a competitive differentiator.
Disconnected data remains the biggest barrier between SEO work and business attribution.
Traditional SERP intelligence remains essential. You still need:
You also need AI SERP intelligence, which analyzes:
In an ideal world, these two groups should appear side by side and provide you with a 360-degree view of your performance.
Prioritize tools that:
These are just some of the examples of practical AI that can really guide you and save you time.
This applies to SEO experts who work with websites in languages other than English.
Many tools are still heavily English-centric. Before choosing a tool, make sure the databases, SERP tracking, and AI insights work across languages, not just English.
Hidden pricing, confusing bundles, and multiple add-ons make evaluation frustrating.
Tools should communicate clearly:
Many vendors change these things quietly, which makes calculating the investment you need difficult and hard to justify.
Dig deeper: How to choose the best AI visibility tool
AI writing
If you can’t input detailed information about your brand, product, and persona, the content you produce will be the same as everyone else’s.
Many tools already offer this and can make your content sound as if it were written by one of your writers.
So the question is whether you need a specialized tool or if a custom GPT can do the job.
Prompt tracking
It’s positioned as the new rank tracking, but it is like looking at one pixel of your monitor.
It gives you only a tiny clue of the whole picture.
AI answers change based on personalization and small differences in prompts, and the variations are endless.
Still, this tactic is helpful in:
Large keyword databases
They still matter for directional research, but are not a true competitive differentiator.
Most modern tools have enough coverage to guide your strategy.
The value now stems from the practical insights derived from the data.
Understanding features is only half the equation.
The real challenge is knowing how to evaluate specialized tools and all-in-one platforms without losing your sanity or blocking your team for weeks.
After going through this process for the tenth time, I’ve found an approach that works for me.
I always begin my evaluation on the pricing page.
With one page, you can get a clear sense of:
Even if you need a demo to get the exact price, the framework should still be relatively transparent.
No checklist will show you more than trying your regular BAU tasks with a couple of tools in parallel.
This reveals:
How difficult the setup is – including whether the learning curve is huge.
I work in a small team, and a tool that takes many hours just to set up likely will not make my final list.
Not all evaluations can rely on BAU tasks.
For example, when we researched tools for prompt and AI visibility tracking, we tested more than ten platforms.
This capability did not exist in our stack, and at first, we had no idea what to check.
In those cases, you need to define a small set of test scenarios from scratch and compare how each tool performs.
Continue refining your scenarios, because each new evaluation will teach you something new.
Dig deeper: Want to improve rankings and traffic? Stop blindly following SEO tool recommendations
Demos are polished. Reality often is not.
If there is no option for a free trial, either walk away or, if the tool is not too expensive, pay for a month.
Always ask yourself who truly needs to be involved in the evaluation.
For example, we are currently assessing a platform used not only by the SEO team but also by two other teams.
We asked those teams for a brief summary of their requirements, but until we have a shortlist, there is no reason to involve them further or slow the process.
And if your company has a heavy procurement or security review, involving too many people too early will slow everything down even more.
At the same time, involve the whole SEO team, because each person will see different strengths and weaknesses and everyone will rely on the tool.
Many features sound like magic wands.
In reality, the magic often works only sometimes, or it works but is very expensive. To understand what you truly need, always ask yourself:
These questions turn the decision into a business conversation rather than a feature debate and help you prepare your “sales” pitch for your boss.
Support has become one of the most overlooked parts of tool evaluation.
Many platforms rely heavily on AI chat and automated replies, which can be extremely frustrating when you are dealing with a time-sensitive issue or have to explain your problem multiple times.
Support quality can significantly affect your team’s efficiency, especially in small teams with limited resources.
When evaluating tools, check:
A great product with weak support can quickly become a bottleneck.
Once you have a shortlist, the quality of your vendor conversations will determine how quickly you can move forward.
And this may be the hardest part – especially for the introverted SEO leads, myself included.
I’m practical, and I don’t like wasting anyone’s time. I have plenty of tasks waiting, so fluff conversations aren’t helpful.
That’s why I start every vendor call by setting clear goals, limitations, a timeline, and next steps.
Over time, I’ve learned that conversations run much more smoothly when I follow a few simple principles.
If you are evaluating a tool, come prepared to the demo.
Ideally, you should have access to a free trial, tested the platform, and created a list of practical questions.
Showing up unprepared is not a good sign, and that applies to both sides.
For example, I am always impressed when a vendor joins the conversation having already researched who we are, what we do, and who our competitors are.
If you have spoken with the vendor before, directly ask what has changed since your last discussion.
When comparing a few tools, I always ask each vendor for a direct comparison.
These comparisons will be biased, but collecting them from all sides can reveal insights I had not considered and give me ideas for specific things to test.
Often, there is no reason to reinvent the wheel.
Annual contracts reduce administrative work and give vendors room to negotiate, which can lead to better pricing.
Many tools include this information on their pricing pages, and we have all seen it.
Ask about any other nuances that might affect the final price – such as additional user seats or add-ons.
Often, the most effective approach is simply to say:
“This is our budget. This is what we need. Can you support this?”
This works especially well with vendors you have used before because both sides already know each other.
Even if you select a tool, that does not mean you will receive the budget for it.
Proving ROI is especially difficult with SEO tools. But there are a few things you can do to increase your chances of getting a yes.
