AI video creation has improved rapidly over the past two years—but for most platforms, ease of use still drops sharply the moment projects become complex or mobile enters the picture. MediaPET.ai is aiming to change that dynamic with the release of MediaPET.ai 2.0, which introduces what the company describes as the first chat-enabled interface built specifically for end-to-end AI video creation.

The update goes beyond a new UI. MediaPET.ai 2.0 reframes how users plan, edit, and scale video projects—especially on mobile—by combining conversational AI, project-level control, and new content formats that extend well beyond ads.

For marketers, creators, and performance teams under pressure to produce more video with fewer resources, the release signals a shift toward conversation-led, mobile-first creative workflows.

From Prompting Clips to Managing Projects

Most AI video tools today still operate at the clip level. Users generate a scene, tweak it, export it, and repeat. MediaPET’s new approach treats the project—not the clip—as the core unit of creation.

The headline feature in version 2.0 is a chat-based interface, designed to feel familiar to anyone who has used ChatGPT or similar conversational tools. But unlike general-purpose chatbots, MediaPET’s interface is highly structured around a video project, guiding users step by step through ideation, scripting, scene generation, and editing.

Instead of juggling timelines, menus, and settings, users can now:

• Ask for creative direction or revisions in plain language

• Move through production stages conversationally

• Apply changes across all scenes at once, not one clip at a time

That last capability is particularly notable. According to MediaPET, no other AI video platform currently supports global, cross-scene edits through natural language. For example, a user can change tone, pacing, branding, or spokesperson style across an entire video with a single instruction.

For teams managing high volumes of content, that shift alone can dramatically reduce production time.

Built for Mobile, Not Just Compatible With It

While many AI video platforms technically work on mobile devices, few are designed around mobile usage. MediaPET.ai 2.0 takes a different stance, positioning itself as the first truly mobile-friendly AI video creation platform.

The chat-driven workflow plays a key role here. By removing the need for complex UI controls and timelines, MediaPET enables creators to build and edit videos directly from their phones—an increasingly important capability as social-first and UGC-style content dominates distribution channels.

This mobile-first design aligns with broader trends in creator tools, where conversational interfaces are replacing dense dashboards. For marketers and small teams, it also opens the door to faster iteration, on-the-go approvals, and real-time content updates without being tied to a desktop setup.

Expanding Beyond Ads: Three New Creation Modes

MediaPET built its early reputation as an AI-powered ad creation platform, but version 2.0 expands its scope significantly with three new content creation modes:

1. Ads Mode

2. Short-Form Movies

3. Story Mode

Together, these modes reflect how video marketing is evolving—away from rigid formats and toward storytelling, authenticity, and platform-native experiences.

Short-form movies and story-based videos are particularly relevant for brands experimenting with TikTok, Instagram Reels, YouTube Shorts, and emerging CTV formats. These modes allow creators to structure narratives more naturally, rather than forcing everything into a traditional ad framework.

The result is a platform that positions itself as all-in-one video infrastructure, rather than a niche AI ad generator.

Spokesperson Videos and the Rise of AI-Driven UGC

One of the most impactful additions in MediaPET.ai 2.0 is its expanded spokesperson video capability, designed to scale user-generated content (UGC) and product demos.

With the new release, users can create spokesperson videos using:

• Uploaded photos of real people, or

• AI-generated characters

These spokespersons can deliver scripted content, testimonials, or explanations, helping brands produce UGC-style videos without relying on creators or production crews.

More notably, MediaPET now supports rich demo segments generated from a single product photo. That means marketers can showcase products “in use” visually—even when no video footage exists.

This capability taps directly into a growing demand in performance marketing: scalable, authentic-looking video that doesn’t require expensive shoots or influencer coordination.

Why the Chat Interface Matters More Than It Sounds

Chat interfaces are becoming common across AI tools, but MediaPET’s implementation is less about novelty and more about workflow orchestration.

