The flood of AI crawlers and automated agents is forcing publishers and content owners to face a challenger: Deciding who gets access to their content, and under what terms.

To help address that problem, IAB Tech Lab released new guidance today on bot and crawler management strategies, opening the document for public comment through June 26, 2026.

The guidance is designed to help publishers, content owners, and ad tech companies think through how they manage non-human traffic, including AI systems that scrape or access content. It also complements the organization’s recently released CoMP API V1, a framework intended to support communication and permissions between AI systems and publishers.

“Giving content owners clear, practical guidance is key if we want adoption to move forward in a meaningful way,” Anthony Katsur, CEO of IAB Tech Lab, said in a statement. “This work helps simplify a complex area so companies can make decisions that fit their business while supporting a more sustainable marketplace.”

Also Read: When Silence Hurts Brands in the Age of AI

Should You Give AI Access to Your Content?

The IAB paper underlines how quickly AI crawling became a business issue across publishing and advertising. Many organizations don’t have formal policies on bots and crawlers, despite growing concerns about AI systems consuming content, straining infrastructure, and complicating monetization.

According to IAB Tech Lab, the guidance was created because many publishers and content owners were struggling to navigate the trade-offs involved in managing AI access. Blanket bot blocking, the group argues, is no longer practical as AI systems become more embedded across the web ecosystem.

Instead, the document outlines a range of approaches companies can take, along with the operational, financial, and strategic implications of each.

The guidance is aimed more at business leaders than at engineers, to help them understand the costs and consequences of AI crawler management.

Also Read: Agile Marketers Win: Elizabeth Maxson on AI, Composability and Creativity

“Content owners are being asked to make important decisions quickly, often without clear frameworks,” said Shailley Singh, EVP and COO at IAB Tech Lab. “This guidance helps break down the options so they can choose an approach that aligns with their goals.”

IAB Tech Lab said it will continue working with members and industry participants to support the implementation of both the bot management guidance and the CoMP API. The organization is accepting public comments on the proposal through June 26 before finalizing the framework.

The post Who Gets to Crawl Your Content? IAB Has an Answer. appeared first on MartechView.

HubSpot has launched AEO Sensor, a free, publicly accessible dashboard designed to track how AI answer engines — ChatGPT, Gemini, and Perplexity — are behaving toward brands in real time. The launch arrives alongside a data point that will focus attention: according to HubSpot’s own infrastructure, ChatGPT sent its lowest volume of traffic to businesses in April 2026 compared to any point in the preceding 12 months.

The tool is available without a subscription or account, positioning it as a shared industry instrument rather than a proprietary commercial product — a deliberate contrast to HubSpot AEO, the paid brand-level monitoring and optimization service the company launched in April 2026 at $50 per month.

Also Read: The CIO Who Says Governance Can Actually Speed Up AI

What AEO Sensor Measures

The dashboard is organized around three data streams, each addressing a distinct dimension of the AI visibility problem.

The first is an Answer Engine Volatility Tracker, which generates a daily score derived from four inputs: mention rate, citation rate, citation type, and AI-referred traffic, each measured against rolling averages across ChatGPT, Gemini, and Perplexity. The scoring system uses four classifications — Calm (0–29), Moderate (30–59), Elevated (60–89), and Extreme (90 and above). On launch day, May 14, the tracker registered 20, placing conditions in the Calm range. HubSpot notes that daily scores may adjust for up to three days after calculation, reflecting the time required for data from all three platforms to stabilize — meaning the dashboard is designed for directional trend reading rather than precise point-in-time measurement.

The second is an AI-Referred Traffic Trends chart, drawing on anonymized data from HubSpot’s customer base and modeling it to show aggregated weekly page visit trends from all three platforms. The chart at launch covers data running back to February 16, 2026, providing approximately three months of historical context. HubSpot notes that actual results may differ by region and language, and that it can take several hours for traffic data to populate after each weekly close.

The third covers AI Visibility Benchmarks by Industry, allowing users to track visibility score and citation share across sectors using representative brand examples. The manufacturing segment, for instance, tracks Nvidia, TSMC, Ford, and Volkswagen Group, with data running from late March through early May 2026. Both visibility score and citation share use a 0–100 percent range. HubSpot is explicit that benchmarks reflect trends from representative brands within each sector rather than exhaustive market coverage.

The April Traffic Signal

The data point with the most immediate implications for the marketing community is the April traffic finding. HubSpot’s internal analysis shows ChatGPT sent its lowest volume of referral traffic to businesses in April 2026 relative to the previous 12 months. The company also flags significant citation share fluctuations among information technology companies during the same period.

