Category: Opinion

Navigating AI’s Impact on Search: A Guide for Leadership Conversations
Hey there, I know we’re in some murky waters right now. The drop in organic traffic is concerning, and it seems that the little bit of referral traffic we’re getting from LLMs like ChatGPT isn’t making up for it.

The truth is, the belief that “traffic is just coming from different places” isn’t entirely accurate. Sure, the way people search and engage is shifting, but click-through rates are plummeting in almost every sector.

Understandably, there’s a lot of anxiety in the industry about SEO’s future and whether AI will make our roles redundant. Bringing these concerns to the C-suite can be daunting, but now is not the time to shy away.

The reality is, it’s the perfect moment to tackle these issues head-first. Our leadership needs to know what’s happening and most importantly, how we’re responding.

This is a great opportunity to educate, realign expectations, and outline how our SEO strategy is evolving. Schedule that meeting, and let’s get this conversation started.

Here’s my plan to maximize the value of this crucial discussion.

Don’t avoid leadership — address AI visibility head-on

I’m not suggesting you picture leadership in their underwear to make conversations easier. Let’s leave the awkwardness aside.

Instead, show up ready to lead the dialogue. Here’s how to guide the discussion effectively.

Set the tone from the outset. They’ll appreciate you broaching the topic proactively rather than having someone else initiate it later.

Explain things honestly, provide clarity, and avoid sugarcoating the reality of what’s happening.

Let’s dive into the key facts to bring to leadership for a clearer picture.

Why SEO is down and how that impacts business

This is our chance to present the facts clearly rather than invoking fear. An honest overview of how the industry’s changes affect us is vital.

Here are critical events impacting performance:
- Tools like ChatGPT, Gemini, and Perplexity are reshaping user behavior, diverting searches away from Google.
- Google’s AI Overviews (AIOs) are increasing in search result pages, reducing clicks to third-party sites significantly. (Some report a 61% decrease in CTR.)
- Despite LLMs sending some traffic, it’s minimal compared to what’s been lost from traditional search.
- Bing’s AI-powered search summaries had limited impact due to a smaller market share.
Next, give a concise, data-driven picture of what’s changed for us and its impact. If organic traffic has dropped by 30% and revenue dipped, be upfront about it.

Anchor the talks in measurable results and their alignment with our goals. Ensure accuracy with your analytics team.

Here’s the data we need to present.

Share revenue, leads (or key actions), and organic traffic data over time, ideally with year-over-year figures.

These figures ground the discussion in business impact, not mere ranking metrics. Comparing data yearly helps separate seasonality from actual declines.

Export keyword data you’ve been tracking, as it’s valuable for Google and Bing. LLM tracking adds further context.

Rankings shouldn’t be a standalone performance metric. However, in times like these, understanding rankings is crucial for identifying lost demand or search shifts.

Analyze click/impression and CTR data in Google Search Console and Bing Webmaster Tools. Identify if SERPs with decreased CTR showcase AIOs.

This showcases real performance slides or industry-wide impacts. If pages losing clicks also show AI overviews, competitors are likely in the same boat — another crucial piece of the puzzle.

Once you share the business’s current state, brace for questions. Don’t wait for them; steer the narrative. Describe the broader shifts, industry trends, and emerging tech driving these changes. Possible action steps include:
- Fetch traffic estimates and keyword rankings for top competitors. Are they experiencing similar downsides?
- Use Google Trends and Exploding Topics to observe growing or waning interest in topics/products in our industry.
- Utilize AI visibility reports to demonstrate brand presence in active conversation platforms (LLMs).
This isn’t about placing blame. It’s about showing comprehension and adapting to landscape shifts impacting performance.

What we’ve learned so far and where we’re going

Now’s the time to prove that we’re not just diagnosing problems but devising solutions. Leadership might not favor all answers, but they’ll respect your forward-thinking mindset.

Make it clear that, although the rules are changing, our team is swiftly adapting for upcoming search challenges. Then specify your needs, whether it’s budget, headcount, data support, or cross-functional alignment, to execute rather than merely presenting a problem.

Here are strategies to progress:

We’re enhancing our brand’s visibility beyond traditional search, focusing on AI-generated answers and new discovery platforms.

This involves tracking essential buyer queries and understanding our brand’s current position to prioritize content, PR, and partnerships for optimal visibility.

The aim is straightforward: if answers don’t draw clicks, our brand must still appear in those solutions. Consistent mentions/citations across the web facilitate this.

We’re revamping content strategy to stress entities and topics, not just keywords and rankings.

LLMs favor brands with comprehensive, consistent topic coverage and expertise signals. This affects our publishing, content structuring, and PR/product collaborations to build authority. This is SEO content 2.0, demanding effort, but the rewards will be significant.

We’re investing in visibility measurement for both traditional and new search channels.

Google organic traffic isn’t the sole truth anymore. We’re developing reporting to include AI surfaces, social discovery, referrals, and offline demand for a comprehensive perspective.

AI Overviews represent a lasting shift.

This requires recalibrating traffic baselines, forecasts, and targets to account for fewer classic blue link clicks. We plan for a reality where this becomes normal.

“AI Mode” might become Google’s default by 2026.

If more searches receive direct answers from Google, fewer visitors reach us. This alters lead/sales expectations and demands a strategy overhaul, including budgeting.

How we’ll be proactive and adapt to the new search landscape

Having explained what’s happening and how we’re adapting, it’s essential to stress that success requires alignment, resources, and continuous support.

Use this chance to outline needs, making it easier for leadership to approve plans without overwhelming decisions.

Here are essential adjustments to consider.

Search success in the AI era is a new measure; optimization takes time.

Agree upfront on timelines, leading indicators, and reporting frequency. Rankings, traffic, and last-click revenue won’t always align, so patience in adapting is necessary.

