Adobe is doubling down on AI-powered marketing. The company announced a $1.9 billion deal to acquire Semrush, a widely used SEO and brand visibility platform. Adobe will pay $12 per share in cash, and the deal should close in the first half of 2026. Semrush stock surged more than 70% after the news, while Adobe shares dipped about 2%.

This move marks Adobe’s biggest push yet into AI search optimization. The company wants to strengthen its grip on digital experience tools at a time when algorithms, not humans, shape discovery. The shift toward LLM search is accelerating, and Adobe intends to meet that moment.

Semrush offers tools that thousands of marketers use to track visibility, manage SEO, and monitor audience reach. It also brings a decade of expertise in what it calls generative engine optimization. The approach blends search optimization with AI-driven discovery across platforms such as ChatGPT and Google Gemini. Brands need those tools as customers increasingly rely on AI agents for recommendations and decisions.

Adobe argues that this shift will redefine how brands stay visible. The company cites new Adobe Analytics data showing a massive 1,200% spike in traffic from AI-driven sources to U.S. retail sites over the past year. That growth signals a new distribution channel. It also explains why Adobe wants Semrush before the landscape moves further.

Marketers feel the pressure as search habits evolve. Traditional SEO is only part of the equation now. AI summaries and chat-based search reshape what customers see and trust. Many CMOs know that visibility is slipping into algorithmic black boxes. Adobe wants to sell them a way out. Semrush’s tools will fold into Adobe’s stack, joining AEM, Adobe Analytics, and Adobe’s emerging Brand Concierge service.

Semrush enters this acquisition with momentum. The company recently reported 33% year-over-year ARR growth in its enterprise segment. Its biggest customers include Amazon, JPMorgan Chase, and TikTok. Adobe believes the platform will accelerate its push into the agentic AI era, where autonomous systems manage everything from content workflows to customer interactions.

Competition across martech remains intense. Many SaaS platforms have struggled this year as AI eats into legacy software budgets. Adobe has felt that pain as well. Its stock is down more than 25% in 2025. Semrush, however, held steady. The pairing may help Adobe regain ground in a market where AI-native platforms expand quickly.

This also is not Adobe’s first major bid to reshape its ecosystem. The company attempted a $20 billion acquisition of Figma in 2022, but regulators blocked the deal in late 2023. Figma went public this July as the IPO market reopened. Regulators will likely take a close look at this Semrush deal as well, though it sits in a less crowded segment.

Both company boards have already approved the acquisition. Semrush founders and major shareholders, representing more than 75% of voting power, agreed to support the sale. Semrush will soon file a proxy statement with the SEC. Shareholders will then vote on the agreement.

If the deal clears regulatory review, Adobe gains a powerful asset in the new race for AI-driven brand visibility. Semrush gains the scale and distribution of one of the world’s most established software giants. And marketers get one more sign that AI search will soon rival traditional search as a discovery channel.

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Interact Marketing is stepping deeper into AI-powered marketing, and the timing couldn’t be more urgent. With Google’s AI Mode and Deep Search reshaping how users find information, traditional SEO is losing ground. Many brands have watched their organic visibility evaporate, and click-through rates tumble by as much as 61 percent. Interact Marketing’s answer is a specialized AI Mode Marketing Program engineered to reclaim that lost traction—and keep brands visible inside the next generation of search.

The agency’s move follows a major industry inflection point. As AI-generated answers replace conventional blue links, search behavior is shifting. Users are relying more on AI summaries, conversational chat responses, and multi-step deep searches that bypass traditional SERPs. For marketers, this means less screen time, fewer impressions, and lower conversions—unless you adapt.

Joe Beccalori, CEO of Interact Marketing, says the program wasn’t an overnight reaction. “GPT sparked a massive shift in how people search, and we began building this program two years ago in response. When Google launched AI Overviews in May 2025, it validated the need,” he explained. Their strategy now centers on the mechanics behind AI responses—citations, relevance scoring, and content structures that large language models prioritize.

Rather than tweaking old SEO frameworks, the agency focused on Google’s AI Mode from the start. This approach includes real-time response testing, performance tracking tuned to generative results, and AI-driven workflows that don’t exist in standard agency packages. Interact Marketing’s team appears to be betting on a simple reality: If AI is becoming the new front page of the internet, then optimizing for it isn’t optional.

The program zeroes in on several areas where brands stand to lose—or gain—the most:

Improved Rankings
AI Mode prioritizes a different set of ranking factors. Interact Marketing uses targeted optimization to help brands secure premium placement inside generated answers.