Unlike chat systems that simply generate outputs, MediaPET’s conversational UI:

• Understands project context

• Maintains continuity across scenes

• Applies instructions globally

• Guides users through structured stages of creation

This design reduces the cognitive load of video production, especially for non-experts. Instead of learning video editing concepts, users focus on intent—what they want the video to communicate—while the system handles execution.

For MarTech teams, this lowers the barrier to entry for video creation and makes it easier to distribute production across marketing, growth, and even product teams.

Executive Perspective: Differentiation Through Usability

MediaPET CEO Dr. Duane Varan frames version 2.0 as a clear step-change rather than an incremental update.

“Version 2.0 is a game-changing release adding new features that further differentiate MediaPET,” said Varan. “The chat feature makes AI video content creation easier than ever and mobile friendly. And the spokesperson mode radically advances UGC creation—particularly with its demo features that allow you to truly highlight the product in use.”

That emphasis on differentiation is telling. The AI video space is increasingly crowded, with competitors racing to add generative features. MediaPET is instead leaning into usability, structure, and end-to-end flow as its core advantage.

Competitive Context: Where MediaPET Fits

The broader AI video market has split into a few camps:

• Text-to-video generators focused on novelty

• Avatar-based platforms centered on talking heads

• Ad-centric tools optimized for performance media

MediaPET.ai 2.0 attempts to unify these approaches under a single interface, while adding project-level intelligence that many rivals lack.

The ability to manage entire video projects conversationally—and to apply edits across scenes—positions MediaPET closer to a creative operating system than a point solution.

For teams juggling speed, cost, and consistency, that distinction matters.

Pricing and Availability

MediaPET.ai 2.0 is available now to all users, with plans starting at $24.99 per month. The new chat-enabled interface, creation modes, and spokesperson features are live as part of the rollout.

That entry price places MediaPET competitively within the AI video market, particularly given its mobile-first design and all-in-one positioning.

The Bigger Picture: Conversational Creative Tools

MediaPET’s update reflects a broader shift in MarTech and creative tooling: interfaces are becoming conversational, and complexity is moving behind the scenes.

As AI capabilities mature, differentiation is increasingly about how intuitively humans can direct those systems. MediaPET.ai 2.0 suggests that chat-based, project-aware interfaces may be the next step in making AI-generated video practical at scale—not just impressive in demos.

For brands and creators trying to keep up with the relentless demand for video, that evolution couldn’t come at a better time.

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With new vehicle sales growth expected to remain constrained in 2026, automotive dealers are being forced to rethink where growth really comes from. The answer, increasingly, lies not just on the lot—but in the conversations happening every day across sales, service, and BDC operations.

That’s the context behind Marchex’s appearance at the 2026 National Automobile Dealers Association (NADA) Show, where the company will exhibit at booth #7337N. Marchex plans to spotlight how its AI-powered conversation intelligence platform turns customer calls and interactions into measurable business outcomes at a time when every missed opportunity carries more weight.

As consumers hold onto vehicles longer and lean more heavily on service departments, dealers face rising call volumes, higher operational complexity, and increased pressure to convert conversations into revenue. Marchex is positioning its platform as a way to bring clarity—and accountability—to those interactions.

Why Conversations Matter More in 2026

The automotive market has shifted dramatically over the past few years. Inventory volatility, margin pressure, and changing consumer behavior have pushed dealers to rely less on pure vehicle sales volume and more on service, retention, and experience-driven differentiation.

Service departments, in particular, are emerging as growth engines. Longer vehicle ownership cycles mean more maintenance, more repairs, and more high-value RO opportunities—but only if dealerships can capture intent, respond effectively, and avoid breakdowns in communication.

That’s where Marchex believes conversation intelligence can make the biggest impact.

“By analyzing customer conversations, Marchex equips automotive retailers to maximize vehicle sales, generate higher-value repair orders, increase scheduled appointments, improve RO close rates, and improve agent performance across the customer journey,” said Troy Hartless, President and CRO of Marchex.

The emphasis isn’t just on listening—it’s on prescriptive insights that tell dealers exactly what to do next.

