I’ve discovered that Google has enhanced the Google Ads call campaign measurement with a new AI-qualified call leads feature. This upgrade focuses on boosting lead quality, moving beyond just measuring call length.
What’s new. Through machine learning, AI-qualified call leads analyze calls to determine if they represent valuable business opportunities. The system seamlessly integrates this data into bidding and reporting for improved results.
Zoom in. As an advertiser, I now receive AI-generated call summaries and tags, providing clearer visibility into each interaction. This transparency allows smart bidding to prioritize leads of higher value instead of relying solely on call duration.
Why I care. Call campaigns have traditionally depended on call duration to gauge value. With this update, I can shift the focus to actual lead quality, filtering out low-value interactions, including spam and robocalls. This change means better ROI, reduced wasted spend, and a clearer understanding of which calls really make a difference.
How it works. Recording calls is a default feature for most advertisers, allowing AI to evaluate call quality effectively. However, sectors like healthcare and financial services are exceptions. Advertisers, including myself, can adjust call length thresholds or opt to disable recording in account settings.
The fine print. Currently, this feature is available only for calls within the U.S. and Canada.
AI has reshaped how we think about acquisition strategy. It’s no longer about starting at the top of the funnel with broad awareness campaigns. Instead, we begin at the bottom, focusing on building understanding, credibility, and reach in the right sequence.
For the past 30 years, the industry followed a top-down model: raising awareness, gaining visibility, and then guiding potential customers through the purchase funnel. This approach made sense during the broadcast era and was somewhat effective in the search era, but today, in AI-driven environments, it’s outdated.
Today’s search engines and AI-powered assistants build brand recommendations from the ground up. They need to grasp who we are before they can evaluate our credibility. Only after establishing credibility can they recommend us. If we prioritize top-down strategies, we’re essentially wasting budget on awareness without a strong foundational understanding for AI to work with.
AI systems hold the key to successful brand recommendations — if they don’t understand our brand, or find us less credible compared to our competitors, they’ll likely recommend someone else. This AI-led shift is what I call the ultimate zero-sum game: the unseen recommendation to prospects we might not even know about.
The acquisition funnel hasn’t altered for users. They still journey from awareness to consideration to decision. Essentially, Elias St. Elmo Lewis’s model from 1898 still applies. All marketing models have been based on this, although channels have evolved. The mantra remains: reach first, relationship second, commitment third.
In my experience, the digital landscape changed with Google’s Knowledge Graph in 2012. It allowed machines to form independent opinions about brands, highlighting the need for brand understanding and reputation over mere awareness. Since then, my focus has centered on these aspects because AI-driven engines and agents rely on it to direct users towards credible destinations.
This marks a structural shift in marketing since 1898. While the user still travels from awareness to decision, in AI engines and agents, it’s our understanding and credibility that position us at the top of their funnel, achieved by training AI to guide users to us.
The coexistence of top-down and bottom-up strategies is real. We can still build awareness through controlled channels—paid media, broadcasts, and direct outreach. However, in the realm of organic engines, we must start from the bottom of the funnel, building a foundation for AI to guide users efficiently.
Every algorithm, AI engine, and agent operates based on entity and brand signals. Social media reach, too, hinges on brand recognition and engagement. Therefore, investing in a solid brand understanding orients us favorably within the AI framework, where roadmaps to our brand are increasingly machine-built.
This content reflects my approach to developing robust brand presences that resonate with both AI systems and human audiences.
I’ve recently discovered that Google has introduced some exciting AI safety features in their Ads Advisor, which could really transform how we manage campaigns. This update promises to automate policy fixes, enhance security, and expedite certifications, all to help us run our campaigns more efficiently.
As someone who spends a lot of time tackling policy issues and managing certifications, this news is music to my ears. With advertising campaigns becoming increasingly complex, having AI handle these time-consuming tasks could significantly boost our productivity and performance.
What’s New. The latest update brings proactive troubleshooting, continuous security monitoring, and immediate certifications. Thanks to AI and Google’s Gemini capabilities, these features promise to be a real game-changer.
Zoom In:
Ads Advisor can now automatically flag and resolve policy violations before they even catch our attention. This proactive approach ensures we stay ahead of potential issues.
The new security dashboard is always on the lookout for risks such as suspicious domains or dormant users. It’s like having an ever-vigilant guard protecting our accounts 24/7.
Imagine getting certifications that used to take weeks, approved instantly with just a click. This means we can focus on strategy rather than paperwork.
