I’ve discovered that content businesses flourish when the economic model, systems in place, and editorial insight work harmoniously. However, challenges arise when these vital components begin to operate in silos.
Managing content operations on a small scale can really rely on instincts. When I have a dedicated editorial team, a select few reliable writers, and a solid grasp of our unique voice, everything tends to run smoothly.
However, in larger setups like media rollups or vast affiliate networks, producing vast quantities of content daily becomes not only feasible but essential. For some, content isn’t a mere marketing tool—it is the business model itself.
At these formidable scales, breakdowns often happen not because of the content but due to a disconnect among the economic goals, operational systems, and editorial decision-making.
Not every type of content can handle being scaled like this. In B2B, for instance, if you’re marketing a niche ERP system, such content volume is unnecessary and would ultimately lead to wasteful spending.
Yet, some categories like sports can support high-volume publishing due to the constant and diverse demand for new content—from game insights to player interviews.
For example, a platform like The Athletic thrives under such volume demands thanks to varied revenue streams including subscriptions and advertisements, generating substantial figures like $54 million in a single quarter.
With the bulk of revenue stemming from direct consumer subscriptions, maintaining high editorial standards shifts from being optional to absolutely critical.
In contrast, models heavily reliant on programmatic display ads can be unstable. Such a system drives monetization through shear output of low-production-cost articles.
Here’s the simple breakdown:
Revenue = (Pageviews ÷ 1,000) × RPM
Profit = ((Pageviews ÷ 1,000) × RPM) − Production Cost
When generating $64 per article via 4,000 pageviews at a $16 RPM, tight profit margins necessitate bulk publishing with sustained quality.
Without careful management, these strategies can falter.
As operations scale, there’s a paramount need for robust systems and data analysis, which help prevent operational collapse. Yet, truly sustaining these operations requires not just infrastructure, but judgment too.
As someone deeply invested in improving my SEO processes, I’ve discovered an innovative way to transform my workflows using AI-powered tools that adapt to my unique methods.
By leveraging platforms like ChatGPT and Google’s Gemini, I can get standard on-page SEO reviews. However, these initial responses often feel generic and devoid of specific context related to my business needs.
This generic nature of AI is both its limitation and its potential opportunity. While out-of-the-box AI provides broad solutions, it lacks the personalization that comes from my own business insights.
Fortunately, tools like GPTs, Gems, and Claude Projects allow me to embed my SEO process into custom assistants, making the complex seem straightforward without needing complex coding skills.
I’ve also learned that large language models predict responses from a vast array of internet data, often resulting in average opinions rather than tailored advice for my business specifics.
In SEO, these broad opinions typically revolve around general content improvements and link building, which might not address the unique challenges I face.
What I needed was a tool that factored in my business’s unique landscape, including customer needs and competitive environment. That’s where the personalization of AI tools comes into play.
Contextualizing inputs to AI tools transforms them into powerful assistants that enhance my specific workflow, making it less about generic data and more about strategic insights.
The process of creating a customized AI tool is more about narrating my workflows rather than needing a deep technical background. Tools like GPTs and Gems have become essential as I package my expertise into reusable, intelligent assistants.
Among the various AI platforms, I find GPTs, Gems, and Claude Projects especially user-friendly for most of my SEO tasks. These platforms are intuitive, allowing even non-developers like me to transform repetitive tasks into automated, efficient processes.
However, generic SEO tools, despite their widespread use, don’t pay attention to my company’s unique strategic priorities, unlike the AI applications I’ve tailored to fit my specific needs.
Moreover, crafting personalized AI apps not only aids in SEO but also transforms how I manage and execute marketing strategies, encompassing tasks like keyword research and content strategy more effectively.
My takeaway is that the true value lies not in AI itself but in the expertise I embed into it. My hard-earned industry skills are the real product, and AI simply empowers me to scale my efforts more efficiently.
