In this episode of PPC Live The Podcast, I sit with Veronika Höller to unpack a real-world PPC mistake — from campaigns that looked perfect on the surface to the deeper issues that were quietly killing performance.

From “perfect” campaigns to zero revenue

Veronika Holler didn’t walk into a broken account. Quite the opposite. Everything looked right — clean structure, strong creatives, solid budgets, conversions coming in. On paper, it was a high-performing PPC setup.

But there was one problem: it wasn’t driving revenue.

That disconnect forced a deeper look beyond surface-level metrics. Because while impressions, clicks and conversions were ticking up, the campaigns weren’t actually delivering business impact — and that’s where things started to unravel.

The real issue: nothing stood out

The turning point didn’t come from inside the account. It came from looking outside it.

During competitor research, Veronika realised the brand sounded just like everyone else. The messaging blended into the market. There was no clear reason for a user to choose them over competitors.

From a user perspective, the ads weren’t wrong — they were just forgettable. And in a crowded category, “good” isn’t enough.

That insight reframed the entire problem: it wasn’t a performance issue. It was a positioning issue.

Starting again — from scratch

Instead of tweaking the existing campaigns, Veronika made a bold call: rebuild everything.

That meant new messaging, new creatives, and a new strategic foundation. One key shift was defining not just ideal customers, but also who they didn’t want to target — using anti-ICPs to sharpen the messaging.

They also introduced stronger localisation, tailored landing pages by market, and platform-specific strategies instead of copying campaigns across channels.

It wasn’t optimisation. It was a reset. And it worked.

The mistake that nearly broke everything

But earlier in her career, Veronika made a far more painful mistake — one that many PPC marketers will recognise.

She applied a recommended target CPA… without increasing the budget.

The result? Campaigns stopped delivering. Performance tanked. And worst of all, it went unnoticed over a weekend.

By Monday, the damage was clear — and the client was not happy.

Owning the mistake — and fixing it fast

There was no hiding from it.

Veronika immediately admitted the mistake, explained what happened, and took responsibility. That honesty changed the outcome. While the client was initially frustrated, the situation de-escalated quickly because there was no deflection — just a clear plan to fix it.

The lesson stuck: don’t blindly apply recommendations, and always understand the full context before making changes.

Why failure is part of getting good

For Veronika, mistakes aren’t something to avoid — they’re essential.

“You can only be good if you fail,” she said.

That mindset now shapes how she works and how she mentors others. Mistakes aren’t a sign of incompetence — they’re a sign that work is being done, tested, and improved.

And more importantly, sharing those mistakes helps others avoid repeating them.

The biggest issue she still sees today

Despite all the changes in PPC, one problem keeps showing up: tracking.

Broken implementations, over-reliance on micro conversions, and poor setup in tools like Google Tag Manager are still common.

In a world of smart bidding and automation, bad data doesn’t just limit performance — it actively misleads it.

Without clean tracking, even the best campaigns will fail.

AI won’t fix average marketing

Veronika is clear on one thing: AI is not a shortcut to better performance.

If you feed it average data, you’ll get average results.

Too many marketers rely on AI tools to analyse accounts without first understanding what needs to be improved. But AI can’t create differentiation — it can only optimise what’s already there.

Standing out still requires human thinking, strategy, and creativity.

The mindset that matters now

The biggest takeaway isn’t tactical — it’s mental.

Don’t aim for perfection. Don’t blindly follow recommendations. And don’t assume tools will do the thinking for you.

Instead, trust your instincts, test your ideas, and accept that mistakes are part of the process.

Because in performance marketing, the real risk isn’t failing — it’s playing it safe and blending in.

Google appears to be pulling parts of the Google Tag Manager interface directly into Google Ads — a move that could simplify how advertisers manage tracking and tags.

What’s happening. Advertisers are spotting a new “Manage” option inside the Data Manager section of Google Ads that opens Tag Manager controls without leaving the platform.

The update was first shared by Marthijn Hoiting and Adriaan Dekker, who posted screenshots showing Tag Manager elements embedded within the Google Ads environment.

Why we care. Tag setup and troubleshooting have long been a friction point for advertisers, often requiring multiple tools and technical handoffs.

Bringing Tag Manager functionality into Google Ads could reduce that complexity — especially for smaller teams or advertisers without dedicated dev support.

Zoom in. Inside the Data Manager interface, users can see connected data sources (including Tag Manager) and trigger management actions directly from within Google Ads.

That suggests Google is moving toward a more unified measurement workflow, where tagging, data connections and campaign setup live closer together.