This shows you have done your homework, not just picked the first thing you found. Present your leadership with:
Providing this view builds trust in your ability to make decisions.
Tools improve efficiency, but they cannot guarantee outcomes – especially in SEO, GEO, or whatever you call it.
Spend time explaining how quickly things are changing and how many factors are outside your control. Managing expectations will strengthen your team’s credibility.
But even with thorough evaluation and negotiation, we still face the same issue: the SEO tooling market has not caught up with what companies now expect.
Let’s hope the future brings something closer to the clarity we see in Google Ads.
Dig deeper: How to master the enterprise SEO procurement process
The next generation of SEO tools must move beyond vanity metrics.
Trained AI agents and custom GPTs can already automate much of the work.
In a landscape where companies want to reduce employee and operational costs, you need concrete business numbers to justify high tool prices.
The platforms that can connect searches, traffic, and revenue will become the new premium category in SEO technology.
For now, most SEO teams will continue to hear “no” when requesting budgets because that connection does not yet exist.
And the moment a tool finally solves this attribution problem, it will redefine the entire SEO technology market.
In 2026 and well beyond, a core part of the performance marketer’s charter is learning to leverage AI to drive growth and efficiency.
Anyone who isn’t actively evaluating new AI tools to improve or streamline their PPC work is doing their brand or clients a disservice.
The challenge is that keeping up with these tools has become almost a full-time job, which is why my agency has made AI a priority in our structured knowledge-sharing.
As a team, we’ve honed in on favorites across creative, campaign management, and AI search measurement.
This article breaks down key options in each category, with brief reviews and a callout of my current pick.
One overarching recommendation before we dive in: be cautious about signing long-term contracts for AI tools or platforms.
At the pace things are moving, the tool that catches your eye in December could be an afterthought by April.
There’s no shortage of tools that can generate creative assets, and each comes with benefits as well as the risks of producing AI slop.
Regardless of the tool you choose, it must be thoroughly vetted and supported by a strong human-in-the-loop process to ensure quality, accuracy, and brand alignment.
Here’s a quick breakdown of the tools we’ve tested:
Our current tool of choice is AdCreative.ai. It’s easy to use and especially helpful for quickly brainstorming creative angles and variations to test.
Like its competitors, it offers meaningful advantages, including:
The usual caveats apply across all creative tools:
Dig deeper: How to get smarter with AI in PPC
There are plenty of workflow automation tools on the market, including long-standing options, like Zapier, Workato, and Microsoft Power Automate.
Our preferred choice, though, is n8n. Its agentic workflows and built-in connections across ad platforms, CRMs, and reporting tools have been invaluable in automating redundant tasks.
Here are my agency’s primary use cases for n8n:
As with any tool, n8n comes with caveats to keep in mind:
Dig deeper: Top AI tools and tactics you should be using in PPC
Most SEOs already have preferred platforms for measurement and insights – Semrush, Moz, SE Ranking, and others.
While many now offer reports on brand visibility in AI search results from ChatGPT, Perplexity, Gemini, and similar tools, these features are add-ons to products built for traditional SEO.
To track how our brands show up in AI search results, we use Profound.
While other purpose-built tools exist, we’ve found that it offers differentiated persona-level and competitor-level analysis and ties its reporting to strategic levers like content and PR or sentiment, making it clear how to act on the data.
These platforms can provide near real-time insights such as:
No matter which tool you choose, the key is to adopt one quickly.
The more data you gather on rapidly evolving AI search trends, the more agile you can be in adjusting your strategy to capture the growing share of users turning to AI tools during their purchase journey.
Dig deeper: Scaling PPC with AI automation: Scripts, data, and custom tools
I like to think most of my content for this publication ages well, but I’m not expecting this one to follow suit.
Anyone reading it a few months after it runs will likely see it as more of a time capsule than a set of current recommendations – and that’s fine.
What does feel evergreen is the need to:
We’re well past head-in-the-sand territory with AI in performance marketing, yet there’s still room for differentiation among teams that move quickly, test strategically, and pivot together as needed.
Dig deeper: AI agents in PPC: What to know and build today
Google added weekly and monthly views to Search Console performance reports. These options give you clearer, longer-term insights instead of relying only on the 24-hour view.
What it looks like. Here are a few photos I took during the announcement at the Google Search Central event in Zurich this morning:
Why we care. This small update gives SEOs, publishers, and site owners access to more detailed data. It can help you pinpoint why your performance shifted in a specific month, week, or day.
Google is being forced to cap all default search and AI app deals at one year. This will end the long-term agreements (think: Apple, Samsung) that helped secure its default status on billions of devices. Just don’t expect this to end Google’s search dynasty anytime soon.
Driving the news. Judge Amit Mehta on Friday called the one-year cap a “hard-and-fast termination requirement” needed to enforce antitrust remedies after his 2024 ruling that Google illegally monopolized search and search ads, Business Insider reported. In September, Mehta ruled on Google search deals:
Why we care. A more fragmented search landscape means user queries could start anywhere. If AI-powered rivals like OpenAI, Perplexity, or Microsoft make even small gains in search, you’ll face a broader and more complicated world to compete in.
Reality check. This is a speed bump, not a shake-up. Google’s cash, brand power, and user habits still give it a big edge in yearly talks.
Google CEO Sundar Pichai explained why understanding AI starts with seeing the bigger picture around it.
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