Neither figure is accompanied by a precise percentage decline or absolute traffic number — HubSpot frames both as directional observations from its anonymized customer dataset. But the pattern is consistent with a sequence of documented disruptions that stretches back more than a year.

ChatGPT’s referral traffic dropped 52 percent in July 2025 when OpenAI adjusted how its retrieval-augmented generation system weighted sources, according to research published by Profound drawing on more than one billion ChatGPT citations. That was the first major documented instance of a model-level citation reconfiguration producing measurable traffic effects across publishers. Analysis of the ChatGPT 5.3 update in March 2026 found that the average number of unique domains per response fell from approximately 19.8 to 15.9 — a roughly 20 percent reduction in outbound linking per response. A separate study from February 2026 found that ChatGPT sends approximately 190 times less traffic than Google despite accounting for around 12 percent of search volume, with an estimated click-through rate of 1.3 percent.

If the April low that HubSpot describes is confirmed across a wider dataset, it would extend this sequence and suggest the traffic contraction from AI answer engines is not stabilizing.

Also Read: Is Your CRM Making Your Customer Service Worse?

The Product Stack It Completes

AEO Sensor is the third instrument in HubSpot’s answer engine product architecture, following HubSpot AEO and the AEO Grader.

The three tools operate at different levels of specificity. AEO Grader provides a one-time snapshot of a single brand’s sentiment, recognition, and competitive standing. HubSpot AEO provides ongoing daily monitoring and recommendations for a specific brand, priced at $50 per month. AEO Sensor sits above both as a macro-level instrument — no brand-specific data, no subscription, no login.

The product stack is built substantially on the infrastructure and methodology of XFunnel, an answer engine optimization platform that HubSpot acquired on October 31, 2025. XFunnel had, at the time of acquisition, analyzed 1,500 companies, collected more than five million AI responses, and examined more than 25 million citations. Its co-founder Beeri Amiel now serves as HubSpot’s Director of Product Development.

What the Tool Can and Cannot Do

AEO Sensor does not resolve the attribution problem that has been complicating AI traffic measurement since mid-2024. It does not tell a brand how it specifically is being cited, whether a competitor is gaining ground, or why citation patterns have shifted. What it provides is an industry-level temperature reading — a shared reference point that allows a marketer to distinguish between volatility that is affecting everyone and volatility that is specific to their own content or brand positioning.

A high daily volatility score, for instance, signals that citation patterns across the tracked platforms are shifting broadly — providing essential context for interpreting changes in a brand’s own visibility data that might otherwise appear to reflect a specific content or strategy failure.

The dashboard’s scope covers ChatGPT, Gemini, and Perplexity — the three platforms for which HubSpot has sufficient traffic and citation data to construct meaningful baselines. Notably absent are Microsoft Copilot, Claude, and other large language models that increasingly receive direct search queries. The three-platform focus means the dashboard does not capture the full picture of answer engine behavior affecting brand visibility, a limitation HubSpot acknowledges.

Also Read: Three Myths That Are Keeping Brands Away From AI

Why This Matters

The structural problem AEO Sensor addresses has been building for several years. HubSpot’s own customer data showed organic traffic falling 27 percent year over year as of the April 2026 AEO product launch — a figure that put quantitative weight behind a qualitative observation circulating in marketing circles since mid-2025: traditional search optimization is becoming a less reliable traffic source at precisely the moment AI answer engines are not yet reliable enough as a replacement to build strategy around.

Research from the Wall Street Journal documented AI chatbot referrals growing from under one million visits in early 2024 to more than 230 million monthly by September 2025 — significant growth in absolute terms, but distributed across the entire web in a pattern that most individual brands cannot yet measure reliably. Citation sets in AI responses change between 40 and 60 percent within a single month, according to research on the ChatGPT 5.3 update — a rate of turnover that makes stable brand representation difficult to assume in any competitive category.

The free positioning of AEO Sensor reflects the same logic that market research firms have long used with publicly released benchmark reports: surface the scale of the problem at no cost, and capture the monetizable demand for solutions in the paid product layer. The marketing community gets a shared instrument. HubSpot gets a sustained top-of-funnel argument for HubSpot AEO.

AEO Sensor is accessible at HubSpot’s marketing properties without a subscription or account.

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Adobe has completed its acquisition of Semrush, the brand visibility and search intelligence platform, in a move designed to extend its customer experience tools into an era in which AI agents are increasingly determining how consumers discover and evaluate brands.

The deal brings together Semrush’s search engine optimization capabilities and agentic search intelligence with Adobe’s existing portfolio of content, commerce and customer engagement products — including Adobe Experience Manager, Adobe LLM Optimizer and Adobe Experience Platform. The combined offering is designed to give marketers a single integrated system covering how their brands appear across traditional search engines, large language models and AI-powered agents.

