I’m excited to share that Microsoft is making a significant update that simplifies the way we set up automated bidding in Microsoft Advertising.
By consolidating performance targets, Microsoft aims to reduce complexity, making bidding more streamlined without sacrificing the control over critical performance metrics.
What’s happening: The platform is integrating common targets like Target CPA and Target ROAS into broader automated strategies. This means these targets will now form part of a more comprehensive bidding approach instead of standing alone.
From now on, I’ll be choosing between two main strategies: Maximize Conversions or Maximize Conversion Value, with optional performance targets that can be added as needed.
Credit – Hana Kobzova of PPC News Feed
How it works: For campaigns focused on conversions, I’ll select Maximize Conversions and may set a target CPA if desired. For campaigns aiming at maximizing value, I’ll choose Maximize Conversion Value with the option of setting a target ROAS.
Microsoft reassures that this update doesn’t change the fundamental bidding behavior — it simply makes the setup more user-friendly.
Why we care: This change enhances accessibility to Microsoft Advertising’s tools, making automated bidding more straightforward and efficient, which is especially beneficial when managing large-scale campaigns.
For us advertisers, this means faster setup times, more consistent optimization across accounts, and fewer complexities when managing campaigns focused on conversion or value.
What’s staying the same: Existing campaigns using Target CPA or Target ROAS will continue seamlessly, requiring no updates. Portfolio bid strategies are unaffected as well.
The bigger picture: This move is part of Microsoft’s larger effort to simplify automated bidding while ensuring performance control remains intact.
Bottom line:Microsoft is refining bidding options to make them more accessible without losing our ability to fine-tune performance through familiar controls.
From my experience, it’s clear that Google is moving forward by retiring several older ad format policies. This change highlights the transition toward innovative, automated campaign strategies in Google Ads.
What’s happening. On March 17th, Google decided to phase out numerous legacy ad format policies, including those concerning form ads, image quality, and more.
What changed. The rationale behind this is that many of these formats have evolved into modern campaigns, making previous policy frameworks obsolete.
Why we care. For us advertisers, this development streamlines Google Ads’ policy landscape, reducing potential confusion from older requirements.
What advertisers should do. It’s essential for us to focus on current Google Ads policies that regulate newer, automated, and AI-driven ad formats.
The bottom line. By streamlining policies, Google is reinforcing a shift toward fewer, more unified standards for today’s modern ad formats.
I recently tuned into an episode of Google’s Ads Decoded podcast where Brandon Ervin, Director of Product Management for Google Search Ads, shared insights on campaign consolidation, AI Max, and the future of advertiser control as we approach 2026. It was enlightening to hear a product team so in tune with advertiser concerns.
However, I felt the podcast left some gaps. There’s a significant disconnect between Google’s narrative and what advertisers truly experience on the ground. While Ervin’s team is making strides, the fast-evolving platform presents new challenges, shifting performance measurement onto economic standards. This change fundamentally alters how we should approach search ad audits.
As I reflect on recent improvements, it’s clear that enhancements like brand exclusions in Performance Max and Demand Gen, exclusion of site visitors in PMax campaigns, and improved search term visibility are crucial. These are responses to issues caused by bundling and aggressive automation. It’s worth noting that these controls arrived after advertisers were already knee-deep in implementation.
In an era where Google’s product team pushes for advancement, it’s vital for us to audit whether these new tools genuinely expand control or simply restore baseline transparency lost with earlier automation efforts.
In building the foundation for a 2026 search audit, we need to start with the basics, ensuring full ad extensions, strategic automated bidding, and maintaining negative keyword lists, among others. These are undeniable essentials that set the stage for deeper audits.
Focusing on the intricacies of signal architecture, I realize that while traditional controls like exact match and manual bids gave us direct oversight, the new controls shift focus to data quality, density, and selectivity. These influence the algorithm, which ultimately makes the decisions.
An effective audit in this context addresses three core aspects: the quality of the data imported, the density of high-quality data available for modeling, and the selectivity of the data shared with Google. These elements are pivotal in shaping campaign success.
Being mindful of incrementality is another key consideration. Google optimizes towards reported conversions, often encompassing brand search and retargeting signals that may not truly reflect incremental gains.
