Google is piloting a Labs section inside the Google Ads platform, giving advertisers a centralized place to access early-stage experiments and tools.
Why we care. Instead of quietly testing features across scattered accounts, Google appears to be consolidating experiments into a single hub, making it easier for advertisers to discover and try new features before full rollout.
Details.
The Labs section shows up at the bottom of the left-hand menu in select accounts.
Early tests include features like “missed growth opportunities,” which highlight potential campaign improvements through bid or budget adjustments.
Not all advertisers will have access to it.
First seen. The update was first spotted by Vojtěch Audy, PPC specialist at Na volné noze.
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Google is enhancing Meridian, its open-source Marketing Mix Model (MMM), to help marketers make smarter, more precise budget decisions.
Why we care. Understanding ROI across channels is increasingly critical. These new Meridian updates allow for a more precise understanding of what drives sales, factoring in both media spend and non-media variables like pricing and promotions.
The big picture. Here’s what’s new:
Non-media variables: Marketers can now include pricing, promotions, and other business levers to measure their impact on sales more accurately.
Channel-level contribution priors: New features let you guide the MMM with your own business knowledge, improving the relevance of insights.
Longer-term effects: Enhanced binomial adstock decay functions track how upper-funnel media influences purchases weeks later.
Optimized spending: Marginal ROI (mROI) priors help pinpoint where the next dollar delivers the highest return, using past campaign performance.
Support and community. Meridian now has 30 certified global partners and an active Discord community, offering guidance to deploy the tool effectively and translate insights into business growth.
The bottom line: These updates aim to transform Meridian from a measurement tool to a strategic partner for budget allocation. This means Meridian can help marketers optimize spend across immediate conversions and longer-term brand impact.
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The history of search offers clues about where we’re headed in the AI era – but there’s more to learn and more to do to move forward with practical steps.
AI changes the way people search. Instead of short queries that once required digging through blended results, users can now ask complex questions and get direct answers.
Much of the work to optimize for AI, though, overlaps with what SEO professionals have been doing for years. Our community is already adapting and well-positioned to take on this shift.
This article outlines practical steps to navigate the evolving landscape.
SEO, AEO, GEO: Defining the new terms
Before diving in, it’s worth addressing the terminology. It’s still new, and no single label has fully crystallized for this AI layer on top of SEO.
Two terms have gained traction:
AEO (answer engine optimization), which focuses on optimizing content so it’s chosen as the answer in AI-driven results like Google’s AI Overviews.
GEO (generative engine optimization), which describes a broader approach across generative AI platforms.
Neither feels perfect. AEO is a bit clunky, and GEO risks confusion with geography and local search.
Still, these are the terms currently in use. For simplicity, I’ll use GEO in this article.
While tools like ChatGPT are undoubtedly cool – and early adopters, myself included, are spending a lot of time tinkering – the broader story is different.
For most people, AI will matter less as a standalone tool and more through its integration into phones, web browsers, and search engines.
GEO is a new layer that sits above SEO.
Often, tools like ChatGPT will search and compile information.
These tools provide a layer of abstraction and do much of the grunt work.
They still scan the digital world and then collate that information to simplify things for the user, while search engines like Google continue to provide the best overall digital map of the world.
If traditional SEO was about matching keywords, AEO is about being the best answer – and the easiest to integrate into an AI response.
GEO: Strategic foundations
There’s no firm consensus on GEO tactics, but most of what’s recommended is simply good SEO.
That said, tactics that lost ground in the zero-click landscape may regain utility in AI Mode, where the AI does the deep dive and collates information for users.
Here are some basic strategic foundations to put in place to set yourself up for visibility in AI tools.
1. Focus on your customers
I’ve long championed bringing traditional marketing thinking into SEO, and GEO is the natural evolution of that approach.
Know your audience. Create personas, gather feedback, and define their goals, pain points, and the jobs they rely on your product or service to support.
