I’m seeing Google add a new Channel Diagnostics feature to Performance Max, and it gives advertisers a more centralized way to understand asset issues that may be holding back campaign delivery across Google’s channels.
The new Channel Diagnostics section is available inside Insights & Reports > Channel Performance for Performance Max campaigns. For me, the value is that advertisers no longer have to dig as deeply to figure out whether missing or disapproved assets are limiting where a campaign can serve.
With this update, I can review diagnostics across all Performance Max channels or drill into a specific channel when I need more detail. I can also identify missing or disapproved assets that affect campaign eligibility and see which asset types, such as headlines, descriptions, or images, need attention.
This matters because Performance Max has often been criticized for limited visibility into campaign issues. I see Channel Diagnostics as a useful step toward making those issues easier to spot, especially when missing creative assets may prevent campaigns from serving across Search, Display, YouTube, Discover, Gmail, and Maps.
By surfacing channel-specific asset gaps in one place, Google is giving advertisers more actionable insight without forcing them to manually audit every asset group. That can make troubleshooting faster and help teams prioritize the fixes most likely to restore eligibility or improve delivery.
The bottom line is that Channel Diagnostics gives Performance Max advertisers a quicker way to identify and fix missing assets. I see it as a practical improvement for keeping campaigns eligible across Google’s full range of inventory.
This update was spotted by a Google Ads Specialist who shared it on LinkedIn.
I can now use Google Trends to quickly add previous time period data to a chart, making it easier to see how search interest compares with the same length of time immediately before it.
Google announced the update on LinkedIn, saying that I can now compare how a trend has changed against preceding periods directly inside Google Trends.
What it looks like. Google shared a GIF showing the feature in action, with a comparison line added directly to the Trends chart for faster context.
How it works. I can go to Google Trends, enter a search term or topic, and then use the new chips that appear above the timeline. Those chips surface percentage changes across different periods, including month-over-month, week-over-week, and specific year-over-year comparisons.
With one click, I can overlay the historical comparison line onto the graph and immediately see whether interest is rising, falling, or following a familiar seasonal pattern.
Why I care. Google Trends is already a helpful source for spotting topics, keywords, and audience interest patterns. When I am planning content, SEO priorities, or marketing campaigns, being able to compare current demand against a previous period gives me a clearer read on timing and momentum.
This update gives me more historical perspective inside Google Trends, which can make trend analysis faster and more useful for content strategy and marketing planning.
After nearly 30 years at Microsoft, I am seeing one of Bing’s most influential search leaders close a remarkable chapter. Fabrice Canel announced that he is retiring from Microsoft, writing on LinkedIn, “I am retiring from Microsoft, effective today July 1st.” He also reflected, “Today marks nearly 30 years with Microsoft. Thirty years…”
When I think about Fabrice Canel’s impact, I think first about the foundation of Microsoft Bing Search. He was responsible for indexing at Bing, including crawling, URL discovery, content selection, and content processing. Those areas are core to how search engines understand the web, and Fabrice helped shape them at massive scale.
He was also the person behind the IndexNow initiative, and he played a major role in creating and powering Bing Webmaster Tools. For anyone working in SEO, publishing, or technical search, those contributions matter because they helped make discovery, indexing, and webmaster communication faster and more practical.
I have watched Fabrice contribute far beyond product work. He has spoken at countless industry events, including SMX, and has written extensively about how search works, how sites can perform better in Bing, and how search is evolving with generative AI. He helped run one of the world’s most important search engines, while also giving the SEO community tools, education, and direct insight.
In his retirement message, Fabrice addressed fellow Microsoftees, engineers, attorneys, marketers, webmasters, publishers, SEO champions, product leaders, journalists, people across search and AI, and even friends at Google. His note was warm, personal, and full of gratitude for the people who shaped his Microsoft journey.
He described his three decades at Microsoft as a wonderful adventure, from solving real business problems with IndexNow to helping webmasters and publishers thrive in the constantly changing world of SEO and AI. He thanked colleagues, partners, publishers, and the people he trained and mentored, saying they are ready to carry the mission forward.
Fabrice also shared that, after many conversations with family and friends, he decided to take advantage of Microsoft’s Voluntary Retirement Program. His message ended with the same sense of warmth and storybook style that many in the industry have come to associate with him: gratitude for Microsoft, confidence in the Bing team’s future, and a final wish that everyone stay curious, keep innovating, and make content easier to find.