The acquisition comes as AI-driven traffic to retail sites surges. Adobe data shows AI-generated traffic to US retail sites increased 269% year over year as of March 2026, while the company says most businesses have significant gaps in how their brands appear across AI surfaces.

“The rules of brand discovery and commerce are being rewritten in real time, and marketers who aren’t optimizing for that world today will find themselves invisible tomorrow,” said Anil Chakravarthy, president of Adobe’s customer experience orchestration business. “Together with Semrush’s leading SEO platform and agentic search intelligence, Adobe will empower our customers with the full picture of how their brands show up to consumers — from discoverability in search engines and LLMs to content creation, customer engagement and conversion, all in one integrated system at scale.”

Bill Wagner, chief executive of Semrush, said the combination represented an opportunity to build a definitive platform for brand visibility in an AI-driven world. “Semrush has spent more than 17 years helping marketers scale and grow — and that mission has never been more important than it is today,” he said. “By joining Adobe, we see an incredible opportunity to help marketers ensure their brands are found, trusted and chosen at every touchpoint.”

Also Read: Three Myths That Are Keeping Brands Away From AI

Adobe framed the acquisition as part of a broader repositioning around what it calls customer experience orchestration — an approach that treats content supply chain, customer engagement and brand visibility as interconnected rather than separate functions. The company recently introduced Adobe CX Enterprise, an agentic AI system designed to bring those functions together under a single intelligence and governance layer.

Semrush customers can expect continued product investment and an expanded roadmap as the two companies integrate.

The post Adobe Acquires Semrush to Win the AI Search Battle appeared first on MartechView.

Social media advertising crossed a symbolic threshold in 2025, overtaking search to claim the largest share of the digital advertising market, according to the Internet Advertising Bureau’s annual assessment of the industry. The shift reflects a sustained reallocation of marketing budgets that has been building for several years and is now reshaping the competitive order among the platforms that have long defined the sector.

Social media ad spending rose 32.6% year over year to $117.7 billion in 2025, giving the channel a 40% share of the total digital market — the first time it has held the top position. Search, which built Google into the dominant force in digital advertising, generated $114.2 billion, but its growth rate declined by nearly 5 percentage points in 2024 as AI-powered tools alter how users find information and how that activity can be monetized.

The downstream consequences for Google are significant. Meta, the owner of Instagram and Facebook, is projected to surpass Google in both US and global advertising revenue for the first time in 2026, according to a recent eMarketer forecast — a milestone that would have seemed unlikely as recently as three years ago.

The IAB’s report also marks a formal reclassification of creator content. What was once treated as an experimental add-on to campaign budgets is now described by the trade group as a “core media channel.” Creator economy spending reached $37 billion in 2025 and is forecast to reach $44 billion in 2026, driven by a shift from one-off brand partnerships toward always-on strategies that combine micro-influencers, affiliate programs, and performance-focused creators.

“Brands are embedding creators into the foundations of their media strategies, operational workflows and even product development,” the IAB said in the report.

Part of what is driving creator value beyond raw follower counts is commerce. More creators are linking brand awareness activity directly to transactions, capitalizing on the growth of commerce media, which rose 18% last year to $63.4 billion — though its rate of growth also slowed by five percentage points from 2024. The human quality of creator content is also becoming a competitive advantage as algorithmic feeds fill with low-quality AI-generated material, a phenomenon the industry has dubbed “slop.”

AI is creating other complications. The IAB estimates that more than 50% of global web traffic now originates from automated bots — a figure the trade group cited in calling for stronger industry measurement and interoperability standards. The bureau’s recently launched Project Eidos initiative is its formal response to that challenge.

Elsewhere in the report, video advertising — spanning connected TV, social video and online video — grew 25.4% to $78 billion in 2025, accelerating from the prior year. Video game and esports advertising rose 22%, driven by more sophisticated in-game ad formats, stronger demand and improved measurement.

Despite the shifting dynamics, the overall structure of digital advertising is consolidating rather than fragmenting. After brands diversified some spending toward mid-sized platforms in 2024, the pendulum has swung back. The top ten global media platforms increased their collective share of digital ad revenue by 3.4 percentage points in 2025, capturing 84.1% of the total market, leaving less room for smaller players than at any point in recent years.

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For nearly two decades, SEO was the digital marketer’s most reliable tool. Entire strategies and teams were built around a stable premise: rank well on Google, get seen. Visibility followed ranking, and traffic followed visibility. The system relied on Google’s shifting algorithms, but at least it was comprehensible.

