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Tag: AI

Study Reveals AI Recommendations Rarely Repeat: What It Means

I recently came across an intriguing study about AI recommendation lists that caught my attention. It revealed that AI systems like ChatGPT, Claude, and Google’s AI don’t often repeat the same recommendations when asked for brands or products. This means if I ask them the same question multiple times, I’ll likely get different lists each time.

This finding came from Rand Fishkin of SparkToro and Patrick O’Donnell of Gumshoe.ai. They investigated how consistent generative AI recommendations are, and their results were quite fascinating.

What They Tested. Over 600 volunteers used 12 identical prompts on ChatGPT, Claude, and Google’s AI nearly 3,000 times. What they found was quite revealing.

Each AI response was turned into an ordered list of brands or products, and the overlaps, order, and repetitions were compared to see how often the same answers appeared.

The short answer: almost never. Achieving identical lists twice was incredibly rare, with odds of under 1 in 100, and getting the same list in the same order was even less likely at 1 in 1,000.

Even the length of the lists varied. Some responses listed only two or three options, while others had more than ten. If I’m dissatisfied with the result, simply asking again might yield a better outcome.

Why This Matters. We often hear about personalization in AI answers, but this study is the first to provide real data to support that claim, showing a clear departure from traditional SEO.

Design and Randomness. This variability isn’t a flaw — it’s intentional. These systems are probability engines designed to create diverse outcomes, not stable ordered results like Google’s blue links.

One Consistent Metric. Despite fluctuating rankings, one metric that proved more stable than expected was visibility percentage. Some brands repeatedly appeared in a majority of responses.

Consistent presence in these lists carries more weight than exact ranking, especially across multiple runs and intent changes.

Context Size Counts. The consistency of AI answers improves in smaller, niche markets compared to larger categories, where results scatter significantly.

Real-World Prompts. Testing with actual human prompts showed varied results — as people phrased their queries differently, semantic similarity was low.

Yet, AI still returned similar brands for the same intent, proving that AI captures the underlying purpose behind the queries.

The Power of Intent. Even with hundreds of unique prompts for headphone recommendations, prominent brands like Bose, Sony, and Apple surfaced consistently.

When I change the purpose — say, to gaming or noise-canceling — the brand results shift accordingly, indicating that AI comprehends intent despite varied prompts.

What Doesn’t Help. Tracking exact positions in AI answers is unreliable because these rankings are too unstable to mean anything.

What Could Work. A more effective approach might be to track how frequently my brand appears over many prompts, even if it seems complex and imperfect.

Unanswered Questions. There are still gaps to explore, like determining how many attempts are needed for reliable visibility stats or whether API-based results align with real user behavior.

Conclusion. AI recommendation lists are inherently variable, but with large-scale, careful visibility measurement, I can derive actionable insights. Just don’t mistake this for traditional ranking metrics.

For more details, you can read the full report here.

Inspired by this post on Search Engine Land.

