As I look ahead to 2026, Google’s innovative strides in AI are truly reshaping digital advertising and commerce. Thanks to the leadership of Vidhya Srinivasan, VP/GM of Ads & Commerce, AI is significantly enhancing the shopping and advertising landscape, making it more efficient and personalized for everyone involved.
Key Trends:
Creators to commerce: In my experience, YouTube is increasingly becoming a go-to platform for discovery, largely because creators act as influential tastemakers. AI plays a pivotal role in pairing the right creators with brands, transforming influence into tangible business outcomes.
Search ads evolve: With conversational and visual searches gaining popularity, AI Mode is revolutionizing ads to seamlessly integrate into the user’s discovery process. Innovative formats like sponsored retail listings and Direct Offers are crafted to assist users in their shopping journey while offering brands meaningful conversion opportunities.
Agentic commerce arrives: Through Google’s Universal Commerce Protocol (UCP), AI-driven shopping experiences are becoming standardized. This advancement allows users to browse, purchase, and finalize transactions effortlessly. Early adopters like Etsy and Wayfair have already started using this system, with giants like Shopify, Target, and Walmart soon joining the bandwagon.
AI-powered creative and performance: I’m thrilled to see how tools powered by Gemini 3 are enhancing creative production and campaign optimization. Generative platforms like Nano Banana and Veo 3 help advertisers produce high-quality assets swiftly, while AI Max boosts reach and performance.
Trust as a foundation: It’s reassuring to know that each advancement prioritizes privacy and security. Strong data management practices, alongside transparent ad personalization, are founded on Google’s legacy of trust.
Why we care: 2026 is poised to be a groundbreaking year, with AI enhancing every facet of the consumer journey. With cutting-edge tools like Gemini 3, Nano Banana, Veo 3, and AI Mode, brands like mine can efficiently create superior content, target the perfect audience, and seamlessly convert interest into purchases during search and discovery.
The advent of agentic commerce through UCP presents a novel approach, connecting advertisers to consumers at critical purchasing moments, all while preserving trust and transparency.
The big picture: The year 2026 heralds an expansive era for digital commerce and advertising, where the fusion of speed, personalization, and AI-driven insights eliminates barriers, facilitating smoother transitions from discovery to purchase while keeping trust paramount.
I’ve noticed something puzzling in my local business performance lately. Despite high rankings, the number of calls and website visits from Google Business Profiles seems to be dropping at an alarming rate.
This disconnect is becoming increasingly common in local search. Rankings are stable, but visibility and customer engagement are not keeping pace.
The alligator of local SEO, if you will, has made its presence known.
The visibility crisis behind stable rankings
I’ve observed that across various U.S. industries, the familiar local 3-packs are often getting replaced or supplemented by AI-run local packs. These new formats differ significantly from the traditional map results many of us are used to optimizing.
According to Sterling Sky’s analysis of Google Business Profiles, a startling pattern emerges. Clicks-to-call are taking a nosedive, particularly for law firms managed by Jepto.
When AI-powered packs take over, the landscape changes notably in four key areas:
Shrinking real estate: AI packs frequently display only two businesses instead of the usual three.
Missing call buttons: The summaries generated by AI often omit the instant click-to-call functionality, complicating the customer’s journey.
Different businesses appear: Companies featured in AI packs do not necessarily align with those in the traditional 3-pack.
Accelerated monetization of local search: The presence of paid ads increasingly results in the loss of direct call and website buttons in traditional 3-packs, thereby reducing opportunities for organic conversion.
There’s an additional challenge compounding this issue:
Measurement blind spots: Most rank trackers have yet to account for AI local packs. A business may hold a top spot in a traditional 3-pack that users rarely encounter.
In 2026, AI local packs surfaced only 32% as many unique businesses as traditional map packs, according to Sterling Sky. Astonishingly, in 88% of the 322 markets examined, the total number of visible businesses plummeted.