The past two years have broken that logic entirely.

Rankings are holding — sometimes improving. But website traffic is declining. Dashboards look healthy. First-page positions remain. The clicks, however, are not there. In 2025, 60 percent of Google searches ended without a single click to any website.

The cause is not a mystery. AI-powered search engines now answer queries directly, absorbing the clicks that websites used to capture. We have entered a zero-click world, where AI synthesizes information and delivers it to users without ever requiring them to visit the source.

For marketers, the implication is significant: the goalposts have not just moved — they have been replaced entirely.

Ranking No Longer Leads to Reach

The old model of search rewarded technical precision. Keywords, backlinks, site architecture, and page speed sent signals that helped search engines determine relevance and authority. In today’s AI-mediated environment, relevance is no longer discovered — it is constructed.

AI chatbots do not surface results. They synthesize them. They draw from multiple sources, weigh credibility signals, and generate responses that may never require a user to visit the referenced content. Ranking highly on a search results page does not mean your brand will appear in an AI-generated answer. And if it does not appear in that answer, visibility effectively disappears — regardless of where the blue link sits.

The new definition of visibility is not where you rank. It is whether you are recognized as a source worth citing.

The End of Silos

Because AI changes how visibility is earned, it dissolves the boundaries that once separated public relations, SEO, and paid content. Historically, these functions operated independently because they influenced different outcomes. SEO drove rankings. PR generated coverage. Paid content drove traffic. Each was measured within its own channel.

Today, all of it is evaluated together — by the AI model deciding whose voice to include in an answer.

AI systems do not draw from a single source. They scan and combine articles, websites, forums, comments, and sponsored content across the entire web to construct a response. The implicit question these systems are asking is: Does this brand appear consistently, clearly, and credibly across multiple sources?

A PR placement is no longer just visibility in one publication — it becomes part of the dataset AI systems are trained on and referenced going forward. SEO content is no longer just about ranking — it is a structured repository of information for AI to extract. Paid placements do not just drive clicks — they shape how frequently and favorably a brand appears across the web.

The problem is consistency. A company might describe itself one way on its website, another way in media coverage, and appear differently still in paid placements. To a human reader, that is messy but navigable. To an AI model, it is noise, and noise weakens the signal. A brand that is difficult for AI to categorize is a brand more likely to be left out of the answer entirely.

Redefining Strategy for the AI Era

If the goal is no longer to rank but to be referenced, the question becomes: how do you build a presence that AI systems will not ignore?

In 2026, an effective search strategy rests on three foundations.

The first is audience-driven visibility. AI systems weigh what real people say and think — customer reviews, forum discussions, and community conversations — more heavily than brand-produced marketing copy. The frequency with which AI Overviews and ChatGPT reference Reddit is not coincidental. If people are discussing your brand in credible, relevant spaces, those discussions become signals AI systems recognize and draw from. Marketers should actively encourage user-generated content and participate in the communities — forums, social channels, industry platforms — where their customers already gather. The goal is contribution, not promotion.

The second is strategic amplification. Search engines have long deprioritized paid content, but AI systems often make no distinction between organic and sponsored material. When placed thoughtfully on high-authority platforms, sponsored content can reinforce a brand’s presence in exactly the sources AI is most likely to reference. Paid and organic content are increasingly parts of the same ecosystem — and should be planned as such.

The third is clarity. AI models prioritize specificity. Users querying AI tools provide far more detail than they would in a traditional search — use cases, specifications, context. For content to be referenced, it must match that level of detail. Brands need to be explicit about what they do, how their products work, and what differentiates them — even when those details feel self-evident. If it is not clearly stated, it is less likely to be surfaced.

The New Definition of Success

The language of SEO has not yet caught up to the shift it is navigating. Rankings, keywords, and traffic remain the default metrics for measuring reach and success. But those metrics are growing increasingly detached from actual visibility in an AI-first environment.

The questions that matter now are different: Are you being cited? Are you being synthesized into answers? Are you present in the conversations and sources that shape those answers?

If the answer is no, it does not matter where you rank.

SEO, as it was understood for two decades, is less useful than it once was. But search is not disappearing — it is being redefined. The brands that understand that distinction and build for it deliberately will not simply adapt to the zero-click era. They will set the terms.

The post SEO Is Not Dead. But It No Longer Works Alone. appeared first on MartechView.

Years and even decades of results-based research and training have conditioned marketers to hunt SEO, optimizing for keywords, propagating backlinks, and competing for placement within the familiar “10 blue links”. But the rules have been rewritten, seemingly almost overnight. SERPs are undergoing a fundamental shift, prioritizing direct answers and AI-generated summaries while pushing traditional organic results further down the page. 

