I’m thrilled to share some fantastic news with you. We’ve just launched support for Claude Fable within Profound, and it’s an upgrade that I’m genuinely excited about.
Incorporating Claude Fable into our system not only enhances user experience but also brings a new level of efficiency to our platform. This integration is designed to provide seamless functionality and improve overall productivity.
I’m confident that this addition will greatly benefit all users by offering enhanced capabilities and features that are both intuitive and powerful. Stay tuned for more updates as we continue to innovate and evolve.
Not too long ago, I remember broad match being hailed as the future of paid search. Today, AI Max has taken on that mantle.
Over recent months, I’ve received plenty of suggestions to activate AI Max on brand campaigns, even when these campaigns are performing just as I want them to.
The reality is, many accounts still aren’t equipped with the essentials AI Max requires for optimum function. Conversion tracking issues, the lack of offline conversion imports, and budget-constrained generic campaigns are common hurdles.
AI Max thrives on robust conversion signals, adequate volume, and enough variation for effective learning. I often find that brand campaigns provide most of these signals.
However, applying AI Max to brand campaigns means layering additional automation over our most efficient and predictable traffic source.
The promise and limitations of AI Max
AI Max can broaden search targeting beyond your key phrases by using keywords, landing pages, and site content as signals instead of specific targeting criteria.
Much like dynamic search ads (DSA), AI Max can align with queries you didn’t explicitly target, and it ventures even further by transcending the intent limits set by your keyword arsenal.
Google portrays AI Max as the future of Search automation, preparing to merge DSA, automatically created assets, and broad match settings into AI Max this September.
With controls like brand exclusions, URL exclusions, text guidelines, and location targeting, AI Max might tap into growth opportunities in accounts rich with strong conversion signals and enough search volume.
Yet, many accounts haven’t reached that point.
With Google’s AI Surface eligibility expanding, it’s tempting to dive headfirst into AI Max. But it’s essential to focus on account fundamentals—measurement accuracy, conversion integrity, and solid account structures—before relying solely on AI Max.
Why AI surface eligibility isn’t reason enough to rush into AI Max
The growing interest in AI Max is fueled by Google’s push toward AI-powered search experiences. AI Overviews now engage approximately 2.5 billion users monthly, presenting ads in 25.6% of AI Overview results, according to Semrush data.
While maintaining visibility in these surprising new fields is advisable, rushing to apply AI Max without assessing your campaign structure and conversion strategies can be detrimental.
Typically, Google Ads representatives pitch AI Max for brand campaigns to ensure their eligibility in AI Mode and associated AI Overviews. However, this isn’t always the truth.
Ginny Marvin, a Google Ads liaison, confirmed that three campaign types are eligible for AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search. Meanwhile, exact match keywords never qualify for AI Overviews.
Thus, PMax and AI Max generally serve the same purpose concerning AI surface eligibility. Running PMax brand campaigns already gives you the necessary coverage, without the need for adding another layer of automation.
Before adding AI Max into your mix, examine whether it’s genuinely necessary over addressing your account’s foundational needs.
Test data doesn’t fully endorse Google’s AI Max assertions
Google claims that enabling AI Max could increase conversions by 14%, and those employing exact and phrase matches might experience a 27% increase. Nevertheless, independent tests have yielded a wide array of results.
The evidence for AI Max remains mixed
In tests covering 600 accounts, Smarter Ecommerce observed AI Max produced 35% lower ROAS than traditional match types. This outcome aligns with intentional budget minimization by advertisers.
Through a four-month examination, Xavier Mantica discovered AI Max resulted in the priciest conversions compared to phrase and exact match. While Mantica noted $100.37 per conversion with AI Max, phrase match was at $43.97, and exact match was at $52.69.
Moreover, 99% of impressions during Ezra Sackett’s 30,000 search term analysis returned zero conversions under AI Max.
Significantly, none of this data is brand-focused. AI Max may provide benefits in certain settings, but a successful, exact match defensive brand campaign may not be the right candidate for testing new automation.
If your brand is still the standout performer in your account, you may want to question why the rest of your campaigns haven’t met similar standards.
What to consider before testing AI Max on brand
Ask yourself these critical questions before branching AI Max into your brand campaigns:
1. Are the conversion signals trustworthy?
Does your setup cleanly distinguish between macro and micro conversions? Are offline imports running smoothly? Does the lead quality feedback enhance platform optimization?