How It Works. Ads Advisor proactively scans accounts and sites, offering up fixes and confirming resolutions without the need for manual intervention. On the security front, it continuously checks account health and even supports passkey use, reducing our dependency on passwords.
Why We Care. These features save us hours that were once spent fixing issues, upping our security game, and dealing with certifications. This proactive system reduces delays and risks, ultimately enhancing campaign speed and efficiency.
What to Watch. Google plans to roll out these features for English-speaking accounts over the coming months, with additional languages to follow.
As I delve into the world of SEO reporting, I realize just how much we’ve outgrown platforms like Data Studio. Let me share what I’ve discovered and the exciting changes on the horizon that promise more efficient workflows powered by AI and APIs.
Imagine this scenario: Our team depends on Data Studio for delivering SEO reports. Just as we’re gearing up for a crucial meeting, Data Studio unexpectedly crashes, leaving us with nothing to showcase. It’s frustratingly common and incredibly embarrassing.
Just last year, I was praising Looker Studio (now Data Studio) for its advantages in SEO reporting. Fast forward, and it seems outdated compared to the dynamic coding tools I’m now utilizing. Here’s why rigid dashboards are holding us back and why transitioning to code-driven SEO reporting is essential.
Data Studio once reigned supreme for customizing SEO reports, but technology advanced, revealing its limitations. From dataset crashes to tedious manual interfaces, let me take you through some challenges I’ve faced with Data Studio.
We’re all familiar with the struggle: vast datasets in Data Studio are prone to breaking, often due to the low limits on rows and fields. Hasn’t it been just one too many times when a minor data addition causes everything to crash?
Manual updates in a slow interface make any iteration seem endless. Even the introduction of AI features addresses only a fraction of report-building issues.
Debugging Data Studio reports feels like a never-ending click maze. Unlike code-based systems where agents breeze through files, I’m often left clicking mindlessly within the interface.
Data Studio’s weak API is another stumbling block. It’s representative of Google’s missed opportunities for API-centric platforms. This flaw severely limits external management capabilities.
Despite recent rebranding efforts, these platforms lag behind modern SEO reporting technologies. Let me show you how everything is shifting with AI, APIs, and coding.
The evolution we’re witnessing is astounding. AI-driven coding tools like Claude Code and OpenAI Codex have changed the game. I describe my SEO reporting needs, and these tools take over, executing multi-step workflows efficiently.
Without needing deep coding expertise, I’m able to set up programmatic report workflows from beginning to end. Tools generate code that directly connects to data sources, eliminating reliance on cumbersome dashboard connectors.
Within minutes, comprehensive reports appear as I get accustomed to these tools. Each offers unique advantages, from reasoning to integration speed, transforming manual, rigid processes into infinitely flexible options.
AI coding tools usher in new possibilities for SEO teams by removing barriers between data management and reporting.
Speed is an unmistakable upside. Coding assistants enable SEOs to achieve in hours what once took days, and what took hours, now takes minutes.
Interacting with data directly through coding instead of dashboard interfaces drastically cuts down wait times for refreshes and modifications.
I’m no longer bound by rigid templates. Alongside on-demand data plotting and diverse frameworks, I can tailor reports to perfectly match needs and provide insightful visualizations.
Setting up these tools requires some initial effort but soon transforms the team’s efficiency, offering clearer data constraints and enhanced process transparency.
I’ve discovered how agentic coding assistants can revolutionize real-world SEO applications, from pre-meeting reports to ad hoc stakeholder requests, reducing late-night work and ensuring quick, reliable data access.
AI is reshaping the landscape for all professionals, not just us in SEO. As we adopt this technology, especially in SEO reporting, studies from Stanford and MIT show increased productivity. The shift isn’t optional; it’s imperative.
Teams leveraging AI tools in SEO witness faster iterations and can tackle complex issues more robustly, transforming analysts into strategists with unprecedented capabilities.
Begin this transformation with a small, repeatable project, connect data sources, and slowly expand your use of code-driven reporting. Early adopters are set to lead in SEO efficiency and results.
Traditional SEO reporting tools no longer meet the fast-paced demands of today’s analytics and strategic needs. Through AI and coding, we can leap ahead in reporting accuracy and timeliness, securing a competitive edge.
I recently came across a fascinating discussion at the Adobe Summit, where Alexis Zamkow and Sandhya Ranganathan Iyer from IBM highlighted the urgent need for brands to modify their approach to SEO. As AI revolutionizes the way brands are discovered, IBM has developed a 12-part GEO playbook that every brand should consider to remain visible in AI-generated decisions.
