Tag: Automation

My New SEO Stack: Tools I Use for Faster AI Search Wins

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.

Inspired by this post on Search Engine Land.

July 3, 2026

How I Use Vibe Coding to Build Practical SEO Tools

I see vibe coding as one of the most accessible ways to create small pieces of software with AI tools like ChatGPT, Cursor, Replit, and Gemini. Instead of writing code line by line, I describe what I want in plain language, receive working code in return, paste it into an environment like Google Colab, run it, and test the result.

Collins Dictionary named “vibe coding” word of the year in 2025, defining it as “the use of artificial intelligence prompted by natural language to write computer code.”

In this guide, I’ll explain how I approach vibe coding, where I think it works well, where it breaks down, and which SEO examples can inspire practical projects of your own.

Vibe coding variations

I use “vibe coding” as a broad term, but it helps to separate it from nearby approaches:

Type	Description	Tools
AI-assisted coding	AI helps write, refactor, explain, or debug code. I usually associate this with developers or engineers who already understand the systems they are building.	GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding	AI handles most of the work after I provide the idea or prompt.	ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms	Platforms handle what I ask for through visual interfaces, drag-and-drop workflows, or background automation. Many now use AI, but they existed before AI became mainstream.	Notion, Zapier, Wix

For this guide, I’m focusing only on vibe coding.

The barrier to entry is low. In most cases, I only need a ChatGPT account, free or paid, and access to a Google account. Depending on the project, I may also need API access or subscriptions to SEO tools such as Semrush or Screaming Frog.

I also like to set expectations early: by the end of this kind of workflow, I’m usually aiming to run a small program in the cloud. If I want to build a SaaS product or software I plan to sell, I treat AI-assisted coding as the more realistic path because it usually requires more technical knowledge, more testing, and more budget.

Vibe coding use cases

I find vibe coding most useful when I’m working with clear buckets of data and need a helpful outcome, not a perfect one. That might mean finding related internal links, adding pre-selected tags to articles, comparing groups of URLs, or building something playful where the output does not need to be exact.

For example, I built an app that creates a daily drawing for my daughter. I type a short phrase about something she told me, such as “I had carrot cake at daycare.” The app uses examples of drawing styles I like and a few pictures of her, then generates a drawing as the final output.

When I ask for precise changes, the tool often gets worse. I once asked it to remove a mustache, and instead it recolored the image. That is exactly the kind of limitation I expect with this approach.

If my daughter were a client reviewing every detail, I would need someone with Photoshop or similar skills to make exact edits. For this use case, though, the result is good enough, and that is where vibe coding shines.

I would be cautious about building commercial applications solely through vibe coding. Some companies may even need vibe coding cleaners to clean up AI-generated work. But for demos, MVPs, internal tools, and quick experiments, I see vibe coding as a useful shortcut.

How I create SEO tools with vibe coding

When I create an SEO tool with vibe coding, I usually follow three steps:

I write a prompt describing the code I need.
I paste the code into a tool such as Google Colab.
I run the code and check whether the results match what I expected.

Here’s a real prompt example from a tool I built to map related links at scale. After crawling a website with Screaming Frog and extracting vector embeddings through the crawler’s OpenAI integration, I vibe coded a tool to compare topical distance between the vectors for each URL.

This is exactly what I wrote in ChatGPT:

I need a Google Colab code that will use OpenAI to:

Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A).

The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.

I’ll upload a CSV with these columns and expect a CSV in return with the answers.

After ChatGPT generated the code, I pasted it into Google Colab, which is a free Jupyter Notebook environment for running Python in a browser. I then used “Run all” to test whether the program produced the output I wanted.

That is the clean version of the process. In practice, AI can make the workflow look perfect while still producing code that does not behave the way I need.

I expect issues along the way, and most of them are simple to troubleshoot if I keep the prompt and testing process clear.

First, I always state the platform I’m using. If I want code for Google Colab, I say that directly in the prompt.

Sometimes I still get code that depends on packages that are not installed. When that happens, I paste the error back into ChatGPT and ask it to fix the code or suggest an alternative. I do not need to fully understand the missing package to move forward. I can also ask Gemini inside Google Colab to identify the problem and update the code directly.

I also check outputs carefully because AI can sound confident while inventing data. One time, I forgot to specify that the source data would come from a CSV file, so the tool created fake URLs, traffic, and graphs. “It looks good” is not the same as “it is correct.”