If the underlying signals falter, AI Max will simply magnify those issues.
2. Have you already explored generic growth?
In the accounts I review, problems like budget constraints, misaligned landing pages, outdated queries, and suboptimal structure frequently hinder generic campaign growth.
Real growth is often found within these issues, rather than an already strong brand campaign.
3. Can the account provide AI with sufficient learning data?
Remember, AI Max is not some sorcery; it mirrors the quality of the signals it receives.
Relying heavily on brand conversions will only amplify these markers and obstruct other growth pathways.
4. Are brand + modifier searches already structured properly?
Search variations like “Brand + pricing” or “Brand + reviews” ought to be treated as separate strategic campaigns. AI Max should not substitute for robust account architecture.
5. Do you have a strategic reason to expand the brand campaign?
Consider testing strategically through experiments, rather than viewing AI Max as a straightforward switch to augment visibility.
AI Max only works as efficiently as the signals guiding it
AI Max might develop into a truly beneficial tool over time, much like PMax did. Automation effective at any level still requires strong foundational signals for success.
The existing issue remains with insufficient solid foundations supporting the automation. Improved conversions, precise measurement, sound account structures, and comprehensive feedback loops are vital to making automation wiser.
Above all, don’t conflate Google’s automation agenda with your campaign objectives.
I recently dove deep into the fascinating world of ChatGPT Ads with insights from Adthena. It turns out, the advertising space on ChatGPT is a treasure trove of competitive information that many search teams are missing out on.
Your competitors are running stealth campaigns via ChatGPT, and the frustrating part is that it’s not immediately visible what they’re bidding on or what creative strategies they’re adopting. Unlike Google Ads, there’s no native way—yet—to get a behind-the-scenes look at this in ChatGPT.
When OpenAI launched advertising within AI-generated responses, brands jumped on board quickly. With the Ads Manager and lowered spending thresholds, this new ad channel grew rapidly. And with plans to expand to U.K. markets soon, there’s a quickly closing window for early adopters to gain a significant advantage.
From the start, we’ve been closely monitoring these developments, and what we’ve found is eye-opening.
What Does the Current ChatGPT Ads Landscape Look Like?
Our analysis spans nearly a million queries across 20 industries in five markets, telling a clear story of the current landscape.
It’s Primarily a U.S. Channel—Other Markets are Catching Up
In the U.S., ads are run on about 4.5% of queries. In contrast, during the same period, the U.K. had none. The U.S. dominates, accounting for 90% of ChatGPT ad placements in our dataset, with Canada and New Zealand also active and Australia at 1.6%.
For U.K. teams, it means while the channel isn’t live yet, U.S. competitors are already fine-tuning prompts and creative strategies, placing them at a strategic advantage when the U.K. market opens.
The Majority of Responses Contain Just One Ad
On average, ChatGPT presents only 1.06 ad items per response in the U.S., implying a single sponsored slot per query. This level of exclusivity changes the game completely compared to multi-slot Google Ads.
Industry Restrictions Still Apply
Certain sectors, like Legal and Pharma, show no ad activity due to what seems to be OpenAI’s deliberate restrictions, although this could change, providing proactive teams an edge.
Unexpected Hot Categories
Logistics, Home & Garden, and Beauty & Cosmetics are leading in ad frequency, indicating high potential for growth in these sectors.
Retail Leads in Ad Spend
Retail & Fashion accounts for a vast share of U.S. ad items, indicating robust advertiser demand, far surpassing the national average. This suggests the significant investments made by retail brands in this space.
Current Challenges in Competitive Intelligence
Without tools like Auction Insights, understanding your competitive landscape on ChatGPT is practically impossible. You’re spending budget where you can barely track competitor activity. It’s a gap that Adthena aims to close.
Achieving Full Market Visibility with Adthena
Adthena’s ChatGPT Ads Intelligence offers broader insights by monitoring a plethora of prompts daily, providing a competitive overview previously unavailable.
You can now see who bids on your prompts, track share of voice, and spot open prompts ripe for targeting before competitors do.
In a new and rapidly evolving channel, being an early mover is an opportunity that shouldn’t be missed. Try ChatGPT Ads Intelligence free for 21 days and unlock the full potential of your advertising strategy.
Beyond Just ChatGPT: Expanding Your Search Horizons
As users move towards AI-driven searches for high-intent queries, such as product recommendations, it’s essential for search practitioners to adapt. Simply put, the game is changing.