Simultaneously, AI-first search experiences on interfaces like ChatGPT are becoming mainstream ways people discover and evaluate information, forcing marketers to source alternative methods to establish visibility, authority, and engagement.

Additionally, as higher volumes of AI-driven content are added to an already crowded consumer ecosystem, marketers are challenged to find more and better ways to reach their target audience—and to capture their attention at just the right time. This all comes at a time when consumers’ focus is flickering, and their attention spans are shrinking. Marketers need to continue making the most of contemporary, proven channels, but they also need to consider options that best fit the current climate and technological undercurrents. 

Additionally, as privacy regulations tighten and consumers grow increasingly wary of invasive tracking, opt-in channels are becoming more valuable than ever. Digital calendars offer marketers a permission-based way to stay visible and engage audiences without relying on cookies, paid reach, or constant retargeting, building trust while maintaining consistent, high-intent engagement.

While calendar marketing tactics may be somewhat less familiar than others, this strategy couldn’t be easier, or more cost-conscious, to implement.

The Benefits of Digital Calendars in a Modern Marketing Environment

As consumer audiences are increasingly bombarded with messaging and visuals across countless channels such as social media, internet ads, television, streaming audio, and all sorts of legacy marketing outputs, brands are in competition with one another in a battle for attention that grows more grueling by the day. The value of direct, timely marketing communication is now at a premium, but because today’s sophisticated consumer is more protective of their time, space, and data, these channels have become more difficult to establish.

That’s what makes the digital calendar one of the most useful tactics in a marketer’s toolbox today. As traditional channels become oversaturated and marketing professionals seek to improve engagement and reach among their audiences, calendars are becoming an essential component of marketing tech stacks. A direct, timely channel that integrates into a consumer’s existing tech platforms and daily routine, the digital calendar can bypass SMS and email limitations and capture audience attention when it is most relevant to a brand.

Digital Calendars: Creating a Simple, Powerful Connection with Consumers

With marketers trying to figure out the new math of internet and social media algorithms and racking their brains over how to crack AI-powered search engines such as ChatGPT, Claude, and Google’s AI Overviews, the digital calendar remains a formidable tactic that is still underutilized in some spaces.

When combined with other channels, a brand website, email campaigns, and social media blasts, for instance, a simple “Add to Calendar” button or subscription to a calendar

can have a powerful effect for marketers. It’s an enduring but dynamic and direct connection with consumers who grant permission to access their calendar platforms, positioning a brand alongside their birthdays, doctor’s appointments, errands, and other everyday activities and reminders, a space those consumers likely visit multiple times daily.

Whether a marketer is advertising a brand’s new hours or important dates, a flash sale or a rewards program, digital calendars create a direct connection with a target audience on a high-value, frequently visited platform. And because calendar marketing is a non-invasive, opt-in tactic, consumers who subscribe are already invested in a brand and therefore interested in the news and promotions a brand decides to share.

Why Digital Calendars Are the Right Channels for the Right Moment

What once paid dividends for marketers may now amount to clutter, wasted efforts, or even false signals. Divining which legacy tactics still have value and which don’t will be part of the calculus for all brands at this critical inflection point. As Duolingo CMO Manu Orssaud recently told Marketing Brew, “The hardest part will be focus. Culture is speeding up, and the volume of ‘reactable moments’ keeps multiplying. But chasing everything is a trap.”

The subtext is this: Marketers must be selective in choosing the best channels for reaching target audiences with shorter attention spans, more messaging to process, and less trust in corporations than perhaps ever before. No single marketing channel will get it done, but certain outreach efforts will provide brands with more bang for their buck. Digital calendars, simple, direct, and dynamic, deeply immersive yet noninvasive, provide marketers with the right tool at the right time to connect their brand with interested target audiences, optimizing engagement without breaking a marketing budget.

The post CMOs Should Turn Calendars Into a Marketing Channel appeared first on MartechView.

For decades, marketers treated direct references to competitors as a red line. Side-by-side comparisons were left to analysts, journalists, or bloggers. Brands stuck to their own talking points, wary of lending attention—or credibility—to rival offerings.

That instinct is understandable. It is also outdated.

Generative AI and chat-based discovery have changed how consumers find information. Instead of scrolling through linear search results, users now ask AI assistants questions like “What’s the best product in this category?” and receive instant summaries that place brands side by side. In this environment, silence is no longer a safe strategy. If brands do not provide content that enables comparison, they risk being excluded from the conversation altogether—or worse, having the comparison defined for them by someone else.