If I connect to an API, especially a paid API from a provider such as Semrush, OpenAI, Google Cloud, or another platform, I need to request my own API key and keep usage costs in mind.

A Semrush subscription screen highlights 2 million Standard API units and a masked API key, underscoring the setup step needed for SEO automation and vibe-coded tools.

If I want an even lower execution barrier than Google Colab, I can use Replit.

With Replit, I can prompt what I want, and the platform can generate the code, design the interface, and let me test everything in one place. That reduces copy-and-paste work and gives me a shareable URL quickly. I still need to review poor outputs and keep iterating until the app behaves properly.

The tradeoff is cost. Google Colab is free unless I use paid API keys, while Replit charges a monthly subscription and usage-based API fees. The more the app runs, the more expensive it can become.

SEO vibe-coded tools that inspire me

Google Colab is the easiest place for me to start, but SEOs are taking vibe coding much further. I’ve seen people create Chrome extensions, Google Sheets automations, and even browser games.

I’m sharing these examples because they show what is possible when useful SEO ideas meet practical AI tooling. If I see a tool and wish it had a different feature, that is often a sign that I could try building a version for myself.

A Replit vibe-coding session turns a simple prompt for toddler-friendly daily jokes into a published web app, illustrating how AI tools can quickly prototype playful ideas.

GBP Reviews Sentiment Analyzer by Celeste Gonzalez

After vibe coding SEO tools in Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, pushed the idea further by creating a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.

Her extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis from reviews over the last 30 days and shows review velocity. It also exports the information to CSV and works on Google Maps and Google Business Profile pages.

Instead of relying only on ChatGPT, Celeste used Claude to create stronger prompts and Cursor to turn those prompts into code.

AI tools used: Claude (Sunner 4.5 model) and Cursor

APIs used: Google Business Profile API (free)

A vibe-coded GBP Sentiment Analyzer turns review data into a quick snapshot, showing negative sentiment trends, key topics, and an export option for SEO workflows.

Platform hosting: Chrome Extension

Knowledge Panel Tracker by Gus Pelogia

I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Later, I discovered that Google’s Knowledge Graph Search API lets me check the confidence score for any entity.

That led me to build a vibe-coded tracker that checks entity scores daily, or at any frequency I choose, and returns the results in a Google Sheet. I can track multiple entities at once and add new ones whenever I need to.

The Knowledge Panel Tracker runs entirely in Google Sheets, and the Knowledge Graph Search API is free to use. This guide explains how to create and run it in your own Google account, or you can see the spreadsheet here and update the API key under Extensions > App Scripts.

AI models used: ChatGPT 5.1

A spreadsheet-based Knowledge Panel Tracker turns entity searches into structured SEO data, comparing names, entity types, descriptions, and confidence scores at a glance.

APIs used: Google Knowledge Graph API (free)

Platform hosting: Google Sheets

Inbox Hero Game by Vince Nero

I also like the idea of vibe coding a link building asset. That is what Vince Nero from BuzzStream did with the Inbox Hero Game. The game asks players to use the keyboard to accept or reject a pitch within seconds, and it ends if they accept too many bad pitches.

Inbox Hero Game is more complex than running a small script in Google Colab, and it took Vince about 20 hours to build from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.

The game was built with HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.

A retro arcade-style Inbox Hero screen turns PR pitch triage into a fast keyboard game, challenging players to accept or reject emails before time runs out.

He also found that longer prompt threads became less useful over time, “to the point where [he’d] have to restart in a new chat.”

That became one of the hardest parts of the project. Vince would add a feature, such as a score, and ChatGPT would “guarantee” it had found the error, update the file, and still return the same problem.

In the end, Inbox Hero Game shows that it is possible to create a simple game without coding knowledge. It also shows where a developer becomes valuable when the goal shifts from “working prototype” to polished product.

AI models used: ChatGPT

APIs used: None

Platform hosting: Webpage

How I think about vibe coding with intent

I do not expect vibe coding to replace developers, and I do not think it should. What I do see is a practical way for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without a heavy technical lift.

The key is realism. I use vibe coding where precision is not mission-critical, I validate outputs carefully, and I stay alert for the moment when a project grows beyond “good enough” and needs stronger technical support.

When I approach vibe coding thoughtfully, it becomes less about shipping perfect software and more about expanding what I can test. For internal tools, proofs of concept, and SEO side projects, the best results come from pairing curiosity with restraint.