January 28, 2026
U.S. Google Searches Drop: The Impact of AI on User Behavior
I recently came across a fascinating Datos/SparkToro report revealing a significant change in our search habits. It’s no surprise that U.S. Google users are searching less than they did a year ago. While Google isn’t losing users, it’s clear they’re experiencing fewer repeat searches.
Why this matters to me. Google still reigns supreme in the search world, but fewer searches mean dwindling opportunities for clicks, ads, and traffic—even if the total search volume seems stable.
The numbers speak for themselves. The report showed a nearly 20% year-over-year decline in desktop searches per U.S. user, based on data from millions of users.
- This sharp decline is unlike the European trend, where searches only fell by 2-3%.
- Despite fewer searches per person, traditional search still constitutes about 10% of all U.S. desktop activity—a share that held steady throughout 2025.
Reasons behind the drop. The rise of AI-powered answers and instant results appears to be the main culprit:
- Users now get the information they need without conducting multiple follow-up searches.
- Zero-click searches remain high but have leveled off in the low-20% range by year-end.
- Little change is observed in repeat searches and clicks within Google-owned properties, hinting at a plateau in user behavior.
The reshaping of search by AI. AI isn’t pulling users away from search; rather, it’s enhancing it. Despite ongoing AI buzz, the report discovered:
- AI tools contribute to less than 1% of total U.S. desktop activity (0.77%), though they’ve seen remarkable growth.
- Google AI Mode remains small, accounting for about 0.06% of U.S. desktop events by December, with steady adoption increase.
Query evolution. One notable behavior change is how we phrase our searches:
- Mid-length queries of six to nine words are increasing rapidly in the U.S.
- Very long queries (15 words or more) are still rare but show significant experimentation and volatility.
- People seem to find it easier to express complex needs directly in their searches.
Discovery becomes a challenge. With concentrated search-driven discovery, breaking into post-search destinations is tougher:
- YouTube, Reddit, Amazon, Wikipedia, and Facebook remain dominant.
- ChatGPT soared to No. 7 among U.S. search destinations, a rare significant mover.
- Meanwhile, Quora has fallen out of the top 15.
AI’s few dominators. AI-driven traffic largely directs users to already established platforms like Google, YouTube, GitHub, and Wikipedia rather than new or independent publishers. When it comes to AI platforms:
- ChatGPT is the leading tool in the U.S., reaching around one-quarter to one-third of desktop AI users.
- Google’s Gemini emerged as a strong No. 2, consistently growing throughout 2025 and surpassing DeepSeek.
- Other tools like Claude, Perplexity, and Copilot stay niche with modest reach.
Industry insight. Rand Fishkin, co-founder and CEO of SparkToro, highlighted in the report:
“The big highlight here is the decline in # of Google searches/searcher from 2024–2025. It’s a nearly 20% decline in the US, though only 2–3% in the EU/UK. Other studies have shown that Google is sending less traffic than in years past, especially to the long-tail of the web, and I suspect that AI answers have dramatically altered the way many users engage with Google, answering their questions before they ever need to click on an organic result or perform a second/third/fourth search.”
The complete report. Discover more in the Q4 State of Search report

Inspired by this post on Search Engine Land.
January 28, 2026
Discover Meta’s AI: The Power of Andromeda and GEM

When I think about Meta’s advertising journey, it amazes me how far we’ve come from the manual days of targeting and account tweaking. Back then, I had to rely on finely tuned audience definitions and schedule constant tests to keep ad performance up.

But as privacy policies evolved and signal clarity dimmed, those methods began to lose their effectiveness. This change prompted Meta to harness the power of AI in reshaping its ad platform.

With Andromeda at the helm, Meta launched its first major AI initiative for personalized ad retrieval, soon followed by the expansive GEM, Meta’s Generative Ads Recommendation Model. These systems reinvent how ads are chosen and delivered across Meta’s ecosystem.

Our role as advertisers has transformed significantly. It’s crucial now to understand how Andromeda and GEM operate in unison and to align our strategies with this AI-first approach that’s defining ad success in 2026.

Let’s dive into the specifics—

Andromeda: Unveiling Meta’s AI Evolution

Andromeda, to me, feels like the beating heart of Meta’s AI transformation. By leveraging past user interactions, it flips traditional targeting on its head, going beyond pre-defined audiences to assess the most engaging ad elements.

Personally, the introduction of Andromeda in 2024 reshaped how I approached advertising. I noticed that broader target groups started to outperform detailed interest-based setups, signaling a shift towards creative-first strategies.

By 2025, it was clear that simplified structures and continuous creative refreshes were the keys to unlocking Andromeda’s potential.

The Shift with Andromeda

With Andromeda, a shift occurred from audience-centric to creative-centric matching, making the creative elements the primary indicators of relevance over traditional targeting metrics.

As I experimented, I found that broader campaigns offered more data for AI to optimize, proving highly effective in meeting diverse campaign objectives.

Source: Engineering at Meta

Enter GEM: The Brain Behind Ad Precision

GEM, the core intelligence engine of Meta’s advertising realm, brought with it a new era of predictive precision. It adds depth by analyzing wide interaction datasets to enhance ad selection and sequencing.