From Call Tracking to Operational Intelligence

Marchex has been embedded in the automotive industry for nearly two decades, supporting more than 5,000 U.S. dealerships and maintaining deep relationships with OEMs. Over that time, the role of call analytics has evolved significantly.

Basic call tracking answered one question: Did the call happen?

Conversation intelligence answers much harder ones:

• Was the customer intent identified correctly?

• Did the agent ask the right questions?

• Were service or sales opportunities missed?

• Did the conversation lead to a booked appointment or RO?

• Where did the process break down?

Marchex’s AI platform analyzes unstructured conversation data—calls, transcripts, and outcomes—and transforms it into actionable operational insights across sales, service, and marketing.

For dealerships, that means visibility into the moments that directly affect revenue, customer satisfaction, and long-term loyalty.

Engage for Sales: Finding and Acting on Buyer Intent

At NADA 2026, Marchex will highlight its Engage for Sales solution, designed to help dealerships capture and convert high-intent buyers more consistently.

Rather than treating every inbound lead equally, Engage for Sales uses AI-driven analysis to:

• Identify conversations that signal strong purchase intent

• Flag missed opportunities in real time

• Prioritize follow-up based on likelihood to convert

• Alert teams when leads require immediate attention

In an environment where lead volumes may soften but competition remains fierce, this kind of prioritization becomes critical. Dealers can focus resources where they matter most, rather than relying on gut instinct or incomplete CRM data.

The result is fewer dropped leads, faster response times, and better close rates—without adding headcount.

Engage for Service: Unlocking RO Growth Hiding in Plain Sight

If sales conversations are about intent, service conversations are about need—and those needs often go unmet.

Marchex’s Engage for Service solution is built to surface what customers are actually asking for, even when they don’t use precise terminology. By analyzing calls, the platform can detect:

• Unmet service needs

• Indicators of major repair opportunities

• Gaps between customer requests and agent responses

• Missed chances to upsell or schedule additional work

For service departments under pressure to increase RO values and throughput, these insights provide a roadmap for action. Managers can identify which calls deserve follow-up, which agents need coaching, and where processes are failing customers.

In a service-driven growth model, that intelligence can make the difference between flat performance and sustained profitability.

Previewing the Agent Performance Suite

One of the most forward-looking elements of Marchex’s NADA presence will be a preview of its upcoming Agent Performance Suite, designed to address a growing challenge in dealership operations: how humans and AI work together on the front lines.

As dealerships experiment with AI-powered agents, chatbots, and automated workflows, performance gaps can emerge. Some interactions improve. Others quietly degrade the customer experience.

Marchex’s new suite aims to make those dynamics visible.

The platform provides insights into:

• Where agents succeed or struggle in conversations

• How handoffs between AI and human agents perform

• Which behaviors correlate with bookings, ROs, and sales

• Where automation helps—or hurts—customer engagement

What sets the suite apart is its focus on coaching and improvement, not just reporting. Conversations are translated into personalized, skill-specific action plans, giving each agent clear guidance on how to perform better.

As labor challenges persist and agent effectiveness becomes a defining growth lever, this kind of performance intelligence is likely to gain traction.

Enterprise Visibility for Dealer Groups

For large dealer groups operating across multiple locations and brands, consistency is often as challenging as growth.

Marchex addresses that need with a unified enterprise view of marketing, sales, and service performance. Leadership teams can:

• Identify trends across rooftops

• Attribute campaigns to real outcomes

• Scale best practices across locations

• Maintain consistent standards and customer experience

That enterprise-wide perspective becomes increasingly valuable as groups look to optimize operations without sacrificing local nuance.

A Timely Bet on Conversation Intelligence

Marchex’s message at NADA 2026 aligns with broader trends in automotive retail and martech alike. As margins tighten and acquisition costs rise, maximizing existing customer interactions becomes one of the most efficient paths to growth.

Conversation intelligence sits at the intersection of CX, AI, and revenue operations—turning what was once dark, unstructured data into a strategic asset.

For dealers facing a year of modest sales growth but rising service demand, the implications are clear: the conversations you already have may be your biggest untapped opportunity.