Meanwhile, paid ads are steadily claiming the space that once belonged to organic results, marking a clear transition toward a pay-to-play environment in local search.
What Google Business Profile data shows
This trend is echoed in the U.S., where Google is proactively testing new local formats, as indicated by data from GMBapi.com. Increased impressions from traditional 3-packs are being nudged out by:
AI-powered local packs.
Paid placements inside traditional map packs: Sponsored listings now appear adjacent to or within the map pack, relegating organic results and removing essential call and website buttons. This interrupts organic customer interactions.
Expanded Google Ads units: Even Local Services Ads are consuming space that once granted organic visibility.
Impression trends continue to vary due to seasonal factors, market disparities, and occasional API glitches. Nevertheless, a clearer picture emerges by focusing on GBP actions rather than mere impressions.
Mentions within AI-generated results still count as impressions, even if they no longer convert into calls, clicks, or visits.
External factors, such as known Google API issues in June, also contribute to these fluctuations. Additionally, the spike in Google Ads investment by significant advertisers towards year-end heavily affects Mobile Maps impressions.
Currently, there’s no method to differentiate these impressions by Google Ads, organic results, or AI Mode.
Despite these challenges, user behavior is undeniably shifting. Interaction rates are dwindling, with fewer direct actions taken from local listings.
Year-on-year data from the U.S. indicates that while impression losses remain moderate and somewhat seasonal, GBP actions are disproportionately affected.
In contrast, data from the Dutch market, where SERP experiments are limited, shows far more stable action trends.
The evidence is clear. AI-driven SERP alterations, increasing Google Ads, and the removal of call and website buttons from the Map Pack are eroding organic real estate. Despite appearances, businesses have fewer opportunities to convert visibility into actual user actions.
Local SEO is becoming an eligibility problem
Traditionally, local optimization focused on key ranking factors like proximity, relevance, prominence, reviews, citations, and engagement.
There’s now an additional layer to consider: eligibility.
Some businesses find themselves absent in AI-powered local results not due to a lack of authority, but because Google’s systems deem them inadequate for the specific query context. Research from Yext and experiences shared by experts like Claudia Tomina emphasize the importance of aligning three core signals:
Business name
Primary category
Real-world services and positioning
Misalignment in these areas can prevent businesses from appearing in certain result types, regardless of how well their Google Business Profile is optimized.
How to future-proof local visibility
Navigating today’s zero-click reality involves moving beyond reliance solely on a well-optimized Google Business Profile. Here’s a new playbook for local SEO.
The eligibility gatekeeper
Inclusion in local packs is now influenced more by perceived relevance and classification than by links or review quantity.
Hyper-local entity authority
AI systems rely on platforms like Reddit, social media, forums, and local directories to evaluate if a business is legitimate and active. Inconsistencies across these platforms can erode visibility without any obvious signs.
Visual trust signals
High-quality and frequently updated photos, along with video, are critical. Google’s AI evaluates visual content to gauge services, intent, and categorization.
Embrace the pay-to-play reality
The hard truth is that Google Ads, particularly Local Services Ads, is now essential to retaining prominent call buttons that organic listings are steadily losing. Adopting a hybrid strategy that merges local SEO with paid search is no longer optional but necessary.
What this means for local search now
Local SEO has evolved beyond a simple directory exercise. Google Business Profiles remain central to local discoverability but now exist within a broader ecosystem informed by AI validation, constant SERP changes, and Google’s pursuit of local search monetization.
Visibility no longer depends solely on where your GBP ranks against local rivals. Search engines, including AI-infused SERP features and advanced models like ChatGPT and Gemini, are increasingly focused on understanding a business’s genuine purpose, not merely its listing position.
Success lies in being widely verified, consistently active, and contextually relevant within the AI-visible ecosystem.
Our findings reveal that there is little correlation between businesses ranking well in traditional Map Packs and those prioritized in Google’s AI-generated local answers. This discrepancy offers a real opportunity for businesses willing to adapt.