For me, the seamless integration of GEM with Andromeda led to noticeable improvements in campaign efficiency by late 2025, driving results more effortlessly than ever before.

Why GEM Transformed the Ads Landscape

GEM isn’t just about displaying an ad—it’s about the continuous learning and anticipation of what should come next. Imagine Andromeda as your ad’s gatekeeper and GEM as its storyteller, predicting the next successful narrative in real-time.

Source: Engineering at Meta

My approach has evolved to value long-term engagement patterns over short-lived peaks, requiring both patience and strategic creativity.
Dig deeper: Rethinking Meta Ads AI: Best practices for better results

Harnessing AI in Advertising: Strategies for 2026

This year, my focus is set on innovative creative strategies and stability, as simplicity in structure seems to generate superior results.

Creative Strategy: The Cornerstone

I’ve learned that providing a rich array of creative content enhances Meta’s AI learning. Tailor content to different personas and employ diverse media formats to keep engagement high.

Streamline for Impact

Simplifying campaign structures has shown remarkable improvements. Fewer campaigns with broader reach enable Andromeda and GEM to identify patterns swiftly.

Giving up granular control wasn’t easy, yet it has proven essential for the AI systems to optimize effectively.

The Power of Patience

I’ve discovered that patience, coupled with a stable strategy, is a game-changer. Avoid making hasty modifications; instead, monitor performance over broader time scales to truly grasp overall trends.

Budget as a Strategic Tool

Generally, larger budgets accelerate learning. Meta’s AI thrives on consistent data flow to optimize performance and develop effective solutions.

Redefining My Role

Today, I see myself less as a manual optimizer and more as a strategic architect, focusing on creative originality and brand fidelity while trusting the AI to handle optimization duties.
Dig deeper: 3 PPC myths you can’t afford to carry into 2026

Mastering Meta’s AI Ecosystem

From observation, AI is the cornerstone of Meta Ads now, transforming how I handle campaigns. Merging human-created strategies with AI insights unlocks immense potential.

By feeding diverse, quality inputs into the system, I’m able to align better with Meta’s AI, which is now the linchpin of ad success.

The rules may have changed, but the opportunity for creative success remains immense.

Inspired by this post on Search Engine Land.

January 28, 2026
Discover Bing’s New AI Performance Insights: A Sneak Peek

I recently discovered that Bing is testing a new AI Performance report within their Webmaster Tools. This has piqued my interest, especially since Microsoft has been teasing the idea of providing better insights into website performance in AI-driven Bing and Copilot searches for months.

It all started back in February 2023, and then in April 2023, Microsoft hinted at delivering data on Bing Chat and AI search impressions. Sadly, our hopes were dashed when they lumped this data together with regular web queries, leaving us still in the dark about our sites’ performance in Bing’s AI experiences. I can’t help but feel a bit let down.

Now, it seems Bing is experimenting with a new report within Bing Webmaster Tools, known as the AI Performance report. This report is in a super limited beta phase, and Microsoft hasn’t officially announced anything yet. A source shared that it showcases citation data from both Microsoft Copilot and its partners, detailing the number of citations and cited pages per day.

With this report, I can see how often Copilot cites my website and across how many pages. However, it still doesn’t reveal how many people clicked through from those citations to my site. The report also presents data categorized by “grounding queries” and “pages.” While “grounding queries” might not represent the exact query entered in Copilot, it shows how Bing interprets them, including insights into the intent behind such queries, like whether they are navigational or informational.

This new report lets me identify the specific pages Copilot cites. While there’s excitement in seeing more AI performance-related data pop up in Bing Webmaster Tools, I can’t shake the feeling of wanting click-through data. Knowing the click-through rate from AI interactions compared to regular web searches is something I, and I’m sure many other publishers and site owners, have been eagerly anticipating.

It feels like all search engines are intentionally keeping this data under wraps, and it’s frustrating not having full transparency.

Inspired by this post on Search Engine Land.

January 27, 2026
Google AI Max: Is Your Account Set for Success?