It’s critical to analyze marginal returns as Google’s system operates on a blended cost-per-action model. Without understanding the incremental cost at each spend tier, advertisers risk overspending without realizing diminishing returns.
Furthermore, as Ervin acknowledged, AI-driven campaigns sometimes misalign with intended targets. Query mapping has deteriorated over time, and AI Max exacerbates irrelevant matches, underlining the need to rigorously classify queries by intent to maintain high-value engagements.
Lastly, the economics of network performance in bundled campaigns like Performance Max and Demand Gen need thorough examination as they obscure valuable insight into actual network-driven outcomes.
By focusing on value redistribution through audits, we can ensure that the surplus value generated by high-intent searches isn’t misallocated into Google’s weaker inventory, thereby optimizing ad spend efficiency and accountability.
I just discovered that Google Ads has given the Asset Optimization layout for Demand Gen a sleek makeover. The updated panel enables advertisers like me to easily streamline creative formatting and placement through a few toggles.
Why we care. If you’re managing a large volume of creative, this central panel makes life much easier. It reduces manual labor by allowing us to enable or disable automation features quickly.
What’s new. This layout refresh organizes three main automation features into a more user-friendly interface:
Auto-generated shorter videos let AI trim existing videos for broader placements.
Automatic video resizing ensures our videos fit multiple aspect ratios, optimizing for wider coverage.
How it works. The new panel displays simple toggles like Resized videos and Image assets, making it straightforward for us to activate or deactivate each feature without sifting through several submenus.
Bottom line. If you’re running Demand Gen campaigns like me, it’s time to dive into the Asset Optimization panel and review which automations are turned on. Don’t miss out on features like video resizing and landing page image pulls as they can expand your reach effortlessly.
And, ensure your landing pages are visually appealing; Google will draw directly from them. As more AI tools roll out, I’m shifting my workflow to focus on high-quality source assets and letting Google handle the optimization of formats and placements.
I’m thrilled to share that Profound Agents can now seamlessly create presentations, documents, and webpages within Gamma as part of my automated workflows. No more hassle of exporting data and rebuilding it elsewhere. My Agent takes the outputs from upstream nodes and crafts them into ready-to-share assets in Gamma, streamlining the entire process.
PPC is becoming an increasingly difficult landscape to navigate, and even though AI provides some help, it doesn’t save the day. Meanwhile, platform transparency continues to decline, leaving us in the dark about budget management.
The latest survey of PPC professionals reveals a challenging environment characterized by less transparent platforms, diminishing effectiveness of traditional measurement methods, and AI tools that have yet to revolutionize our daily routines.
Why I care. As someone deeply invested in PPC, it’s notable that over half of practitioners (53%) believe PPC has become tougher compared to two years ago. The issue isn’t just competition; it’s the increasing number of decisions being made by platforms out of advertisers’ view, which contributes to this growing complexity.
Considering that a whopping 89% of digital ad spend goes to just three companies, those of us who don’t have private measurement tools are essentially navigating without a compass.
By the numbers:
1,306 respondents participated in the survey conducted between November and December 2025, representing agency, freelance, and in-house roles.
62% identified platform opacity as the main reason for increased PPC complexity, with 53% pointing to the loss of effective measurement tools.
5.2 hours/week are saved on average with AI tools, though the majority of us (55%) save only 1–5 hours; almost nobody reports saving over 20 hours.
59% are now using LLMs for ad copy, up significantly from 42% the previous year, marking it as the fastest-growing AI use case.
73% of in-house teams now manage PPC entirely in-house, a significant increase from 44% two years ago.
20% of clients are considering replacing agency work with AI, compared to just 12% planning to switch agencies.
$1 trillion was spent globally on digital ads in 2025, with 89% directed towards Google, Meta, or Amazon.
What they’re saying. Among PPC features, exact match keywords remain the most reliable, with 75% of us using them frequently. However, AI Max for Search sees minimal adoption, with 34% never having used it, possibly due to it being one of Google’s newest updates. Across the board, auto-apply recommendations are viewed with skepticism.
Between the lines. The underlying theme in the report revolves around agency survival. Many of us (62%) highlight the challenges of finding talent and increasing revenue, with the real threat being clients opting to manage PPC internally using AI.