Inspired by this post on Search Engine Land.

July 3, 2026

Google Ads API Ending Smart Campaign Creation: My Take

I see Google’s latest Google Ads API change as another clear move away from legacy automation and toward newer AI-driven campaign types, especially Performance Max.

Beginning August 3, 2026, Google says developers will no longer be able to create new Smart Campaigns through the Google Ads API. For me, the key detail is that this change is about new campaign creation only.

Existing Smart Campaigns are not being shut down. They can keep serving ads, and advertisers and developers will still be able to update and manage those campaigns through the API.

What changes is the ability to create brand-new Smart Campaigns through API workflows. If I depend on automated campaign setup, that is the part I would review now.

I care about this because it signals where Google wants advertisers to go next. Smart Campaigns may continue running, but the path for new API-based campaign creation is moving toward newer products such as Performance Max, Search campaigns, and Demand Gen campaigns.

Google is specifically pointing advertisers toward Performance Max as the primary alternative. Since Performance Max runs across Google’s advertising inventory and uses AI to automate more of the campaign process, it fits the broader direction Google has been taking for years.

I also see this as part of a wider consolidation around automated campaign formats. Google has increasingly emphasized systems that handle bidding, targeting, and creative optimization across channels, and limiting new Smart Campaign creation reinforces that shift.

For developers, the practical next step is to audit any application that creates Smart Campaigns before the August 3, 2026 deadline. The affected requests are campaign creation operations where advertising_channel_type is set to SMART and advertising_channel_sub_type is set to SMART_CAMPAIGN.

After August 3, attempts to create new Smart Campaigns through the API will fail. In version 24 of the Google Ads API, developers will receive a SmartCampaignError.CREATION_FAILED error.

In version 23 and earlier, the same type of request will return an OperationAccessDeniedError.CREATE_OPERATION_NOT_PERMITTED error.

My main takeaway is that advertisers, agencies, and software providers should not treat this as a last-minute technical cleanup. If campaign creation is built into an internal tool, onboarding flow, or platform integration, I would start mapping the replacement path now.

Google is not ending existing Smart Campaigns, but it is removing a key creation path for new ones. To me, that is a strong signal that future campaign planning should center on Performance Max and other AI-driven Google Ads campaign types.

Dig deeper: Changes to Support for Smart Campaigns in the Google Ads API

Inspired by this post on Search Engine Land.

July 3, 2026
Deciding to Build or Buy Your Next SEO Tool with AI Insights
Before I consider requesting a new SEO tool, I always ensure that I understand the trade-offs between custom solutions, SaaS platforms, and hybrid approaches that utilize both.

AI has empowered SEO teams, including mine, to become more ambitious about automation. Tasks that once required engineering support are now tackled easily with tools like Claude or ChatGPT.

This is thrilling, yet it brings a new challenge: the assumption that everything can be automated. In today’s language, it boils down to a single question: Do we build or buy the tool?

The build-versus-buy dilemma is intricate, made even more so by AI advancements. It isn’t merely about cost; it’s about security, maintenance, data access, internal capabilities, workflow fit, and whether a custom solution can stay reliable and useful as time progresses.

How AI Lowers the Barrier to Building

AI has drastically lowered the barrier to experimentation. Even those of us without technical know-how can now create custom GPTs, build workflows, connect data sources, or craft an internal AI assistant.

However, maintaining a tool over the years remains a challenge, even if I managed to build it initially with AI support.

AI significantly aids SEO teams in data analysis, pattern recognition, summarizing information, and recommending actions, saving us a lot of time. Ignoring AI would surely leave us trailing behind.

It’s essential to acknowledge that AI still hasn’t reached the level of human creativity. It excels at working from established patterns and predicting outputs. This could evolve in the coming years.

AI tools also come with unseen costs. Internally developed tools may appear free since their invoices typically bypass our SEO teams, but expenses from token usage, API calls, infrastructure, engineering time, security reviews, and maintenance do exist.

Many organizations, as noted by Reuters, are experiencing “AI sticker shock,” finding themselves unable to forecast usage-based AI costs accurately. Companies like Uber, reported by TechCrunch, have even established AI spending caps after exceeding their annual budget in only a few months.

Currently, marketing teams, including mine, aren’t the largest AI consumers compared to engineering teams. Yet, this could shift rapidly.

When this happens, our expenditures will undoubtedly rise, prompting organizations to evaluate which AI tools and processes genuinely add value as opposed to simply consuming our budget.