Customer insight is key to building a customer-first strategy that helps you stand out in the age of AI.
2. Real expertise wins
The web is full of derivative content that does little to stand out. This creates a problem for AI.
Model collapse happens when AI keeps training on AI-generated content without new signals, leading to increasingly stale and inaccurate results.
The solution is what humans are still best at – fresh insights from:
Interviews.
Original research.
Proprietary data
These provide AI with something new – and worth citing.
That’s an opportunity. Have a voice, and bring something original to the table.
AI is helping people research and make purchase decisions. Your role is to be part of the discussion.
Modern SEOs are in good shape – much of what AI requires builds on the strategic SEO work we already do.
Add in some PR, social media, and content creation (which often sit under the SEO umbrella), and you’re well on your way to a functional GEO strategy.
Getting started is crucial. To stay ahead:
Create content worth quoting: Write the piece an AI (or a human journalist) would want to reference. That means clear answers, evidence, original insights, and a point of view – not filler.
Anticipate the full conversation: Don’t just answer the first question – answer the follow-ups, too. If someone asks, “How does AI change SEO?” they’ll also want to know, “What should I do about it?” Build that into your content.
Structure for machines and humans: Use headings, lists, FAQs, and concise summaries to help AI parse your work. But don’t forget the narrative depth that keeps people reading.
Diversify your discovery footprint: Don’t rely on Google alone. Research your audience, understand their hangouts, and publish in the formats and places where they ask questions today: LinkedIn, YouTube, podcasts,and industry forums. AI tools crawl all of it.
Focus on authority signals: Show the human behind the content. Add author bios, cite sources, and link to your work elsewhere. AI engines, like search engines before them, lean on trust and authority.
Experiment, measure, refine: Try different formats, test and measure how your content shows up in AI summaries, track brand mentions, and adapt. SEO has always been iterative – this new era is no different.
The opportunity in the chaos
As SEO evolves into GEO – or whatever it may end up being called – this really is the best approach.
There’s no doubt a lot of change is happening.
But much of it is part of the same gradual evolution we’ve seen before, where clicks declined and Google started answering questions directly.
AI now makes it even easier for customers to find the information they’re looking for.
They may not read it on your site – at least not initially – but the AI will, and that’s the point.
Another strength we have as SEOs is that change is constant.
If you’ve been in SEO for any length of time, you’ve lived through Panda, Penguin, Mobilegeddon, BERT, helpful content updates – the list is long (and may cause PTSD for many of us).
The key is to treat this as the next evolution. AI is being integrated into search, and it will likely become the way the masses adopt the technology.
Don’t see this as the death of SEO.
Instead, view SEO and AI (or GEO/AEO, etc.) as close, contributing partners, and evolve your plan to match the changing landscape.
Your job as a marketer is to feed these tools the information they need to point customers to you and your clients.
This shift will likely mean fewer short-term manipulations and tactical opportunities – but better results for businesses that do the basics well.
At its core, good SEO/GEO is just good marketing: understanding your customers, meeting their needs, and communicating clearly.
Amid the chaos lies opportunity. For those willing to embrace the challenge, experiment with new tools, and keep going.
That’s what we’ve always done as SEOs, which is why we’re best positioned to embrace this new world.
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Before this, figuring out what AI Max was doing felt like looking into a black box.
Now, we can finally see the data and make smarter decisions.
Evaluating AI Max: Metrics that matter
When you’re looking at AI Max performance, start with the basics.
Open your search term tab and look for the Search terms and landing pages from AI Max option.
This lets you see AI Max results separately from your exact match, phrase match, and broad match keywords.
Compare conversion share and budget share
The first thing to check is how many conversions come from AI Max versus your regular keywords.
If AI Max is bringing in 30% of your conversions but eating up 60% of your budget, you know something needs attention.
Look at the cost per conversion, too.
AI Max might cost more at first, but that’s normal while Google learns what works for your business.