Between the lines. This aligns with Google’s broader push to simplify measurement and improve data accuracy — particularly as privacy changes and signal loss make clean tracking more critical.

It also mirrors recent efforts to make tagging more accessible without heavy technical setup.

What to watch:

• Whether full Tag Manager functionality gets embedded or remains partial
• How this impacts workflows between marketers and developers
• If this becomes the default way to manage tags for advertisers

Bottom line. Google is quietly reducing the gap between campaign setup and measurement — bringing tagging closer to where ads are actually managed.

First seen. This update was shared by Adrian Dekker on LinkedIn, who credited Data and Analytics specialist Marthijn Hoiting for spotting it.

Microsoft Advertising is giving advertisers more flexibility in reporting, with custom columns now supporting all conversion metrics — a move aimed at deeper, more tailored campaign analysis.

What’s happening. According to Microsoft’s product liaison Navah Hopkins, advertisers can now build custom metrics using the full range of conversion data available in the platform.

This includes both all conversions and primary conversions, allowing marketers to align reporting more closely with their specific goals.

Why we care. Standard reporting often doesn’t reflect how businesses actually measure success. By expanding custom columns, Microsoft is enabling advertisers to create metrics that better reflect their own performance definitions — whether that’s based on lead quality, revenue or blended conversion actions.

This is especially useful for advertisers managing multiple conversion types or complex funnels.

More control over performance metrics. Advertisers can now create custom columns using ratios and combinations of metrics that matter most to them — such as cost per qualified lead, blended CPA or conversion rate based on primary goals.

Revenue and ROAS calculations will also reflect the values set at the conversion goal level, giving more accurate insights tied to business outcomes.

Between the lines. This update signals a shift toward more flexible, advertiser-defined measurement — rather than relying solely on platform-standard metrics.

It also reflects ongoing demand for better reporting customisation as campaigns become more automated and complex.

What to watch:

• How advertisers use custom metrics to guide optimisation decisions
• Whether this leads to more consistent reporting across teams and stakeholders
• If similar flexibility expands across other areas of the platform

Bottom line. Microsoft is giving advertisers more control over how they measure success — turning custom columns into a more powerful tool for campaign analysis.

Advertisers are starting to push back on gaps in AI Max capabilities — particularly around landing page control — as Google continues its shift away from legacy Dynamic Search Ads (DSA).

What’s happening. In a LinkedIn exchange, digital marketing expert Gabriele Benedetti raised concerns about AI Max lacking the same level of URL-based targeting controls that DSA campaigns offered.

His point: DSA allowed advertisers to structure campaigns around website architecture — using categories, URL paths and page rules to guide where traffic lands. That level of control, he argued, is not yet fully replicated in AI Max.

Why we care. For many advertisers — especially those managing large or structured websites — aligning campaign structure with site architecture is key to performance. Losing granular control over landing destinations could impact relevance, user experience and ultimately conversion rates.

This highlights a broader tension in Google Ads today: automation vs control.

Google responds. Google Ads Liaison, Ginny Marvin responded, clarifying that AI Max does support several URL-based controls, including:

• URL rules and combinations
• Page feeds with custom labels
• URL inclusions at ad group level and exclusions at campaign level

However, she acknowledged that not all DSA targeting rules are currently supported — such as “page contains” conditions.

Between the lines. Google is not removing control entirely — but it is reshaping how that control works. Instead of granular rule-building, advertisers are being pushed toward structured inputs like page feeds and labels that AI can interpret.

Migration reality check. For advertisers moving from DSA to AI Max, existing URL rules will carry over — but with limitations. Unsupported rules will remain active as read-only, meaning they’ll continue to function but cannot be edited.

That’s a temporary bridge, not a long-term solution.

What’s next. Google says it plans to expand controls further, including bringing content and title-based exclusions to the account level later this year.

This would complement AI Max’s existing “inventory-aware” features, which already exclude out-of-stock items automatically.

Bottom line. AI Max is evolving, but it’s not yet a full replacement for DSA when it comes to granular control — and advertisers are making that clear.

Dig deeper. Full discussion on LinkedIn.

Google is rolling out new AI-driven bidding and budgeting features across Search, Shopping and Performance Max — aimed at helping advertisers capture more demand without increasing manual effort.

What’s happening. Google is expanding its automation stack with updates like Journey-aware Bidding, Smart Bidding Exploration and demand-led budget pacing. Together, these changes are designed to help campaigns respond more dynamically to shifting consumer behaviour.