Start by Defining What You Need

Before choosing whether to build or buy, SEO teams must define their true needs.

Different Ways to Use AI and Automation

I’ve noticed that many teams, including ours, lump various solutions together, yet they differ in cost, complexity, and maintenance.
- A custom tool: Generally a complex internal system necessitating engineering support, often focusing on automation and potentially incorporating AI aspects.
- A custom workflow: A repeatable process built with numerous tools like a custom GPT, spreadsheets, and automation, usually with an AI layer.
- A custom layer on SaaS: Leveraging data from existing tools to shape personalized reporting, prioritization, or recommendation processes.
- A true AI agent: A system capable of taking more autonomous actions, such as scanning Slack and following up on pending communications.
Though similar, these are often misidentified. Overgeneralizing terms like “AI agent” can lead to cost and complexity misjudgments.

Look for Repetitive, Context-Rich Tasks

Our team is still exploring AI capabilities. So far, we have concentrated on daily tasks involving substantial manual work.

For instance, we developed a custom GPT to assess whether our content aligns with our personas and addresses their pain points. The aim is not to replace our copywriters or reviewers, but to ensure that content isn’t generic and suggest pertinent enhancements.

We’ve also leveraged AI for translations, monthly reporting, and creating a weekly summary that integrates meeting notes, Slack, and Jira to identify outstanding tasks or follow-ups.

One of our newest workflows converts internal meeting recordings into structured landing page briefs.

Such tasks are ideal candidates for AI-powered custom workflows, given their dependence on internal context, repeatability, and specific company knowledge.

Not Everything Should Be Built

A case from our team involved a colleague who vibe-coded a prompt tracking tool. Although a good start, data presentation required manual steps for trend graphing, soon becoming a maintenance hassle due to changes in LLM tools.

The core issue was reliability. For AI visibility and prompt tracking, we needed stable data presentation, leading us to switch to a specialized platform like Peec AI, rather than maintain our own version.

This experience was insightful, enhancing our understanding of the problem, complexities, and necessary features when considering external solutions.

Here’s my advice: whether opting to build or purchase a tool, always explore existing market solutions. It helps to narrow down the essential features, preventing reliance on non-essential ones.

Especially for business-critical tools like rank tracking and website crawling, smaller SEO teams without technical support should be cautious of building from scratch. Reliability should be prioritized when data is crucial for decision-making.

Use AI Where Your Data Already Lives

Consider buying a crawler, rank tracker, or AI visibility platform and focus on linking these with custom data like GA or GSC accounts, or CRM data. This integration allows comprehensive analysis in a single view.

MCP connections also warrant consideration. The Model Context Protocol is a standard for linking AI applications with external systems, enhancing current workflows.

Though not necessary to learn coding, understanding enough to ask the right questions is beneficial.

If sensitive data is involved, like proprietary research or customer details, it’s crucial to assess security risks. It may be safer to allocate engineer support to avoid compromising sensitive information.

Deciding on a custom tool requires acknowledging the full cost, including engineering time, security reviews, and API usage, despite invoices not being SEO-related.

Before requesting any tool, SEO teams should articulate the problem, expected value, cost comparison between building and buying, and potential consequences of taking no action.

Effective requests should not start with tool needs, but with the problem, its significance, tested solutions, and the proposed optimal solution.

How to Prioritize What to Build First

No one-size-fits-all matrix exists for prioritizing builds.

Tools vary; from website crawlers to content evaluation systems, each can’t be judged by identical criteria.

In doubt, start by mapping current workflows versus the ideal ones. Patterns often emerge, highlighting primary priorities.

The first group involves tools that aid revenue generation, like identifying content opportunities or improving conversion. Marketing, including SEO, seeks visibility and leads, thus revenue-centric tools can be higher priorities.

The second category concerns tools minimizing repetitive tasks. While they may not directly create revenue, they free up valuable team time for strategic work.

Quick wins should not be ignored. Stakeholders value timely results, thus a small project with potential returns within weeks can build trust and support larger initiatives.

Also, consider cross-team value in your decision. SEO problems often extend beyond one team. Collaborating with other teams can strengthen the business case for shared solutions.

Often, the best tool isn’t the most complex. Starting small could be the strategy for smarter progress.