Why do I care so much about this? Because Fabrice has been a true friend to the search industry. His work will live on through the products, systems, and initiatives he helped create, and his willingness to share knowledge has made a lasting difference for SEOs, publishers, developers, and search professionals.
I know Fabrice has trained a team to continue the work, and I believe Bing remains in good hands. Still, I would be lying if I said I am not sad to see him retire. It has been an honor to work with him and learn from him over the years, and his legacy at Microsoft Bing will be felt for a long time.
I’m seeing Google expand its measurement capabilities for YouTube brand campaigns, and the goal is clear: advertisers are getting better visibility into how video ads influence engagement, brand interest, and downstream business outcomes.
What’s new: I’m paying attention to two updates in particular: Shorts Ad Actions for Video View Campaigns and Attributed Branded Searches.
Shorts Ad Actions for Video View Campaigns: When advertisers run Video View Campaigns that are opted into YouTube Shorts, they will now automatically benefit from Shorts Ad Actions in budget optimization. Google is also adding new reporting columns so advertisers can measure these interactions more clearly.
Attributed Branded Searches: Now available globally in Google Ads, this reporting metric measures branded Google searches that happen after someone sees or views a YouTube ad. I see this as a useful way to understand how awareness campaigns may influence purchase intent before a direct conversion takes place.
Why I care: It has always been difficult to connect upper-funnel YouTube campaigns with measurable business outcomes. These updates give marketers stronger signals that link brand advertising to engagement and search intent, which can make it easier to justify brand investment and improve campaign decisions.
By the numbers: According to Google, YouTube Shorts ads that generated more than 10 seconds of watch time and a like delivered 15% higher brand consideration and 20% higher brand favourability.
Google also says every additional branded search generated is associated with an average $31 increase in sales, which gives advertisers another way to connect brand activity with business impact.
Between the lines: I see Google continuing to blur the distinction between brand and performance marketing by introducing metrics that connect awareness campaigns with downstream actions. Attributed Branded Searches, especially, gives advertisers another way to show that YouTube campaigns can influence high-intent behaviour before a conversion happens.
The bottom line:Google’s latest measurement updates help advertisers better prove the value of YouTube brand campaigns by linking video engagement and branded search activity to business outcomes. For me, the bigger story is that upper-funnel advertising is becoming easier to measure in ways that matter to performance-focused teams.
I’m watching Google update its advertising policy to make clearer how certain ads are limited while the company estimates a user’s age. The change gives advertisers more transparency as Google expands its age assurance technology worldwide.
What I’m seeing: Google has renamed its Default Ads Treatment policy to “Categories restricted while Google is estimating a user’s age.” To me, that wording matters because it makes the policy sound less like a permanent restriction and more like a temporary safeguard while Google’s systems work out whether a user is old enough to see certain types of ads.
What’s changing: I see three main updates here: the policy has a clearer name, the language now emphasizes that these protections are interim measures during the age estimation process, and enforcement remains unchanged.
What’s different: Google has also narrowed the list of ad categories restricted while a user’s age is being estimated. Previously, the restricted categories included adult content and pornography, alcohol, gambling, and shocking content.
Under the updated policy, I now see only three restricted categories: adult content and pornography, alcohol, and gambling. Shocking content no longer appears on that restricted list.
Why I care: This update does not introduce new advertising restrictions, but it does make the policy easier to understand. For advertisers in affected verticals, the key takeaway is that these limits are tied to Google’s age estimation process, not a broader or permanent policy shift.
The bottom line: I do not see any operational change for advertisers, but Google’s updated policy makes it much clearer that restrictions on adult, alcohol, and gambling ads are temporary safeguards while a user’s age is being estimated.
I’m seeing Google Ads roll out a redesigned All Campaigns selector, and the goal is clear: make it easier to move through large, complicated account structures without wasting time hunting for the right campaign.
What’s happening is that Google is refreshing the All Campaigns selector across Google Ads with a cleaner layout and better navigation tools. For advertisers who manage bigger accounts, this should make day-to-day campaign work feel more organized.
The selector has also been moved to a new location in the interface, which means I’d expect some advertisers to need a short adjustment period before the new placement feels familiar.
The biggest improvement I notice is the new expandable hierarchy view. Campaigns now appear in a structure that makes campaign groups and nested setups easier to browse, especially when an account has grown beyond a simple list of campaigns.