Look beyond cost – focus on conversion rates
Don’t just focus on the cost. Pay attention to conversion rates.
AI Max often finds people who are ready to buy but use different words than you expected. These new search terms can be gold mines if you spot the patterns.
One of AI Max’s biggest benefits is finding search terms you never thought to target.
Look at your search terms report and filter for AI Max queries. You’ll probably see some surprises.
Let’s say you sell running shoes and target “best running shoes.” AI Max might show your ads for “comfortable jogging sneakers” or “shoes for morning runs.”
These are people who want the same thing but use different words. The smart move is to add these high-performing terms to your regular keyword lists.
Identify irrelevant traffic
If you’re getting clicks from people searching for “cheap shoes” when you sell premium products, add “cheap” as a negative keyword.
AI Max respects negative keywords, so use them to guide the system toward higher-quality queries.
Every AI system needs time to learn, and AI Max is no different.
Plan for a learning phase
The first few weeks often look expensive because Google is still figuring out what works. Don’t panic if your costs jump initially.
Track your performance daily during the first month
As AI Max learns your patterns, you should see costs stabilize and conversion rates improve. If things keep getting worse after three weeks, then it’s time to make changes.
Keep an eye on the types of search terms AI Max finds over time
Early on, you might see lots of random queries. As the system learns, the terms should become more relevant to your business goals.
The biggest mistake people make is changing too much, too fast.
AI Max needs data to work properly, and constantly adjusting things prevents the system from learning.
That said, some problems need quick fixes.
If AI Max is spending money on completely irrelevant searches, add negative keywords immediately.
If the AI creates ads that violate your brand guidelines, remove those assets right away.
Watch your overall account performance, not just AI Max numbers.
Sometimes, AI Max might look expensive on its own, but it actually helps your other campaigns perform better by capturing different types of traffic.
Planning for the future
AI Max is still evolving, and Google keeps adding new features.
To adapt:
Build reporting systems that can grow with these changes.
Set up automated reports for the metrics that matter most to your business.
Don’t try to control everything.
The businesses seeing the best results from AI Max are the ones that:
Set clear goals.
Provide good data.
Let the system do its job.
Your role shifts from managing keywords to managing strategy.
Start testing AI Max on a small scale if you’re nervous about it. For example:
Create one campaign with AI Max enabled and compare it to your existing campaigns.
Run an AI Max for Search campaign experiment and let Google evaluate if the experiment is statistically valid.
Once you see how it works for your specific business, you can decide whether to expand.
Case studies: AI Max in action
These are early results from a limited data set and shouldn’t be viewed as statistically significant.
I’m sharing them to illustrate what I’ve seen so far – but the sample size is small and the timeframe short. Take these numbers with caution.
Case 1: Tourism and travel
This advertiser already had a solid search setup and was seeing good results.
Growth, however, was difficult because of heavy competition and the fact that strong keywords were already in play within a modern search structure.
Match type
Avg. CPC (€)
CVR (%)
AI Max
€0.11
1.47%
Broad match
€0.09
3.79%
Exact match
€0.53
9.00%
Exact match (close variant)
€0.22
7.11%
Phrase match
€0.16
6.25%
Phrase match (close variant)
€0.11
3.27%
AI Max generated additional conversions, but relative to the existing setup the impact was limited.
The conversion rate was much lower than other match types.
Because the average CPC was low, there was no cost spike, but performance still lagged.
Broad match – also known for surfacing broader, newer queries – had an even lower CPC (€0.09) and a conversion rate more than twice that of AI Max.
In this account, AI Max’s contribution was minor.
Search term overlap analysis showed AI Max had a 22.5% overlap rate, meaning 77.5% of queries were new to the campaign.
That’s a fairly good sign in terms of query discovery.
Case 2: Fashion ecommerce
This account focused on women’s clothing and already had a well-optimized campaign.