In essence, this entails blending local input with central management.
Authentic engagement across multiple channels, locally tailored content, and actual community signals are necessary alongside brand governance, data consistency, and operational scale. Businesses deeply ingrained in their community, discussed, recommended, and referenced, both online and offline, find themselves halfway there.
For agencies and brands with multiple locations, the challenge is balancing control with local nuances and ensuring trusted signals extend beyond Google, encompassing Apple Maps, Tripadvisor, Yelp, Reddit, and other pertinent review ecosystems. Producing locally relevant content and citations at scale without losing authenticity is the real test.
Even if rankings appear stable, true performance is occurring elsewhere.
I’ve just learned that Apple is teaming up with Google to enhance its future AI capabilities, including a much-anticipated upgrade to Siri.
What’s happening: Apple plans to utilize Google’s Gemini AI models and cloud technology to bolster its upcoming Apple Foundation Models. This significant partnership is set to launch later this year.
Why it matters to me: With Gemini’s integration, Siri is poised to transform into a genuine AI-driven answer engine, fundamentally altering how I, along with millions of iOS users, search for information and interact with digital content.
Driving the news: According to Apple, after thorough assessments, they found Google’s Gemini model to be the optimal foundation for their AI objectives.
Apple and Google have entered into a multi-year collaboration under which the next generation of Apple Foundation Models will be based on Google’s Gemini models and cloud technology. These models will help power future Apple Intelligence features, including a more personalized Siri coming this year. After careful evaluation, Apple determined that Google’s Al technology provides the most capable foundation for Apple Foundation Models and is excited about the innovative new experiences it will unlock for Apple users. Apple Intelligence will continue to run on Apple devices and Private Cloud Compute, while maintaining Apple’s industry-leading privacy standards.
The bigger picture: It’s fascinating to see that Google briefly surpassed a $4 trillion market cap last week, overtaking Apple for the first time since 2019.
Google’s Gemini 3 model became available late last year as part of its expansive AI initiatives.
Meanwhile, Apple has largely stayed out of the intense AI competition that took off with ChatGPT’s release in late 2022, while competitors invested heavily in models, chips, and cloud infrastructure.
In my conversation with Joshua Weisberg, CEO of Lambda Finance, we explored how AI is reshaping financial research. As discovery evolves from traditional search to AI-powered insights, platforms must earn trust in an era demanding clarity, accessibility, and centralization.
First Page Sage: Financial research carries significant risks where misinformation can have severe outcomes. Joshua, why do finance sectors experience shifts in search behavior and AI-driven discovery sooner than others?
Joshua Weisberg: In finance, the repercussions of poor information are swift and quantifiable. If research lacks depth or accuracy, the impact is immediately observed in performance. This urgency pushes investors to adapt their research methods faster than other industries.
As AI shapes discovery, investors scrutinize information sources and presentation more acutely. They prefer sources demonstrating depth, consistency, and reasoning, pushing financial platforms to evolve quickly. This also provides a blueprint for trust-centric industries’ behavior.
First Page Sage: With AI underpinning research, the focus shifts from keyword matching to perceived expertise and trust. How does this affect financial platforms’ approach to visibility and authority?
Weisberg: It redefines the objective. Visibility now relies on being consistently useful rather than merely optimized for keywords.
In finance, expertise emerges from effectively linking concepts and illustrating relationships. AI favors sources that provide comprehensive answers. Platforms should focus on delivering a holistic experience that conveys thorough understanding of the topic.
First Page Sage:: Fragmented user experiences can weaken authority from an SEO/GEO perspective. Lambda Finance unifies several research functionalities. Why is this vital in an AI-driven discovery realm?
Weisberg: Fragmentation causes friction for users and affects perceived expertise. When multiple tools are needed for answers, building confidence is challenging.