Executive backing is crucial for prioritizing long-term brand building over quick wins.

Leadership must accept that essential SEO initiatives may not yield immediate results but are vital for sustained visibility in search and AI-driven spaces.

Flexible budgeting to experiment with channels, content formats, and AI tracking tools.

A part of the marketing budget must focus on trials — from AI tools and data implementation to interactive content and strategic partnerships.

Collaboration with other departments is key to altering organic growth measurement.

SEO can’t work solo. We need analytics for new dashboards and coordinated PR and content efforts to align with significant topics.

This is your moment to lead the AI visibility discussion

You’re not merely reacting. You’re guiding through change. AI and LLMs redefine search, discovery, and interaction. This isn’t panic time, nor a case for the “organic search is dead” mantra. It’s about adaptation.

A crucial step is constant monitoring. A one-time pitch is valuable, but marketing efforts always need measurement. Regularly set an AI visibility update metric alongside standard metrics.

As AI and LLMs progress, leverage measured data to update leadership on changes and adaptations.

By initiating discussions, grounding messages in data, and suggesting actionable plans, your strategic acumen becomes evident to executives.

This shift isn’t solely about SEO; it’s about securing future visibility, trust, and traffic across various environments. Whether it’s Google, ChatGPT, or elsewhere, your focus should be on being present where your customers engage.

Inspired by this post on Search Engine Land.
December 23, 2025
Thriving Brand-Agency Partnerships: Insights for 2026 Success

In today’s ever-evolving landscape, brand-agency partnerships look vastly different than they did just a few years ago, and this evolution will only continue to expand by 2026.

I’ve noticed that internal marketing teams have become more sophisticated, digital channels are increasingly specialized, and the role of agencies shifts away from a one-size-fits-all approach.

Interestingly, the companies reaping the most benefits from agency relationships aren’t necessarily the biggest spenders.

Instead, those that succeed are clear about their specific needs and objectives.

Achieving clarity starts with understanding the true role an agency should play in your organization.

Too often, partnerships fail because expectations and responsibilities weren’t clearly aligned from the beginning.

When this foundational understanding is lacking, even the most robust execution can fall short.

Having worked with thousands of businesses across industries and growth stages, I’ve consistently observed that agency success falls into two distinct partnership models. These models are primarily influenced by company size and internal marketing maturity.

Model 1: Execution-first Partnerships for Large Companies

If your company sees over $50 million in annual online revenue, chances are you already have a capable internal marketing team.

Strategy and planning remain in-house, so what you need from an agency is deep platform expertise and exceptional execution.

At this stage, agencies function as specialist operators that activate roadmaps, optimize channel performance, and bring advanced technical knowledge that’s inefficient to replicate internally.

When performance dips, a powerful agency partner doesn’t default to tweaking tactics.

Instead, they help uncover whether the issue stems from execution, market conditions, or a strategic misstep, offering data to guide corrective measures.

Model 2: Integrated Growth Partners for Small to Mid-Size Companies

For companies under $50 million in annual revenue, the agency dynamic shifts.

Internal teams might be lean or still cultivating core digital expertise.

In these situations, agencies do more than execute; they shape your entire growth strategy.

An ideal agency acts as an extension of your marketing team, guiding platform selection, crafting cross-channel strategies, and more.

For growing businesses, this integration provides access to senior-level expertise, balancing speed, strategy, and financial constraints effectively.

Finding the Right Agency Partner

I’ve seen many companies approach agency selection improperly.

Ditch the RFPs

Large companies often rely on the request for proposal (RFP) process, which tends to favor vendors skilled in documentation over performance-driven results.

Instead, I recommend using your professional network. If you’re in charge of a large marketing department, you likely know several professionals who can provide referrals to standout agencies.

Smaller businesses should seek advice from peers about reliable vendors, then check reviews to confirm their findings.

While no agency is perfect and all will have some unhappy clients, patterns of negative reviews are a solid indicator to avoid those agencies.

Request an Audit

Upon narrowing down potential partners, I suggest asking for an audit of your current marketing setup.

Most digital marketing agencies conduct these audits for free, offering honest and constructive feedback.

Depending on your company’s size, audits might vary, with larger firms focusing on specific platforms and smaller ones requiring full-funnel evaluations.

This information helps evaluate how the partnership will integrate with existing processes, paving the way for effective collaboration.

The selection process inherently includes finding partners that mesh well with your internal processes—critical to long-term success.

Setting Achievable Goals

After selecting an agency partner, the next step is defining coherent goals aligned with your business objectives.

Unfortunately, I’ve observed that many leaders set goals disconnected from their business aims, straining the agency relationship from the get-go.

A robust agency questions your goals pre-contract, urging you to adjust expectations realistic to your context and aspirations.

Your chosen partner should grasp your business’s economics and help ensure marketing goals are aligned with broader business objectives.

Maintaining a Productive Partnership

Once everything is underway, you must keep your agency accountable, which involves regular reviews and tracking progress against initial audit benchmarks.

Contract Length

Large enterprises often sign 12-month contracts for stability, but smaller firms might benefit from a more flexible three-month commitment that auto-renews.

In cases where everything seems perpetually smooth, consider that growth might be stagnating, as healthy conflict is a sign of challenge and progress.

Ongoing Accountability

Regularly reviewing opportunities against your agency’s initial audit findings not only keeps progress on track but also provides vital context for adapting strategies.

Context is key, especially if your industry’s dynamics affect your agency’s work—awareness of broader market trends is crucial for realistic appraisal.

Innovation and Testing

Your agency should consistently suggest fresh ideas, especially for smaller businesses, while larger companies should fund dedicated innovation budgets.

Effective agency partnerships without innovation risk falling behind competitors more willing to explore uncharted avenues.

Ultimately, understanding what’s upcoming and strategically positioning your business will keep you competitive.