Consumers Are Already Choosing AI Answers

The shift is no longer theoretical. Roughly 80 percent of search users now rely on AI-generated summaries at least 40 percent of the time. More striking still, about 60 percent of searches end without a click to a traditional website. The familiar “ten blue links” model—where SEO led users directly to brand-owned destinations—is giving way to a zero-click reality.

Today, the answers consumers see and trust are shaped upstream by large language models. Those models draw from content that is clear, structured, and comparable. Discovery no longer happens only on brand websites. It happens inside AI-generated answers. Visibility now depends on whether a brand’s content is included in those summaries.

From SEO to GEO

This shift has given rise to a new discipline: generative engine optimization, or GEO. Where SEO focused on making content legible to search crawlers, GEO is about making it intelligible, indexable, and citable by AI systems.

That begins with answer-first writing, precise metadata, and distribution through credible editorial environments. AI agents prioritize clarity, authority, and structure. Content published in trusted contexts—and written to anticipate real consumer questions—is far more likely to surface in generative responses.

Just as SEO once forced marketers to think differently about keywords, architecture, and intent, GEO demands a comparable mindset shift. The question is no longer simply, “How do we rank in search?” but, “How do we appear in AI-generated answers?”

Why Comparative Content Now Matters

AI systems are not looking for slogans. They are looking for distinctions—benchmarks, trade-offs, and context. The most useful content in this new discovery landscape often requires acknowledging competitors directly.

This does not mean attacking rivals. It means offering clear, fact-based information that helps consumers make informed choices while still highlighting a brand’s strengths. Effective formats include:

• Feature-by-feature comparisons that clarify differences across a category
• “Best for” guides that map products to specific use cases
• Educational explainers that define terminology and market structure
• Buyer checklists that outline decision criteria
• Trend-driven analysis that shows where a category is headed and how players differ

This approach delivers two benefits. It increases the likelihood that a brand’s content is cited in AI summaries. And it positions the brand as a credible authority—even when it is not the only name mentioned.

Getting Comfortable With Discomfort

For many organizations, this shift feels risky. Mentioning competitors in branded content can feel counterintuitive, even threatening. But the rules of discovery have changed. Comparisons are happening whether brands participate or not.

The more effective strategy is to shape the narrative rather than avoid it.

Brands that adapt—by structuring content for generative engines and embracing comparative formats—stand to gain visibility and trust where decisions are increasingly made. Those who cling to old assumptions risk fading from view.

The conclusion is straightforward: in the era of AI summaries, relevance depends on participation. The brands that show up in the conversation will be seen as helpful, credible, and worth choosing. The rest may not be seen at all.

The post When Silence Hurts Brands in the Age of AI appeared first on MartechView.

Ranketta, a Czech-based AI visibility platform, has announced it has raised €1 million in pre-seed funding. The financing round was led by Lighthouse Ventures, with participation from Gi21 Capital. The company is addressing a rapidly emerging challenge for marketers: ensuring brand visibility within AI search and AI shopping results.

The investment comes as consumer habits shift dramatically. Recent data suggests that approximately 50% of consumers already use AI-powered search to inform purchasing decisions, with 44% relying on it more than traditional Google search. Spending influenced by AI search is projected to reach $750 billion by 2028.

Despite this monumental shift, fewer than 40% of marketers prioritize Generative Engine Optimization (GEO), and a mere 16% of brands actively track their AI search performance. This creates a dangerous visibility gap where brands may maintain a strong presence on traditional search but disappear within large language model (LLM) responses.

Also Read: GenAI Search Is Rewriting the Shopper’s Playbook

Measuring and Improving AI Prominence

Ranketta addresses this by providing a platform for D2C, e-commerce, and B2B brands to measure and improve their prominence in AI-generated answers. The platform tracks the frequency at which products and brands appear across tools such as ChatGPT, Gemini, Perplexity, and AI Overview.

The system provides detailed insights into which products LLMs recommend most, the reasoning behind the placement, and data-driven suggestions for adjusting content to influence rankings. Beyond product insights, Ranketta tracks overall brand presence, sentiment, relative ranking, and identifies the exact articles and sources the AI models cite.

An integrated AI Copilot enhances this offering by reviewing the data and providing tailored recommendations on content creation and which external citations are likely to have the greatest impact on AI search visibility.

Also Read: Edition 3: Sharp Minds Decoding Tech’s Next Wave

Founded on Research

Vojtěch Oravec, the 21-year-old CEO and founder of Ranketta, stated that his research confirmed companies’ limited visibility into AI’s influence on purchasing decisions. He emphasized that traditional SEO and analytics no longer capture the full customer journey, necessitating tools that can operate across both conventional and AI-driven channels.