Unifying insights allows them to exist contextually. Connecting technical signals, fundamentals, alternative data, and portfolio analyses enhances user comprehension and signals authoritative understanding to the users.
First Page Sage: In finance, ambiguity is costly. How does effectively explaining complex data grow user trust and digital visibility?
Weisberg: Clarity is surprisingly advantageous in financial research. Even seasoned investors benefit from understanding why something is significant, not just the event itself.
By prioritizing explanation, platforms engage users deeply, leading to sustained reliance. Over time, this trust enhances digital visibility. Platforms excelling at detailing complexities often become references for both users and AI systems seeking comprehensive answers.
First Page Sage:: What error do digital leaders in finance commonly make preparing for AI-driven search? And what should they emphasize instead?
Weisberg: A common mistake is seeing AI-driven search as merely a technical challenge. While optimization is important, it doesn’t replace substantive content, especially in complex sectors like finance.
Long-term visibility relies on depth—accurate data, insightful analysis, and clear communication. Companies focusing on these fundamentals are well-equipped as search evolves, aligning with user preferences. Authority in high-stakes industries is earned through consistent utility.
This past Black Friday and Cyber Monday, I delved into the fascinating insights from our Black Friday Index, crafted from a vast pool of 400 million genuine conversations. It was enlightening to see which brands stood out as AI’s top recommendations, especially as so many of us relied on Answer Engines to hunt down the best deals.
As I explored the data, the impact of AI on shopping trends became crystal clear. The technology not only streamlined how we search for deals but also influenced brand visibility and consumer choices. The excitement of seeing how AI is reshaping shopping habits made this year’s Black Friday and Cyber Monday particularly intriguing for me.
The findings from the Black Friday Index are a testament to the growing importance of AI in retail, showing us how indispensable it has become for both consumers and brands. Being part of this evolution makes me look forward to what future shopping events will bring, especially as technology continues to advance.
When I think about Google’s Local Pack, I realize it’s not a random selection process. It’s a calculated move to reward ‘signal-fit’ brands that truly reflect user expectations.
From my experience, I see that Google isn’t prioritizing brands based on flashy ads or perfect images. Instead, they favor businesses that align with immediate user needs. This is why the traditional checklist for local SEO is outdated; it fails to account for varying customer behaviors.
In essence, Google is selective, but it favors those who fit the ‘signal-fit’ criteria. Their algorithm is far from arbitrary—it is finely attuned to intent and behavior within specific categories.
Recent trends challenge the old assumptions about Google’s algorithm. It’s not a one-size-fits-all formula; rather, it adjusts based on how individuals search. Expecting a generic strategy to work across different industries—like a burger place versus a dental practice—is unrealistic.
What the Data Shows
Through Yext’s analysis of 8.7 million Google Business Profiles, it’s clear that neither brand size nor ad budget guarantee visibility. What truly makes a difference is ‘signal fit’—how well a listing meets local users’ expectations. (Disclosure: I’m the senior director of Yext Research.)
Factors like review frequency, photo quality, and profile completeness all matter, but their impact varies by industry and region. Google’s priorities differ based on these specifics, highlighting its preference for alignment with local contexts and user needs.
For businesses with multiple locations, a distinct strategy for each is essential. You can’t force your way into the Local Pack. Industry-specific signals are key to success in this dynamic environment.
The concept of ‘signal-fit’ is best seen through industry-specific nuances where Google’s algorithm adapts to unique consumer expectations.
Hospitality: Practical information outweighs visual appeal. Hours, descriptions, and comprehensive profiles are crucial, while excessive photos offer little extra value. Travelers prioritize essential details over pretty pictures.
Healthcare: Patient satisfaction and accessibility are paramount, with reviews, accurate hours, and clear location details being more impactful than visuals. In healthcare, trust stems from reliability.
Retail: Customer opinions carry significant weight. Review volume and sentiment sharply define leaders from laggards, second only to healthcare. A polished listing indicates a well-run store, while neglect hints at mismanagement.