The evolution of search is something I’m experiencing firsthand. AI tools now answer questions, compare products, and recommend brands without users even needing to visit a website. This means that if my brand isn’t included in this AI-generated narrative, I’m potentially out of the picture when decisions are made.
To stay relevant, merely updating tactics won’t suffice. A holistic system, namely a GEO (Generative Engine Optimization) playbook, is key. During their presentation, aptly named ‘Adapt or Disappear: How Brands Win with AI-Powered Search,’ Zamkow and Iyer emphasized this shift.
Embracing the AI Shift: Marketing to Machines
I’ve realized that AI agents now mediate the interaction between me and my customers. They simplify complex markets and often represent my brand to potential customers.
As Zamkow aptly put it, “These machines are disintermediating the brand experience.”
In this new landscape, consumers heavily rely on AI for research and decision-making, businesses are quick to adopt AI solutions, and many searches conclude without any clicks.
Zamkow estimates that in the next couple of years, AI agents could account for 75% of search visibility, highlighting the importance of being included in the AI-generated answers themselves.
The GEO Playbook: 12 Essential Components
To navigate this shift, the speakers unveiled a 12-part playbook focusing on content, technology, and operations. It starts with creating a strategic content foundation which ensures that my messaging is clear and consistent across all platforms, building trust for both users and machines.
Ensuring my content meets retrieval-grade passage standards is crucial. Since AI extracts answers rather than ranking webpages, content clarity is key. I need to present information in concise, easy-to-understand sections.
Technical foundations can’t be ignored. It’s essential that my content is machine-readable with clean HTML, structured data, and pages that load content directly to maximize AI extraction.
I started by optimizing my on-site search to align with GenAI, making sure it can easily find relevant answers — a foundation for external AI search visibility.
Equally important is the AI search citation qualification model. Not just being mentioned, but cited by AI, boosts trust and credibility through consistent messaging and recognized expertise.
Through extraction optimization, I ensure my content is structured and rich in context to be easily pulled by AI tools.
Understanding that 85% of mentions come from external domains, I focus on a third-party strategy involving content mentioned across platforms like Reddit and social media, recognizing that PR and social teams are critical for search success.
Tracking new KPIs, such as AI mention frequency and citation locations, becomes essential, shifting my focus from mere traffic to AI recommendations.
I implement SOPs to maintain consistency in how my content is written, structured, and published, preventing confusion for AI systems.
With searches becoming conversational, I adopt prompting best practices, crafting content that aligns with users describing their queries in a more natural way.
Managing change across the entire organization involves training, goal alignment, and breaking down silos, emphasizing that this evolution is more than a marketing update; it’s transformational.
Continuous governance and versioning are critical. AI and competitor content change rapidly, making it vital to monitor, update, and maintain ownership of content changes.
From SEO Tactics to Comprehensive GEO Systems
We’re moving beyond traditional SEO, transitioning from keywords to prompts, links to citations, and from traffic-based metrics to validating our presence in AI answers. Importantly, it’s about building a system to continuously supply AI with accurate information.
A Leadership Issue
This transformation is rapidly becoming a leadership concern. As shared by Zamkow, this is no longer solely a matter for the SEO team; it’s a priority for CEOs, who need to recognize the importance of brand visibility in AI-based recommendations.
Adapt or Disappear
The AI-driven world is reshaping brand discovery. It’s trusted by consumers, utilized by businesses, and expanding quickly. Brands prepared with a comprehensive GEO playbook are poised to maintain visibility, while others risk being invisible in the digital landscape.
I recently dove into Google Ads Asset Studio to see what all the hype was about. I’ve heard declarations like, “Google just ended all excuses for not running video ads!” and “It’s a total game-changer; no production budget needed!”
The process is supposed to be simple: upload some images and get campaign-ready videos in minutes. Using Google Ads > Tools > Asset Studio, I can manage and scale images and videos effortlessly across various ad formats.
Recent additions like Veo, Google’s AI video model, and Nano Banana Pro suggest we can transform a few product images into engaging video ads almost instantly.
But does it really change the advertising game? Let’s explore if it’s truly worth our time.
From the Think with Google article about AI-generated ads, such as those for Cosmorama, I tried to reverse-engineer their imaginative approach. Unfortunately, despite using Nano Banana and Veo, I encountered many limitations.
For instance, I found the lack of scene-level control problematic. No prompting for video scenes meant I couldn’t guide the animation’s motion or pacing.