The goal was to expand reach during the competitive holiday season, when exact match keywords became increasingly expensive.
Match type
Avg. CPC (€)
CVR (%)
AI Max
€0.08
2.15%
Broad match
€0.12
2.89%
Exact match
€0.67
8.45%
Exact match (close variant)
€0.28
6.78%
Phrase match
€0.19
5.92%
Phrase match (close variant)
€0.14
4.11%
AI Max performed well here, delivering the lowest CPC at €0.08 and a respectable 2.15% conversion rate.
Although conversion rates were lower than exact and phrase match, the cheaper clicks kept cost per acquisition competitive.
AI Max also captured fashion-related long-tail searches and seasonal queries that the existing keyword set had missed.
Notably, AI Max outperformed broad match with both lower costs and better conversion rates.
This suggests its ability to better understand product context and user intent – especially important in fashion, where search terminology is diverse.
Search term overlap analysis showed only an 18.7% overlap rate, meaning 81.3% of queries were completely new.
That level of query discovery was valuable for extending reach in a highly competitive market.
Case 3: B2B SaaS
This account promoted project management software and had a mature strategy focused on high-intent keywords.
Conversion tracking was strong, measuring both MQLs and SQLs.
The client wanted to test AI Max for additional lead generation opportunities.
Match type
Avg. CPC (€)
CVR (%)
AI Max
€0.89
0.76%
Broad match
€0.72
1.23%
Exact match
€1.84
4.67%
Exact match (close variant)
€1.22
3.91%
Phrase match
€1.05
3.44%
Phrase match (close variant)
€0.94
2.88%
In this case, AI Max struggled. Despite a reasonable CPC of €0.89, the conversion rate was just 0.76%.
That pushed CPA well above the client’s target, making AI Max the worst-performing match type in the account.
It tended to capture too many informational searches from users not yet ready to convert.
Even broad match, typically associated with lower-intent traffic, outperformed AI Max with a 1.23% conversion rate at a lower CPC.
The complexity of the B2B buying cycle favored exact and phrase match keywords over AI Max’s broader interpretation.
Search term overlap analysis showed a 31.4% overlap rate, leaving 68.6% of queries as new.
However, these were mostly low-intent informational searches that didn’t align with SQL goals – underscoring the importance of high-quality conversion tracking when evaluating AI Max.
Wider industry sentiment
Advertiser feedback so far mirrors these mixed results.
In a recent poll by Adriaan Dekker, more than 50% of respondents reported neutral outcomes from AI Max, while 16% saw good results and 28% reported poor performance.
Tips to analyze AI Max search terms
You can analyze AI Max queries in Google Sheets using a few simple formulas. If your search term report has the term in column A and match type in column B:
To check whether a search term appears in both AI Max and another match type:
To count how many match types trigger a given term:
=COUNTIFS($A:$A;A2) can be used to count how many match types trigger on that search term.
To measure query length with an n-gram analysis:
=(LEN(A1)-LEN(SUBSTITUTE(A1," ","")))+1
These checks show whether AI Max is surfacing unique queries, overlapping with existing match types, or favoring short-tail vs. long-tail terms.
Because AI Max is still in early stages, it’s hard to draw firm conclusions.
Performance may improve as the system learns from more data, or remain flat if your setup already covers most transactional queries.
That’s the question advertisers will answer in the coming months as more tests and learnings emerge.
So far, results can be positive, neutral, or negative.
In my experience, neutral to negative outcomes are more common – especially in accounts with strong existing setups, where AI Max has fewer opportunities to add value.
A Google Ads script to uncover AI Max insights
To make analyzing AI Max performance easier, I created a Google Ads script that automatically pulls data into Google Sheets for deeper analysis.
It saves hours of manual work and includes the exact formulas mentioned earlier in this article, so you can immediately spot overlap rates and query patterns without manual setup.