It’s been enlightening to see how enhancing my AI tools with my knowledge improves productivity, ultimately strengthening my business impact. This process of encoding my SEO knowledge into AI-propelled systems is groundbreaking and transformative.
I’m excited to share how Adobe’s latest tool is changing the game for businesses eager to boost their brand visibility in AI-driven searches.
With the backing of 300 million AI prompts and the comprehensive data of Semrush, this platform is adept at tracking mentions, gauging share of voice, and identifying content gaps across prominent AI platforms.
Adobe introduced a pioneering solution for brands aiming to bolster their visibility and trustworthiness across AI interfaces. As part of the Adobe CX Enterprise, this tool offers an agentic AI system to streamline customer lifecycle management, covering everything from initial acquisition to fostering long-term loyalty.
AI traffic is skyrocketing. The way LLMs are utilized for product and service research represents a major pivot for both marketers and consumers. Recently, Adobe revealed data underlining this massive surge in AI traffic to U.S. retail sites—up by an impressive 1,324% from October 2024 to May 2026. The travel industry saw an even greater increase of 2,215% in the same timeframe.
As Vice President of strategy and product, Loni Stark, remarked to MarTech, “We used to get back the same thing—a SERP page with links. Now results seem random, but aren’t when scaled, and companies lack tools for this.”
Understanding brand visibility in AI search. Adobe Brand Visibility marks Adobe’s first venture into generative engine optimization (GEO), following its acquisition of Semrush. By integrating Adobe LLM Optimizer with Semrush’s AI Optimization tool, it provides unmatched insights.
Drawing from a staggering database of 300 million real-world AI search prompts, Adobe Brand Visibility helps teams pinpoint which prompts lead to brand exposure or loss.
Additionally, utilizing Adobe’s first-party data from owned channels, marketers gain a holistic view of how their brands appear on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics encompass mention frequency, reach, competitive share of voice, and content gaps, allowing AI agents to offer prioritized recommendations that teams can rapidly implement and evaluate results.
Competitive intelligence unleashed. Adobe Brand Visibility offers tools for competitive brand analysis, comparison, and trend tracking, enabling marketers to effectively benchmark against competitors.
Featuring advanced SEO intelligence driven by Semrush’s extensive data of 28.5 billion keywords and 43 trillion backlinks, this platform underscores the continued importance of SEO fundamentals for AI search visibility. It shows the potential for existing search authority to yield AI citations and identifies opportunities for content investments across channels.
While there’s still much to learn about leveraging LLMs for brand visibility, Stark is confident in Adobe’s leadership position in this emerging space.
As Stark stated, “Adobe had proprietary data while Semrush offered data and trends. Though we may not have all answers, we possess unrivaled data.”
In 2026, PPC budgeting goes beyond simply setting spending levels. It’s about understanding when to adjust budgets, scaling campaigns effectively, and how data informs Google’s automation in these decisions.
Over the years, Google’s automation has been driven by the signals supplied to it. In 2026, these signals are processed faster and more precisely, making clean signal architecture more crucial than ever.
While the fundamentals of budget management remain constant, the speed at which a poorly structured account can drain your budget has increased significantly.
Two Budget Mechanics You Must Grasp Now
Before tweaking targets, audiences, or bid strategies, it’s essential to comprehend how these two budget controls operate.
The Ad Scheduling Pacing Change
Google now paces campaigns with ad scheduling towards the full 30.4x monthly billing cap, regardless of how many days your ads run. Previously, a $100 daily budget targeted around $2,200 across 22 weekdays. Now, it targets $3,040 in the same period, and the billing ceiling remains unchanged.
If your campaigns utilize ad scheduling, you need to recalibrate your daily budget based on your total monthly spend rather than active days, setting it by dividing your monthly target by 30.4. For example, a $2,200 monthly target becomes a $72 per day budget if calculated this way. However, 24/7 campaigns remain unaffected.