When to Make an Agency Change

Occasionally, a brand-agency partnership doesn’t thrive. Trust your instincts if you feel things could improve or something is amiss.

Your Business Isn’t Growing

Marketing should focus on acquiring new-to-brand customers. If growth stalls while your industry maintains, it’s time to reassess your agency’s role.

Your Agency Isn’t Pushing Innovation

If new ideas aren’t forthcoming or you’re not exploring novel methods to engage customers, seek an external audit to identify gaps.

Your Agency Can’t Explain Performance

An inability to contextualize performance suggests a knowledge gap in your sales funnel, where interconnected activities impact overall success.

For smaller businesses, agents should grasp comprehensive marketing operations and how various elements influence each other.

The Marketing Reality Check

Great marketing can’t compensate for a flawed business model. Successful growth stems from the synergy of good business, leadership, and agency collaboration.

If any component is lacking, marketing falls short of potential. Meaningful growth arises when agency roles align with specific business needs.

Agency selection is an ongoing journey involving ongoing dialogue, accountability, and refinement, even when this involves constructive disagreements.

Inspired by this post on Search Engine Land.

December 23, 2025
How SEO Fundamentals Beat AI in Driving Your Website Traffic

I’ve been observing how AI is transforming search, yet the timeless principles of SEO still seem to bring in the majority of traffic. It’s fascinating to look at data that show which strategies really work.

Generative AI is a huge trend right now. It’s featured in every conference and is all over my LinkedIn. Businesses, mine included, are rethinking organic search.

We’re all in a race to optimize for AI Overviews, work on vector embeddings, and reconfigure content models around LLMs. But what’s less talked about is the simple truth: AI isn’t yet the primary driver of web traffic for most of us.

While AI-driven search is gaining momentum, the LLM platforms collectively account for just a tiny fraction, about 2-3%, of the organic traffic that Google alone provides.

However, I’ve noticed that many teams, maybe even yours, are investing more energy in AI strategies instead of reinforcing essential SEO fundamentals that still deliver tangible results. Focusing too much on the future means we’re not making the most of today’s opportunities.

In my experience, looking closely at proven SEO tactics and real-world data can highlight how they still effectively move the needle today.

Quick SEO Wins Still Deliver Substantial Gains

It’s easy to overlook minor updates when we’re caught up with trends like vector embeddings and semantic SEO. Yet, these small changes can have a significant impact.

Take title tags, for instance. They’re among the simplest and most effective SEO tools. I’ve seen many websites fail to use them effectively, often neglecting to target the right keywords, include key variations, or use any keywords at all.

Just recently, a simple change of adding “& [keyword]” to a client’s homepage title tag resulted in a surge in keyword rankings, clicks, and impressions. No other changes were made, yet the results were significant.

Combining this with other strategies like on-page copy edits, internal linking, and backlinks can lead to ongoing growth. It might sound basic, but these tactics continue to work wonders. Don’t let advanced GEO strategies blind you to simple, impactful tactics.

The Importance of Content Freshness and Authority

The rise of AI might have pushed some tactics like the skyscraper technique into the shadows.

This approach involves crafting superior content for keywords and topics that are already ranking, aiming to outperform existing results. While the internet is flooded with similar content, focusing on keyword authority and freshness can be incredibly effective.

I’ve witnessed this success multiple times. Recently, a client’s article on a well-established topic quickly climbed to the second spot, generating new clicks and impressions almost instantly.

The success was due to the site’s strong authority and because much of the competing content was outdated. Although this strategy may not suit every situation, ignoring it could mean missing out on clear wins.

User Experience: A Key Conversion Lever

Although there’s buzz around AI-driven shopping experiences, the core principles of website optimization remain irreplaceable. Some argue that AI will soon take over interactions and conversions, but this is far from the present reality.

Many websites still rely on traditional search-driven traffic and website-based conversions. Whether visitors come from organic search, paid ads, AI referrals, or direct, what matters is a fast site, an excellent user experience, and a well-defined conversion funnel.

Optimizing these aspects can lead to remarkable performance gains, as I’ve seen through a simple CTR test with a client, which yielded impressive results.

Brands prioritizing user experience and conversion rate optimization will continue to outperform those who don’t. This competitive advantage will only grow if teams delay waiting for AI to perfect conversion mechanisms.

AI’s Role in Search and the Power of Existing Strategies

AI is indeed reshaping search by altering user behavior, influencing SERP appearances, and complicating attribution. Yet, the real risk lies in overreacting to AI at the expense of proven strategies.

For most sites, traditional organic search continues to be the primary traffic source. When well-executed, SEO fundamentals still deliver results. Quick wins and high-quality content are rewarded, and optimizing user experience remains critical.

These efforts support each other, improving organic visibility and complementing paid search and LLM visibility. Staying updated on AI developments is vital but not at the cost of current growth-driving strategies.

Inspired by this post on Search Engine Land.

December 22, 2025
Google’s Surge in Review Deletions: What It Means for Your Business
In 2025, I’ve noticed Google is removing reviews at an unprecedented rate, and this isn’t by chance.

According to our industry analysis of 60,000 Google Business Profiles, review deletions are driven by several factors:
- Automated moderation techniques.
- Industry-specific risk considerations.
- Greater enforcement against incentivized reviews.
- Local regulatory pressures.
Together, these elements greatly impact business visibility in local search.

Review Deletions Are Increasing Globally

Data from tens of thousands of Google Business Profile listings show a notable rise in deleted reviews between January and July 2025.

The increase gained momentum toward the end of Q1, with more locations seeing at least one review removed weekly.

This isn’t confined to negative reviews alone.

Five-star reviews are now frequently removed, indicating Google’s stricter enforcement to maintain authenticity and trust.