When generating videos, anything that resembled a human face—AI-generated or not—caused errors. This restriction limited my asset options significantly.
The audio options were also very limited. Unlike Cosmorama’s videos with cinematic scores, I was stuck with a small set of preloaded audio without the ability to upload custom tracks.
Overall, while Veo 3 introduced significant restrictions within Asset Studio, requiring a shift from expectations of advanced creative freedom.
While simplifying production could be beneficial, if you were expecting full creative control, you might be disappointed.
Thinking about whether Asset Studio truly saves time and effort, my experience suggests it’s a mixed bag. For brands previously in need of full production teams, Asset Studio might offer a faster and more cost-effective solution. However, for agencies or individuals incorporating this into existing workloads, it turns creative constraints into a newfound responsibility.
Regarding AI ad compliance, it’s worth noting there are no current U.S. federal laws against using AI in ads. However, places like New York are setting new precedents with upcoming laws requiring disclosure of AI use.
On the brighter side, if you use Asset Studio with ethical transparency in mind, although there’s no watermark or disclosure methods built-in, Google’s SynthID supports invisible AI tagging.
Could this tool live up to its potential without succumbing to ‘AI slop’? Josh Spanier from Google suggests not to worry, yet it’s essential to maintain control to avoid low-quality AI-generated ads from being published unwittingly.
Asset Studio indeed offers a streamlined way to bring product images to life, optimized for product integrity through tools like Nano Banana 2.
Features like quick trimming and leveraging simple templates show promise in turning around high-performing, concise ad creatives, even doubling CTR compared to previous client efforts.
In conclusion, while Asset Studio isn’t a complete game-changer, it provides tools that democratize creative access for those lacking a full production budget. However, it’s vital to measure the outcomes in terms of conversions and sales.
I’m running tests to see what truly holds up. Stay tuned.
I first got into SEO not because I had a crystal ball, but because I had no other choice. Back in the early 2000s, I was part of a small web business with my mom in Seattle. We once hired another company for SEO work, but when we couldn’t afford to continue, I found myself diving into search marketing.
Fast forward more than 20 years, and here I am, one of the loudest voices in SEO, and admittedly, one of Google’s fiercest critics. In a recent interview, I took a deep dive into how search has evolved, what’s gone astray, and what the future might hold.
Early SEO was a wild ride. The digital landscape today may seem convoluted, but nothing beats the chaos of the early days. It was a time ruled by forums like WebmasterWorld and Search Engine Watch, where people shared tactics rather openly. Risky as it was, buying links was common and effective—myself included. However, a public reprimand from Google’s Matt Cutts was a turning point for me, steering my focus towards ‘white hat’ practices aligned with Google’s guidelines.
Over time, I’ve begun to question if following those guidelines perhaps went too far, given Google’s own evolving practices. Yet, what continues to stand out from the early industry days are not just the tactics but the relationships I’ve built.
Many attribute AI as the seismic shift in search, but I beg to differ. It all started around 2011 when ‘zero-click search’ emerged—Google began answering queries directly on the results page. Initial features were simple, like weather boxes, but the concept expanded significantly with time.
Indeed, by around 2016–2017, nearly half of all searches ended without a click, growing to more than two-thirds today. This trend didn’t just appear out of nowhere with AI; it’s been brewing for over a decade.
I reckon publishers had a missed chance to take action long ago. At that time, media conglomerates could have united to challenge Google’s growing dominance, perhaps by demanding compensation or limiting usage of their content. Instead, they let Google expand its reach unhindered.
The publishing industry missed a window, but adaptation is key now. It’s time to pivot towards creating subscription businesses and monetizing attention rather than just traffic, as demonstrated by companies like The New York Times.
As for Google, I don’t believe its search services have worsened for users, though it’s become increasingly tough for publishers and creators. As Google grew and went public, priorities shifted, succumbing to growth and revenue pressures, thus becoming aligned with investor expectations.
When it comes to AI, I see a common misconception. People often mistake AI’s outputs as solid and consistent, akin to search results, but that’s rarely the case. Answers can vary widely. I recommend not relying on a single response; instead, ask multiple times and look for consistencies.
Reflecting on the early days of SEO, I don’t yearn for any specific tactic, but I do miss the opportunities for smaller creators and independent sites. Back then, traffic wasn’t just for the giants—it was more democratically distributed.
As I look forward, I imagine the media and search landscape might mirror the past: A few powerful platforms dictating the flow of information while individuals continue to create content within their domains. And yet, I’m hopeful the web will continue to evolve.