Remember, effective scoping leads to good decisions. Even with AI easing the build process, proper scoping of what to build remains essential.
- Define the problem, expected value, user base, and post-launch maintenance.
- Engage with your team and other departments, identifying whether it’s solely an SEO issue or a broader business challenge.
- Avoid building for AI’s sake, or being swayed by impressive demos.
Neglecting scoping risks acquiring costly tools that don’t integrate with workflows or building internal tools beyond maintenance capabilities.

Thoughtful consideration of scope is crucial before opting to build, buy, or customize a solution.

Inspired by this post on Search Engine Land.
June 23, 2026
Discover the Power of Google’s New AI Agent for Ad Manager

I’m excited to share that Google has taken a significant step in integrating Artificial Intelligence into publisher workflows by launching a new AI agent called Ask Ad Manager. This innovative tool leverages a Gemini-powered assistant to help us analyze performance and take action seamlessly through a user-friendly chat interface.

Google is embedding AI into publisher workflows, making it easier to analyze performance and act on insights from a chat interface.

Incorporating generative AI into Google Ad Manager, Ask Ad Manager is specifically crafted to assist publishers like myself in analyzing performance, troubleshooting issues, and navigating the Ad Manager platform effortlessly by using natural language.

The beta version is set to roll out this month, marking Google’s deeper foray into AI-supported ad operations.

What’s happening. Ask Ad Manager acts as a conversational AI agent dedicated to Google Ad Manager users who are publishers. Unlike conventional reporting tools, it allows us to pose questions in everyday language and receive tailored answers, recommendations, and reports based on our own Ad Manager data.

Google assures that this tool is engineered to help us swiftly transition from analysis to action, drastically reducing the time spent on generating reports, diagnosing issues, and navigating the Ad Manager platform.

What it can do:

Troubleshoot delivery issues. Instead of manually gathering reports to understand why certain line items are underperforming, I can now ask the AI agent questions and receive insights on the possible causes and recommended next steps.

Generate reports on demand. With a simple prompt, I can request customized metrics, benchmarks, and performance reports without the hassle of building multiple reports manually.

Navigate Ad Manager faster. Ask Ad Manager guides me to relevant pages on the platform and automatically applies suitable filters and settings rooted in the ongoing conversation.

Why we care. As a publisher managing large inventories and complex campaigns, having the capability to quickly uncover insights and diagnose issues can significantly reduce operational workloads and speed up decision-making processes.

Moreover, this feature signifies a broader trend in ad tech towards employing AI agents that not only generate information but also enhance workflows and task execution.

Looking ahead. According to Google, Ask Ad Manager marks just the start toward a future they envision as being more “agentic”, enhancing advertising operations comprehensively.

Google plans to unveil additional AI features throughout the year, incorporating developer tools like REST APIs and an MCP server aimed at supporting workflow automation and integration efforts.

They’re also working on developing specialized agents that could assist publishers and agencies in exploring inventory, negotiating deals, and executing campaigns with improved efficiency.

Bottom line. Ask Ad Manager introduces Gemini-powered assistance directly within Google Ad Manager. It offers a novel way for us publishers to access insights, resolve issues, and manage advertising operations all through natural language prompts.

Inspired by this post on Search Engine Land.

June 18, 2026
Top 11 Common PPC Mistakes and How to Avoid Them

As I delve deeper into the world of ad platforms, it’s fascinating to see how algorithms are transforming the landscape—putting a spotlight on marketers and their expertise.

Managing ad accounts today is a whole new ball game compared to just a few years back. With automation taking center stage through tools like Meta’s Advantage+ and Google’s Performance Max, the skills needed for effective account management have evolved significantly.

Recently, I’ve conducted numerous B2B account audits, for companies both in-house and those transitioning from agencies. It’s striking how rare it is to find operators who truly grasp these systems.

Even with advanced technology at our fingertips, many proficient B2B marketers continue to make costly errors. Here are the frequent mistakes I’ve identified recently on LinkedIn and Google.

Mistakes Advertisers Are Still Making on LinkedIn

LinkedIn is indispensable for B2B outreach, yet some advertisers persist with avoidable blunders like these.

1. Ignoring Audience Targeting

Shockingly, I still see this rookie error. Ads end up targeting entry-level individuals, students, and irrelevant businesses instead of the intended audience.

2. Neglecting to Adjust Targeting Over Time

LinkedIn’s professional targeting can be excellent, but not infallible. It often lets unrelated roles and titles slip through, wasting budget on irrelevant audiences.

3. Over-relying on Automated Settings

This is more common than a mere oversight. Keeping options like audience expansion active leads to ads appearing in less relevant, low-quality spots.