Enhanced Citations
Credible citations heavily influence whether a brand appears in AI responses. The program ensures accuracy, consistency, and authority across all digital listings.

Increased Conversions
AI responses may reduce page visits, but high-quality content can still drive intent. Interact’s strategies aim to convert those interactions into measurable business outcomes.

What sets the program apart is its early adoption curve. While many agencies are still trying to decode Google’s shifting search ecosystem, Interact Marketing built its framework before AI Overviews even rolled out. As Google continues refining its AI-driven search experience, the agency’s approach centers on continual testing, cross-channel analytics, and rapid adaptation.

The real story here isn’t another SEO product—it’s a preview of the next competitive battlefield. As AI-driven search becomes the default experience, brands that optimize for Deep Search and generative results will shape the visibility landscape. Everyone else risks disappearing from it.

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Automotive dealerships have no shortage of data. What they lack is coherence. Today, Data Axle and Tealium announced a partnership designed to fix that problem by unifying fragmented dealership systems and turning scattered records into actionable intelligence. For an industry long plagued by inconsistent communications and conflicting customer profiles, the timing is ideal.

The collaboration blends Data Axle’s verified consumer and vehicle datasets with Tealium’s real-time customer data platform. Together, the companies aim to help dealers and OEMs merge disjointed customer information, eliminate duplicate records, and guide marketing, sales, and service decisions with far greater accuracy.

The need is clear. A new survey commissioned by Data Axle shows consumers are tired of mixed signals from dealerships. Nearly half of respondents reported receiving duplicate or contradictory messages from the same dealer. Even worse, 68 percent said dealership outreach often feels inconsistent or irrelevant. Yet consumers are willing to share core data—emails, phone numbers, and vehicle history—so long as they receive real value in exchange, such as accurate trade-in estimates or relevant service offers.

According to Chris McTague, managing director of automotive at Data Axle, the issue isn’t a shortage of information. “Dealerships are sitting on a wealth of data that rarely works in concert. Cars have become interchangeable, but data isn’t. What separates one dealer from the next is how intelligently they use their information,” he said. For McTague, the partnership offers a foundation that turns verified insights into better decision-making and more trustworthy customer experiences.

At the core of the integration is Data Axle’s deterministic identity graph, which feeds directly into Tealium’s CDP to clean and enrich customer profiles in real time. Dealers gain a unified view of each customer, allowing them to refine audience models, tailor outreach, and strengthen service engagement without relying on guesswork. It’s a shift from fragmented databases to measurable impact.

Stephen Smith, RVP of Partnerships at Tealium, emphasized that automotive data challenges run deeper than outdated CRMs. “The auto industry faces unique challenges and often navigates fragmented data systems, making it difficult for dealers to fully understand and engage their customers,” he said. By combining Tealium’s orchestration engine with Data Axle’s verified datasets, the partnership brings “clarity, control, and connected data” into the dealer’s workflow.

With pressure mounting for dealerships to modernize how they identify and reach buyers, the partnership arrives at a strategic moment. As digital-first shoppers expect relevant and timely interactions, the dealer that masters its data—not just its inventory—wins. Data Axle and Tealium’s solution offers a path toward more credible, efficient, and intelligent customer engagement.

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Thanks, the customer-first native ad network known for reimagining post-purchase experiences, is expanding into live events through a new partnership with Oztix, Australia’s largest independent ticketing company. The collaboration brings Thanks’ AI-powered media monetisation platform directly into Oztix’s digital ecosystem, turning the high-intent moment after a ticket purchase into a smarter, more curated discovery experience for fans.

Instead of generic ads or irrelevant offers, buyers will now see contextually aligned recommendations—travel options, local dining, merch, and experience upgrades—delivered at the precise moment their excitement peaks. It’s a strategy already trusted by brands like eBay, Linktree, and Booking.com, but this marks its first major expansion into the live-events sector.

The partnership extends beyond commerce. As part of the rollout, Thanks and Oztix will jointly support Make-A-Wish Australia, dedicating a portion of proceeds each quarter to help grant wishes for children with critical illnesses. The initiative brings an added layer of purpose to every ticket sale, allowing fans to contribute to a national cause simply by completing their purchase.

“Oztix is a cornerstone of Australia’s live-event scene, connecting millions of fans to the experiences they love,” said Steve Tesoriero, Founder and Co-CEO of Thanks. He noted that Oztix’s audience-first ethos made the partnership a natural fit. The goal, he added, is to “add more meaning to every ticket purchase” while expanding the same AI-driven platform used by global brands into a new industry.