The focus: letting AI identify and act on opportunities advertisers may not see themselves.

Why we care. These updates aim to capture more conversions without increasing manual work, using AI to find new demand and optimise spend in real time. By improving how bids respond to full-funnel signals and how budgets adapt to peak demand, campaigns can become more efficient and less reliant on constant adjustments.

Ultimately, it’s about getting more value from the same budget while staying competitive in a fast-changing search landscape.

Smarter bidding gets more context. Journey-aware Bidding (beta) allows advertisers to feed more of the customer journey into optimisation, including non-biddable conversions. This gives Google AI a fuller picture of what leads to actual sales — not just initial actions like form fills.

At the same time, Smart Bidding Exploration is expanding beyond Search. Already delivering an average 27% increase in unique converting users, it will soon roll out to Performance Max and Shopping campaigns, helping advertisers tap into less obvious, incremental queries.

Budgets that follow demand. On the budgeting side, Google is building on its campaign total budgets feature, which allows advertisers to set spend across a defined period instead of relying on daily limits.

The next step is demand-led pacing — where AI automatically adjusts spend based on real-time demand, increasing budgets on high-opportunity days and pulling back during slower periods, without exceeding overall limits.

Advertisers using total budgets have already seen a reported 66% reduction in manual budget adjustments.

Why this is a big deal. Budget management has historically been one of the most manual parts of campaign optimisation. By automating pacing, Google is reducing the need for constant monitoring while aiming to improve efficiency.

What to watch:

• How much control advertisers are willing to give up for automation
• Whether incremental gains from exploration translate into profitable growth
• How transparent these systems remain as they scale

Bottom line. Google is directing advertisers to AI to handle both bidding and budgeting — shifting the advertiser role from manual optimisation to guiding inputs and trusting the system to find growth.

Google is expanding its Analytics Data API to include cross-channel conversion reporting — giving developers programmatic access to paid and organic performance data.

What’s happening. The new feature, currently in alpha, allows Google Analytics and Google Ads users to pull conversion data across channels via the API — mirroring what’s available in the Conversion performance report in the Analytics interface.

This means developers can now access the same insights without relying on manual reporting.

Why we care. As measurement becomes more complex, advertisers need unified views of performance across paid and organic channels. This update enables teams to automate reporting, integrate data into their own systems and build more advanced analysis workflows.

It’s particularly valuable for businesses managing multiple platforms and looking to centralise performance data.

The caveat. This feature may not be available to every Google Analytics property yet. Google says it is actively working to expand access, and advertisers should check with their support teams to confirm eligibility.

What to watch:

• When the feature moves beyond alpha and becomes widely available
• How advertisers use API access to build custom attribution models
• Whether more reporting capabilities are added to the Data API

Bottom line. By bringing cross-channel conversion data into the API, Google is giving advertisers and developers more control over how they access, analyse and act on performance data.

OpenAI is taking the next step in building its ChatGPT ads platform — introducing self-serve buying, CPC bidding and improved measurement to bring more advertisers into the ecosystem

What’s happening. Ads in ChatGPT are moving beyond a limited pilot, with new ways for businesses to buy and manage campaigns. Advertisers can now access inventory through agency and tech partners — or directly via a new beta Ads Manager rolling out in the U.S.

This marks a shift from a controlled test environment to a more scalable ad platform.

Why we care. Until now, access to ChatGPT ads has been restricted and expensive, limiting participation to large advertisers. These updates lower the barrier to entry, opening the door for SMBs, startups and a wider range of brands to test the channel.

At the same time, introducing CPC bidding brings ChatGPT closer to established performance platforms, allowing advertisers to optimise for actions — not just impressions.

Self-serve Ads Manager. The new Ads Manager gives advertisers direct control over campaigns, including budgeting, bidding, creative uploads and performance tracking.

While still in beta, it signals OpenAI’s intention to build a full-service ad platform — not just a partner-led ecosystem.

Between the lines. This is a familiar playbook. Platforms typically start with high-touch, partner-led campaigns before moving to self-serve tools that unlock scale. ChatGPT is now entering that second phase.

CPC bidding arrives. Previously, ChatGPT ads were sold on a CPM basis. The addition of CPC means advertisers can now align spend with user actions — a critical step for performance marketers.

Given the nature of ChatGPT queries — often exploratory, comparative and decision-driven — clicks could become a strong proxy for intent.

Measurement catches up. OpenAI is also rolling out pixel-based tracking and a Conversions API, allowing advertisers to measure actions like purchases, sign-ups and leads.