As I look around, it seems like everyone is scrambling to harness AI’s power. However, I’m realizing that fundamental identity gaps and issues like fraud and unreliable inputs are not getting resolved, but rather they are magnified by AI models.
AI has quickly become one of the most confidently discussed items in our modern marketing strategies. Budgets are reallocated, teams restructured, and vendors evaluated primarily by how “AI-powered” they appear. The belief is strong that once the right AI models are in place, performance metrics—such as targeting, segmentation, and conversion—will simply fall into place.
Yet, I’ve discovered a quieter truth. While organizations aren’t necessarily struggling with using AI, they face challenges feeding it adequate data. And often, the data they are supplying AI isn’t nearly as reliable as assumed.
This realization leads me to the uncomfortable truth about inputs. AI doesn’t produce truths; it magnifies what’s provided. If data is fragmented, outdated, or manipulated, AI doesn’t correct it—it scales it confidently.
Marketers have invested heavily in data infrastructures, only to find that an abundance of data and signals doesn’t necessarily equate to readiness. Large volumes do not guarantee validity. For instance, customer profiles built from various identifiers don’t assure a unified identity, and AI models are not inherently designed to question these flawed inputs.
Identity is at the core of this issue. Every AI-driven marketing effort assumes accurate identity for analysis and targeting, yet identity remains a fluctuating component in our data stacks. Consumers frequently move across devices and change profiles, making it tricky to track accurately over time. However, most systems treat a snapshot identity as a constant, and AI inherits this flawed assumption.
Additionally, not all data issues stem from outdated sources. Some are intentionally deceptive due to evolving fraud tactics, becoming more challenging to distinguish without additional context. Fraudulent behavior can significantly distort model outputs and performance metrics, creating a feedback loop where AI unintentionally perpetuates the very issues it should mitigate.
Traditional data strategies often focus on structure over substance, and clean data doesn’t equate to accuracy. AI demands an in-depth understanding of identity validity, activity authenticity, and risk awareness, which traditional strategies may overlook.
The illusion of AI readiness becomes apparent when dashboards show excellent match rates and models yield seemingly precise outputs. However, metrics of identity reachability and engagement accuracy become crucial yet often disregarded questions.
True AI readiness starts with ensuring that our data inputs are trustworthy. It focuses on verifying identity accuracy, validating meaningful activities, and acknowledging risks rather than simply accumulating data records.
By addressing these foundational elements, organizations can suppress low-value identities, optimize outreach, and mitigate misuse before it skews results. Over time, this creates a structural advantage for AI operations, leading to more reliable predictions and efficient campaigns.
I’ve come to understand that AI’s impact on marketing is undeniable, yet it cannot independently resolve inherent data challenges. Organizations need to prioritize and invest in understanding the integrity of their data systems.
The real question isn’t about applying AI but assessing whether our data is worthy of AI. This deeper level of scrutiny defines true readiness and distinguishes the truly prepared from those merely rushing ahead.
Traffic from Google searches is declining, and I know it firsthand because I’ve invested years in organic strategies. Seeing this shift in real-time is unsettling but also enlightening.
I’ve observed this change particularly in my SaaS clients. The educational, top-of-funnel (TOFU) content that once consistently drew traffic is losing steam. This isn’t due to declining quality; users simply don’t need to click anymore. AI Overviews are handling their queries.
This led me to a crucial choice: defend the old strategy or adapt to the new landscape. I decided to adapt.
Surprisingly, while informational content is getting fewer clicks, bottom-of-funnel (BOFU) content is not only steady but often driving more qualified leads.
This shift signifies a new understanding of value creation through search.
The pivot: Making BOFU the priority
My new approach focuses 60% to 80% of my efforts on bottom- and mid-funnel content. The rest fills in gaps with TOFU topics, supporting content clusters and timely industry discussions.
When I proposed this change to clients, I put it plainly:
“You can choose between traffic and leads. If leads are your goal, here’s our path, though it may mean less traffic.”
I was transparent that traffic might dip, but conversions would likely increase. Clients saw the appeal of a qualified pipeline over mere traffic.
Comprehensive comparison guides and listicles aimed at high-intent queries are highly effective BOFU content.
Take, for example, a guide on the best time-tracking software for construction. I created a reusable review methodology for the client, addressing pros and cons transparently, including their product. This honesty builds trust with evaluating readers.
The guide was factual, precise, and targeted at decision-makers in the purchasing phase, not casual browsers.