The big picture. We’ve developed a cautious yet practical approach to incorporating AI — leveraging it for tasks like copywriting and research while being wary of its ability to make autonomous decisions. The more pressing issue that remains unaddressed is that platforms are gaining control and giving us less control over visibility, with no easy solution on the horizon.
Automation and AI are revolutionizing the PPC landscape. Now, PPC teams are transforming into data teams, mastering data infrastructure, measurement, analysis, and experimentation.
Like many people, I worry about AI taking over jobs. Where do my ‘old school’ PPC skills fit in an AI-dominated landscape?
Relax. It’s not a binary situation. The shift is towards data and strategy. Media buying might look automated from the outside, but don’t be misled. The role is simply evolving once more.
Having been in PPC for over 15 years, I’ve learned that there’s nothing to fear. The real question is: am I riding the wave or getting left behind?
Let’s explore what the current PPC landscape looks like with ad network automation, and more importantly, where today’s PPC teams truly add value.
The Return of the Technical PPC Team
A decade ago, technical PPC agencies distinguished themselves by developing scripts, managing data on a large scale, and overseeing complex structures. As automation matured, many teams pivoted towards strategy and creativity.
Now, with AI’s help, producing quality creatives or analyzing massive datasets to create strategies is easier than ever. However, these outputs aren’t flawless.
From a client’s perspective, the typical creative-centric or strategy-focused agency might be out of the game. Therefore, rejoice, PPC folks: the technical edge is back, albeit in a different form. It’s time to bring back the spreadsheet enthusiasts from the 2010s who can now drive the PPC industry forward.
Still skeptical? Let’s rewind and get a clearer view of the necessary skill sets.
The PPC Edge: From Spreadsheet Skills to Data Nerds
Today, successful PPC agencies sell something vastly different than a decade ago, though the core mindset remains the same.
Why? Let’s consider the key performance drivers nowadays:
Integrating down-funnel data into strategy.
Building a data infrastructure to support strategy.
Providing accurate signals to ad algorithms.
Building systems to scale operations, including creative tasks.
See the pattern? A broken data model can’t be solved just by prompts. This is your advantage, what clients value most. Automation enhances the value of technical literacy rather than diminishing it.
Who do you turn to for technical literacy? The seasoned PPC marketers who thrived on manipulating paid search ads using custom Excel macros or managing extensive product feed items. They have the mindset: a love for automation, data, and math.
1. Data Engineer
The data engineer builds and maintains the infrastructure. Although they might come after the tracking specialist in the data chain, they are central, which is why we mention them first.
In today’s multi-platform world, think of CRM integration with Google Ads or blending online and offline data sets to strategize effectively.
Without a comprehensive data model, strategies become vague gut feelings needing constant reality checks. The data engineer’s role is to set a strong foundation to prevent such situations.
Without this role, you face repetitive manual exports and inconsistent numbers across teams, leading to sluggish decision-making.
What is the Data Engineer’s Scope?
Building a data infrastructure follows an ETL process: extract data, manipulate it, and make it usable in tools like Looker Studio, Power BI, or Tableau.
Build data pipelines from ad platforms, analytics, or CRM tools into the warehouse for data like spend and revenue.
Structure tables for these sources and merge them for specific use cases.
Maintain datasets and perform automated QA, including refresh schedules.
What Skill Sets and Tools Does the Data Engineer Use?
In a Google-centric world, we often hear about BigQuery, but there are alternatives like Microsoft Azure. The essential skills are coding, particularly SQL and Python.
These languages are used to structure tables within the data warehouse (using SQL) and to create data pipelines (using Python).
2. Tracking and Measurement Architect
Some might think this role overlaps with data engineers, but I strongly disagree. This person focuses solely on maintaining signal quality within tight deadlines when issues arise.
Tracking failures mean lost conversion data, impacting ad platforms’ performance because they’re built on conversion data insights.
Notice this when CPAs fluctuate unexpectedly or in-platform data varies drastically from your ‘source of truth’ (GA, CRM, others). These architects help stabilize bidding and improve event match quality for better data in Google Ads.
What is the Tracking Architect’s Scope?
They design comprehensive, regulation-compliant data collection mechanisms, making sure everything is aligned with privacy compliance.
Align tracking with privacy regulations.
Design client- and server-side tracking.
Implement GTM and server containers.
Co-manage Conversions API integrations with the data engineer.