The script creates two tabs in your Sheet:
AI Max: Performance Max search term data with headlines, landing pages, and performance metrics.
Search term analysis: A full comparison of all match types, including AI Max, with automated formulas.
The analysis covers:
Overlap detection between AI Max and other match types.
Query length analysis (short-tail vs. long-tail).
Match type frequency counts to identify competitive terms.
Automatic cost conversion from Google’s micro format into readable currency.
How to use it:
Create a new Google Sheet and copy the URL.
In Google Ads, go to Tools > Scripts.
Paste the script code and update the SHEET_URL variable.
Run the script to automatically populate your analysis.
With this setup, you can quickly calculate the same metrics I used in the case studies – like the 22.5% overlap rate in the tourism account or the 81.3% new query discovery in fashion.
The automated workflow makes it easier to see whether AI Max is surfacing genuine new opportunities or simply redistributing existing traffic.
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Google AI Mode is getting more visual by providing a more graphical response to some of your queries, including your shopping search queries. Google can do this by using its new visual version of the query fan-out technique it has used with AI Overviews and AI Mode.
Google AI Mode, for certain queries, particularly those related to shopping, will respond with images and graphics. These are aimed at sparking inspiration, Robby Stein, VP of Product Management at Google Search, told Search Engine Land.
AI Mode will be able to not just understand your query in a text-based manner, but also understand your query visually and respond with both textual and visual responses. It is a new and updated fluid, ongoing conversation in AI Mode that “sparks inspiration,” Google said.
Visual search fan-out technique. Google’s fan out technique is able breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf. Now, Google can also do this visually by looking at the image and text query input for query analysis and various image region analysis, including meta data and context around the image. Then Google AI Mode can render a visual grid of responses to your query.
“This means AI Mode can perform a more comprehensive analysis of an image, recognizing subtle details and secondary objects in addition to the primary subjects – and then runs multiple queries in the background. This helps it understand the full visual context and the nuance of your natural language question to deliver highly relevant visual results,” Google wrote.
AI Mode is more visual. Now when you search for some queries in AI Mode, the responses will be much more graphical and visual, right up front. Yes, AI Mode may have responded with images before, but now the images are higher and more prominent in some of the responses.
Plus, you can conduct follow up questions on the visual responses.
“You’ll see rich visuals that match the vibe you’re looking for, and can follow up in whatever way is most natural for you, like asking for more options with dark tones and bold prints. Each image has a link, so you can click out and learn more when something catches your eye. And because the experience is multimodal, you can also start your search by uploading an image or snapping a photo,” Google added.
Shopping in AI Mode. Lilian Rincon, VP Product Management Google Shopping, told us one of the best places for a visual experience is with shopping in Google Search. A more visual AI Mode helps you shop conversationally with fresh shopping data, can lead to a better shopping experience.
With the addition of Google’s Shopping Graph of 50 billion product listings, where 2 billion products are refreshed daily every hour, the responses are not just inspirational but detailed and helpful.
AI Mode for Shopping can not just give you ideas on what to put in your living room but also help you find the perfect article of clothing, in your color, style and fit.
Here is a video of this in action:
More details. This is launching today in Google AI Mode in the US in English. These are free listings, not shopping ads, and currently have no paid model, including no affiliate model. While Google has ads in AI Overviews, Google is only experimenting with ads in AI Mode, and there are no more details on ads right now for this experience.
Agentic experiences, like helping you buy and find what your looking for, is here for some areas now in Search Labs. But Google said they want the final purchase to happen directly on the retailer’s site.
Why we care. A new, more visual and graphical experience in AI Mode, may be a better search experience for some searchers and for some queries. Google is experimenting with a lot of changes to Google Search and is rapidly trying new interfaces and technologies.
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Engineering teams usually have a quality assurance (QA) process.
Without it, they risk releasing work that hurts the user experience and creates unforeseen technical issues – including major SEO problems.