Importantly, this data is aggregated, with no access to individual conversations — reinforcing OpenAI’s emphasis on privacy.

Why this is a big deal. Measurement has been one of the biggest gaps in early ChatGPT ads. Without it, advertisers struggled to justify spend. These updates begin to close that gap and make optimisation more viable.

The ecosystem grows. OpenAI is also expanding its partner network, working with agencies like WPP and Publicis Groupe, as well as tech platforms such as Criteo and Adobe.

This allows advertisers to buy ChatGPT ads through tools and workflows they already use.

What to watch:

• How quickly self-serve adoption scales
• Whether CPC performance holds as competition increases
• How measurement evolves to match advertiser expectations

Bottom line. ChatGPT ads are moving from experiment to platform — and with self-serve tools, CPC bidding and better measurement, OpenAI is laying the groundwork for scale.

Google is rolling out new tools to help advertisers better understand performance across increasingly complex customer journeys.

What’s happening. As AI continues to transform campaigns, creatives and targeting, Google is introducing updates focused on data integration, experimentation and media mix modelling — all aimed at helping marketers turn fragmented signals into actionable insights.

Why we care. Automation has made it easier to run campaigns, but harder to understand what’s actually working. These updates make it easier to connect data, prove what’s actually driving results, and make smarter budget decisions across channels. As AI handles more of the execution, having strong measurement in place becomes the key differentiator for performance and growth.

Data is the starting point. Google is expanding its Data Manager to give advertisers a clearer view of how their data flows across platforms like BigQuery, HubSpot and Shopify.

A new map-based interface will help marketers visualise connections between data sources and identify gaps in tracking or configuration. At the same time, updates to the Google tag aim to simplify setup, allowing advertisers to upgrade existing tags without additional coding.

The goal: make it easier to unify signals and improve data quality — which directly impacts campaign performance.

Between the lines. Google is acknowledging a long-standing issue — advertisers struggle more with data setup and integration than with campaign execution itself.

By simplifying tagging and data flows, Google is trying to remove one of the biggest blockers to effective AI adoption.

Proving what actually works. Google is also introducing Meridian GeoX, a new geo-experimentation tool designed to measure incremental impact across regions.

Built on an open-source framework, GeoX feeds into Google’s broader Marketing Mix Model, Meridian, giving advertisers a more defensible way to validate performance — especially when presenting results to finance teams.

This signals a shift toward causal measurement, not just correlation.

Why it matters. As privacy changes reduce visibility and attribution becomes more complex, marketers are under pressure to prove impact. Tools like GeoX aim to provide that “ground truth” — something many attribution models struggle to deliver.

Simplifying media mix modelling. To address the complexity of Marketing Mix Models (MMMs), Google is launching Meridian Studio — a Google Cloud-powered platform that helps teams build, customise and scale models more easily.

The focus is on operationalising MMMs, making them less resource-intensive and more accessible for enterprise teams managing large datasets.

What to watch:

• Whether advertisers adopt MMMs more widely with simplified tools
• How effective GeoX is in proving incremental impact
• If improved data visibility translates into better campaign performance

Bottom line. Google is making a strategic shift: in an AI-driven world, better measurement — not just better automation — will determine who wins.

Initial reports from SimilarWeb indicate ChatGPT ads are outperforming traditional benchmarks on engagement — but with limited inventory and small-scale tests, it’s too early to call this a long-term trend.

What’s happening. According to early analysis, ads appearing in ChatGPT conversations are generating strong click-through rates vs Display and Podcast channels, likely driven by high-intent user queries and the native way ads are integrated into responses.

Unlike traditional search ads, these placements appear directly within conversational answers, making them feel more contextual and less disruptive.

Why we care . If these early CTRs hold at scale, ChatGPT could become a serious performance channel — especially for advertisers looking to reach users at the moment of intent.

But there’s a catch: inventory is still limited, and early performance often looks better before wider rollout introduces more competition and variability.

Between the lines. High CTRs don’t necessarily mean high performance. Conversion quality, cost efficiency and scalability will ultimately determine whether ChatGPT ads can compete with established platforms like Google Ads.

There’s also the novelty factor — users may be more likely to engage simply because the format is new.

Zoom in. Some categories are already showing stronger signals than others.

Mother’s Day-related prompts are far more likely to trigger ads—about three times more than average—because they signal strong purchase intent, with brands like Etsy, Nordstrom and flower retailers already showing strong visibility.