Co-ensure deduplication logic with the media buyer.
What Skill Sets and Tools Does the Tracking Architect Use?
While many PPCs have used Google Tag Manager, few have set up server-side tagging. This role needs a deep understanding of Consent Mode frameworks, CAPI, among other tools.
3. Data Analyst
If data engineers build the pipes and tracking architects secure the signals, data analysts interpret what the data implies. It’s a role quite affected by AI, yet crucial due to the risk of misinterpretation.
Wrong interpretations can lead to costly errors. Fully relying on AI over data analysts could be a grave mistake, as misinterpreted metrics like ROAS versus actual contribution margins or CPA disparities can derail strategies.
What is the Data Analyst’s Scope?
While outsiders might think they only build dashboards, data analysts handle much more, like designing models aligned with KPIs and rigorous analysis, all while questioning platform narratives.
Align data models with business KPIs.
Analyze performance cohorts, churn rates, and profitability.
What Skill Sets and Tools Does the Data Analyst Use?
Think of data analysts as translators; understanding numbers doesn’t mean you’re ready to interpret them correctly. They need SQL for warehouse queries and modeling skills for strategic planning, along with strong statistical reasoning.
4. CRO and Experimentation Lead
Once data is cleaned and analyzed, CROs leverage insights to enhance visitor economics. A low conversion rate can mean higher CPA, which no one wants. Their expertise helps scale operations efficiently rather than throwing money at inefficient processes.
What is the CRO’s Scope?
CRO roles are not just about landing pages but full-funnel optimizations, identifying friction points, structuring tests, and working with creative teams to position offers effectively.
Navigate from impression to revenue.
Utilize heat maps to locate friction points.
Use proper methodologies instead of random experiments.
Coordinate with creative and product teams for best offer placements.
What Skill Sets and Tools Does the CRO Lead Use?
Core tools include GA4 and heat mapping software, with options to scale based on needs. Critical skills involve a firm grasp of statistical reasoning and translation of business metrics into actionable insights.
From Media Buyers to Data Teams
Today’s PPC teams resemble hybrids of marketing, data, and product roles rather than mere media buyers. Successful teams deliberately build capabilities around understanding algorithms, data dynamics, and economics, enabling AI to become a strategic asset rather than a threat.
As an advertiser reaching out to Google Ads support, I’ve discovered there’s a new step involved in the process. Now, I must authorize any support-led changes to my account while still being accountable for the outcomes.
When I contact Google Ads support, I encounter a beta AI chat first. If I choose to fill out a support form instead, I need to check an ‘Authorization’ box. This allows a Google Ads specialist to access my account and deal directly with the issues by making necessary changes.
The fine print makes it clear that while Google may assist, they don’t guarantee any specific results. Any alterations are at my own risk, meaning I am fully responsible for any impact on my campaigns’ performance and costs.
Why do we care about this change? The new requirement places more responsibility on us, the advertisers. Even in an era of automation and AI, if any changes are applied by support, I still bear the risks associated with campaign performance and spending adjustments.
This situation presents a dilemma for people like me, as it offers a trade-off between speed and control. Allowing access can quicken the troubleshooting process, but it also means potential changes at the account level that might affect live campaigns without a guarantee of better results.
The bottom line? To obtain support, I might now have to temporarily hand over control, but I still need to remain accountable for my account’s future performance.
This change was first observed by PPC specialist Arpan Banerjee, who shared the message on LinkedIn.
Stepping into the world of automation has always intrigued me. It brings a level of efficiency that every SEO team craves. Today, AI agents like n8n are revolutionizing how we automate SEO workflows, from data scraping to structured delivery—plus, they have their set of challenges.
What makes n8n particularly captivating is its flexibility and control. Let me walk you through how this platform functions and how it can be harnessed in modern SEO operations.
Understanding How n8n AI Agents are Deployed
Think of modern AI agent platforms as a more intelligent version of Zapier. Platforms like n8n don’t just shuffle data between steps—they interpret, modify, and decide on the next move.
Starting with n8n involves choosing your deployment method: cloud-hosted or self-hosted. While letting n8n host your environment could sound appealing, it has its downsides:
The environment can feel limited.
Customization, like modifying server interactions, becomes difficult.
No community nodes can be installed or utilized.