I recently discovered the potential of Google AI Max and, like many of us, wondered if my account is ready to harness its power. Google AI Max promises to unlock additional conversions if set up correctly. Before jumping in, I knew I had to ensure everything was primed and in place.

Google’s AI Max is designed to transcend traditional keyword targeting by utilizing various signals to determine ad displays. It’s a game-changer for those with a history of broad match success. However, if not optimized, it could quickly deplete your budget.

One important clarification: using AI Max is not mandatory for ad appearances in AI Overviews. Broad match keywords can place ads in AI Overviews regardless of AI Max usage. I see AI Max more as a tool to expand conversions beyond mere AI Overviews.

We’ll explore the essential steps to review before testing AI Max. These insights are crucial to ensure our campaigns are fully prepared.

What to Check Before Enabling AI Max

Accurate Conversion Tracking

Having precise conversion tracking is vital. AI Max optimizes based on our defined success metrics. Inaccurate or inflated conversions can lead to poor AI decisions. This insight made me double-check everything.

Automated Bidding with a Conversion-Focused Strategy

For broad match to function optimally, a conversion-centered bid strategy is necessary. Options like ‘Maximize Conversion Value’ or ‘Target CPA’ should align with your updated strategy. My experiments indicated more consistent results with target bids than max bids.

Using max bids without watching over budget and collected data might not yield the best results. I’ve learned to keep a careful eye on it.

Conversion Volume

AI Max needs sufficient data to perform well. With over 100 conversions monthly, its reliability has been strong, provided there’s a positive history with broad match. Based on this, I aimed to test in campaigns with at least 30 monthly conversions.

No Impression Share Lost Due to Budget

If budget constraints already hinder impression share, AI Max could exacerbate this issue. Prioritize spending on top keywords and let AI Max utilize remaining funds for experimentation.

Proven Broad Match Success

AI Max treats keywords as broad match and extends beyond them. Without past success, it could be ineffective. Preparing through ad group optimization and new ad testing has been my strategy.

Should You Use URL Expansion?

Enabling URL expansion allows Google to pick any webpage for landing when AI Max triggers an ad. However, indiscriminate use can be detrimental—excluding non-conversion-oriented pages mitigates risks.

Those who created landing pages for specific geographies should carefully manage page exclusions to avoid mismatching.

Should You Try Automatically Created Assets?

I’m hopeful about automatically created assets. They can significantly enhance messaging but require caution to avoid irrelevant sitelinks and incompatible callouts. Establishing clear guidelines ensures alignment with brand objectives.

How to Test AI Max

Because of its performance inconsistencies with brand keywords, I’ve found it best to initially focus on non-brand keywords in AI Max tests. Starting with successful ad groups rich in conversion data offers the best chance to test its potential.

Operating AI Max at the ad group level via the Google Ads Editor proved efficient in my testing experience.

Is Your Account Ready to Test AI Max?

As AI Max continues to evolve, its integration into our existing systems may provide significant advantages. But, readiness involves assessing if our accounts meet all setup criteria before diving in. By following my steps, you’ll recognize its readiness and potential for success.

Inspired by this post on Search Engine Land.

January 27, 2026
EU Focuses on Google’s AI and Search Data: What It Means for Competition

I’ve noticed the European Union is turning its gaze towards Google once more, scrutinizing how it handles its AI and search data. This could lead to changes that might open up its Android features and search data, ultimately reshaping the competitive landscape.

The European Commission is now formally outlining the ways Google must share specific Android functionalities and its search data with competitors, in line with the Digital Markets Act.

Tuesday marked the start of two official proceedings by the Commission, aimed at establishing a structured approach for Google to meet key obligations under the DMA. It’s fascinating to see these regulatory dialogues become more concrete.

Why I care. This move by the European Commission could alter the dynamics in mobile AI and search. With Google potentially needing to share its search data and Android AI capabilities, it could boost the competition from other search engines and AI services. Such changes might impact where advertisers allocate budgets, alter the availability of advertising inventory, and shift campaign dependencies away from Google’s platforms.