Google has also added search inside the selector, which should help advertisers quickly find specific campaigns or campaign groups instead of manually scanning through long account lists.
Why I care: this update could save meaningful time for anyone managing large Google Ads accounts. When campaigns are split across multiple groups or complex organisational structures, faster navigation can make daily optimization work less frustrating.
The bottom line is that Google’s redesigned All Campaigns selector is meant to streamline campaign management with a clearer hierarchy and built-in search, helping advertisers navigate complex accounts more efficiently.
The update was first spotted by performance marketer Vivek Gupta on LinkedIn. Since the rollout is gradual, I would not expect it to be available in every Google Ads account immediately.
I’m seeing Microsoft bring experimentation into Performance Max campaigns, giving advertisers a more practical way to test campaign changes and measure incremental impact without disrupting live performance.
What’s new: Microsoft is adding two Performance Max experiment types designed to help advertisers understand whether their campaigns are truly driving better results.
Uplift experiments help me measure the incremental impact of Performance Max campaigns by comparing results against a control group.
Upgrade experiments give me a way to compare an existing campaign with an upgraded Performance Max version before I fully roll out the change.
For eligible accounts, both experiment types are available under Campaigns > Experiments.
Why I care: Until now, Microsoft Ads experiments were limited to Search campaigns. Bringing testing into Performance Max gives advertisers a safer path to validate changes, improve performance, and make more data-driven decisions before committing budget.
Between the lines: As Microsoft expands experimentation, it has also renamed its existing experiment offering to Search optimization experiments. That distinction helps separate traditional Search testing from the new Performance Max testing capabilities.
I see this as part of Microsoft’s broader push to give advertisers more advanced optimization tools across automated campaign formats.
The bottom line: Microsoft is closing an important gap in its Performance Max offering. With dedicated uplift and upgrade experiments, advertisers can test with more confidence and get a clearer view of the real impact of automated campaigns.
First spotted: The help docs were spotted by PPC News Feed founder Hana Kobzová.
I’m seeing Google make recipe results in AI Mode more publisher friendly with a new visual treatment that gives recipe creators more visibility. For some recipe responses, Google is now showing details such as the creator name, recipe ratings, and the number of ingredients.
What is new. Google’s Robby Stein said AI Mode now includes “prominent links at the top of responses with useful details and images,” including creator names, ratings, and ingredient counts. From my view, the key shift is that Google is trying to make recipe sources easier to recognize and visit directly from AI Mode.
What it looks like. The new treatment places recipe links, images, and useful recipe details more prominently in the AI Mode experience, giving users a clearer path from the AI-generated response back to the original recipe page.
Previously. Back in March, Robby Stein announced earlier changes to recipe results in AI Mode. At the time, he said Google had heard feedback and was making updates to better connect people with recipe creators across the web.
I see this latest update as part of Google’s effort to address concerns around AI recipe slop and to make original recipe content more visible when people search for cooking ideas through AI-powered results.
Why I care. Recipe bloggers, and content creators more broadly, have been frustrated that Google’s AI experiences often send less traffic than traditional search results. This update suggests Google is trying to encourage more searchers to click through from AI Mode to the publishers and creators behind the recipes.
If Google continues adding more clickable link units into AI search experiences, I think it could help ease some of the tension between publishers and Google. The bigger question is whether these changes will drive enough meaningful traffic back to recipe sites and other content creators.
Earlier this year, I made the case that the core fundamentals of international SEO still matter. I still believe that. Hreflang, localization, technical excellence, and market-specific content remain essential because search engines and LLMs still need to discover, understand, and connect content with the right audiences.
What has changed is the environment those fundamentals now operate in.
For decades, I watched multinational organizations treat markets as mostly separate digital ecosystems. Content created in one market usually stayed there, and governance focused on managing websites, content, and technical implementation across different regions.
Today, those boundaries are much harder to see.
AI systems can translate content, synthesize information from multiple sources, and increasingly sit between organizations and their customers. Information that once lived inside one market can now shape visibility, recommendations, and customer experiences across many regions.
As those boundaries blur, I see the governance challenge expanding. International SEO is no longer only about managing websites across countries. It now requires organizations to manage the knowledge, expertise, and information that search engines and AI systems use to represent them globally.
Why I believe the governance model must change
Historically, many website and localization decisions were built around operational efficiency. Headquarters created content, technology platforms, and standards for global distribution, while local markets adapted those assets for their own audiences.