What to watch:

• Whether CTRs hold as inventory expands
• How conversion rates compare to search and social
• If pricing models evolve beyond early testing phases

Bottom line. ChatGPT ads are off to a strong start on engagement — but until scale, cost and conversion data catch up, advertisers should treat this as a promising test channel, not a proven one.

Dig deeper. Advertising in AI: Insights from Real User Behavior

Automation doesn’t fail on its own — it does exactly what it’s trained to do. The problem is that when Google Ads is fed incomplete, misaligned, or overly broad signals, it can optimize toward the wrong outcome faster than most advertisers realize.

In our second installment of SMX Now, our new monthly series, Ameet Khabra of Hop Skip Media will break down a real account where a 417% jump in conversions turned out to be the wrong kind of success. She’ll use that case study to explain the four key ways automation drift enters an account: signal drift, query drift, inventory drift, and creative drift.

You’ll leave with a practical framework for diagnosing drift early, understanding where human oversight matters most, and managing automation more deliberately so it works toward real business goals — not just platform-reported wins.

Join us May 6 at noon ET.

Save your spot

Microsoft Advertising is expanding its Performance Max reporting with publisher-level conversion and spend data — giving advertisers more visibility into where results are actually coming from

What’s happening. According to Microsoft Ads Product liaison Navah Hopkins, the PMax Website Publisher URL report now includes conversion and spend metrics, moving beyond basic placement visibility into actionable performance data.

This gives advertisers clearer insight into which placements are driving real outcomes — not just impressions or clicks.

Why we care. This update gives advertisers visibility into which placements are actually driving conversions and spend — not just impressions. That means better optimisation decisions, from scaling winning inventory to cutting wasted spend. It also makes it easier to trust and justify Performance Max performance with concrete data, rather than relying on aggregated reporting.

How advertisers can use it. The update opens up several practical use cases. High-performing placements can now inform Audience Ads strategies, such as building remarketing campaigns or impression-based audiences from winning inventory.

At the same time, advertisers can identify poor-fit placements and exclude them using account-level URL exclusion lists, helping protect brand safety and improve efficiency.

Between the lines. This is another step toward making automated campaigns more transparent. Rather than replacing control entirely, platforms are starting to give advertisers clearer signals on what’s working — and where to act.

What to watch:

• Whether this level of transparency expands further across PMax reporting
• How advertisers balance automation with manual optimisation
• If similar reporting features roll out across other platforms

Bottom line. With conversion and spend data now visible at the placement level, Microsoft is making Performance Max a little less of a black box — and a lot more actionable.

Google is enforcing a hard cutoff for older API versions, meaning advertisers and developers who don’t upgrade risk losing access to critical campaign management tools.

What’s happening. Google Ads API v20 will officially sunset on June 10, 2026. From that date onward, all requests to v20 will fail, requiring migration to a newer version to maintain uninterrupted API access.

Why we care. If you rely on the Google Ads API and don’t upgrade in time, automated workflows — including reporting, bidding and campaign management — could suddenly stop working. This could lead to data gaps, performance issues and operational disruption. Migrating early ensures continuity and avoids last-minute fixes that can impact campaign performance.

What to do. Google is urging users to upgrade as soon as possible and provides resources like release notes and upgrade guides to support the transition. Developers can also use the Google Cloud Console to review recent API activity, including which methods and versions their projects are calling.

Between the lines. API sunsets are routine, but the impact can be significant for advertisers relying on custom scripts, tools or third-party platforms. Missing the deadline could disrupt reporting, bidding or campaign automation workflows.

The bottom line. This is a firm deadline with real consequences: upgrade to a newer Google Ads API version before June 10 or risk losing access entirely.

Google is trying to simplify one of its most complex products, helping advertisers and analysts get more value from Google Analytics without deep technical expertise.

What’s new. Google Analytics is rolling out Task Assistant, a guided workflow tool that surfaces tailored recommendations to improve property setup, data collection and reporting.

How it works. Available in the left-hand navigation, Task Assistant organizes recommendations into clear categories like connecting accounts, enhancing reporting and fixing data issues. Users can mark tasks as complete as they go or skip items that don’t align with their business goals, creating a more flexible setup experience.

Why we care. Google is making it easier to identify gaps in tracking and fix them quickly, which leads to more reliable data and better decision-making. Task Assistant helps ensure Analytics is properly configured without requiring deep expertise, reducing the risk of missed insights or inaccurate reporting. Ultimately, better data setup means more confident optimization of campaigns and budgets.