4. Stagnating Creative Content

Veteran LinkedIn marketers know that LinkedIn’s ad structure complicates creative testing, emphasizing the need for creative refreshes and theme variations.
Dig deeper: 5 B2B LinkedIn Ads tests to run in 2026
Mistakes Advertisers Still Making on Google

Here are the top pitfalls I’ve discovered in Google Ads accounts recently.

5. Using Conversion Strategies Without Adequate Data

Ad accounts employing conversion-based strategies without any conversion data often mislead algorithms, targeting the cheapest and least effective actions.

6. Defaulting to Display and Search Partners

This blunder reallocates budget from high-intent searches to less effective, lower-quality placements, wasting resources.

7. Forgetting Sitelinks

Omitting sitelinks diminishes the potential to occupy more space in search results, reducing appeal to potential customers.

8. Ignoring Non-Converting Keywords

Failure to scrutinize and modify ads for non-converting keywords results in wasted investment.

9. Overusing Broad Match

Exclusively using broad match without employing phrase or exact match can lead to unnecessary expenditure.

10. Unrestricted PMax Utilization

While PMax for B2B has evolved, neglecting enhanced conversions and educational setup is a recipe for low-quality leads.

11. Discounting AI Max Testing

With Google constantly innovating ad serving and optimization through AI, early adoption can offer a competitive advantage.
Dig deeper: Top 10 Google Ads mistakes to avoid in 2026
Automation Still Needs Skilled Oversight

Critical gaps often occur post-data collection, where follow-up sequences and accurate data linking are necessary to optimize Return on Ad Spend (ROAS).

Focusing solely on past practices like granular negative keywords without adapting to newer automation capabilities highlights inefficiencies.

Mistakes might appear minor, but they signal deeper mismanagement when left unattended, stressing the need for skilled supervision even in an automated environment.

Remember, effective ad management still necessitates quality inputs, accurate measurement, and vigilant oversight.

Inspired by this post on Search Engine Land.

June 10, 2026
Is Your Brand Campaign Truly Ready for AI’s Prime Time?

Not too long ago, I remember broad match being hailed as the future of paid search. Today, AI Max has taken on that mantle.

Over recent months, I’ve received plenty of suggestions to activate AI Max on brand campaigns, even when these campaigns are performing just as I want them to.

The reality is, many accounts still aren’t equipped with the essentials AI Max requires for optimum function. Conversion tracking issues, the lack of offline conversion imports, and budget-constrained generic campaigns are common hurdles.

AI Max thrives on robust conversion signals, adequate volume, and enough variation for effective learning. I often find that brand campaigns provide most of these signals.

However, applying AI Max to brand campaigns means layering additional automation over our most efficient and predictable traffic source.

The promise and limitations of AI Max

AI Max can broaden search targeting beyond your key phrases by using keywords, landing pages, and site content as signals instead of specific targeting criteria.

Much like dynamic search ads (DSA), AI Max can align with queries you didn’t explicitly target, and it ventures even further by transcending the intent limits set by your keyword arsenal.

Google portrays AI Max as the future of Search automation, preparing to merge DSA, automatically created assets, and broad match settings into AI Max this September.

With controls like brand exclusions, URL exclusions, text guidelines, and location targeting, AI Max might tap into growth opportunities in accounts rich with strong conversion signals and enough search volume.

Yet, many accounts haven’t reached that point.

With Google’s AI Surface eligibility expanding, it’s tempting to dive headfirst into AI Max. But it’s essential to focus on account fundamentals—measurement accuracy, conversion integrity, and solid account structures—before relying solely on AI Max.

Why AI surface eligibility isn’t reason enough to rush into AI Max

The growing interest in AI Max is fueled by Google’s push toward AI-powered search experiences. AI Overviews now engage approximately 2.5 billion users monthly, presenting ads in 25.6% of AI Overview results, according to Semrush data.

While maintaining visibility in these surprising new fields is advisable, rushing to apply AI Max without assessing your campaign structure and conversion strategies can be detrimental.

Typically, Google Ads representatives pitch AI Max for brand campaigns to ensure their eligibility in AI Mode and associated AI Overviews. However, this isn’t always the truth.

Ginny Marvin, a Google Ads liaison, confirmed that three campaign types are eligible for AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search. Meanwhile, exact match keywords never qualify for AI Overviews.