That model worked because scale often outweighed the limitations of localization. Consistency improved, costs came down, and organizations could deploy content and technology across dozens of markets far more efficiently than local teams could manage independently.
The challenge now is that AI systems are changing what gets rewarded.
Scale and standardization still matter, but search engines and AI systems increasingly look for signals of expertise, relevance, and geographic specificity. Content that reflects local regulations, market conditions, customer expectations, and industry practices often provides context that translation alone cannot reproduce.
At the same time, AI systems can magnify inconsistency. Contradictory product information, conflicting entity definitions, inaccurate regulatory guidance, and fragmented technical implementations can create confusion across search engines, answer engines, and AI-powered experiences.
That is why I do not think organizations can optimize only for efficiency or only for localization anymore. They need governance models that protect global consistency while giving local markets room to contribute the expertise and context that increasingly drive visibility and trust.
Hreflang solved routing, not understanding
In my previous hreflang article, I argued that hreflang still belongs in an international search strategy, even in the age of AI. I stand by that view.
But hreflang does not decide which market perspective should be prioritized when AI systems synthesize information from multiple sources. It also does not determine which content demonstrates the strongest expertise when AI-generated answers are produced.
As search moves from retrieval toward synthesis, I believe organizations need to think beyond routing users to the right page. They also need to govern the knowledge that powers those answers.
What I would centralize
My simplest rule is this: if an activity creates enterprise risk when it is handled inconsistently, it should usually be governed centrally.
Technical SEO standards are a clear example. Search engines and AI systems do not evaluate websites one market at a time. They evaluate the broader ecosystem of signals an organization provides. CMS governance, structured data standards, entity definitions, AI crawler policies, measurement frameworks, and technical infrastructure all benefit from consistency.
Many international organizations have already faced a version of this problem.
Years ago, before hreflang existed, many global companies used IP detection to route users to the market website they believed was most appropriate. The problem was that Google primarily crawled from U.S.-based IP addresses. When Google tried to access French or Japanese content, it was often redirected to the U.S. site instead.
Individual markets could not solve that on their own because the routing rules affected every market at once. The solution required global governance with local input.
I see AI crawler management creating a very similar challenge today.
Organizations now have to decide which AI systems can access their content and whether those systems can reach the market-specific information they are meant to understand. For companies still relying on geographic routing, market gateways, or IP detection, the governance issue should feel familiar even if the technology is new.
The platforms have changed, but the lesson has not. Some decisions are too interconnected to manage market by market.
What I would localize
If technical infrastructure benefits from consistency, content benefits from expertise.
For years, multinational organizations followed a simple model: create content in the primary market, then translate, adapt, and distribute it globally. That approach delivered real efficiencies. It helped organizations scale content production, maintain brand consistency, and support dozens of markets with shared resources and common technology platforms.
Traditional search engines could lean on signals like hreflang and country targeting to understand regional relevance. AI systems increasingly evaluate the content itself. When multiple markets publish nearly identical versions of the same information, language models may treat them as variations of one source rather than distinct expressions of expertise.
To stand on its own, content increasingly needs market-specific signals such as local regulations, terminology, customer expectations, industry practices, and other forms of geographic specificity.
That is why I believe content ownership, audience research, local authority building, regulatory content, and market expertise should usually stay close to the market. The goal is not localization for its own sake. The goal is to make sure expertise comes from the people closest to the customer and that the content reflects the realities of the market it serves.
The strongest multinational organizations will still use global content frameworks, shared resources, and common technology platforms because those efficiencies remain valuable. The hard part is preserving those efficiencies while giving local markets enough space to contribute expertise that is visible, differentiated, and meaningful.
For years, organizations balanced scale against localization. Increasingly, I think they are balancing scale against representation. The markets that remain visible in AI-driven search experiences will often be the ones that contribute enough unique expertise to stand on their own, rather than simply echo the dominant market version.
What I think needs shared ownership
Governance ultimately comes down to accountability. Whether responsibility sits with a Chief Digital Officer, CMO, enterprise search team, or AI governance group matters less than whether ownership is clear. As search becomes more connected to marketing, technology, product, legal, and AI initiatives, organizations need clear decision rights, escalation paths, and accountability.
The companies that succeed will not necessarily be the ones with the largest SEO teams or the most advanced AI tools. I expect the winners to be the organizations that know exactly how knowledge is created, governed, validated, and represented across markets.
My practical rule for determining ownership
For me, the distinction comes down to risk and expertise.