That’s where SEO QA comes in. Adding SEO-specific checks to existing QA protocols helps teams catch and fix issues before they go live.
But this step is less common than you’d think. Too often, it’s overlooked.
This article outlines what it takes to build an effective SEO QA discipline and provides a checklist SEOs and QA engineers can use to cover their bases.
Why SEO QA gets overlooked
Unless SEO is fully integrated with engineering, SEO-specific QA often gets overlooked.
As a result, SEOs may not flag problems until a tech audit – or worse, when they show up as organic KPI declines.
This is especially common when SEO teams sit under marketing instead of product or engineering, since they’re excluded from regular milestones and lifecycle meetings.
That makes it harder to communicate SEO’s importance, win buy-in, and establish it as part of everyday development.
Having a QA team within engineering is also no longer a given.
In agile environments, some teams prioritize speed over fully clean rollouts.
Others rely on AI tools to automate QA or monitor for technical issues, instead of employing dedicated QA engineers.
In short, there are plenty of reasons many teams lack a well-developed SEO QA practice.
What are the benefits of SEO QA?
For SEOs to be able to proactively find and resolve issues before they go out into the world, they need two things on a regular basis:
Opportunities to view upcoming engineering tickets and flag any that may have potential SEO impact. (A great reason for an SEO representative to be a part of sprint planning meetings.)
A chance to QA any of the flagged tickets before they hit production.
This has a few key benefits for the business:
Minimize the chances of deploying code that hurts SEO.
Catch and correct errors that hit production before they register with search engines.
Capitalize on SEO opportunities related to engineering work that’s already slotted for development.
The last bullet is just as much of a reason to implement SEO QA as the first two.
It’s not just about catching bugs, it’s about maximizing value while minimizing resources.
When SEOs have a chance to see what’s coming up, it allows them to connect the dots between SEO roadmap items and upcoming engineering initiatives to find potential areas of overlap.
In turn, the business reaps the SEO benefits of work that’s already in motion, rather than spending additional resources to achieve the same goal later.
Best practices: The 4 Ws of SEO QA
Alright, now that we’ve established why brands need SEO QA, let’s get into the logistics.
Structured data: Is it crawlable, parsable, accurate, and reflecting visible information on the page? (Note: Google’s Schema Markup Testing Tool won’t work on staging URLs since crawlers are (hopefully!) blocked.)
You can see CSS issues because they visually impact the page.
For JavaScript issues, you’ll need tools to understand whether crawlers can access critical content.
Unless you’re already running a sitewide crawl with JavaScript enabled, test one or two pages from the affected template (i.e., blog, listing, product detail) using a tool like Rendering Difference Engine.
Page elements applicable to the template are available and functioning as intended, including pop-outs, filtering, sort function, and pagination.
Even worse, until it’s fixed, that’s historical data that you won’t get back.
Before launch on staging, check if:
All pages and templates have tracking code available.
The day after launch, verify that:
Internal analytics platform doesn’t show significant declines in KPIs or discrepancies with external reporting tools (e.g., GSC).
Optional: A/B testing
Not all A/B testing tools distinguish the control and variant for crawlers.
They’re usually served one or the other version of the page randomly, which means your variant could impact SEO.
Aside from the variable, the pages should be identical to a crawler.
Refine over time
With every round of QA, engineers and SEOs will learn nuances and find new connections.
You’ll discover that certain types of updates are more likely to cause certain types of SEO issues, certain plugins are linked to certain types of problems, etc.
Your SEO QA checklist is a living, breathing document and a place to document all of this to make SEO QA more effective – and avoid repeating mistakes – no matter who’s carrying it out.
Start with the list below and make it your own over time.
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Pinterest is rolling out Top of Search ads, a new ad format that places brands directly in the first 10 slots of search results and Related Pins, where nearly half of user clicks occur.