Oztix has spent more than two decades building partnerships across promoters, venues, artists, and community organisations. Its reputation for strengthening the live-events ecosystem makes the collaboration especially aligned with its mission. The integration with Thanks introduces a new way for fans to both receive value and give back—connecting entertainment with meaningful social impact.

Stuart Field, Co-Founder and Managing Director of Oztix, emphasized the human side of the initiative. “Together, we’re showing how retail media and community can work side by side—creating rewarding experiences for fans while giving back,” he said. For many on the Oztix team, the partnership with Make-A-Wish is personal. “It’s about spreading the same joy and connection we see at live events to children and families who need it most.”

For Thanks, the collaboration marks another milestone in its Australian expansion and reinforces its mission to bring more relevance, humanity, and value to the moments brands often overlook. As retail media continues its shift toward high-intent, first-party environments, the post-purchase audience is becoming one of the most strategically important surfaces in digital marketing. By pairing real-time contextual intelligence with charitable impact, Thanks and Oztix are betting that the future of advertising isn’t just more efficient—it’s more meaningful.

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Live events have never struggled to attract people—they’ve struggled to measure what actually matters. ROME Insights, a new analytics and AI startup, wants to fix that. Today, the company unveiled ROME (Return on Memorable Experiences), a measurement framework built to capture human attention, emotional engagement, and the lasting impact of in-person experiences. For an industry built on connection, it’s a shift that feels long overdue.

Co-founded by event-technology veteran Justin Zebell and marketing strategist and AI researcher Bob Hutchins, ROME Insights was created in response to a long-standing issue: traditional event metrics don’t tell the full story. Attendance numbers, badge scans, and booth visits offer surface-level signals. What they miss is the depth—the moments that spark memory, influence behavior, and justify sponsorship spend.

“We built ROME because the events industry has been measuring the wrong things,” Zebell said. His argument is simple: event organizers, sponsors, and internal stakeholders need proof of value grounded in real human experience, not just headcounts. ROME provides that proof.

The ROME framework blends multiple data sources—behavioral attention tracking, qualitative feedback, and quantitative analytics—to produce a composite engagement score. It captures both immediate reactions and longer-term recall, offering what the company calls a clearer, more defensible metric for understanding event impact.

Hutchins sees ROME as a tool that protects the value of live gatherings in an increasingly digital world. “Live events are one of the few places where people still gather in person to learn, connect, and be moved by ideas,” he said. “ROME helps organizers protect and prove the value of that experience in a world that demands measurable outcomes.”

ROME Insights will work directly with conference organizers, trade show producers, and corporate event teams to integrate the framework across different formats. The company also provides consulting services to help teams interpret findings and use those insights to shape future events—whether that means adjusting programming, reallocating budget, or refining sponsor packages.

Early adopters are already seeing results. Event organizers using ROME have reported higher sponsor renewal rates, smoother budget approvals, and a sharper understanding of which moments drive the most value. For a sector where gut instinct has often outrun measurement, ROME’s data-backed approach could become a competitive advantage.

In an era defined by metrics, the company is betting that the most important event KPIs aren’t clicks or counts—they’re the moments people remember.

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AI has already transformed market research, but according to the new 2026 Market Research Trends report from Qualtrics, the divide between teams using basic AI and those embracing purpose-built capabilities is widening rapidly. The stakes aren’t small: research groups relying only on generic tools are four times more likely to lose influence inside their organizations.

Meanwhile, 72% of teams using synthetic responses, agentic AI, and AI-native workflows say their organizations rely on research far more than they did last year—momentum that’s translating directly into higher budgets. Traditional teams, however, are almost twice as likely to face stagnant or shrinking demand for their work.

“In today’s fast-moving economies, rapid access to consumer insights is a huge advantage,” said Ali Henriques, Executive Director of Edge at Qualtrics. “The teams embracing AI are reimagining what research looks like, asking bigger questions, and moving earlier in the innovation cycle.”

Purpose-Built AI Becomes the New Standard

AI adoption has crossed a maturity threshold. More than half of researchers now use AI regularly, and nearly nine in ten have experimented with it, but the report shows a clear shift away from generic chatbots toward AI embedded directly in research platforms. Purpose-built capabilities grew from 62% to 66% adoption, while usage of general-purpose tools dropped by nearly ten points.

The teams gaining the most traction are those leaning into specialist functions. Conversational analytics and visual content analysis—both at 49% adoption—give researchers deeper qualitative insight at a fraction of the time. What once took weeks can now be processed in hours.

Synthetic data is driving an even more dramatic evolution. Researchers using synthetic datasets are:

• 11% more likely to engage in early-stage innovation

• 7% more likely to run go-to-market studies

• 5% more likely to perform final product testing

Among those who’ve adopted it, 45% now consider synthetic data their most reliable source, surpassing traditional online panels—a remarkable shift for an industry built on human surveys.