First focus — Android and AI interoperability. The regulators are delving into how Google must enable third-party developers to access Android hardware and software features as freely as Google’s own AI services, like Gemini.

– The objective is to allow rival AI providers the same level of integration with Android devices as Google’s native tools.

Second focus — search data sharing. The Commission aims to define how Google should provide anonymized search data including ranking, queries, clicks, and views to rival search engines under fair, reasonable, and non-discriminatory conditions.

– This includes specifying the types of data to be shared, how it will be anonymized, eligibility for access, and whether AI chatbot providers can use this dataset.

Between the lines. It’s not just about ticking off compliance boxes. The Commission is making it clear that AI services are under the DMA’s watchful eye, especially where data and device control could influence emerging markets.

What’s next: Within three months, the Commission plans to send Google its initial findings and recommended actions. The full proceedings should wrap up within six months, accompanied by non-confidential summaries for public input.

The backdrop. Since March 2024, Google has been required to comply with DMA obligations, having been identified as a gatekeeper in services like Search, Android, and YouTube.

Bottom line. The EU is moving from planning to action with the DMA, testing how strongly it will influence competition by overseeing Google’s AI functions and search data management.

Inspired by this post on Search Engine Land.

January 27, 2026
Explore ChatGPT’s Costly Ads: Visibility at a Premium

I’ve noticed that OpenAI is introducing premium-priced ads on ChatGPT, but here’s something interesting: the data provided to advertisers is significantly limited compared to what we’re used to.

What’s happening. Reports indicate that OpenAI is offering ChatGPT ads at around $60 per 1,000 impressions. That’s about three times the rate of standard Meta advertisements! Yet, even with this higher cost, advertisers only receive basic metrics like total impressions or clicks, without insight into actions like purchases.

Why we care. ChatGPT is becoming a fresh, highly engaging ad space, but it’s not without its challenges. The hefty CPMs and limited insights mean that early advertising efforts will lean more toward enhancing brand presence and gathering learnings than achieving performance-driven efficiency.

For marketers who are open to trying new avenues, this presents a unique chance to gain insights into how ads function within AI-driven conversations before the format becomes more widespread or measurable.

The tradeoff. OpenAI is contemplating expanding its measurement capabilities in the future, yet it remains committed to user privacy. It has pledged not to sell user data or invade the confidentiality of conversations, which limits traditional targeting and attribution possibilities that platforms like Google and Meta offer.

Who will see ads. Initially, these ads will be available to those using ChatGPT’s free and lower-cost Go tiers, but won’t be shown to users under 18 or in conversations concerning sensitive topics like mental health or politics.

Between the lines. OpenAI is branding ChatGPT ads as a top-tier, trustworthy product, banking on the idea that context, focus, and brand safety can validate the higher pricing, despite the lack of detailed performance data.

Bottom line. Brands eager for prominent visibility in a cutting-edge AI-driven environment may find ChatGPT ads appealing, but those focused on performance metrics might hesitate due to the absence of detailed measurement.

Dig deeper. OpenAI Seeks Premium Prices in Early Ads Push (Subscription needed)

Inspired by this post on Search Engine Land.

January 26, 2026
Harnessing AI: Transform Your Prompting with Rubrics

Generative AI is an integral part of my search, content, and analytical workflows these days.

However, with increased usage, I’ve noticed a recurring and expensive issue: confidently incorrect outputs.

Often referred to as “hallucinations,” this problem arises not because the AI is faulty, but due to vague instructions, or more specifically, unclear prompts.

Imagine asking AI for just a “cookie recipe” without any specifics. The result? Christmas cookies in July, or a peanut-filled recipe regardless of allergies!

To mitigate this, I try to expect missteps and set clear guardrails with the help of rubrics.

In this discussion, I’ll explore how rubric-based prompting can enhance factual reliability and how you can implement it to achieve more dependable AI results.

Fluency vs. Restraint: What Matters More?

When I request polished answers from AI without specifying how to handle uncertainties, the system usually opts for fluency over restraint.