Google is also asking its Local Guide community about incentivized reviews, likely triggered by AI-flagged suspicious activities.

Industry Variations in Review Treatment

Patterns of review deletions differ significantly depending on the business sector.

Restaurants lead in review deletions, followed by home services, retail, and construction. Both recent and older reviews are subject to removal.

By contrast, medical, beauty, and professional services experience fewer deletions, but unique patterns are consistent within these categories.

What Review Ratings Reveal

Review deletion patterns reveal clear moderation trends. In sectors like restaurants, reviews are deleted across all star ratings.

Medical and home services show a bias toward removing five-star reviews, indicating more scrutiny in regulated industries.

This variation is not due to manual policies but reflects Google’s automated adjustments based on perceived industry risks.

The Timing of Review Deletions

The age of a review often determines when it’s removed. In medical and home services, many reviews are deleted within six months.

In restaurants and retail, older reviews are frequently removed, suggesting ongoing improvement in detection systems.

For businesses, this means reviews might disappear long after being posted, often without warning.

Geographical Differences in Review Deletions

Location adds complexity to review deletion trends. In many English-speaking countries, five-star reviews face increased scrutiny.

However, in Germany, low-rated reviews are more commonly deleted shortly after being posted, aligning with strict defamation laws.

In summary:
- AI-driven enforcement is prevalent in English-speaking markets.
- Legal challenges are more impactful in Germany.
Implications for Local SEO and Business Owners

Increased review deletions pose two major challenges:
- Trust erosion: Disappearing legitimate reviews weaken confidence in platforms.
- Data distortion: Removed reviews alter performance metrics essential for SEO.
For businesses, monitoring review activity is crucial. Understanding removal patterns is now as vital as acquiring new reviews.

The Future of Review Visibility

Three key developments are shaping how reviews are managed:
- Greater reliance on automated moderation.
- Legal influences in regions with strict defamation laws.
- Increased use of third-party monitoring tools for independent tracking.
As moderation practices evolve, recent and detailed reviews will continue to be critical for SEO authority signals.

Maintaining a strong reputation now requires constant vigilance.

Inspired by this post on Search Engine Land.
December 22, 2025
Master Broad Match: Control Smart Bidding Effectively
I’ve learned that broad match now operates alongside Smart Bidding. It’s fascinating how drift happens, why it’s important, and how to align performance with genuine intent.

Broad match, once synonymous with “more reach, less relevance,” now depends on a machine learning layer to define relevance.

Over time, Google has nudged us, the advertisers, towards fewer complexities like fewer match types and more automation.

Since July 2024, broad match has become the default for new Search campaigns, signaling a shift in how we ought to think about it.

If you’re stuck in the mindset of broad match being the “loosest match type,” you’re stuck in 2016, and that’s where problems like CPC inflation and irrelevant leads arise.

Today’s broad match works within a system, collaborating with query matching, Smart Bidding, conversion signals, and optional tools like audiences and negatives.

Google leverages broad match as a growth mechanism for Smart Bidding campaigns rather than a solitary reach tactic.

In this article, I explore the changes, Google’s motivations behind them, and safe practices to maintain standards while using broad match.

The real risk with broad match isn’t relevance, it’s direction

Broad match tends to drift rather than fail completely.

With shallow optimization goals, broad match coupled with Smart Bidding can find quick ways to meet them, sometimes resulting in:
- Queries that trigger cheap forms without real sales potential.
- Users who convert but never purchase.
- Leads that look good in Google Ads but don’t end up profitable.
Even when everything seems fine in the interface, the account might drift away from commercial intent.

This illustrates why understanding broad match’s current behavior is crucial.

What broad match actually is now

Broad match no longer stands alone as a keyword setting but works within a larger optimization system.

It’s built to work with Smart Bidding

Google specifies that broad match is intended to run with Smart Bidding, as bidding decisions are now made during auctions using signals like:
- Device
- Location
- Time of day
- Query context
- User behavior
Broad match increases eligible queries. Smart Bidding evaluates which ones merit investment.

Running broad match without Smart Bidding deviates from its intended design.

Google has materially improved broad match matching

Google claims that recent AI enhancements have uplifted broad match campaigns using Smart Bidding by 10%.

This doesn’t imply broad match is inherently safe, but Google feels its matching layer justifies broader use.

It’s no longer positioned as optional

Starting July 2024, new Search campaigns activate broad match by default.

The campaign-level setting enforces broad match when conversion-based Smart Bidding is active, marking a significant paradigm shift.

Why Google wants advertisers to adopt broad match

Google’s rationale is straightforward:
- Search behavior is increasingly unpredictable and long-tail.
- Manual keyword lists fail to keep up with language and intent shifts.
- Machine learning can interpret intent at auction time better than rigid logic.
Google positions broad match as a growth tool for Smart Bidding campaigns, providing algorithms with more opportunities to optimize for conversions.

You might not agree with this philosophy, but when advertising on Google Search, you’re part of this system.

A framework for using broad match without losing control

Broad match expands your reach. Maintaining control requires thoughtful constraints.

Conversion goals that reflect quality, not convenience

Smart Bidding optimizes based on defined conversion actions and values.

If your primary conversions are low-intent, broad match will scale this low intent.

Successful setups often include:
- Optimizing for deeper conversion actions.
- Applying conversion values to identify lead quality tiers.
- Importing offline conversions, like qualifying leads or revenue.
This tackles the issue of associating cheap volume with success.

Intent filters through audience signals

Broad match identifies queries. Audience signals dictate ad visibility for those queries.

Audiences should provide context, not just report data:
- Customer lists favor known buyers.
- Remarketing lists for measured expansion.
- Audience insights to recognize quality-segment correlations.
Even in observation mode, these signals help verify if broad match growth benefits the right areas.

Negative keyword structures that scale

With broad match, negative keywords transform from mere cleanup to structural elements.