Between the lines. Analytics platforms are powerful but often underutilized due to poor configuration. Task Assistant is Google’s attempt to reduce that friction by turning setup into a step-by-step process rather than a manual audit.

The bottom line. Task Assistant aims to make Google Analytics more actionable, guiding users toward better data quality and more effective measurement with less guesswork.

Google is filling a key measurement gap between awareness and consideration, giving advertisers a clearer view of how their brand is actually perceived — not just remembered.

What’s new. Google Ads has introduced a new “Association” metric within Brand Lift Studies. Advertisers can define a concept, category or attribute, and Google will ask users a survey-style question: which brands they associate with that specific idea.

How it works. Instead of measuring simple recall, the metric evaluates whether audiences connect your brand to a desired positioning. That could mean “premium,” “sustainable,” or even a product category — offering a more nuanced read on brand perception.

Why we care. Google is giving you a way to measure brand positioning, not just awareness or recall. The new Association metric helps determine whether campaigns are actually shaping how consumers perceive a brand — a critical step between being known and being chosen. It also enables more strategic optimization of creative and messaging, especially for brands trying to own specific attributes or categories.

Between the lines. Brand Lift has traditionally focused on awareness, recall and consideration. Association sits in between, helping advertisers understand whether their messaging is shaping how people think about the brand, not just whether they recognize it.

The catch. There’s still a constraint: advertisers can only select three Brand Lift metrics per study, so adding Association means making trade-offs with existing KPIs.

The bottom line. Association gives advertisers a more strategic lens on brand building — measuring not just visibility, but whether campaigns are landing the intended message.

First seen. This update was first spotted by Google Ads expert, Thomas Eccel who shared the update on LinkedIn.

Google is doubling down on AI-driven ads just as search behavior shifts toward conversational queries, giving advertisers more automation while trying to preserve control.

What’s new.

AI Max expands beyond Search: Now rolling out to Shopping campaigns and travel-specific formats, broadening reach across more advertiser types.

AI Brief (powered by Gemini): A new interface that lets advertisers steer AI using natural language inputs.

Text disclaimers + URL automation: Compliance-friendly updates to pair with automated landing page selection.

Why we care. Google is making AI Max a core layer across Search, Shopping and Travel, meaning automation will increasingly determine how ads are matched to user intent. This update expands reach into more conversational, high-intent queries that traditional keyword strategies miss, helping brands capture demand earlier in the journey.

At the same time, tools like AI Brief and new compliance features give advertisers more control over messaging and targeting, reducing the risk of fully automated campaigns feeling like a “black box.”

Shopping gets smarter. For retailers, AI Max for Shopping uses Merchant Center data to generate more adaptive ads that can respond to long-tail and exploratory queries, helping brands appear earlier in the discovery phase rather than only at the point of purchase. The rollout is positioned as a simple upgrade for existing Shopping campaigns, suggesting Google wants rapid adoption.

Travel gets consolidated. Travel advertisers get a consolidation play. Search Campaigns for Travel bring previously fragmented formats into a single interface with unified reporting and integrated AI Max capabilities. The move reduces operational complexity while reinforcing Google’s push toward centralized, AI-driven campaign management.

More control with AI Brief. The most notable addition is AI Brief, which attempts to solve a long-standing advertiser concern: lack of compliance control in automated systems. Advertisers can define messaging rules, specify which queries to prioritize or avoid, and shape how different audiences are addressed. The system then generates previews, allowing feedback before campaigns go live.

Automation meets compliance. Google is refining how traffic is directed to websites. Final URL expansion uses AI to select the most relevant landing page for each query, and the new text disclaimer feature ensures required legal messaging remains intact even when automation is active. This signals a push to make AI usable in more regulated industries without sacrificing compliance.

The bottom line. AI Max is evolving from a Search add-on into a foundational layer across Google Ads, combining automation, cross-format reach and advertiser input to adapt to a more AI-driven, conversational search landscape.

Looking to take the next step in your search marketing career?

Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.

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• Paid Media & Influencer Marketing Internship (Summer 2026)
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• Account Manager, Paid Social
  May 8, 2026
  Job Description Job Description Who We Are Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our 600+ employees work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world’s most premium platforms. Taking a […]
• Marketing Specialist – New York, NY
  May 8, 2026
  Corcoran Sunshine Marketing Group is seeking a Marketing Specialist to join its New York headquarters, reporting to the VP of Marketing and working closely with both the Marketing and Project teams. This role supports the execution of project‑based marketing initiatives across multiple channels, including print and digital advertising, project‑specific social media, portfolio‑wide and site‑specific events, […]

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