Available for Demand Gen, Search, Standard Shopping, Performance Max, and YouTube campaigns, campaign total budgets let me set a fixed spending ceiling over a defined period instead of managing a daily limit. This window is from three to 90 days for some campaigns, while others can extend up to a year.
While there is no daily spend cap, allowing flexibility, it’s crucial to monitor these closely, especially when running alongside ongoing campaigns. Additionally, the budget type cannot be altered post-campaign creation, making committed decisions at setup vital.
What Actually Governs Google Ads Budget Spending
Efficiency Targets Usually Constrain Spend Before Budgets
In Smart Bidding strategies, efficiency targets often restrict spending before budget caps do. With a set tCPA of $50, if leads cost $80, the system reduces bids to avoid surpassing your target. It appears as if there’s a budget problem, but it’s actually a target problem.
I must initially set targets closer to the market conversion rates and then fine-tune them to align with my true goals. When close, the 10%-20% margin aids in navigating those final conversion opportunities effectively.
Performance Max Decides Where Your Budget Goes
Performance Max automatically allocates budget across various channels like Search, Shopping, and YouTube, with Google determining the split, not me. Excluding my brand can prevent paying for redundant conversions from Search campaigns.
Checking my negative keyword lists ensures clarity in branding and budget allocation. This helps avoid misallocation and focuses resources effectively.
AI Max Expands Ad Appearances
AI Max, available since April, expands query matching beyond my keyword list, generates ad copy from existing assets, and dynamically targets landing pages. Monitoring the initial spend distribution closely helps maintain alignment with intended strategies.
An insurance broker using Smart Bidding faced a disconnect: a 416% rise in conversion volume didn’t reflect in revenue due to form starts mistaken for completions. The system optimized for interactions, but the alignment with Cyrillic-language spam was costly without benefiting the pipeline.
This reflects a broader issue in lead generation: equal weight is assigned to all form fills, leaving Smart Bidding unable to distinguish high-value leads from irrelevant submissions.
Primary conversions must be meaningful actions that properly guide Smart Bidding. Secondary engagements belong in reports to avoid skewing bidding data.
For accounts outside the current beta, extending conversion windows to 90 days and assessing performance over these periods can help counteract issues arising from longer sales cycles.
Using First-Party Data for Budget Guidance
Customer Match, with a 540-day max membership duration, remains crucial in guiding automation toward valuable traffic. For effective budget allocation, I focus on exclusion before expansion, targeting acquisition budgets toward new prospects.
Retention strategies should be run separately to maintain consistency in conversion goals. It’s vital that exclusions, available from the start, streamline acquisition efforts effectively.
For ongoing daily budget campaigns, weekly increases of 10-20% are still relevant. For scheduled campaigns, I focus on monthly targets divided by 30.4 instead of daily adjustments.
Using Smart Bidding Exploration in open beta for Performance Max can increase unique conversions by exploring new queries. I evaluate results over 60-day windows to make informed decisions.
Demand-led pacing, complementing daily management, tracks predicted high demand periods to optimize spend within budgetary limits. For B2B accounts, longer evaluation periods safeguard against undervaluing long cycle campaigns.
Recently, I’ve been delving into an intriguing study by Lily Ray, which reveals some unexpected findings about Google’s AI Overviews. Apparently, these Overviews frequently reference brands’ own listicles but tend to recommend their competitors.
The study highlighted that Google AI Overviews cited these self-promotional listicles in a whopping 69% of B2B software-related queries. Yet, they favored rival brands in their recommendations. This got me thinking about the strategies brands employ to influence AI search outcomes.
Detailed Findings. I discovered that the analysis was quite comprehensive. Ray reviewed 100 B2B queries spanning categories like “best [category] software.” She gathered data across three specific periods: April 15, May 15, and June 8.
The study found that out of 80 queries that triggered an AI Overview, self-serving listicles were referenced 323 times, yet in 224 instances, Google didn’t actually recommend those brands. This mismatch intrigued me.