Effective accounts often include:
- Account-level shared negative lists for terms like jobs, free, definition.
- Campaign-level exclusions aligned with intent boundaries.
- Regular search term reviews, crucial early on.
Broad match naturally explores, while negatives determine its limits.

Brand controls to protect intent

Google’s brand controls can substantially reduce unwanted behavior in broad match.

These controls include:
- Brand inclusions restrict matching to queries featuring specified brands.
- Brand exclusions prevent matching on certain brand names.
These controls are handy when broad match starts overlapping with competitor intent or misaligned searches.

How broad match succeeds and where it breaks

A sensible rollout usually includes:
- Choosing a campaign with effective tracking and enough conversion volume.
- Aligning Smart Bidding with meaningful outcomes.
- Launching with predetermined negative keywords.
- Frequent search terms reviews in the initial month.
- Verifying lead quality outside Google Ads before scaling.
Broad match has potential and is beneficial if used wisely. However, it isn’t a simple fix.

Failures often occur due to three common mistakes:
- Choosing the wrong conversion to optimize: The algorithm follows your instructions meticulously.
- Lack of a negative keyword system: Unchecked exploration becomes costly.
- Judging success solely by platform metrics: CPC and CPA can look good, while revenue declines.
Broad match is a system, not a setting

Google favors a systemized approach to Search, moving from simple keyword management to a broader strategy.

Control isn’t lost, but shifted.

Successful broad match campaigns are defined by:
- Clear quality definitions.
- Deliberate intent constraints.
- Success measured beyond the interface.
If used judiciously, broad match can reveal new demand opportunities. Casual use, however, might lead you astray.

Inspired by this post on Search Engine Land.
December 19, 2025
EU’s Google Probe: Impact on SEO, AI, and Content Rights
I’ve been following the significant regulatory move in which the European Commission launched a formal antitrust investigation into Google.

At the heart of this issue is Google’s use of publisher content to develop AI Overviews and other generative AI features, potentially diverting traffic from original publishers.

As someone involved in SEO or content strategy, I’m immediately affected by these developments.

The question I’m pondering is whether Google is overstepping by using publisher content for AI answers, or if it’s just part of being in an open web environment.

With regulators stepping in, I’m seeing the industry reevaluate how we use, manage, and value machine-readable content. It raises questions about the cost to brands, publishers, and agencies if regulation doesn’t catch up with innovation.

Here’s what’s going on, why it’s significant, and how the industry is already responding.

What’s Actually Happening: Core Allegations in the Complaint

This move from the EU is unfolding alongside other legal challenges, like those from publishers taking a stand against OpenAI and Penske Media’s recent antitrust suite targeting Google’s AI offerings.

Many publishers see Google’s actions as a no-choice situation: allow the use of their content for AI, or face losing vital search traffic.

At the same time, I notice how technical tools like robots.txt, Google-Extended, and new noai/nopreview conventions are reflecting an industry that’s striving to reclaim control.

The crux of the issue is whether AI training and answer generation stretch the bounds of traditional indexing and require licensing or proper attribution.

Dig deeper: New web standards could redefine how AI models use your content

What Does the Complaint Target

Publishers have seen their traffic drop by 20–50% on informational queries. The complaint highlights three practices:
- Google’s scraping of publisher content to enhance models like Gemini.
- A lack of meaningful opt-out options that still preserve search visibility.
- AI summaries capturing user attention above organic links, thus reducing clicks to the original content.
Regulators are called to explore three key questions:
- How Google uses publisher content in model training and grounding.
- If publishers can meaningfully opt out without losing their search visibility.
- If AI Overviews enhance Google’s dominance by retaining users within their interface.
Zero-Click Search Evolution: Is the Market Ready?

I see this probe as the onset of a post-click era for SEO, shifting the visibility battle from the SERP to the LLM context window.

The key question on my mind is whether Google is prepared for this transition.

The zero-click search experience often gets talked about, but for it to be successful for everyone involved, a few things need to happen:
- Users must find what they need directly on the SERP, within AI Overviews or AI Mode.
- Google needs to integrate various content types into a coherent experience.
- Publishers must receive fair compensation for participating in this ecosystem.
Although Google is moving towards a zero-click model, they’re not yet able to fully support it:
- Users still face outdated or incorrect answers.
- Assistive chats remain fragmented and can’t deliver full experiences.
- Publishers are unsure about compensation for quoted content.
What is the Opt-Out Version, and How Effective is It?

Google defends its content repurposing by offering opt-out mechanisms like Google-Extended in robots.txt.

While Google-Extended can prevent Gemini training, it doesn’t block AI-generated answers from using live data from publisher websites.

However, opting out of LLM training has its drawbacks:
- Content may still appear in AI Overviews if it’s already indexed.
- The process is opt-out rather than opt-in, requiring awareness and action from publishers.
- No granular control allows for selective blocking between snippets and LLM training.
Why Opting Out May Be a Bad Idea

Many publishers are considering opting out of having their content crawled for AI-generated answers.

Still, as AI answers evolve to become default, relying solely on direct or organic traffic is risky.

In reality, it creates a lose-lose situation.

Blocking usage may protect IP but hurts visibility, while staying open compromises control.

Without regulations, publishers largely have to adapt to the current system.

Dig deeper: How AI answers are disrupting publisher revenue and advertising

The Big Debate: ‘Google Doesn’t Owe You’ vs. ‘It’s Not Their Content’

I often see the assumption that control of web content lies in our hands.

Yet, without search engines, their reach is quite limited.

This tension fuels an ongoing debate dividing SEO perspectives.