Brands like Gabb are already using Qualtrics’ purpose-built synthetic model to reduce fielding costs, accelerate discovery, and test messaging against emerging trends. As Research Director Garred Sheppard described it, “Synthetic data became our cultural radar—cutting timelines from a week to hours while letting us validate high-stakes decisions with human panels.”

AI Agents Push Research Toward End-to-End Automation

Another major shift is the rise of agentic AI. While only 15% of researchers use AI agents today, nearly 80% expect that these tools will handle more than half of research projects end-to-end within the next three years.

Efficiency gains are already visible. Among teams using agentic AI, 84% report significantly higher efficiency, compared with 68% of those who haven’t tried it.

Henriques said the biggest unlock isn’t workload reduction—it’s democratization. Product teams can test ideas without submitting requests. Marketing can evaluate sentiment without waiting on insights teams. Executives can explore new markets directly. “The barrier to insights is no longer specialist knowledge,” she said. “It’s simply asking the right question.”

Leadership vs. Frontline: A Growing Execution Gap

Despite major investments in AI, many organizations aren’t seeing the full return. The report highlights a sharp misalignment between research leaders and individual contributors:

• 39% of leaders say AI has revolutionized their processes vs. 19% of frontline researchers

• Only 5% of leaders fear layoffs due to AI vs. 15% of individual contributors

• 68% of leaders consider themselves synthetic data experts vs. 41% of contributors

• 79% of leaders trust synthetic data quality vs. 61% of contributors

This mismatch results in underused tools, wasted budget, and slower execution—while competitors with tighter alignment surge ahead.

“When frontline teams don’t buy in, expensive AI tools go unused,” Henriques warned. “Organizations need shared definitions of success, real hands-on training, and clarity across levels about the practical applications of new AI capabilities.”

A Global Snapshot of a Rapidly Changing Industry

The findings come from a global Qualtrics study conducted in Q3 2025 with more than 3,000 research professionals across 14 countries. The data reveals a sector in transition—from manual workflows and traditional surveys to hybrid human-synthetic models, autonomous research agents, and a new definition of what strategic research teams look like.

The message from Qualtrics is clear: the teams that invest in purpose-built AI now will set the pace for the next decade of research. The ones that don’t risk being left behind.

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ON24 just added another weapon to its engagement stack. The company secured U.S. Patent No. 12,445,698 B2 for its AI engine that finds and extracts “Key Moments” from long-form video content. The system works across webinars, virtual events, digital conferences, and recorded demos.

The feature solves a long-standing marketing headache. Teams often run events that generate hours of footage but struggle to reuse that content. ON24’s patented technology tackles that gap by automating the discovery of highlight moments. The platform then packages them into short clips that can slip into nurture streams, partner enablement programs, landing pages, and social feeds.

Turning Engagement Behavior Into Short-Form Assets

The AI engine does more than trim videos. It examines engagement signals from the event itself. That includes viewing behavior, interactions, and content consumption patterns. The model then ranks moments that matter and extracts them into short, high-value videos.

This helps B2B marketers extend the shelf life of every event. Instead of a single live session, teams can quickly launch follow-ups, repurposed assets, and targeted campaigns—without manual editing or guesswork.

According to ON24 CEO Sharat Sharan, the core of the company’s strategy is AI-driven engagement. He says the new patent strengthens ON24’s position as an AI-enabled platform that understands audience behavior in real time. It also helps the company scale personalized content across channels.

Built by Engineers and Data Scientists for Demand Teams

The “Key Moments” engine is the product of ON24 developers and machine learning experts. The goal was simple: pull out the most engaging sections from any event. That includes product walkthroughs, customer stories, expert analysis, or Q&A sessions.

ON24 says the tech benefits a wide range of teams—marketing, customer success, sales, partner management, and training groups. Each can reuse “Key Moments” to reinforce messaging or support education workflows.

CTO Jayesh Sahasi says the patent advances ON24’s vision of connected, AI-led engagement. The system ties together behavioral data, automation, and content analysis. It lets teams transform a single event into continuous, data-backed campaigns.

Why This Matters for the B2B Engagement Market

Short-form content is the new currency in B2B marketing. Marketers want rapid content delivery, personalized outreach, and scalable video workflows. ON24’s patent supports that shift by automating one of the slowest steps in the process—manual video review.

Competitors offer clip-generation tools, but ON24’s differentiator is its use of engagement data tied to event behavior. It’s not guessing what content works; it’s identifying what audiences respond to.