This means it prefers to continue smoothly rather than pausing or qualifying a response where information is missing, leading to potentially costly errors.

For instance, Deloitte had to refund substantial costs due to AI errors in a government report, which included fabricated citations, as reported by Associated Press in 2025.

This incident highlights the necessity of keeping AI in the loop but ensuring it’s adequately constrained — defining protocols when uncertainties arise.

Understanding Rubrics: The Guiding Hand AI Needs

Generic safeguards against AI hallucinations exist, but are often ineffective as they describe outcomes instead of a decision-making process.

This is where rubric-based prompting becomes vital, establishing a framework to steer AI behavior.

Just like an academic rubric, AI rubrics define evaluation criteria but apply it to the decision-making process during response creation.

Clear boundaries set by rubrics significantly reduce the likelihood of AI hallucinations.

Writing Better Prompts Isn’t Enough

While refining prompts can improve surface-level results, they don’t address the root cause of hallucinations: insufficient decision-making guidance.

Often, I notice that prompts ask for specific outcomes without providing rules, leaving the AI to fill in substantial gaps autonomously.

This autonomy can lead to generated outputs where fluency trumps accuracy.

Switching from inference to explicit instruction using rubrics helps align AI responses with defined goals and limits.

The Unique Strength of Rubrics

While prompts set tone and format, rubrics tackle uncertainty, defining clear decision paths and reducing ambiguity.

By supplying concrete criteria, rubrics ensure factual accuracy takes precedence over spiraling completeness.

An effective rubric guides the model on how to act if the information is insufficient, significantly improving output reliability.

Anatomy of a Robust AI Rubric

To avoid over-complication, a solid rubric must focus on a concise set of enforceable criteria addressing hallucination risks directly.

Elements such as accuracy requirements, source expectations, and uncertainty handling are essential to include.

By ensuring clarity in these areas, rubrics bolster the AI’s ability to provide truthful and trustworthy responses.

For me, prompting with purpose means shaping AI behavior effectively by foreseeing where assumptions might occur and setting parameters clearly.

With rubrics, I am able to guide AI to halt, pause, or clarify when data is lacking, fostering accurate and dependable outputs.

Inspired by this post on Search Engine Land.

January 23, 2026
Agentic AI: Transforming PPC with Smart Automation

I’ve watched automation quietly transform PPC management over the years with rules, scripts, and API-driven workflows in Google Ads.

Like many other marketers, I’m already very comfortable with automated bidding, data-driven optimization, and a suite of other AI-powered enhancements. But there’s a new shift on the horizon that’s set to redefine how we manage and optimize PPC campaigns.

This time, I’m talking about AI agents and vibe coding. These innovations are ushering in a more autonomous mode of working where AI takes the lead in execution, allowing marketers like me to focus on strategy and creativity.

This evolution promises unprecedented efficiency and flexibility, redefining effective PPC management.

Agentic AI: Google Ads’ Game-Changing Feature

In November 2025, Google rolled out its Agentic Ads Advisor, powered by advanced Gemini models. This tool helps advertisers like me uncover insights and boost campaign performance effortlessly.

Google positions Ads Advisor as an AI partner that enhances campaign management by understanding business contexts, simplifying tasks, and learning from interactions to deliver better outcomes.

However, the pressing question remains: What functionalities should an agentic AI tool embody?

It should function as an autonomous agent, surfacing information as needed but also operating independently. It should identify opportunities for enhancing campaign setups, assets, ad copy, and more.

An ideal agentic AI wouldn’t just make recommendations but also implement essential changes on its own.

Integrating Agentic AI in PPC Workflows

Agentic AI should ideally make decisions autonomously without needing constant human input, thereby managing, adjusting, and optimizing campaigns as they run.

Beyond just advice or reporting, its real value lies in managing bidding, ad placements, and creative testing in real-time, based on live data, seasonality, and user behavior trends.

With agentic AI handling more operational tasks, I can direct my efforts toward strategic decision-making.

The competitive edge will increasingly rely on strategy rather than tools, focusing on marketing fundamentals like positioning, value propositions, and brand awareness.