On one side is the belief that ‘Google doesn’t owe you anything’.
- Many argue that the web is open, allowing search engines to crawl freely grants implicit permission for content use.
- Google facilitates discovery, but clicks or backlinks aren’t guaranteed.
On the flip side, there’s the perspective that ‘It’s not their content’.
- Publishers argue against unlicensed use of content for LLM training and AI responses.
- They see generation without attribution or compensation as disruptive.
This debate is active across social media and discussion forums.

Some suggest focusing on generative engine optimization, or GEO, replacing traditional rankings with AI quotes.

Nonetheless, that approach keeps publishers reliant on Google’s linking decisions.

In practice, there’s validity to both arguments.

Yet, the broader trend reveals the trajectory.

Even if Google faces consequences, search is unlikely to return solely to blue links.

The zero-click conversion is advancing.

The Dark Future of a Web Without Unique Content

Before diving into potential outcomes of the complaint, consider the impact on information itself.

As creators feel their work is reused without reward, the drive for original content wanes.

Simultaneously, AI-generated content is growing, often with minimal human input.

Entire sites now rely heavily on generative systems for content.

This often involves reworking existing text, with occasional inaccuracies.

As this cycle continues, the risk is declining informational quality due to a lack of truly fresh inputs.

The debate over AI training isn’t just about traffic or monetization.

It questions how the web can sustain unique knowledge creation and why protecting publishers is crucial to prevent information quality degradation.

What Can Happen if Google Loses

The traditional Google-publisher agreement was straightforward: “I let you crawl, you give me clicks.”

Generative AI disrupted this balance.

If the EU finds Google’s actions anticompetitive, we could witness major shifts:
- Mandatory opt-out mechanisms: Effective changes could enforce a granular system that protects against AI summaries without sacrificing rankings.
- The licensing economy: Following the music industry model, licensing could become compulsory, splitting organic search into free and premium sectors.
- AEO formalization: Attribution could be legally required, turning source citations into a ranking factor.
Ads and the Shifting Economics of Visibility

While this primarily concerns AI and content rights, ads still significantly impact SERP dynamics.

As organic space shrinks due to AI summaries, paid ads remain a strong visibility tool.

Even if EU pressures curb AI answers, the space for blue links is unlikely to grow.

The landscape will continue to favor revenue-driven Google products.

If AI Overviews reduce organic visibility, CPCs could rise, affecting ad positions.

Whatever the AI outcome, one truth is apparent: the cost of visibility is on the rise.

How to Adapt Your SEO and Content Strategy

Before any EU decision, I see top teams already shifting their strategies from merely ranking for keywords to ensuring they are the main entity answer wherever an AI model scans.

This involves several key actions:
- Enhancing entity clarity with schema and consistent data for accurate AI association.
- Auditing brand representation in AI Overviews and tracking emerging visibility KPIs.
- Reconsidering robots.txt strategies to manage IP protection versus AI visibility.
- Educating leadership that visibility extends beyond traffic, incorporating citation and AI source value.
The strategic goal is remaining readable and rights-conscious while ensuring brand presence where AI answers are most trusted.

Dig deeper: How to build an effective content strategy for 2026

Inspired by this post on Search Engine Land.
December 19, 2025
Boost Visibility in AI Search with GEO Strategies

Ever felt like your organic traffic is dwindling? I assure you, it’s not just in your head. AI Overviews and answer engines are nudging traditional SEO results off the stage.

For brands to maintain visibility, swift adaptation is key.

The upside? You don’t have to overhaul your entire SEO strategy. With some intelligent adjustments, you can transition from SEO to GEO, reclaiming your visibility in the AI era.

GEO, or generative engine optimization, emphasizes entities—like your brand, products, and experts—over mere pages. By amplifying these signals, you boost your chances of appearing in AI-generated answers and conversational search results.

This switch to GEO is crucial because AI search tools diverge from traditional search engines. Instead of just presenting a list of links, AI delivers comprehensive answers that predict follow-up inquiries and provide context. Users benefit from swift insights, while brands may observe a drop in clicks.

The demand for organic search remains, though traffic is shifting. As clicks wane, your presence in AI-generated responses becomes increasingly vital.

What you need to do now

Moving from SEO to GEO doesn’t mean starting from scratch. Instead, it builds on existing principles, placing more emphasis on structure, clarity, and consistency.

1. Prioritize E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness)

AI engines favor content that’s authoritative and signals genuine expertise. Aligning your content with these quality guidelines ensures its likelihood of appearing in AI-generated answers.

2. Make your content easy for AI crawlers to read

While Googlebot processes JavaScript efficiently, other AI crawlers might not. Ensure your content is in fully rendered HTML with a clean structure to facilitate easy referencing by AI systems.

3. Invest in structured data

Using schema markup, complete metadata, and detailed alt text helps AI models understand and connect your content to the right entities, improving visibility in AI-generated interactions.

4. Rethink measurement

Shifting our focus away from traffic as the primary metric, we should now emphasize conversions, deeper funnel impacts, sentiment, and brand visibility within generative search results.

Want to go deeper?

Ready to confidently pivot from SEO to GEO? Check out proven best practices, frameworks, and real-world examples on Contentful’s GEO Hub. It’s your essential resource for understanding the shift and staying ahead.

Inspired by this post on Search Engine Land.

December 19, 2025
Crafting Resilient SEO Plans Amid Constant Change

As I’m deep into the marketing planning season, a familiar tension surfaces that I’ve often heard from CMOs and VPs:

“We build a plan, but the execution never matches the intent.”

If this echoes your experience, know that you’re not alone. The issue isn’t flawed strategies or incorrect goals, but rather that most SEO plans aren’t built to withstand operational hurdles like shifting priorities or unforeseen product launches.

Over the years, after guiding various businesses in developing SEO strategies, I’ve realized that success doesn’t hinge on lavish budgets or cutting-edge tools. Rather, it’s about creating plans that reflect actual workflow realities.