Shopping on Pinterest is inherently visual, and most searches on the platform (96%) are unbranded according to Pinterest. That makes the top of search results a prime spot for discovery – and for brands to reach consumers who are open to new products.
Why we care. Pinterest’s Top of Search ads put your products in front of shoppers at the most valuable moment: when they’re actively browsing but not yet brand-committed. With nearly all searches unbranded, the format offers a powerful way to win new customers and outperform competitors. Early testing shows significant performance lifts, making it a high-return opportunity for brands looking to drive both visibility and conversions.
How it works. Top of Search ads ensure a brand’s products are featured where shopping journeys often begin. The new format also includes a brand-exclusive ad unit that highlights an advertiser’s catalog, giving products prominent placement over competitors.
Early results. According to Pinterest:
Top of Search ads have driven a 29% higher average CTR compared to standard campaigns.
Top of Search ads are 32% more likely to attract new clickers.
Wayfair, an early tester, reported a 237% CTR lift in just two weeks compared to its typical campaigns.
Between the lines. For advertisers, Top of Search ads offer a way to intercept undecided shoppers earlier in their journey, effectively turning Pinterest’s visual-first search into a new performance channel.
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OpenAI is launching Instant Checkout inside ChatGPT to Plus, Pro, and Free users in the U.S.
Users will be able to buy products from Etsy sellers.
Purchases are powered by the new Agentic Commerce Protocol (ACP), co-developed with Stripe.
How it works. Users search in plain language (e.g., “gifts for a ceramics lover”). Then:
ChatGPT returns product recommendations ranked by relevance, not payment.
If an item supports Instant Checkout, users tap “Buy,” confirm shipping and payment details, and complete the order without leaving chat.
Orders, payments, and fulfillment run through the merchant’s existing systems; ChatGPT passes information securely.
Merchants pay a small transaction fee; shoppers pay no extra cost.
Between the lines. OpenAI said products are ranked only by relevance – not sponsorship or whether Instant Checkout is enabled. Merchants remain the merchant of record, keeping control over fulfillment and customer relationships.
What’s next. Coming soon, according to OpenAI:
Multi-item carts.
Expansion to Shopify’s million-plus merchants (e.g., like Glossier, SKIMS, and Spanx)
More regions beyond the U.S.
Why we care. If AI chat becomes a mainstream way for people to discover products, OpenAI is now at the start of that purchase journey. For brands or businesses selling products, this could mean a new channel to optimize for – one that bypasses traditional search and funnels discovery straight into checkout.
How to sign up. Merchants can apply to have their products included in ChatGPT search results and enable Instant Checkout via ACP.
Etsy and Shopify sellers are already eligible and don’t need to apply.
OpenAI is onboarding merchants on a rolling basis through an online application form.
Different from Google. OpenAI is taking a different approach than Google’s agentic search capabilities. Whereas OpenAI is going all the way to completing purchases on behalf of users, Google (for now at least) is letting users take the final conversion action.
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TikTok is introducing a new ad solution, Travel Ads powered by Smart+, designed to help brands connect with travelers during the discovery and booking phases.
Why now. Travel is one of TikTok’s fastest-growing verticals. According to internal TikTok data:
66% of users say the app is their most helpful source of travel inspiration.
Users are 2.6x more likely to book after searching on TikTok.
How it works. Travel Ads leverage TikTok’s travel intent model and catalog integration to automatically serve personalized creatives at scale. Smart+ AI powers campaign setup, creative generation, and delivery optimization, aiming to convert discovery into bookings seamlessly.
Why we care. TikTok Travel Ads put hotels, flights, and destinations in front of a massive audience already using the app for trip planning, with two-thirds calling it their top source of travel inspiration. Smart+ AI tools then streamline setup and optimization, helping brands scale personalized ads that influence bookings at the earliest, most impactful stage.
The details:
Ads can showcase hotels, flights, and destinations using dynamic, visually rich formats.
Advertisers can choose among:
Single video ads: A hero video paired with personalized travel cards (hotel name, flight route, price).