“Ranketta turns AI shopping into a real acquisition channel that companies can monitor, improve, and grow,” Oravec said. “Our mission is to ensure every brand can compete fairly in the AI-driven discovery landscape.”

Oravec, a Czech engineer and AI researcher, developed the technology as a solo prototype in 2025. Before founding Ranketta, he conducted research at the Czech Institute of Informatics, Robotics and Cybernetics and worked as a product manager at Henry Schein.

In the two months since its launch, Ranketta has already secured more than 20 European brands, including Brainmarket and Purple Technology. The new funding will be used to accelerate European expansion, support entry into the U.S. market, expand engineering, product, and sales teams, and advance integrations with key e-commerce platforms, including Shopify.

The post Ranketta Raises €1M to Decode the AI Search Shelf appeared first on MartechView.

Adobe, the creative software giant, agreed on Wednesday to acquire the digital marketing platform Semrush for approximately $1.9 billion in cash, a strategic move designed to help companies navigate the chaotic shift from traditional search engines to artificial intelligence.

The deal, valued at $12.00 per share, represents a significant bet by Adobe that the era of typing keywords into a search bar is fading, replaced by consumers asking complex questions of large language models (LLMs) like ChatGPT and Google’s Gemini.

The Rise of ‘GEO’

For over a decade, Semrush has been a primary tool for marketers attempting to master Search Engine Optimization (SEO)—the art of ranking high on Google. However, the acquisition is centered less on the past decade of search and more on a nascent field known as Generative Engine Optimization (GEO).

As AI chatbots increasingly become the primary interface between brands and consumers, companies are scrambling to ensure their products are recommended by these algorithms.

“Brand visibility is being reshaped by generative AI, and brands that don’t embrace this new opportunity risk losing relevance and revenue,” Anil Chakravarthy, the president of Adobe’s Digital Experience Business, said in a statement.

Mr. Chakravarthy noted that the acquisition is intended to unlock GEO as a “new growth channel,” allowing marketers to manage how their brands appear not just on websites, but inside the “black box” of AI models.

A Shift in Consumer Habits

The acquisition comes amid data suggesting a rapid change in user behavior. According to new figures from Adobe Analytics, traffic from generative AI sources to U.S. retail sites surged by 1,200 percent year-over-year in October.

This explosion in AI-driven traffic has created a pain point for Adobe’s corporate clients—including Coca-Cola and General Motors—who use Adobe’s software to manage their content supply chains but often lack visibility into how their brand is perceived by AI agents.

By integrating Semrush’s data engine with its own Experience Cloud, Adobe aims to provide a “comprehensive solution” that tracks brand visibility across owned channels, traditional search, and the wider web of LLMs.

Bill Wagner, the chief executive of Semrush, stated that the combination will help brands understand “where and how their customers are engaging in these new channels” as the digital landscape evolves.

Deal Details

The transaction has been approved by the boards of directors of both companies. Adobe has already secured voting commitments from Semrush’s founders and major stockholders representing over 75 percent of the voting power, all but ensuring the deal’s approval.

The acquisition is expected to close in the first half of 2026, subject to regulatory approvals.

Wachtell, Lipton, Rosen & Katz is serving as legal advisor to Adobe, while Centerview Partners and Davis Polk & Wardwell are advising Semrush.

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In a marketing landscape reshaped by artificial intelligence and composable content platforms, Elizabeth Maxson, Chief Marketing Officer at Contentful, offers a compelling vision for the future of enterprise marketing. As companies pivot away from traditional monolithic systems, Contentful is helping brands harness agility, scale, and personalization through its innovative approach to content orchestration. 

In this exclusive interview, Maxson explores how marketers can strategically leverage AI to close the personalization gap, rethink customer experience in an age of zero-click search, and balance data-driven insights with creative risk-taking. With real-world examples from leading enterprises, she reveals how composable CMS and AI are not just tools but essential drivers of marketing innovation and operational resilience in 2025.

Full interview; 

What’s the biggest advantage for marketers embracing composability and AI-driven personalization in 2025?

The biggest strategic advantage is agility without compromise. Composable platforms allow marketers to orchestrate content across multiple channels and geographies, while AI tools help deliver personalization at scale. We just released a new research report based on surveys and interviews with hundreds of senior marketing leaders, and we found that while 89% of marketing teams are using AI, only 25% are applying it to audience segmentation and personalization. 

Contentful blends composability and AI so that marketers can close that gap. Our platform helps marketers adapt quickly, reuse modular content, and consistently and confidently deliver personalized experiences at enterprise scale. 