Analysis of Recommendations. While examining specific cases, it became evident that Google sometimes cited a brand’s listicle but opted to recommend more renowned competitors instead. For instance, in the search for “best LMS for selling courses,” Oasis LMS was mentioned, yet Kajabi and others were pushed forward as the preferred options.
This pattern wasn’t just isolated to LMS software; it appeared in multiple domains like help desk tools, task management, and more. It made me ponder over the dominance of stronger brands in recommendations.
Observing Organic Declines. An interesting trend noted was a drop in organic visibility for websites heavily leaning on self-promotional listicles. I noticed beginnings of these declines back in January and observed further drops post-Google’s May 2026 core update.
Interestingly, these sites also seemed to have expanded into AI-generated content and other “best” pages prominently featuring their own brands.
Rise of Third-party Citations. Ray’s analysis also showed an upsurge in Google comprising third-party content for “best” queries. Platforms like Reddit, Forbes, and YouTube gained traction in citations.
Understanding Impact. I believe it’s crucial to realize that merely having your content cited doesn’t equate to a recommendation. This situation offers competitors the chance to snag attention and, ultimately, valuable visibility.
Keeping Up with Changes. Previously, Search Engine Land shared insights on how some SaaS and B2B businesses witnessed visibility losses after banking on self-ranked “best” lists. The risks are significant when company-driven content doesn’t transparently disclose material relationships as mandated by the FTC’s Consumer Review Rule.
About Ray’s Data. To reach her conclusions, Ray employed Ahrefs Brand Radar to examine numerous AI Overview responses. Her analysis spanned 100 B2B software queries, focusing on citations versus actual recommendations.
I recently came across an intriguing development regarding Google and its operations in the UK. The UK’s Competition and Markets Authority (CMA) has taken a proactive stance, requiring Google to not only allow site owners a way to opt out of AI Overviews but also to clarify how they rank search results.
In addition, Google is required to enable users to port their search data to specific third-party services, a move aimed at increasing data portability.
Transparency on search rankings. The CMA’s demand for Google is to enhance transparency and fairness in ranking search results, with an implementation deadline of six months.
Many UK businesses have voiced concerns to the CMA, claiming that Google’s ranking practices lack fairness and transparency. They argue that changes are implemented without sufficient notice, impacting their operations without providing them with adequate avenues to express their concerns.
Yes, we cover Google search updates frequently, and it’s evident that Google is constantly refining its algorithms to make search results more relevant and to deter manipulation attempts.
According to the CMA, Google must:
Establish clear processes for businesses to voice concerns about Google’s ranking methods, ensuring these concerns are addressed effectively.
Use objective and non-discriminatory criteria to rank ‘organic’ search results, which includes AI Overviews but excludes sponsored results.
Offer businesses greater transparency on ranking mechanics and provide advance notice of significant changes.
Data portability. The CMA also seeks Google’s cooperation to “Allow users to port their search data to authorized third parties, such as rewards platforms or businesses offering personalized offers or discount codes”, aiming for this within three months.
The potential for third-party companies to access Google’s search data could open new avenues for personalized services, such as tailored travel suggestions and more relevant shopping deals, enhancing consumer experiences.
Why we care. Despite these orders, I’m skeptical that Google will comply, as doing so might compromise its highly valued search ranking algorithm, risking exposure to competitors and potential manipulation.
This isn’t the first time such demands have been made and undoubtedly won’t be the last. Google is likely to resist these orders firmly.
I recently discovered how Meta is revolutionizing online shopping on Facebook and Instagram. Their new features aim to simplify the purchase process and enhance how advertisers turn casual browsing into actual sales.
Exploring New Possibilities. Meta is making a significant move by spreading Live Video Ads globally on Facebook, and now they’re introducing these to Instagram. This expansion allows businesses to reach more people during live shopping events, potentially increasing sales directly from these experiences.
In the U.S., Meta is partnering with several live commerce providers such as CommentSold and TalkShopLive to help sellers transform live streams into ads that can connect with untapped audiences.