What Dealers Can Expect at NADA 2026

Dealers attending NADA 2026 are invited to visit Marchex at booth #7337N, where they can:

• See live product demonstrations

• Receive real-time account reviews

• Explore how AI-driven insights apply to their operations

• Learn how conversation intelligence can drive growth in 2026 and beyond

As the automotive industry adapts to new realities, Marchex is betting that better conversations—and better insight into them—will separate high-performing dealerships from the rest.

Enterprises are pouring money into AI, but many are still building on shaky foundations. New research cited by Datalinx AI suggests 63% of enterprises admit they lack the data management practices required to support AI at scale. The result is a familiar pattern: ambitious AI roadmaps, expensive consulting engagements, and fragile data pipelines that break just when they’re needed most.

Datalinx AI believes that’s the real bottleneck—and investors are buying in.

The company, which positions itself as an AI data refinery, has raised $4.2 million in oversubscribed Seed funding to help enterprise marketing and data teams transform raw, fragmented data into AI- and application-ready assets. The round was led by High Alpha, with participation from Databricks Ventures and Aperiam, alongside a notable group of strategic angels with deep roots in enterprise software, advertising, and data infrastructure.

For a market obsessed with models, copilots, and generative interfaces, Datalinx is betting that data readiness—not model sophistication—is the real differentiator.

The Real Cost of “Broken” Enterprise Data

Most large organizations already run on modern cloud warehouses and analytics stacks. Yet AI initiatives still stall. According to Datalinx, the problem isn’t access to tools—it’s the complexity and brittleness of the data pipelines feeding them.

Enterprises often spend millions on systems integrators or divert highly paid engineers into what Datalinx bluntly describes as janitorial work: discovering datasets, cleaning them, validating schemas, resolving inconsistencies, and rebuilding pipelines when they inevitably fail.

Even then, the output is often opaque, hard to trust, and poorly documented. That fragility makes it nearly impossible to build predictive, production-grade AI systems—especially in marketing, advertising, and commercial analytics, where data is messy, fast-moving, and deeply contextual.

Datalinx is targeting that pain point head-on.

What Datalinx Actually Does

At its core, Datalinx aims to automate the most failure-prone parts of enterprise data work—from discovery to activation—using a combination of AI agents, domain-specific knowledge, and modular architecture.

The company describes its platform as the first “agentic data utility”, designed to:

• Discover relevant datasets across complex enterprise environments

• Clean and validate data automatically

• Apply commercial and marketing-specific ontologies

• Produce high-fidelity, outcome-ready data products

• Maintain transparency and predictability throughout the process

Rather than focusing on dashboards or surface-level analytics, Datalinx concentrates on data products—assets designed explicitly to drive downstream outcomes in AI models, marketing activation, and data science workflows.

The pitch is simple but ambitious: 10x faster time-to-value using a fraction of the resources typically required.

Built for AI, Not Just Analytics

One of the more subtle distinctions in Datalinx’s positioning is its emphasis on AI readiness, not just data cleanliness.

Traditional data engineering workflows often stop at “good enough” for reporting. AI systems, especially those driving personalization, prediction, or automated decision-making, demand far more consistency, context, and semantic clarity.

Enterprise teams frequently struggle with questions like:

• Which version of this data should the model use?

• How should fields be structured for predictive performance?

• What hidden assumptions exist in the data?

• How do we ensure changes don’t silently break downstream systems?

Datalinx addresses these challenges by embedding domain expertise and context graphing directly into the data refinement process. Instead of treating all data as interchangeable, it applies specialized knowledge—particularly around commercial, marketing, and advertising data—to guide how assets are shaped and activated.

This focus aligns with a growing realization in the market: AI systems fail less often because of bad models than because of misunderstood data.

A Team with Enterprise Scars

Datalinx is led by Joe Luchs, CEO and co-founder, a multi-time founder and former executive at Amazon and Oracle. That background shows in the company’s framing of the problem.