Thus, PMax and AI Max generally serve the same purpose concerning AI surface eligibility. Running PMax brand campaigns already gives you the necessary coverage, without the need for adding another layer of automation.

Before adding AI Max into your mix, examine whether it’s genuinely necessary over addressing your account’s foundational needs.

Test data doesn’t fully endorse Google’s AI Max assertions

Google claims that enabling AI Max could increase conversions by 14%, and those employing exact and phrase matches might experience a 27% increase. Nevertheless, independent tests have yielded a wide array of results.

The evidence for AI Max remains mixed

In tests covering 600 accounts, Smarter Ecommerce observed AI Max produced 35% lower ROAS than traditional match types. This outcome aligns with intentional budget minimization by advertisers.

Through a four-month examination, Xavier Mantica discovered AI Max resulted in the priciest conversions compared to phrase and exact match. While Mantica noted $100.37 per conversion with AI Max, phrase match was at $43.97, and exact match was at $52.69.

Moreover, 99% of impressions during Ezra Sackett’s 30,000 search term analysis returned zero conversions under AI Max.

Significantly, none of this data is brand-focused. AI Max may provide benefits in certain settings, but a successful, exact match defensive brand campaign may not be the right candidate for testing new automation.

If your brand is still the standout performer in your account, you may want to question why the rest of your campaigns haven’t met similar standards.

What to consider before testing AI Max on brand

Ask yourself these critical questions before branching AI Max into your brand campaigns:

1. Are the conversion signals trustworthy?

Does your setup cleanly distinguish between macro and micro conversions? Are offline imports running smoothly? Does the lead quality feedback enhance platform optimization?

If the underlying signals falter, AI Max will simply magnify those issues.

2. Have you already explored generic growth?

In the accounts I review, problems like budget constraints, misaligned landing pages, outdated queries, and suboptimal structure frequently hinder generic campaign growth.

Real growth is often found within these issues, rather than an already strong brand campaign.

3. Can the account provide AI with sufficient learning data?

Remember, AI Max is not some sorcery; it mirrors the quality of the signals it receives.

Relying heavily on brand conversions will only amplify these markers and obstruct other growth pathways.

4. Are brand + modifier searches already structured properly?

Search variations like “Brand + pricing” or “Brand + reviews” ought to be treated as separate strategic campaigns. AI Max should not substitute for robust account architecture.

5. Do you have a strategic reason to expand the brand campaign?

Consider testing strategically through experiments, rather than viewing AI Max as a straightforward switch to augment visibility.

AI Max only works as efficiently as the signals guiding it

AI Max might develop into a truly beneficial tool over time, much like PMax did. Automation effective at any level still requires strong foundational signals for success.

The existing issue remains with insufficient solid foundations supporting the automation. Improved conversions, precise measurement, sound account structures, and comprehensive feedback loops are vital to making automation wiser.

Above all, don’t conflate Google’s automation agenda with your campaign objectives.

Inspired by this post on Search Engine Land.

June 9, 2026
Effortlessly Boost Your Brand with Automated Citations

Have you ever wondered how to effortlessly get your brand mentioned in the most important third-party citations? Well, now you can, thanks to Profound and Noble’s latest automation feature. This groundbreaking technology allows me to seamlessly integrate my brand into key online listings, saving time and enhancing visibility.
The convenience doesn’t end there. By automating the citation placement process, I can focus more on strategic activities rather than getting bogged down in the details. It’s all about maximizing impact with minimal effort.

Inspired by this post on Try Profound Blog.

May 26, 2026
Revolutionizing Ad Campaigns: Microsoft’s AI Bidding and Reporting

When I hear about Microsoft rolling out its latest AI-powered features for advertisers, I can’t help but feel excited about the potential ease it could bring to multi-platform ad campaigns.

The unveiling of the new Import Center really caught my attention. It’s designed to streamline the way we can transfer campaigns from Google Ads and Meta Ads into Microsoft Advertising.

This impressive hub offers me the ability to search and filter campaign imports, edit or pause them as needed, access those imported campaigns with ease, view troubleshooting guidance, and even get performance recommendations once the imports are done.

Microsoft assures that this is all about minimizing the hassle of manual troubleshooting and simplifying how we manage campaigns across different platforms.

I find the expansion of AI-powered bidding capabilities particularly appealing as it includes cross-account portfolio bidding for both Search and Shopping campaigns. This addition allows me to handle portfolio bid strategies efficiently across various accounts, optimizing my budget by pooling significant signals.