Let me guide you through crafting an SEO annual plan that’s not just aspirational but actionable in the real world. We’ll explore setting clear, actionable goals and establishing quarterly systems to keep us on track even when the unexpected arises.

Why Annual Planning Still Works

It might seem outdated to engage in annual planning when new tools like AI Overviews, ChatGPT, and Perplexity change the landscape overnight. The impulsiveness of frequent algorithm changes can make a 12-month plan seem laughable.

Yet, companies that avoid long-term planning often end up merely reacting, chasing trends without accumulating the assets necessary for sustained growth.

Annual plans should provide guidance and resource allocation frameworks that enable smart decision-making when adjustments inevitably occur.

The Need for Better Planning in a Fragmented Search Landscape

With your audience seeking answers from AI-generated summaries and multiple platforms competing for attention, SEO success involves more than just Google rankings. You need to build brand authority, so AI systems recognize and reference your content.

Your strategy has to unify brand authority and topical depth, applicable across various search situations—from traditional queries to conversational AI.

An effective SEO plan should lead to business results, competitive advantages through authority, and preparedness for market changes.

Setting Action-Driven Goals

It’s common for many SEO plans to falter by prioritizing metrics detached from actual business outcomes, like focusing on rankings or traffic that don’t translate to revenue or conversions.

1. Start with Performance Metrics

Identify what success means for your business—be it ecommerce revenue from organic traffic, SaaS trials, or qualified leads for services.

Analyze these metrics at granular levels, ensuring resource investment is targeted towards high-revenue opportunities.

2. Add Contextual Visibility Metrics

Rather than focusing on isolated keyword rankings, track keyword groups that represent business themes. This offers a comprehensive view of market segment performance.

3. Establish Leading Indicators

Identify metrics that signal future changes, allowing timely interventions to maintain performance. Such metrics might include publication rates or indexation issues.

The Baseline Audit: Know Your Current Position

A thorough assessment of your current stance, focusing on technical health, content gaps, and authority signals, is crucial to prioritize effectively.

Strategy Around Constraints

Most planning falters when it doesn’t account for resource limitations or shifting priorities. Use an effort-versus-impact matrix to prioritize tasks effectively.

Quarterly Execution

Break annual goals into achievable quarterly targets, reserving part of your bandwidth for unexpected challenges. This ensures plans remain actionable, not just theoretical.

Cross-Functional Alignment

SEO isn’t isolated. Regular collaboration with product, content, and PR teams ensures consistency and reinforces shared goals.

Common Pitfalls

Avoid rigidity, competitor mimicry, and neglecting fundamentals in your SEO strategy. Focus on aligning plans with business realities and remaining flexible.

Bridging the Gap Between Planning and Execution

Avoiding execution gaps requires plans that reflect real-world conditions, enabling flexibility and focus on impactful metrics.

Inspired by this post on Search Engine Land.

December 18, 2025
Master AI Search: Techniques to Enhance Your SERP Strategy

As I dive into the evolving landscape of search, I’ve noticed a shift from traditional keywords to more conversational prompts. In today’s digital world, searchers are replacing shorter queries with detailed prompts, seeking comprehensive answers rather than a mere list of links.

Until we’re equipped with an AI-specific Google Search Console or Bing Webmaster Tools, understanding our audience’s behavior on AI platforms feels like a guessing game. But fear not, as we can still trace their journey using data proxies. By leveraging these proxies, I can uncover how my audience might be searching and track those prompts with my preferred AI Tracking Tool.

One invaluable tool is the ‘People Also Ask’ feature on search engines. This well-known SERP component can help transition from keywords to questions. Introduced in 2014, it suggests related questions, allowing me to explore queries that echo conversational prompts.

Using platforms like AlsoAsked, I can extract these questions at scale, finding long conversational queries that closely resemble AI prompts.

Another avenue I explore is through Userbots such as ChatGPT-User and Perplexity-User. These bots offer insights into how my content is utilized in AI search, highlighting pages that are frequently cited without needing to guess the relevance of prompts.

The process, called RAG (Retrieval-Augmented Generation), effectively grounds language models in factual data. It’s fascinating to consider how my content can play a role in shaping user responses, even if it doesn’t result in a direct click.

Gaining insights from long queries through tools like Google Search Console is another method I employ. By utilizing innovative techniques like Ziggy Shtrosberg’s complex regex filters, I can unearth queries that simulate AI search behavior.

It’s essential to approach this data cautiously, as some patterns might stem from automated trackers rather than genuine human interaction. For instance, high-appearance queries with zero clicks could indicate non-human usage.

Engaging with Perplexity AI’s follow-up feature is also enlightening. This feature can hint at how users might prompt AI systems, aiding my understanding of expected human interaction.

Finally, the Semrush AI Visibility Tool provides an ingenious way to manage the scaling challenge of unique prompts. By merging prompts into broader topics and using AI to distill their meanings, I gain valuable insights into intent and brand mentions across different regions.

In a rapidly changing tech environment, staying grounded in data is vital. Not all prompts engage Retrieval-Augmented Generation (RAG), which means those needing answers already in training data may bypass linking to new page sources.

However, when users seek recommendations (for example, dining options or attractions), page visibility within AI-generated answers can still convert offline interactions, benefiting brand exposure.

Checking the background operations of ChatGPT reveals search prompts within Chrome Dev Tools. By identifying searches and their relevancy to RAG, I can strategize to optimize this invisible layer of search behavior.

The quest to master AI search dynamics is ongoing. New AI models and evolving user behaviors necessitate continuous adaptation to comprehend and leverage audience interactions effectively.

Inspired by this post on Search Engine Land.

December 17, 2025
Mastering Google AI Bidding: Taking Control When It Breaks
I’ve noticed that Google’s AI-powered bidding can truly be enticing. It promises to optimize my campaigns if I just feed it my conversion data and set a target, allowing me to focus on the bigger picture of strategy.