Rather than pitching AI as a silver bullet, Luchs emphasizes the operational realities enterprises face.

“You can’t reap the benefits of AI innovation on a foundation of broken data,” Luchs said. “We’re providing the first agentic data utility, designed to bring enterprises clean, actionable, and performant data products with minimal work and full transparency.”

The emphasis on transparency is notable. One of the persistent complaints about automated data tooling is that it replaces manual work with black boxes. Datalinx argues that enterprises need automation and visibility—especially when data underpins revenue-generating systems.

Early Traction and Enterprise Validation

While Datalinx is still early, it’s already working with large organizations and platform partners.

The company was one of just five startups selected for the inaugural Databricks AI Accelerator Cohort in 2025, a signal that its approach resonates with major data infrastructure players.

That partnership extends beyond branding. Datalinx integrates deeply with Databricks, aligning its data refinement capabilities with modern lakehouse architectures and AI workflows.

Andrew Ferguson, VP at Databricks Ventures, framed the value proposition clearly:

“The most successful AI strategies are built on a foundation of clean, high-quality data. By combining our infrastructure and AI tools with marketing and advertising data models, Datalinx creates seamless connections between CMOs and their data teams.”

That last point—bridging CMOs and data teams—is strategically important. Many AI initiatives stall not because of technology gaps, but because business and technical stakeholders lack a shared data language.

A Real-World Use Case: Sallie Mae

Datalinx has also landed early enterprise collaborators. Sallie Mae, for example, selected Datalinx as a co-development partner to accelerate data product development across its data and media initiatives.

According to Li Lin, VP of Engineering at Sallie Mae, the appeal was automation combined with accessibility.

By automating time-consuming pipeline work, enabling natural-language data exploration, and embedding domain expertise into data product design, Datalinx is already showing early promise in speeding up go-to-market execution.

That blend—technical depth paired with usability—is increasingly critical as enterprises try to scale AI beyond experimental teams.

Why Investors Are Paying Attention

The investor list behind Datalinx reads like a who’s who of enterprise software and ad tech experience.

Alongside High Alpha, Databricks Ventures, and Aperiam, the round includes:

• Frederic Kerrest, co-founder of Okta and 515 Ventures

• Ari Paparo, founder and CEO of Beeswax and Marketecture

• Arup Banerjee, founder and CEO of Windfall Data

These aren’t passive investors chasing AI hype cycles. Many have lived through multiple infrastructure shifts and understand how long-standing data problems resurface with each new wave of technology.

High Alpha partner Mike Langellier summed up the opportunity succinctly: Datalinx could become the essential utility layer for enterprises using data in AI, advertising, and marketing.

That framing positions Datalinx less as a point solution and more as foundational infrastructure—an ambitious but potentially defensible role if the company executes well.

The Bigger Trend: Data Readiness as the New Bottleneck

Datalinx’s timing is hard to ignore. As generative AI moves from experimentation to production, enterprises are discovering that data readiness is now the rate-limiting step.

Models can be swapped. APIs can be integrated. But messy, undocumented, fragmented data slows everything.

This has created a new category of tooling focused on:

• Semantic layers and ontologies

• Data observability and trust

• Automated data product generation

• Agentic workflows that reduce manual engineering

Datalinx sits squarely in that emerging space, with a specific focus on commercial and marketing data—areas where AI-driven personalization and automation promise outsized returns, but only if the data holds up.

What Comes Next

With $4.2 million in fresh capital, Datalinx plans to scale operations and meet growing demand from enterprise teams under pressure to deliver AI results faster.

The challenge ahead will be execution: proving that agentic automation can handle the nuance and edge cases that have historically required human judgment. If Datalinx can maintain trust while reducing effort, it could carve out a durable position in the enterprise AI stack.

For now, the message is clear: AI innovation doesn’t fail because of a lack of ambition—it fails because the data isn’t ready. Datalinx is betting that fixing that problem is one of the biggest opportunities of the AI era.