How should marketing teams adapt customer experience strategies to stay visible in the era of zero-click search and AI discovery?

Even in the era of AI-powered discovery and zero-click search, marketing fundamentals haven’t changed: we’re still in the business of telling stories that resonate and build trust. What has changed is how customers discover and engage with those stories. Search engines and LLMs now surface answers directly, which means our content needs to be more structured, conversational, and optimized for generative engine discovery. The good news is that strong SEO fundamentals — clear structure, relevant keywords, and consistent updates — already give brands a strong foundation. From there, it’s about adapting for AI discovery by using our customers’ language and leaning into the insights only our brand can provide.

At the same time, we need to evolve how we define success. Traditional traffic metrics will decline, but the customers who arrive are more qualified because they’ve already done their research in AI results — and they’re converting at higher rates. That means the KPIs that matter most are conversion, pipeline impact, and share of voice within AI and search results. Tracking branded searches and direct traffic also helps us understand whether our efforts are creating demand across channels.

So the strategy is threefold: first, ensure solid SEO fundamentals; second, keep leaning into what only our brand can uniquely provide — insights, perspectives, and storytelling; and third, measure success through impact and visibility rather than raw traffic. That’s how we stay discoverable, trusted, and differentiated in the age of AI discovery.

How do you balance data-driven decisions with fostering creative risk-taking in marketing teams?

We call this evidence-based creativity, and it’s a must-have skill set for modern marketers.  Our findings show that data analysis and interpretation are now the top-ranked skills for marketers. This tells us that marketers are expected to be both creative and analytical. For me, it’s about giving data and creativity equal weight. I encourage my teams to use data as a compass, not as a constraint. Data allows us to see what’s resonating, but creativity sparks ideas people remember. I want my teams to take creative risks while validating ideas through testing frameworks, such as A/B testing, and customer feedback loops. This balance ensures campaigns are imaginative and anchored in measurable outcomes. 

How does Contentful’s AI integration ensure scalable, accurate personalization in multi-language and regulated industries?

AI-powered personalization is only as effective as the guardrails around it.  Our regional data found that 58% of EMEA marketers test AI selectively with a defined plan, and nearly a third emphasize governance skills like brand voice, compliance, and quality standards. At Contentful, we’ve built composability and native AI into the platform so teams can scale personalization across languages and markets while ensuring consistency, compliance, and brand integrity. For example, Biogen, a leading biotechnology company, uses our platform to unify its content strategy and has dramatically streamlined translation workflows to support roughly 1500 pages published per year, across 30 languages. Tools like Contentful are critical in regulated industries where accuracy isn’t optional, and teams need to publish faster, more consistently, and at scale.

How do headless CMS and composable architectures help enterprises deliver omnichannel experiences with AR, video, and conversational AI?

Headless and composable systems are the foundation for meeting audiences across new formats. By separating content from the front end, marketers can create once and deploy everywhere. For example, a marketer can design a single piece of content and deploy it across different channels such as AR and video (and whatever else comes next!) without reinventing the wheel every time. This approach helps brands adopt new platforms faster without worrying about creating inconsistencies in the customer experience.  

How does Contentful ensure quality and trust in AI-generated content, and what guardrails should marketers use?

AI is valuable not just because of speed but because it gives marketers the freedom to focus on tasks only humans can do. Our research shows that 42% of marketers define campaign success by content quality and consistency, which tells us that trust and creativity can’t be outsourced to machines. We recommend giving AI clear guardrails while keeping humans at the center of judgment and reviews.   Marketers are already tackling the issue, with 45% of organizations offering AI training. At Contentful, we encourage teams to use AI as a creative accelerator for drafting, generating options, or testing variations, while keeping humans in the loop to make the final judgment on quality, compliance, and brand voice.

With privacy regulations tightening globally, how is Contentful evolving its data governance and transparency features to give marketers actionable insights while ensuring compliance and ethical personalization?

At Contentful, we see data governance and transparency as core to building customer trust and enabling marketers to work responsibly with AI and personalization. That’s why we’ve built our platform to meet the highest standards, including SOC 2 Type 2 compliance and features like EU data residency that help our customers store their user data in the EU, while staying aligned with global regulations like GDPR.

Another way we do this is through our Privacy Plugin, which gives brands the flexibility to decide what data is collected and how it’s used with clear consent options for customers. This means marketers can personalize experiences responsibly, tailoring them to regional requirements without being boxed in by one-size-fits-all limits.

Ultimately, we’re helping marketers derive actionable insights from compliant data, so they can personalize experiences to respect user boundaries, maintain brand integrity, and strengthen long-term customer relationships.

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