Read more: Agentic PPC: What Performance Marketing Could Look Like in 2030

Why Agentic AI is Key for Advanced PPC Marketers

Agentic AI appeals to experienced PPC marketers like myself because it scales campaigns without compromising strategic control, proving to be a true game-changer.

With real-time optimization, data-driven creativity, and reduced human error, it redefines my role by allowing more time for strategy rather than execution.

Despite its capabilities, informed oversight is essential to ensure alignment with broader marketing objectives, highlighting the need for ongoing professional engagement.

Agentic AI isn’t replacing PPC professionals. Instead, it extends our capabilities, reduces manual effort, and facilitates better outcomes with minimal friction.

Vibe Coding: Creating Your Marketing Toolbox

In tandem with agentic AI, vibe coding is redefining how I work with AI-powered platforms, allowing me to create personalized, intuitive marketing tools and campaigns.

Tools like Cursor and AI Studio have enabled me to articulate and realize specific needs seamlessly, even without being a developer.

Incorporating vibe coding led me to build an SEO schema markup generator, an SEO audit tool, and a marketing idea generator, proving its practical value in my professional life.

The possibilities expand when combining vibe coding with agentic AI, empowering marketers to engineer their AI agents tailored for PPC work.

With this combination, I integrated these tools effectively within my marketing workflows, enhancing performance and strategy development at scale.

Explore further: How Vibe Coding is Changing Search Marketing Workflows

The Future: Navigating PPC with Agentic AI and Vibe Coding

Agentic AI and vibe coding present immense opportunities to streamline PPC operations, enhance performance, and maintain competitiveness in a fast-evolving landscape.

The future is about leveraging these technologies for more autonomous, data-driven, and personalized marketing strategies that benefit both internal teams and customers alike.

As a PPC professional, it is crucial to embrace these advancements, ensuring adaptability and continued relevance in an AI-powered future.

Follow experts like Alfred Simon, Mike Rhodes, and Ales Sturala to see practical applications of these innovative technologies in real-world scenarios.

Inspired by this post on Search Engine Land.

January 23, 2026
Experience Personalized AI in Google Search’s New AI Mode
I’m excited to share that Google is introducing Personal Intelligence to its AI Mode in Google Search! In a recent Labs experiment, AI Pro and Ultra subscribers in the U.S. can now opt-in to this feature over the next few days.

Personal Intelligence was first introduced on the Gemini app last week and is now making its way to Google Search for certain users. According to Robby Stein, VP of Product at Google Search, starting today, subscribers can securely connect their Gmail and Google Photos to AI Mode, enhancing their search experiences.

This feature allows me to receive more personalized responses by connecting across my Google ecosystem, including Gmail, Photos, and YouTube history, right in Google Search. This rollout will be completed in a few days for AI Pro and Ultra subscribers in the U.S.

How to Access Personal Intelligence

This innovative feature is part of a Labs experiment, and opting in is straightforward. It’s available for personal accounts in the U.S., but currently not for Workspace business, enterprise, or education users. Subscribers will automatically gain access as the feature becomes available.

If you’d like to enable it manually, follow these steps:

1) Open Search and tap your profile.

2) Click on Search personalization.

3) Select Connected Content Apps.

4) Connect Workspace and Google Photos.

Inspiring Examples

Imagine asking Google a question like these:
- Help me plan a weekend getaway with my family based on things we like to do.
- Make a scavenger hunt for [partner’s name] to celebrate our anniversary, including a hint about us for each location.
- I’m decorating [child’s name] bedroom; give me theme ideas and decor suggestions.
- If I were the hero/heroine from a book, who would I be?
- What specific era of fashion suits me best?
- Recommend books that fit my current life phase.
- If I were a perfume, what would my top notes and base notes be?
To truly see it in action, you can watch this video:

Why This Matters

This feature, currently available as an opt-in, could become a standard part of Google Search in AI Mode. As a result, searches will become increasingly personalized, making it more challenging to track different website citations for individual users.

Inspired by this post on Search Engine Land.
January 22, 2026

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