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Artificial intelligence may be transforming customer service dashboards and call routing systems, but when things go wrong in the real world—no heat, a flooded basement, a medical concern—most consumers still want to talk to a person. And they want that option immediately.

That’s the central takeaway from ServiceForge’s newly released research report, “Keep Service Human,” which digs into how consumers actually feel about AI-driven customer service. The answer, based on original survey data, is blunt: speed alone doesn’t equal satisfaction, and automation can cost businesses real revenue when it replaces human interaction too aggressively.

For home services, skilled trades, and other high-stakes service industries, the findings land as both a warning and a strategic opportunity.

Consumers Are Clear: Don’t Replace Humans When It Matters

According to the report, 85% of consumers prefer speaking with a real human when contacting a local service business. Even more striking, one in three respondents said they would hang up immediately if they reached an AI bot.

That’s not a mild preference—it’s active resistance.

For service-driven businesses that rely on inbound calls to book jobs, that behavior translates directly into missed appointments, lost revenue, and damaged brand perception. An unanswered call is one thing; a call that ends in frustration is worse.

ServiceForge frames this as a growing disconnect between how companies deploy AI and how customers experience it.

Speed Isn’t the Priority—Resolution Is

Much of the hype around AI in customer service focuses on faster response times, lower costs, and always-on availability. But the data suggests consumers are optimizing for something else entirely: getting the problem solved.

Key findings from the Keep Service Human report include:

• 73% say resolution matters more than how fast the call is answered

• 54% describe AI-powered customer service as frustrating

• 83% have actively requested to speak with a human instead of AI

These numbers challenge a common assumption in CX strategy—that faster equals better. For essential services, customers appear willing to wait a bit longer if it means empathy, clarity, and confidence that someone understands the situation.

Why This Hits Harder for Local and Essential Services

ServiceForge focuses on software for skilled trades and home service businesses, and that context is crucial. When a customer calls a plumber, HVAC technician, or electrician, the situation is often urgent, emotional, or disruptive to daily life.

In those moments, AI’s strengths—efficiency, consistency, scale—don’t fully match the customer’s needs.

“When a customer is calling because their heat is out or their basement is flooding, they want things AI can’t deliver: empathy, understanding and reassurance,” said Jane Blanchard, head of brand and marketing at ServiceForge.

That insight aligns with broader CX research showing that emotional intelligence and trust play an outsized role in service satisfaction, particularly in crisis or high-cost scenarios.

This Isn’t Just a Home Services Problem

While the report centers on skilled trades, the implications stretch much further.

Respondents expressed similar discomfort with AI-led customer service in healthcare, real estate, legal services, and other relationship-driven industries. In other words, this isn’t about pipes and furnaces—it’s about contexts where decisions feel personal, urgent, or risky.

That distinction matters as AI adoption accelerates across industries. The data suggests that blanket automation strategies may work for transactional interactions, but fall apart when customers need guidance, reassurance, or nuanced explanations.

The AI Paradox: Efficiency vs. Experience

None of this means AI has no place in customer service. ServiceForge is careful to draw that line clearly.

The report acknowledges that AI can be highly effective in back-office automation, scheduling, data entry, and internal efficiency. Used correctly, it can free up human agents to focus on the conversations that actually require judgment and empathy.

The problem arises when AI becomes the front door instead of the support system.

For businesses chasing cost savings, it’s tempting to push customers toward bots by default. But the data suggests that approach may erode trust—and ultimately revenue—especially in competitive local markets where reputation and reviews carry outsized weight.

Human Support as a Competitive Advantage

One of the more strategic insights from the report is how human-led service correlates with brand outcomes.

ServiceForge found that customers are significantly more likely to:

• Leave positive online reviews

• Express trust in the company

• Recommend the business to others

In crowded local markets, those signals can be decisive. Star ratings, word-of-mouth, and perceived responsiveness often matter more than price alone.

“For home service businesses, the human touch isn’t just nice to have; it can be a major competitive advantage,” Blanchard noted.

That framing positions human customer service not as a cost center, but as a differentiator—something AI-first strategies risk undervaluing.