Food and Dining: This category is competitive, with review ratings and consistent engagement being the most important signals. Profile completeness matters less than responsiveness and active feedback.
Financial Services: Trust is built through reputation and real-world experience, with genuine reviews far outweighing polished photos in establishing confidence.
Regional variations influence these rules slightly but don’t overturn them. For instance, Northeast restaurants benefit from social media links, while healthcare listings in certain areas value other attributes.
Google’s notion of ‘relevance’ remains inherently local, always aligning with regional consumer expectations.
How to Align Each Location with Local Consumer Signals
Optimizing Google Business Profiles requires attention to vertical-specific nuances. Treating each location identically simplifies processes but sacrifices visibility where it counts.
Local SEO strategies must be regularly reassessed because a universal checklist approach is no longer viable. Agility is key.
Measure the localization effects: Evaluate each location within its unique context, understanding user interactions and preferences.
Prioritize relevant signals: Focus on GBP features that matter most for your business category, optimizing for relevance rather than routine.
Implement continuous testing: Treat local SEO as an ongoing experiment. Utilize test markets to compare strategies and identify effective approaches rapidly.
Foster authentic engagement: Engage with reviews as part of an ongoing conversation. Quick, sincere responses build credibility with both customers and algorithms.
Maintain your digital footprint: Keep information current. Even small updates can lead to significant gains; a 1% increase in updates can boost Google clicks by 2.23%.
Why Precision Will Decide Who Gets Seen Next
Google continually evolves with user behavior, learning and adapting. Generic SEO approaches have their limits and can cost revenue.
While ‘best practices’ might keep you on the radar, they won’t ensure success in a competitive landscape. As AI condenses search choices, visibility depends more than ever on precision.
A localized GBP strategy isn’t just beneficial—it’s essential. Google’s Local Pack rewards relevance, not routine. By transcending generic methods and embracing precision, marketers can leverage local SEO powerfully.
Align with consumer signals, and your brand will keep its visibility even when the SEO playbook changes.
The real threat is not doing anything differently; it’s doing the same thing everywhere.
Imagine being able to simply type what data you want in a report, and, voilà, Google creates it for you on the spot. That’s exactly what’s happening with Google’s new experimental feature in Search Console!
Recently, I learned about Google’s exciting “AI-powered configuration” update within the Search Console Performance report. This experimental feature allows you to request a specific report, and Google will instantly configure it for you. Not everyone can access it just yet, but it’s definitely something to keep an eye on.
I immediately thought of the AI-powered advisors Google offers for Ads and Analytics. Now, similar technology is being harnessed for Search Console. According to Google, this AI-powered configuration lets you describe the analysis you want in everyday language. Your description is then transformed into appropriate filters and settings, configuring the report instantly!
Curious about how it looks? There’s a GIF demonstration that perfectly showcases how it generates reports based on your questions, making the process seem almost magical.
The cool part is that this feature streamlines your analysis by handling several key elements. First, it automatically selects metrics like Clicks, Impressions, Average CTR, and Average Position based on your query.
It also applies filters to narrow down data by different factors such as query, page, country, device, search appearance, or date range. Additionally, you can set up complex comparisons without having to fiddle with manual setup.
Although the rollout is currently slow, Google plans to expand this feature over time. But, being early days, the feature does have some limitations. It’s solely meant for configuration tasks and doesn’t perform actions like sorting tables or exporting data.
The feature currently only supports the Performance report for Search results and isn’t available for Discover or News reports. Also, since the AI might occasionally misinterpret requests, I recommend reviewing the suggested filters to make sure they align with your needs before diving into data analysis.
Honestly, I’m excited about this tool because it could potentially unveil insights in reports that were otherwise challenging to discover using standard filters. While most of us still need to wait for this feature to be accessible, it’s worth a try once available to explore new data insights.
If you’re looking for more information, head over to the Google help documentation. There’s plenty to learn and get familiar with!