The enhanced bid strategy reporting metrics such as Avg. Target ROAS, Avg. Target CPA, and Avg. Target impression share are promising tools that let me comprehend bid performances better and adjust targets from within the UI.

Reporting has become even more flexible thanks to the new custom column capabilities. This expansion gives me access to all conversion metrics in custom columns, allows segment reports by goal name, and lets me dive into additional metrics like CPA and ROAS, enhancing transparency and optimization insights.

In my perspective, these updates make campaign management far more seamless across all platforms, including Google, Meta, and Microsoft Ads, while expanding AI-powered bidding and automation.

I’m also catching up with two previously announced updates from Microsoft that are now widely available: seasonality adjustments for portfolio bidding and shared budgets, and the data-driven attribution for automated bid strategies.

By assigning conversion credit across the customer’s journey in campaigns that use Maximize Conversions, Maximize Conversion Value, and Enhanced CPC bidding strategies, these features could be transformative.

In conclusion, Microsoft is progressively adopting AI-assisted campaign management with an aim to reduce operational friction for advertisers juggling campaigns across the Google, Meta, and Microsoft platforms.

Inspired by this post on Search Engine Land.

May 20, 2026
Rising Google Ads Costs in 2025: Boosting Conversion Success

In 2025, I’ve noticed that while the costs of Google Ads continue to climb, there’s been a silver lining. Advertisers like myself have been improving conversion efficiency, which means growth is still within reach.

I’ve observed that although we’re paying more per click, the data from WordStream by LocaliQ shows we’re getting better at converting those clicks. The benchmark report, analyzing over 16,000 campaigns, highlights an increase in average CPC to $5.42, up from $4.66 last year, with 87% of industries seeing a rise.

Despite this jump in CPC, the average conversion rate has improved to 8.18%. This indicates we’re becoming more efficient, even as traffic costs rise.

Why advertisers should care. The benchmarks clearly point out that inexpensive traffic is fading fast. For us advertisers, this means absolute reliance on volume is not sustainable anymore.

To maintain profitability, I’ve realized that focusing on stronger targeting, creative enhancements, better landing pages, and smarter automation is vital.

The report suggests advertisers who adapt well to automation and intent-driven targeting are improving conversion efficiency, despite the rising costs.

By the numbers. Here’s what stands out:

$5.26 — Average Google Ads CPC in 2025, increased from $4.66 in 2024.

87% — Percentage of industries experiencing CPC hikes annually.

7.52% — Across-the-board average conversion rate in 2025.

$70.11 — Average cost per lead in Google Ads, 2025.

Highest CPCs. Industries like Attorneys & Legal Services led with $8.58, while areas like Finance & Insurance, and Home Improvement consistently hovered in the $7+ range.

Lowest CPCs. The Arts & Entertainment and Travel & Hospitality sectors fell in the $2–$3 range, benefitting from reduced competition.

Highest conversion rates (strong intent / local services)

Automotive Repair led with 14.67%, followed by other high-intent services like home services ranging from 12–14%.

Lowest conversion rates (complex or high-consideration journeys)

Finance & Insurance was at the bottom with 2.55%, and B2B, legal, and high-ticket items were between 3–5%.

The cost-per-lead is stabilizing, thankfully. Although the average CPL rose modestly by 5.13% to $70.11 in 2025, it’s a relief after years of sharper increases. Legal services remain costly, while auto repair is more cost-effective.

Automation is changing performance benchmarks. I’ve seen how Google Ads has embraced AI-driven optimization. As conversion rates rise, smarter bidding systems and improved intent matching are effectively connecting advertisers with high-quality users.

While automation like Smart Bidding and Performance Max is shaping campaigns, I know that not every account is thriving. Some have zero conversions, and failure to optimize or poorly set up tracking continues to waste spend.

Interestingly, accounts using negative keywords experience conversion rates up to three times higher, underscoring how foundational practices are essential even in an AI era.

Between the lines. The benchmarks present a mixed message. Costs are rising, yet Google’s automation aids efficiency for those optimizing their campaigns effectively.

The biggest challenge now isn’t finding cheap clicks—it’s enhancing conversion quality and maximizing value from expensive traffic.

Bottom line. Google Ads is more costly than ever, but by embracing automation, focusing on conversion quality, and improving account efficiency, growth is still possible.

Inspired by this post on Search Engine Land.

May 18, 2026