What This Means for CX and MarTech Leaders

The Keep Service Human report lands at a moment when AI is being rapidly deployed across customer engagement stacks, often under pressure to “do more with less.” For MarTech and CX leaders, the findings suggest a need for more nuanced design choices.

Key implications include:

• Hybrid models outperform AI-only approaches in high-stakes interactions

• Customer intent should dictate automation levels, not cost targets alone

• Human availability needs to be visible and accessible, not hidden behind bots

• Trust and empathy remain core CX metrics, even in AI-powered environments

In other words, AI works best when it amplifies humans—not when it replaces them outright.

A Reality Check for AI-First Customer Service

As generative AI and conversational bots continue to improve, the temptation will be to assume customer resistance is temporary. ServiceForge’s data suggests otherwise.

Consumers aren’t rejecting AI because it’s new—they’re rejecting it because, in certain moments, it feels insufficient. And no amount of speed can compensate for the absence of understanding when something important is on the line.

The message from the research is straightforward: keep service human, especially when it counts.

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Hightouch’s elevation to Leader in the 2025 Gartner Magic Quadrant for Customer Data Platforms (CDPs) is more than a badge of honor for a fast-growing data startup. It’s a marker of where the CDP market is heading—and how quickly the old rules are being rewritten.

This is Hightouch’s first-ever appearance in the Magic Quadrant, and it didn’t just sneak in. Gartner positioned the company squarely among Leaders, citing its Completeness of Vision and Ability to Execute. For a category long dominated by monolithic, all-in-one platforms, the recognition validates a different approach: warehouse-native, composable customer data activation.

In plain terms, Gartner is signaling that CDPs no longer need to sit on top of a data stack, duplicating information and slowing teams down. Instead, they can live inside the modern data warehouse—and that architectural choice is becoming a competitive advantage.

Why This Matters: The CDP Market Is Being Rewritten

For most of the last decade, CDPs followed a familiar pattern. Vendors promised a single system of record for customer data, ingesting information from dozens of sources, transforming it internally, and then pushing it out to marketing and advertising tools. That model worked—until it didn’t.

As cloud data warehouses like Snowflake, Databricks, and BigQuery became the real source of truth for enterprises, cracks started to show:

• Data duplication drove up costs

• Sync delays made “real-time” personalization aspirational at best

• Engineering teams became bottlenecks for marketing and growth teams

• Governance and security became harder, not easier

Hightouch emerged with a contrarian idea: don’t move the data at all.

Instead of copying customer data into yet another platform, Hightouch activates it directly from the warehouse, using the same governed, analytics-ready data that already powers BI and machine learning. Marketing, sales, and customer success teams get fresh, reliable data—without asking engineering to rebuild pipelines or manage new silos.

Gartner’s recognition suggests that this model is no longer fringe. It’s becoming mainstream.

What Sets Hightouch Apart

Hightouch describes itself as a warehouse-native customer data platform, but the distinction goes beyond buzzwords.

At its core, the platform connects cloud data warehouses directly to downstream tools—think ad networks, marketing automation platforms, CRMs, customer support systems, and even connected TV and retail media networks.

Key differentiators include:

No Data Replication
Hightouch doesn’t require companies to copy customer data into a proprietary store. That reduces infrastructure costs and eliminates sync lag—two pain points that have plagued traditional CDPs.

Built for Modern Data Stacks
Rather than replacing tools like Snowflake or Databricks, Hightouch assumes they’re already central. It layers activation, orchestration, and governance on top of existing investments.

Cross-Team Usability
Marketing, growth, and lifecycle teams can build audiences and launch campaigns without SQL-heavy workflows, while data teams retain control over schemas, permissions, and data quality.

AI-Powered Activation
Hightouch is leaning into AI to help teams optimize performance across channels—automating decisions around targeting, timing, and personalization based on warehouse data.

This focus aligns neatly with what Gartner and other analysts have been tracking: a move away from monolithic CDPs toward composable architectures that integrate cleanly into enterprise data ecosystems.