In weeks, it became our most referenced article in LLM responses. Now a cornerstone piece, it often appears in conversion pathways, driving qualified leads.
That single piece outperformed a dozen previous informational posts in pipeline impact because it directly answers a buyer’s question.
Many SEOs see this as a binary choice. But I haven’t abandoned TOFU content; I’ve simply repositioned it.
TOFU now builds topical authority, supporting the ranking of BOFU pages. It’s the structure beneath the main act. Guides and educational content should:
Support content clusters.
Establish expertise in Google’s eyes.
Pass link equity to BOFU pages.
We’ve revised top-performing TOFU pieces to connect directly to clients’ products, supported by screenshots and expert insights.
Calls to action were redesigned for context and strategically placed throughout the content, not just at the end.
These changes significantly increased visitor engagement with demo request pages, without altering the informational purpose.
The key is still producing valuable TOFU content but ensuring it has a unique perspective—something fresh and insightful.
Specificity in a sea of AI-generated content sets us apart.
Why this strategy excels in AI-driven search
Visitors from AI platforms arrive informed and ready to weigh options. This aligns with how AI Overviews serve search results.
AI Overviews are more frequent for informational than commercial queries. E-commerce searches trigger them less, safeguarding BOFU content for now, though commercial coverage is growing.
This change in behavior modifies what content performs well. As informational value diminishes with upfront answers, decision-stage content gains importance, aiding users in comparison and validation.
That’s why BOFU content thrives; it matches users’ decision-making phase, not just their search.
The time tracking software comparison piece is a prime example. It often appears in discussions on construction time tracking tools. While it might not always convert instantly, its impact is evident in branded searches and lead generation.
The attribution challenge to embrace
Here’s the dilemma: BOFU content’s true value often isn’t reflected in traditional analytics.
When someone discovers your solution via an AI response, then proceeds via direct or branded search to convert, it often appears as direct traffic in GA4, masking SEO’s role.
Therefore, I’ve guided clients to emphasize broader performance metrics, including:
Trends in brand search volume.
Citation frequency in LLM platforms.
Increases in direct traffic post-publication.
Conversions even with stable traffic levels.
The ROI of BOFU and LLM-focused content exceeds dashboard insights. Relying solely on immediate click metrics misses SEO’s true value creation.
Your playbook for transitioning to BOFU
Here’s a practical guide to capitalizing on this shift:
Audit for BOFU gaps: Identify purchase-stage queries lacking coverage. These high-intent gaps offer quick opportunities.
Create comparison content: Use a consistent review framework, openly address pros and cons for credibility and citations.
Enhance leading TOFU articles: Incorporate product links, contextual CTAs, and expert testimony for dual-purpose content.
Set up LLM tracking in GA4: Use regex segments to track AI referrer traffic and gain insights often overlooked.
Refocus client metrics dialogue: Shift focus from traffic to lead quality and conversion rates, reflecting modern SEO’s impact.
AI Overviews have reshaped informational content economics.
This disruption opens strategic doors. BOFU content traditionally converts better, and AI highlights the need to focus on content that drives revenue rather than mere site visits.
The opportunity for strategic realignment is here, but it won’t last indefinitely.
Search advertising continued to lead the pack in 2025, although its growth took a slight dip as digital advertising landscape evolved. What really struck me was how U.S. search ad revenue soared to $114.2 billion.
Despite being the largest ad channel, growth slowed down a bit, indicating a shift towards exciting AI-driven ad formats. It’s fascinating to see how advertisers are reallocating budgets towards these new trends.
Throughout 2025, the digital advertising market in the U.S. climbed to a phenomenal $294.6 billion, even without major cyclical events like elections or the Olympics driving it. The final quarter alone brought in a whopping $85 billion.
When I delve into the growth figures, video, social, and programmatic formats emerged as the fastest-growing sectors. Digital video revenue jumped by an impressive 25.4%, reaching $78 billion, while social platforms saw a 32.6% increase to $117.7 billion.
The influence of AI is undeniably reshaping the advertising landscape. It’s not just a tool anymore; it’s transforming how we discover, purchase, and measure ads across various platforms.
What truly captured my attention is the concentration of market control. The top 10 players now hold 84.1% of the market share, leveraging AI and large-scale data to assert dominance.
For anyone involved in digital advertising, it’s crucial to adapt to these shifts. With search as a somewhat stable force, emerging formats like video and social offer more exciting opportunities backed by automation and AI.
The insights come from the IAB/PwC’s comprehensive study of U.S. internet advertising revenue, giving us a look into the future of digital marketing.