Thanks to Facebook’s Live Shopping Tools, users can now browse and purchase products without leaving the livestream, making shopping more seamless than ever before.
Introducing a New Checkout Experience. Starting this summer, Meta will be offering a virtual card payment feature on both Facebook and Instagram through a collaboration with Mastercard and Visa.
What excites me about this feature is that it generates temporary, one-time card numbers linked to my existing cards. This means I can shop without sharing my real card details, enhancing both security and trust among users.
Benefits for Advertisers. Meta is integrating product data as a core aspect of all Sales campaigns. This streamlines the advertising process by allowing advertisers to combine product feeds with creative assets, all while Meta’s AI assembles the most engaging ads tailored to individual users.
By using product details like pricing and availability, advertisers can craft detailed and high-performance shopping campaigns.
Why This Matters. Meta’s innovations offer brands more ways to convert browsing into purchases without shoppers leaving the app. With these new features, advertisers can potentially reach larger audiences through live shopping events and AI-driven ads, optimizing their approach to sales.
The introduction of virtual card checkout aims to reduce barriers in the purchase process and build consumer trust, possibly boosting conversion rates.
A Glimpse into the Future. Meta sees AI as a game-changer in product discovery, emphasizing how recommendations now organically appear in content feeds and creator videos over traditional searches.
By leveraging product catalogs as vital data points, Meta empowers these discoveries across various platforms like creator content and business recommendations.
I’m thrilled to introduce the latest addition to Profound: the External MCP Connectors. With these, I’ve found it incredibly easy to link my favorite CMS tools, project trackers, and team communication platforms directly to Profound via MCP.
This seamless integration has transformed the way I manage projects, allowing me to streamline workflows and enhance team collaboration. Now, all my critical tools are accessible from one central hub, boosting my productivity like never before.
Try it out and see how Profound can help you connect everything you need in one cohesive system. It’s a game-changer for efficiency and team synergy.
Starting in August 2026, Google will begin to automatically categorize customer types in conversion-based lists, removing some of the control we advertisers once had. I must now provide Google’s systems with clearer signals on where audiences are in their customer journey.
As someone deeply involved in advertising, I know the importance of precise audience targeting. With these changes, I’m urged to review and update my classifications in the Google Audience Manager before they kick in.
What’s Changing? From August 2026, Google Ads will automatically classify customer lists into categories like:
Existing customers
New customers
Other customer segments
Why Google’s Making This Shift. It appears that Google aims to enhance audience consistency across its tools for customer acquisition and retention. This standardization allows for better optimization decisions in Google’s automated bidding and targeting systems by clearly defining prospecting from retention audiences.
Why This Matters to Us. As an advertiser utilizing customer acquisition strategies, the precise classification of these lists is crucial. Any misclassification could impact Google’s optimization of users throughout their lifecycle, affecting campaign performance.
What We Should Do. It’s vital for us to audit our Customer Match lists—based on conversion data—before August. Consider these questions:
Are my customer lists categorized correctly?
Do they represent existing customers versus acquisition targets?
Will Google’s automatic classification align with my internal definitions?
Reviewing these settings now could prevent unexpected changes when Google enforces these classifications.
The Bottom Line. Google is taking an active role in managing audiences, further streamlining the signals powering their automated advertising systems by assigning lifecycle labels to conversion-based lists.
First Spotted. This update was noticed by Google Ads expert Bia Camargo, who shared the alert on LinkedIn.
AI search is reshaping the marketing landscape faster than anything I’ve seen before.
During my time at Zero Click NY, I witnessed how significantly AI search has evolved over the last six months and identified emerging features that might define its future.
Among all the discussions, these seven trends were the most compelling.
From the emergence of marketing engineers, to the way Claude and ChatGPT differ in results, and Claude’s rapid ascent in the business world over the past year, here are the key insights I gathered.