If you’re attentive to ChatGPT Ads now, you’ll be hard to budge later. Our data shows a window of opportunity open now, similar to the early days of Google Ads. Capitalize on this before it closes.
Start your free 21-day trial of Adthena’s ChatGPT Ads Intelligence today to discover what’s unfolding in the ChatGPT ad space.
I’ve discovered that Profound is the ultimate hub for marketers aiming to excel in the AI-driven landscape. It’s where I run my visibility, sentiment, and accuracy analyses.
This platform is my go-to for building marketing Agents and uncovering new opportunities. It’s here that I generate innovative content and take action based on deep insights.
Given all these functions, it’s only natural that Documents have found a home here too. Profound seamlessly integrates document management into my existing marketing workflow.
For the first time ever, I discovered that bots are now responsible for the majority of webpage requests worldwide, as shared by Cloudflare’s CEO, Matthew Prince. It’s fascinating to see how the digital landscape is evolving.
In a recent post on X by Prince, I learned that automated traffic currently represents 57.3% of global HTTP requests to HTML content, leaving just 42.7% to us humans, according to Cloudflare’s analytics.
Prince’s Prediction Hits Early. Interestingly, Prince had forecasted in March during SXSW that AI bots would outnumber humans online by early 2027. He anticipated this shift due to the increasing prevalence of agent-driven browsing. Yet, it seems that the future arrived ahead of his expected timeline.
Why this Matters to Me. We are now stepping into an ‘agentic’ era of search, where bots might soon dominate webpage requests. This change underscores the need for us to make content that is not only machine-readable but also authoritative and easily interpretable by AI systems.
Changing Browsing Patterns. Prince has pointed out that AI agents generate significantly more web activity compared to us. While I might browse a few sites when shopping, an AI agent could hit thousands, resulting in genuine traffic without the usual clicks or ad views.
The Measurement Dilemma. This shift presents a fresh challenge for publishers, retailers, and brands like mine: while traffic numbers may rise, human engagement and revenue may not follow suit.
The Big Question. Prince earlier raised a thought-provoking question: with bots now forming the majority, what funds the web? This transition from human to bot dominance makes this question critical to ponder.
Have you ever found yourself immersed in the SEO world, only to be told by an AI that everything you know is wrong? That’s exactly what happened to me, and not just once, but three times in a single week with Gemini.
It’s not the mistakes that rattled me—it was how credible they sounded. The answers from Gemini were polished and convincing, enough so that most would accept them without question.
When it comes to topics you’re not deeply versed in, how do you even begin to challenge such confident wrongness?
Laughably, I caught two, but the third one hit me where it hurts—my wallet. All this unfolded within a week.
Here’s a closer look at what went down.
In one scenario, Gemini misguidedly walked me through technical SEO for a client. During a site migration task on Shopify, where canonical tags were misbehaving, I turned to Gemini for solutions.
The advice was not just misleading but used terms that would raise red flags with leadership—talk about penalties!
Semantic clarity is crucial here; an internal misstep with jargon can make stakeholders halt essential projects.
Gemini further compounded the issue with incorrect guidance on URL parameters hosting.
The experience echoes another incident where Gemini’s mechanical advice almost led me to make a $3,000 error on my Jeep SRT. The AI’s confident proclamation of a rear differential issue had me nearly misappropriating my resources.
After sharing more data, Gemini pivoted, claiming it had leapt to conclusions without sufficient evidence.
In yet another amusing episode, my Madden game finance strategy, courtesy of Gemini, resulted in a fictional $20 million oversight. Although the stakes were virtual, it was a stark reminder of why critical thinking remains indispensable.
These anecdotes underline that it’s not AI replacing experts but rather pushing out those who stop questioning.
The real skill remains in smelling the bull and asking deeper, more insightful questions.
I’ve always found it fascinating how existing tools for tracking sentiment in AI responses barely scratch the surface. They might show me if sentiment is up or down, sometimes even by platform, yet they leave me with the most daunting task: understanding what’s actually behind these shifts and figuring out my next steps.
This bottleneck is where many AEO strategies come to a halt. I realized there was a need for a more comprehensive solution, which led us to rebuild Sentiment within Profound. Our aim was to eliminate the guesswork and provide actionable insights that truly empower us to shape AI narratives effectively.