Responsibilities that create enterprise-wide consequences when implemented inconsistently generally belong closer to headquarters. Activities that depend on local customer knowledge, regulations, language, or market conditions are usually best managed in-market.
Many of the most important decisions need both perspectives, which means they are best handled through shared governance.
10 governance decisions I would review with every global SEO team
The exact structure will vary by organization, but I would encourage most multinational companies to evaluate ownership across these areas.
Typically centralized
1. Technical SEO standards
I would centralize these standards to ensure consistency in crawling, indexing, structured data, and technical implementation across markets.
2. CMS and infrastructure governance
I would govern this centrally to prevent fragmentation while maintaining a common technology foundation.
3. Entity definitions and taxonomies
I would keep these consistent so products, services, brands, and organizational relationships are represented clearly across markets.
4. AI crawler and bot governance
I would establish consistent policies for crawler access, monitoring, verification, geographic routing, and exception management. Governance should usually sit with headquarters, while markets should still be able to request business-specific exceptions.
5. Measurement and reporting frameworks
I would centralize reporting definitions so markets are evaluated with comparable success metrics.
Typically localized
6. Market-specific content
I would keep creation and validation close to local teams so content reflects customer needs, regulations, terminology, market conditions, and the geographic signals that help AI systems recognize local relevance. Global content frameworks can still support that work where appropriate.
7. Audience and search behavior research
I would manage this in-market to capture differences in language, intent, customer expectations, and emerging market trends.
8. Local authority building
I would localize this work because market-specific expertise, trust, partnerships, citations, and visibility cannot be fully manufactured from headquarters.
Typically shared
9. Product and knowledge management
I would treat this as shared ownership because it needs global consistency as well as local validation, market expertise, and regulatory accuracy. Headquarters should define the framework, while markets validate that products, services, and policies reflect local realities.
10. AI visibility and representation
I would also make this shared. Headquarters should establish monitoring and escalation processes, while local teams validate market-specific accuracy and identify emerging issues in how products, services, and brands are represented across AI systems.
The new global SEO mandate
I do not think the objective is to centralize everything or localize everything. The real mandate is to place ownership where decisions can be managed most effectively, so the organization can balance consistency with expertise.
I see generative AI and automation creating both excitement and anxiety across the SEO industry. With 87% of Americans reading AI summaries, I believe any SEO team that is not adapting its toolset is already starting to fall behind.
When I move away from rigid enterprise tools and toward agile, AI-driven workflows, I can work faster, spot new search signals earlier, and show clients or internal stakeholders that I understand where search is heading.
In this guide, I’ll walk through what the old SEO stack looked like, what I now add to it, and how I combine both approaches without abandoning the fundamentals that still matter.
Here’s what an old SEO stack looks like
I still believe traditional SEO practices matter because generative AI search experiences continue to depend on core search ranking systems, quality systems, and the broader signals search engines have used for years.
That said, the classic SEO stack was built for a simpler search environment. It usually centered on rank tracking, keyword research, and technical site audits.
Rank trackers
For a long time, I treated keyword rankings as the heartbeat of an SEO campaign. I would add target keywords, monitor SERP positions, and expect higher rankings to translate into more search traffic. But rankings have become far more fragmented.
Now I need to pay attention to AI Overviews, local packs, shopping carousels, and many other search features that can change the value of a ranking completely.
A third-place local pack ranking, for example, may drive two or three times more traffic than a number one ranking in an AI Overview. That makes old-school rank tracking useful, but incomplete.
Keyword tools
Keyword tools still help me understand what people search for, how competitive a topic might be, and which queries match specific user intent. In the past, that information often felt close to a crystal ball.
I would choose keywords based on difficulty, search volume, intent, and other factors. The better the data, the easier it was to shape a campaign around the right opportunities.
The problem is that search volume has always looked backward. A keyword may have shown 10,000 monthly searches last month, but that does not mean it will perform the same way this month. Demand can rise, fall, or shift quickly.
Today, the bigger issue is opportunity loss. A keyword that generated tens of thousands of clicks in 2022 may now be answered directly inside an AI Overview. Even when search volume has not dropped, zero-click behavior can reduce the traffic I can realistically capture.
Site audit tools
I still rely on site audit tools because crawlers still crawl websites, interpret content, and surface technical issues. I need to know whether search engines can access, understand, and navigate the pages I care about.