1. Every AI relies on different content
According to Profound data, only 8% of citations are shared between ChatGPT and Claude. This means 92% of the sources that ChatGPT refers to would not be recognized by Claude for the same inquiry. Essentially, a brand may have high visibility in one AI and not exist in another.
Moreover, each AI favors different types of content.
ChatGPT frequently indexes community content: Reddit, Quora, and forums make up around 16% of its citations.
In contrast, Claude cites listicles 36% and opinion content 13.2% of the time, compared to ChatGPT’s ~20% and 7.2%, respectively.
The disparity also applies to traditional search. A significant 64% of websites Claude cites appear in Google’s top 50 for equivalent queries, whereas it’s only 37% with ChatGPT.
Takeaway: It’s vital to inform stakeholders that AI visibility differs between LLMs, and strategic prioritization is necessary to reach your audience.
Track your visibility by engine because effective strategies in one platform may not translate to another. UGC helps drive ChatGPT while listicles boost presence on Claude.
2. Claude is quietly winning B2B — so sequence your optimization by audience
Claude may appear insubstantial in AI traffic-share charts, but it’s a different story when it comes to enterprise usage.
Web traffic doesn’t tell the whole tale. Anthropic derives about 85% of its revenue from enterprise and API usage, not visible in consumer data.
A critical chart from Ramp’s AI Index reveals the true penetration of Anthropic in the business sector. A year ago, only a small number of businesses used Anthropic. Now, it’s at 34.4%, surpassing OpenAI at 32.3%.
This insight led me to reconsider: if more business users are engaging with Claude and consumers are on ChatGPT, shouldn’t our optimization priorities reflect audience preferences?
Should B2B entities focus on Claude first, while B2C aim for ChatGPT visibility?
Currently, few distinguish between ChatGPT, Gemini, or Claude usage. This distinction is bound to grow.
3. ChatGPT ads are here, and this is what we’re seeing
The game has changed: competitors are securing visibility through ChatGPT ads. These ads are now live and available for self-serve directly within the chat interface.
Recent weeks also saw the debut of GPT 5.5, citation chips morphing into clickable links (leading to a 60% spike in referral traffic overnight), and Google integrating AI Mode into its main search functionality.
This wasn’t incidental. The hyperlinks are crucial for an ads business. Analyzing over 100,000 ad placements highlighted three essential revelations.
ChatGPT Ads match on topic
Ads align with topic similarity, not intent. Only 14% of real user prompts express commercial intent, yet 20% show ads, even if the prompt involves a math problem.
Embedding analysis indicates that ad titles and descriptions significantly influence which conversations you appear in, transforming them into tactical targeting tools.
Paying for ads
We have entered a “pay-to-play” era. Approximately one-fifth of ad placements appear when a direct competitor is mentioned, but only 8% of organic references belong to the associated brand.
Competitors are twice as likely to advertise around your brand’s organic mentions than you are.
For instance, Startup CRM Adia is targeting prompts mentioning Salesforce, with Salesforce responding by showing paid ads 40% of the time, defending their position even when organically mentioned.
Ad inventory is scarce and expensive
Currently, ChatGPT presents about one ad per conversation, with the median exchange spanning three turns. Only 30% of eligible users ever see ads, and CPMs/CPCs are about four times Meta’s rates.
Expect future changes: additional ad slots per reply, ads woven deeper into conversations, and engineered suggestions to prolong interactions, ultimately increasing inventory.
The insight: Understanding both organic AEO and paid defense strategies is essential. Monitoring your brand’s organic citations without tracking who advertises against them offers a partial view.
4. Claude is the most directly optimizable AI right now
Claude sources web content directly from Brave searches, not merely influenced by it, as discussed in the presentation I attended.
In recent testing by Profound, 79.2% of Claude’s citations were directly aligned with Brave’s top 10 search results for equivalent queries.
Reshuffling is minimal; no other AI model trusts its search provider so extensively.