The idea is that machine learning will take care of everything else. But, what Google doesn’t really highlight is that its algorithms prioritize Google’s outcomes, which might not align with my goals.

As I delve into 2026, it’s clearer than ever that with Smart Bidding becoming more opaque and Performance Max absorbing more campaign types, discerning when to direct the algorithm—and when to take charge—has become an essential skill for exceptional PPC managers.

AI bidding can yield impressive results, but there’s also a risk of it undermining profitable campaigns by prioritizing volume over efficiency. The key isn’t in the technology itself but in knowing when the algorithm requires direction, tighter constraints, or a complete override.

This article will guide you through:
- How AI bidding actually operates.
- Recognizing the warning signs when it’s failing.
- The intervention points where human judgment is crucial.
How AI Bidding Actually Works – And What Google Doesn’t Tell You

Smart Bidding offers various strategies, such as Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value. Each uses machine learning to predict conversion likelihood and adjusts bids in real time.

The algorithm evaluates numerous signals during auctions—device type, location, time of day, and more—to determine an optimal bid. During the “learning period” of typically seven to 14 days, the algorithm probes the bid landscape to understand the conversion probability curve.

Although Google advises patience during this phase, sometimes campaigns get stuck in perpetual learning and fail to stabilize.

Dig deeper: When to trust Google Ads AI and when you shouldn’t

Google’s Optimization Goals vs. Your Business Goals

The algorithm optimizes for metrics that increase Google’s revenue, which might not align with my profitability goals. For instance, setting a Target ROAS at 400% might prompt the system to maximize total conversion value, focusing on spending the full budget rather than understanding the varied nuances of my business.

My business goals might require a different approach, such as a specific volume threshold or maintaining varying margin requirements across products. The algorithm doesn’t account for these intricacies, like cash flow constraints.

Key Signals the Algorithm Can’t Understand

While AI bidding is effective, it has its limitations. Without intervention, several factors may go unaccounted for, like seasonal patterns, product margin differences, and changes in market conditions.

For example, the algorithm might not recognize the distinction between products with different profit margins. A $100 sale on Product A with a 60% margin is distinct from a sale on Product B with a 15% margin, yet the algorithm treats them equally, highlighting the need for profit tracking and margin-based segmentation.

Warning Signs Your AI Bidding Strategy Is Failing

The Perpetual Learning Phase

Extended learning periods are a major red flag. If my campaign’s “Learning” status persists for over two weeks, it indicates a problem. The causes could range from low conversion volume to frequent changes that reset the learning phase.

When to Intervene
- Boost the budget to speed data collection.
- Relax the target for higher conversions.
- Switch to a less aggressive strategy, like Enhanced CPC.
Budget Pacing Issues

Healthy AI campaigns show smooth budget pacing. If I observe erratic patterns like front-loaded spending or consistent underspending, it signals a lack of algorithm confidence.

The Efficiency Cliff

This refers to when performance starts strong but then deteriorates. It’s usually visible in Target ROAS campaigns where, month after month, the ROAS declines as the algorithm exhausts efficient segments and expands into less qualified traffic.

Traffic Quality Deterioration

Even when metrics seem fine, qualitative signals might suggest otherwise. I might notice a drop in engagement or shifts in geographic targeting, indicating the algorithm is prioritizing cheaper clicks which don’t necessarily convert better.

The Search Terms Report Reveals the Truth

Regularly exporting the search terms report helps identify issues. I look for irrelevant expansions or low-intent queries that consume budget with little conversion value, such as a luxury retailer finding clicks for “free furniture donation pickup.”

Strategic Intervention Points: When and How to Take Control

Segmentation for Better Control

When it comes to AI bidding, a one-size-fits-all approach might not work for diverse business models. By segmenting my campaigns, I can tailor algorithms to meet specific goals, using separate campaigns for high- and low-margin products or different regional performances.

Bid Strategy Layering

Sometimes, a hybrid approach serves better. I might run a Target ROAS under normal conditions and adjust it manually during peak times to capture volume, or use Maximize Conversion Value with bid caps to honor unit cost constraints.

The Hybrid Approach

Pairing AI with manual campaigns can optimize effectiveness. Allocating a percentage of the budget to each allows for capturing valuable traffic through manual efforts while still leveraging AI for broader campaign management.

COGS and Cart Data Reporting (Plus Profit Optimization Beta)

Google now supports reporting cost of goods sold and cart data, allowing a clearer view of profitability within Ads reporting. Although still in testing, this feature could soon enable profit-focused bidding rather than revenue-focused, enhancing performance analysis.

Dig deeper: Margin-based tracking: 3 advanced strategies for Google Shopping profitability

When AI Bidding Actually Works

AI bidding thrives under solid fundamentals, such as sufficient conversion volume and a stable business model with clear margins. In these contexts, AI often surpasses manual bidding by processing more variables than a human possibly could.

This tends to hold true for mature ecommerce accounts, stable lead generation programs, and SaaS models with predictable conversion paths.

Preparing for AI-First Advertising

As Google continues to simplify advertisement management through automation, my role has evolved from bid management to being an AI strategy director. My focus is now on setting clear goals, providing context, and intervening when needed.

Despite the reduction in advertiser control, certain strategic decisions remain human-driven, ones that require intelligence beyond what an algorithm alone can provide.

Master the Algorithm, Don’t Serve It

AI-powered bidding is a remarkable tool for optimization that delivers unparalleled results when conditions are optimal. However, the key lies in mastering it, ensuring that my business context informs the algorithm’s decisions, and knowing when to take control to align it with my strategic goals.

The strongest PPC leaders today are those who don’t just manage bids but helm the systems that manage them.

Inspired by this post on Search Engine Land.
December 16, 2025