Costs are usually higher.
But there’s a silver lining:
Less management is required—n8n takes care of updates and patches.
It’s user-friendly with little technical expertise required.
Maintenance stress is reduced significantly.
n8n offers various license packages. The self-hosted option is free, though it poses challenges for larger teams due to limitations in version control and change tracking.
How n8n Workflows Run in Practice
API credentials from providers like Google and OpenAI are necessary to leverage AI models and LLMs. Once installed, n8n’s interface is reminiscent of Zapier—a simple canvas for process design.
You can add nodes and pull data from external sources. Workflows can be triggered via webhooks, schedule, or another system interaction.
The executed workflows transmit outputs to places like Gmail, Microsoft Teams, or HTTP request nodes, triggering further n8n workflows or interacting with external APIs.
Take, for instance, a workflow that scrapes RSS feeds, generating a summarized update. It’s not a full-scale article, but it trims down recap times substantially.
Building AI Agent Workflows in n8n
Within a webhook trigger node, you can generate a webhook URL that Microsoft Teams calls, activating the n8n workflow. It streamlines requests for search news updates directly in a Teams channel.
Once the workflow runs, AI agent nodes communicate with LLMs like those from OpenAI and Google. This opens up numerous possibilities.
Variables from the scraping node, including content from multiple RSS feeds, get transferred to the prompt for summarization. Both user and system prompts guide the AI in processing and formatting this data.
While a single AI node handles summarization, a second node converts this summary into HTML, proving effective for specific tasks where dual AI nodes function best.
The summarized news is delivered through Teams and Gmail, offering a look at efficient workflow execution.
n8n SEO Automations and Other Applications
While I’ve shared a rather straightforward project, n8n’s capabilities extend much further in SEO and digital applications, such as:
Creating full-length, in-depth content.
Crafting meta and Open Graph data snippets.
Analyzing content from a UX perspective.
Developing simple SEO scanners.
And much more!
Inspired by a colleague’s comment, “If I can think it, I can build it,” I ventured into complex systems using n8n to meet the changing needs of SEO.
Drawbacks of n8n
Despite its potential, n8n isn’t without limitations:
Platform immaturity can lead to transaction hiccups during updates.
Resistance might stem from fears about job redundancy or ethics.
The focus should be on supplementing roles, not replacing them.
Its utility is limited in extensive technical audits or large-scale data analysis.
Beginning with repetitive or tedious tasks and automating them might be the key to reducing friction within your team.
SEO’s Shift Toward Automation and Orchestration
AI agents don’t replace human expertise, but they enhance it. They free us from mundane tasks, allowing us to focus on strategic areas, showing the positive shift in SEO toward automation rather than the discipline’s demise.
The evolution of tools may continue, yet the trend toward automation and orchestration is undeniable. Building proficiency in these systems is on the horizon as a vital skill for SEOs.
I’ve recently discovered an intriguing feature in Google Ads that provides advertisers, like myself, with enhanced visibility into how our landing page images can be automatically converted into ad creatives in Performance Max (PMax) campaigns. It’s fascinating to see the potential of these visuals beyond their traditional use.
Imagine having the ability to transform your website’s visuals into dynamic ads. By opting into this feature, Google can extract images from your landing pages and present them as ads. As I set up my campaigns, I can preview these automated creations before they go live, which grants me significant control over my advertising strategy.
Why this matters to us. With PMax, our website isn’t just a storefront but a vital component of our ad strategy. Any image—from banners to product visuals—can appear across platforms like Search, Display, YouTube, or Discover. This update offers a clear understanding of how our landing page images could become part of these campaigns, helping us visualize our potential reach.
I no longer have to speculate how Google might utilize my site’s visuals. Now, I can foresee, scrutinize, and regulate what content is utilized in my ads. This feature enables me to refine my landing pages and align them with my campaigns, minimizing surprises.
Between the lines: While automation is growing, so is the need for careful creative oversight. This update serves as a crucial tool for advertisers, ensuring we’re informed about what content goes live before it happens.
Bottom line: Our websites have transcended their roles as mere landing pages; they’re now integral to our ad engines, driving our marketing efforts forward.
First seen. Digital Marketer Thomas Eccel was among the first to highlight this development on LinkedIn, showcasing a practical example.