This transparency makes Claude the most straightforward AI to optimize for: a visible index, checkable rankings, and, as we’ll explore next, predictable retrieval.
If I’ve convinced you of the importance of Claude for B2B, here’s your approach: identify where you stand on Brave for key prompts and use that as your roadmap for Claude visibility.
This level of transparency won’t last forever. Take advantage now while it’s possible.
5. Claude only performs web searches a third of the time
There’s a significant caveat: ChatGPT initiates web searches for nearly 95% of prompts, but Claude does so only a third of the time, likely due to cost ($5 per thousand searches via Brave’s API).
You can optimize Claude effectively only when it conducts a search.
The encouraging part is its predictable search habits. Prompts framed around recent events (“best X in 2026”) initiate searches about 81% of the time.
Ranking-related prompts lead to 67% search initiation, location-specific prompts 55%, and comparisons 51%.
Prompts concerning definitions and procedures rarely trigger searches, making them poor targets for Claude optimization.
The lesson: Before investing to enhance Claude visibility for a prompt category, determine if Claude actually conducts searches for it.
Focus on recent events, rankings, locations, and comparisons for effective Claude optimization using Brave rankings as a guide.
Other areas rely on internal memory beyond our reach.
6. Query fan-out: A raffle on one platform, near-deterministic on another
Two speakers offered perspectives on query fan-out, presenting a contrast worth exploring.
Query fan-out entails background synthetic queries to collect content prior to providing an AI-generated response.
Mike King of iPullRank viewed it as a raffle: The task is to gain more tickets through a wider content range across owned, earned, and shared channels, and the right content formats make all the difference.
Even if you rank for a fanned-out query, the wrong format renders you ineligible.
According to his research, content-to-query cosine similarity and information gain strongly correlate with success in AI search.
Conversely, Josh Blyskal from Profound notes that Claude’s fan-outs are highly predictable; the same prompt results in consistent fan-out strings 65% of the time. Interestingly, 94% of Claude’s fan-outs are current-year stamped, unlike ChatGPT’s 17%
Where ChatGPT’s fan-outs constantly evolve, Claude’s remain relatively stable. Thus, both perspectives may hold true for different engines.
With stable fan-outs like in Claude, content creation can directly focus on them. The year-stamping trend suggests using the current year in titles.
For volatile fan-outs as in ChatGPT, King’s approach applies: maximize exposure through format variety.
One mechanism demands two strategies, tailored by engine, potentially requiring prioritization between them.
7. The marketing engineer is here, and agents are the new workforce
The role of a “marketing engineer” might sound like a buzzword, but the hiring trends prove otherwise.
Google’s recently hired its first marketing engineer, Figma has an opening at a $295,000 salary, and both RBC and Autodesk have placed hires.
It’s a rapidly growing search term, and Google’s AI marketing lead dubbed it “the hire for 2026.”
What makes someone ideal for this role? Is the priority given to an engineer learning marketing or vice-versa?
The emerging profile emphasizes marketing experiences such as someone with channel expertise who builds and runs AI systems, reports to the marketing head, and supports the team by removing obstacles. They are marketers advancing the state-of-the-art.
The underlying concept is that marketing functions decompose into pipelines: data extraction, transformations, and loading into useful formats. Agents can now automate these pipelines.
Monitoring competitor pricing and auto-generating sales content.
Scheduling and assessing AEO presence and landing page efficiency.
Analyzing sales call objections and drafting relevant content solutions.
What previously were backlogged tasks now become brief agent-building exercises. Creativity replaces headcount as the limiting factor.
If marketing engineering isn’t a role in your team yet, it’s likely only a matter of time before it is.
The job now: Figuring out how this all works
There remains no definitive roadmap for AI search. When a guidebook emerges, the key step will be prioritizing one LLM contingent upon who you wish to reach.
In many instances, that “who” will now be agents, simultaneously assisting us in our endeavors and highlighting the rising need for professionals adept at engineering such systems.