Audit tools help me find broken links, redirect problems, missing metadata, slow pages, thin content, and other technical issues that can hold a site back.
But I do not expect crawl audits alone to tell me whether my content will appear in AI-driven search experiences. Technical health is necessary, but it is no longer the full picture.
Signals such as brand mentions can influence whether a site is included in LLM outputs from tools like ChatGPT, Claude, and Gemini. Many older site audit tools were not built to track those signals.
That is why I still keep parts of the old stack, but I now add tools and workflows that help me understand AI visibility, brand presence, and faster data-driven decision-making.
Here’s what a new SEO stack looks like
If I am optimizing only for Google’s traditional results, I am missing where search behavior is moving. Between the first and second half of 2025, LLM referral traffic grew by 80%. Conversion rates reached 18%, even though LLM referrals still represented 2% or less of total traffic in the dataset.
That tells me the channel is still small, but meaningful. Now is the time to build a stack that helps me understand, measure, and improve performance across AI-driven discovery.
LLMs
I want my site to appear in LLM responses, but I also use LLMs to strengthen my SEO process. These tools can support analysis, content review, competitor research, metadata refinement, and structured data work.
For example, I can connect ChatGPT with Google Search Console to automate SEO analysis, use Claude to refine copy and conduct content audits, or use Gemini to generate schema markup and compare competitor pages against my own.
I use the LLM that best fits the task, but I keep human oversight in place. These tools help me improve speed and performance; they do not replace judgment, strategy, or editorial review.
The biggest shift is speed. Large datasets that once took hours, days, or weeks to review can now be explored in minutes when I use LLMs carefully and integrate them into a repeatable workflow.
APIs
The old workflow often meant logging into dashboards, exporting CSV files, and cleaning everything in Excel. I still do that when needed, but APIs let me pull data directly from platforms like Google Search Console and Google Analytics.
APIs can sound intimidating, but LLMs make the learning curve easier. I can use them to help with authentication, JSON parsing, and the basic structure of repeatable data workflows.
Once I can connect to APIs, I can stop waiting on manual exports and start building faster reporting, monitoring, and analysis systems around the data I already use.
Lightweight scripts
Python scripts are now within reach for many SEOs, especially with tools like Claude Code and similar coding support inside ChatGPT or Gemini. I do not need to be a full-time developer to automate repetitive SEO work.
I can create scripts that pull top pages from Google Search Console, compare title tags against character limits, flag 30-day performance changes, or generate a clean CSV output for review.
Instead of waiting for a vendor to add the exact feature I need, I can build a small script that removes a bottleneck. A hundred-line script can replace hours of manual work without requiring another SaaS license.
I also like that scripts make the logic visible. If I hand the workflow to another teammate, they can inspect what the script does and understand how the output was created.
Notebooks and local workflows
SEO teams usually have data scattered across shared folders, Google Sheets, Notion docs, monthly CSV dumps, and long-running audit trackers. I have seen how quickly that fragmentation slows decisions down.
Notebooks and local workflows help me turn scattered files into a working system. A script can pull the data, an API can surface the signal, and an LLM can help interpret the results before the output lands in a notebook or spreadsheet.
The value is consistency. I get cleaner data formats, shared access, and documented logic instead of rebuilding the same process every time someone needs a report or audit update.
As search optimization becomes more connected to generative AI, I need workflows that scale. Local workflows help me keep data consistent while giving the team a faster way to act on what we find.
Creating hybrid workflows that mix old and new SEO stacks
I do not think the old SEO stack is obsolete. I also do not think the new tools replace everything. The strongest approach is a hybrid workflow that keeps proven SEO fundamentals while adding AI, APIs, scripts, and notebooks where they create real leverage.
Tool + custom script + AI layer
To build a practical hybrid workflow, I would start with a familiar audit tool such as Screaming Frog, then run a Python script that joins the crawl data with Google Search Console data.
From there, I could flag pages with high impressions and low clicks, send those pages to an LLM for title and intent analysis, place the output into a notebook or spreadsheet for editors, and turn approved recommendations into change logs.
Work like this used to take weeks, so many teams pushed it aside. At enterprise scale, the amount of data could easily become overwhelming. With a hybrid SEO stack, I can complete larger projects in a fraction of the time.
For me, the goal is not to chase every new tool. The goal is to build a more agile SEO stack that can handle today’s massive datasets, identify AI search signals, and help teams move faster without losing the core SEO basics.