As AI becomes central to B2B engagement platforms, this patent strengthens ON24’s position in a crowded market. It also signals the company’s larger push toward AI-powered orchestration across the full customer journey.

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Alembic Technologies just pulled off one of the biggest jumps in enterprise AI valuation this year. The Causal AI startup secured $145 million in Series B and growth funding, marking a 15.7x valuation increase since its last round. The investment was led by Prysm Capital and Accenture, signaling strong institutional confidence in a company aiming to redefine how enterprises understand cause and effect.

The move comes at a moment when every B2B vendor claims AI leadership. Yet Alembic’s pitch is sharper: while competitors rely on generic models, it focuses on proprietary data, causal inference, and a compute layer powerful enough to keep up with Fortune 500 demand. That combination has drawn interest from companies such as Delta Air Lines, Mars, NVIDIA, and others seeking clarity in a messy measurement landscape.

A Data Flywheel With Real Competitive Weight

At the core of Alembic’s strategy is a simple idea with major implications: better insights create better decisions, which then generate better data. This closed loop forms what CEO Tomás Puig calls a compounding data flywheel, and it’s difficult for rivals to replicate.

Many enterprises are drowning in dashboards and correlation-based reporting. However, Alembic claims its Causal AI identifies what actually drives outcomes. That advantage positions the company as a key intelligence layer rather than another analytics feature.

Accenture’s involvement reinforces that view. The consulting firm will integrate Alembic’s Causal engine into its enterprise transformation work, giving clients tools that move beyond pattern recognition toward evidence-based decisions. As Accenture CEO Julie Sweet put it, large companies need “verifiable, cause-and-effect insights” to act quickly and safely.

Big Customers, Bigger Impact

Enterprise customers are already using the platform to quantify decisions that were previously guesswork. Delta Air Lines linked Olympic sponsorship activations to ticket sales within days. Mars measured the exact dollar impact of viral brand moments. North Sails optimized digital spend for record returns. One Fortune 500 company grew its sales pipeline by 37% with precise attribution.

These examples illustrate why Alembic is drawing attention. Most organizations are not short on data; they are short on certainty. Causal AI promises to close that gap.

A Supercomputer Strategy Built for the Long Game

Alembic is pairing its funding with serious infrastructure investment. The company will deploy a new NVIDIA NVL72 superPOD cluster at Equinix’s SV11 data center, running NVIDIA AI Enterprise across its stack. This system is engineered for spiking neural networks, high-speed graph processing, and continuous-learning workloads.

This will be the company’s second dedicated supercomputing cluster, creating bi-coastal redundancy and ensuring the compute headroom needed for real-time causal analysis. Alembic positions this as a strategic moat. Instead of relying on shared cloud resources, it will operate a private AI fleet optimized for its unique workloads.

The deeper tie to NVIDIA also reflects a broader trend: enterprise AI platforms increasingly require custom compute infrastructure to maintain speed, security, and differentiation.

Defining the Next Layer of Enterprise Intelligence

Investors see Alembic as more than a vertical analytics solution. Many describe it as an emerging foundational model for enterprise decision-making. Instead of generating text or images, it generates causal truth, and brands are paying attention.

Prysm Capital’s team sees the company as a “mission-critical intelligence layer,” while WndrCo partners highlight the platform’s ability to deliver what marketers have sought for decades: clear, quantifiable attribution that informs where every dollar should go.

The company is also building a reputation for marrying deep research with commercial relevance. Its approach combines spiking neural networks, advanced graph modeling, real-time simulation, and high-performance compute into a system that updates continuously as new data enters the ecosystem.

The Market Impact

With this round, Alembic is positioned to influence several fast-shifting categories:

• Marketing measurement, where correlation-based models are losing credibility

• AI-driven budgeting, as brands face pressure for provable ROI

• Enterprise intelligence platforms, which increasingly compete on proprietary data

• Custom AI infrastructure, especially among companies needing guaranteed compute

Causal AI sits at the intersection of all four. That gives Alembic a strategic lane with few direct rivals and strong tailwinds as enterprises rebuild their data strategies around reliability rather than volume.

The Bottom Line

Alembic now has the capital, compute, and customer base to accelerate its push into enterprise AI. The company’s focus on Causal intelligence sets it apart in a market dominated by generative hype. With a new superPOD, deeper ties to Accenture, and accelerating Fortune 500 adoption, it is shaping a category that could define the next era of decision intelligence.

If Alembic delivers on its promise, the industry may soon shift from asking what happened to understanding why—and acting with far more confidence.

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