Catalog video ads: Auto-built from product catalogs with tailored calls to action.
What they’re saying. David Hoctor, TikTok’s head of US verticals for travel and gaming, said:
“Travel on TikTok goes beyond the For You feed, unlocking real-life travel experiences. Every swipe can be a step toward conversion.”
Between the lines. By integrating catalog feeds with intent signals, TikTok is pitching Travel Ads as a direct competitor to Google and Meta in the lucrative travel advertising market, where inspiration and conversion increasingly overlap.
In episode 325 of PPC Live The Podcast, I sat down with Inderpaul “Indi” Rai, group account director at WeDiscover, to explore the lessons learned from mistakes, team dynamics, and the evolving role of automation and AI in paid search.
Indi, a veteran with over a decade of experience in AdTech, MarTech, SEO, analytics, and multilingual paid search, shared candid insights on how errors can shape careers and client relationships.
Embracing mistakes to grow
Indi opened up about one of the most significant mistakes in his career: an automated budget feature in Search Ads 360 went unchecked during his holiday, resulting in the U.S. account overspending by a substantial amount.
Despite the magnitude of the error, the client’s response was surprisingly supportive – they recognized it as an honest mistake and collaborated to mitigate the impact.
Key takeaway. Mistakes are inevitable, but transparency, a calm response, and collaborative problem-solving can turn potential disasters into learning opportunities. Indi emphasizes that experiencing and managing errors is essential for personal and professional growth.
The importance of team communication and handover
Indi reflected on how the overspend could have been avoided with better team preparation:
Ensuring critical information isn’t solely in the manager’s head.
Documenting handovers thoroughly and leaving room for junior team members to step in effectively.
Establishing multiple layers of oversight to prevent single points of failure.
He stressed that team members should feel empowered to act and communicate when issues arise, even if the manager is unavailable. In his experience, the junior team caught the overspend themselves and waited for Indi’s return rather than panicking – a testament to clear communication and a supportive team culture.
Lesson for managers: Maintain composure during crises, focus on solutions over blame, and ensure your team knows their role in resolving issues.
Automation and AI: Tools, not crutches
The discussion turned to automation and AI, where Indi shared practical advice:
Treat AI as an assistant, not a replacement. Blind reliance on AI can lead to errors, especially if users don’t understand the subject matter.
Always validate outputs and run rigorous testing before implementing AI-driven changes. He cited an example from his past work rewriting product descriptions: AI produced repetitive, generic content that wasn’t an improvement over the original, highlighting the need for careful oversight.
Automation can enhance efficiency but requires clear rules, regular checks, and accountability to prevent mistakes like unmonitored budget overspend.
Insight. Automation amplifies efficiency but cannot replace thoughtful human oversight and strategic decision-making.
Client relationships matter
A recurring theme in Indi’s story was the importance of cultivating strong client relationships. The supportive response of a previously “difficult” client revealed that mutual respect, trust, and proven value can turn challenging situations into opportunities for stronger partnerships.
Lessons in leadership and mindset
Indi also shared broader reflections applicable beyond PPC:
Resilience matters: he likened his career journey to Rocky, emphasizing that success isn’t about avoiding hits but about how you respond and keep moving forward.
Learning through experience: setbacks are essential; they teach you to handle pressure, improve processes, and grow professionally.
Balanced guidance: leaders should manage crises calmly, focus on facts, and support their teams without panicking.
From mistakes to momentum
The conversation underscores that mistakes are not the end – they are a catalyst for learning, collaboration, and improvement.
From automation missteps to client communication, Indi’s insights provide a roadmap for PPC professionals aiming to thrive in a fast-evolving industry.
Whether refining handovers, managing automation, or responsibly leveraging AI, the core lesson remains the same: anticipate errors, respond calmly, communicate clearly, and use each experience to build stronger teams and smarter processes.
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