I’ve noticed how AI-driven Google Ads has revolutionized the PPC landscape. My role has evolved from merely executing campaigns to designing signals and guiding the conversion system.
In the past, PPC was all about having control – managing keywords, match types, bids, crafting ad copy, and structuring campaigns to make the algorithm follow my lead.
Back then, proficiency in Excel and pivot tables distinguished the best ad managers. Agencies and PPC experts thrived on their execution skills. Greater control over variables meant better job execution, a strategy that worked well for PPC’s first decade.
However, Google Marketing Live (GML) 2026 heralded a significant shift for PPC. The focus moved from tactical control to system optimization, from managing keywords to signal design, and from setting up campaigns to aligning with machine strategy.
With AI-driven Google Ads, it’s evident that execution alone is no longer a competitive advantage. As Selin Song from Google Customer Solutions emphasized, execution has become a commodity.
Here’s what the new skill set involves.
I’ve learned to design inputs – the new keyword research. Knowing what inputs to provide the system helps it find the right audience on my behalf.
With AI Max for Search, I’m using a mix of broad match, keywordless targeting, text customization, and URL expansion. This strategy surfaces queries my keyword list wouldn’t catch, leading to an average of 7% more conversions or conversion value at a similar CPA/ROAS.
Feeding the system accurate conversion data is crucial. If conversion actions are irrelevant, the system solves the wrong problems, and that responsibility falls on me.
In terms of product and feed data, optimizing feeds with Conversational Attributes helps display products effectively in AI-generated responses. Ensuring audience signals are precise also shapes system operation, particularly with new prospects.
The days of relying solely on keyword lists are long gone; today’s system demands a strategic approach with the right inputs to automation.
Value signal architecture has replaced traditional bid management. My focus is now on providing robust signals like first-party data and accurate conversion values to Smart Bidding.
The advent of demand-led budget pacing means I set parameters rather than control pacing. Understanding product margins, inventory, lifetime value, and cash flow guides me in providing the right signals instead of merely setting bids.
Journey-aware bidding allows me to optimize the full conversion journey, not just the endpoint, requiring a well-instrumented conversion path connected back to the ad platform for effectiveness.
System prompting is today’s copywriting. AI Brief powered by Gemini helps me guide AI Max using brand-specific briefs to ensure it represents the brand accurately without over-constraining creativity.
I’ve learned to write briefs that effectively convey brand strategy, assisting AI in maintaining brand integrity in every campaign impression.
Budget architecture has taken precedence over daily budget adjustments. Campaign total budgets automate the process, and interpreting auction behavior in predictive systems has become my focus.
I rely on missed opportunity reporting to make informed decisions about budget constraints and optimize growth opportunities within the architecture I construct.
Measurement literacy has surpassed mere Quality Score management. Feeding the system quality signals helps it make informed decisions and optimizes bidding behavior through robust data integration.
It’s crucial now to ask business-relevant questions that the system can optimize toward meaningful outcomes. Communicating system behavior in business language is becoming a survival skill, alongside maintaining human oversight to ensure strategic alignment.
GML 2026 confirmed we’re already in this new phase. Thriving today means understanding the system’s needs and strategically providing those inputs to achieve business objectives.
Have you ever wondered how AI search platforms have evolved from simple Retrieval-Augmented Generation (RAG) to sophisticated agentic systems? These days, AI search has advanced beyond mere RAG, transforming into something far more complex and dynamic. In this article, I’ll guide you through how today’s advanced AI retrieval systems determine if your content is showcased or left in the shadows.
About two and a half years ago, I penned an article for Search Engine Land on how RAG represents the future of search. It wasn’t just a reactionary measure from Google in response to ChatGPT, but rather an architecture in development since the REALM paper in August 2020. Observing developments since then, everything has aligned with what I speculated.
The RAG pipeline of the past, which I outlined as a query transforming to an answer with citations, is already outdated. Major AI search platforms like Google AI Mode and ChatGPT Search have transitioned to a more complex architecture. They now possess planning capabilities, tool-routing options, and iterative retrieval methods that continuously refine results until they reach a suitable conclusion. The one-retrieval-to-answer model is defunct.
This sophisticated approach is what we now refer to as agentic RAG, a framework that’s become the industry standard. If your content strategy still relies on single-shot retrieval, you’re optimizing for a non-existent system. What’s more, in agentic RAG, you can’t witness the gatekeeping process—only the final outcome shows if your content made it.
By the time you finish reading, you’ll have a functional understanding of agentic RAG, the patent evidence showing its application by companies like Google, insights into what each major platform is doing, and concrete tactics to enhance your content strategy. You’ll also gain my important takeaway of the year: the future hinges on model distillation.
The October 2023 perspective is still relevant. Passage-level retrieval remains essential to relevance, and knowledge graphs work in tandem with LLMs. Search systems aim to lower what are known as Delphic costs, minimizing the effort users expend to find answers. Google’s guiding principle has always seen traffic as a means rather than an end. This aspect of my argument needs no change.
What has evolved is the structure of the retrieval pipeline. Back in 2023, RAG was straightforward and linear. A query was encoded, top passages were retrieved, and an answer was generated. If your content was within the top set of results, you had visibility; if not, you were invisible. This framework served its purpose at the time.
Today’s pipelines boast abilities absent from linear models: planning, tool usage, multi-hop iteration, and reflection. Rather than being a single occurrence, retrieval now involves up to twenty sub-retrievals orchestrated by a central agent, which refines its foundation of evidence continuously before finalizing an answer.
My earlier writing hinted at these upgrades without naming them precisely.
The word “agentic” is used liberally, but its structural definition is specific. Understanding agentic RAG requires grasping four properties each system must embody to wear the label.
1. Planning involves restructuring the user query into a research plan, breaking it down into sub-queries, pre-selecting tools, and strategizing retrieval sequences. The system doesn’t just respond; it plans each step with precision.
2. Tool usage extends beyond basic retrieval to include inquiries through APIs, code execution, live web browsing, and more. The agent selects the best method for each task, weaving these tools into cohesive outputs.
3. Iteration or multi-hop retrieval is where the agent refines its findings by visiting the source multiple times, continually improving the evidence base.
4. Reflection involves the agent critiquing its own output, determining its sufficiency and quality, and retrieving more information if needed to resolve discrepancies or improve source diversity.
These are the qualities that set agentic RAG apart and what make it the new default for AI search platforms.
Drawing a contrast between the classic RAG and agentic RAG, imagine the former as a direct process and the latter as a comprehensive loop where steps can be revisited until the solution is optimal. This is what my content needs to withstand.
The six shifts required for effective content engineering in the realm of agentic RAG are clear. I need to optimize for a spectrum of sub-retrievals, present well-structured and cohesive passages, leverage bridge entities, offer tool-callable content, and ensure freshness within my content.
The path forward involves navigating measurement’s increasingly complex landscape with the aid of model distillation. By understanding the full lifecycle from internal query generation to external execution, I can effectively target content positioning and citation strategy.
Engaging with this agentic environment demands observation, adjustment, and perpetual calibration. The choice is simple: evolve to survive and thrive or remain static and risk obscurity.
I’m excited to share that Google is rolling out an innovative AI targeting mode designed specifically for advertisers who want to reach fresh, brand-unaware audiences early in their discovery phase.
Google is introducing this new “prospects” targeting mode to help advertisers, like myself, connect with consumers who have yet to engage with our brands.
What’s happening. Google is enhancing its New Customer Acquisition tools by introducing the “new prospects” mode, set to launch this year.
Unlike traditional methods, which target users who haven’t made a purchase, this mode aims to reach those completely unfamiliar with my brand.
Google ensures the system automatically excludes users who have:
purchased previously,
searched for brand terms,
visited a website or app,
or engaged with brand content across Google and YouTube.
The main goal is to focus advertising spend entirely on “cold” audiences, those who are still in the discovery phase.
Why this matters. For brands like mine, this gives us more control over pursuing incremental growth, rather than just continually targeting those we’ve already reached.
The new mode promises to connect us with new users earlier in their buying journey while improving efficiency through AI-driven exclusions and automation.
The bigger picture. Google is positioning AI-driven targeting as a method for balancing growth with efficiency.
Advertisers using the New Customer Acquisition Value Mode, like me, have seen a noticeable 9% improvement in ROAS when valuing new customers at twice the average order value.
Between the lines. As AI-driven targeting expands, platforms increasingly rely on behavioral signals and first-party data to identify potential customers earlier in their purchase journey.
What to watch. The effectiveness of the “new prospects” mode will largely depend on Google’s accuracy in identifying brand-unaware users and balancing reach with privacy concerns.