A First-Time Leader—And That’s the Story

What makes this placement particularly notable is that 2025 marks Hightouch’s first inclusion in the Magic Quadrant. Vendors often spend years moving from Niche Player to Visionary before earning a Leader spot.

That jump reflects both execution speed and market timing.

According to Hightouch, Gartner evaluated vendors on their ability to deliver against current CDP needs while articulating a credible vision for where the market is going. In a category undergoing architectural change, vision matters as much as feature checklists.

Hightouch’s leadership team sees the recognition as confirmation that the CDP market is aligning with ideas the company has pushed since its early days.

“Organizations want to power personalized marketing with their complete data, move faster without data replication, and use AI to optimize performance across channels continuously,” said Tejas Manohar, co-founder and co-CEO of Hightouch. “That combination has been core to Hightouch from the beginning.”

Translation: the market finally caught up.

How Hightouch Compares to Traditional CDPs

To understand the impact of Gartner’s positioning, it helps to look at what Hightouch is not.

Traditional CDPs often bundle identity resolution, storage, analytics, and activation into a single system. While convenient on paper, this approach can clash with modern enterprise realities, where data teams already rely on best-of-breed tools.

Hightouch flips that model:

This difference is especially relevant as enterprises scale. When billions of rows of customer data are involved, duplication isn’t just inefficient—it’s expensive and risky.

Industry Implications: CDPs Meet the Composable Era

Hightouch’s Leader placement also reflects a broader trend across the martech landscape: composability.

Just as headless CMSs reshaped content management and modular data stacks redefined analytics, CDPs are being unbundled. Enterprises increasingly prefer tools that do one thing well and integrate cleanly, rather than platforms that try to do everything.

Gartner’s Magic Quadrant plays a powerful role here. For enterprise buyers, it’s often a filtering mechanism long before demos or RFPs begin. Seeing a warehouse-native vendor among Leaders sends a clear message: this architecture is no longer experimental.

Expect ripple effects:

• Increased scrutiny of data duplication practices

• More CDP vendors adopting warehouse-first roadmaps

• Greater alignment between marketing and data teams

• AI-driven activation becoming table stakes, not optional

The Growing Role of AI in Activation

One subtle but important aspect of Hightouch’s positioning is its emphasis on AI-powered activation.

While many CDPs talk about AI in abstract terms—predictions, scores, recommendations—Hightouch is focused on applying AI directly to campaign execution. That includes optimizing audience definitions, channel selection, and performance over time.

This matters because AI models are only as good as the data feeding them. By working directly on warehouse data, Hightouch reduces the risk of stale or incomplete inputs—a common issue when data is copied across systems.

As advertising, retail media, and connected TV ecosystems become more fragmented, AI-assisted orchestration is shifting from “nice to have” to essential.

What Gartner’s Magic Quadrant Signals to Buyers

For enterprise technology buyers, the Magic Quadrant remains a shorthand for market maturity and vendor credibility. Gartner combines analyst research with validated customer feedback, offering a view that goes beyond marketing claims.

Hightouch’s placement suggests that:

• Warehouse-native CDPs are viable for large enterprises

• The market rewards execution speed and architectural clarity

• Buyers should question whether they still need standalone CDP storage

It doesn’t mean traditional CDPs are obsolete overnight—but it does suggest their dominance is no longer guaranteed.

The Competitive Landscape Is Heating Up

Hightouch isn’t alone in pushing the warehouse-native narrative, but its Leader status gives it a visibility boost at a critical moment.

As legacy CDP vendors modernize their stacks and new entrants emerge with composable-first designs, differentiation will come down to usability, governance, and performance at scale. Gartner’s evaluation implies that Hightouch is executing well on all three—at least for now.

The real test will be how quickly competitors adapt, and whether enterprises are willing to rethink long-held assumptions about where customer data “should” live.

Bottom Line

Hightouch’s debut as a Leader in the 2025 Gartner Magic Quadrant for Customer Data Platforms is a milestone not just for the company, but for the CDP category itself.

It reinforces a growing consensus: the future of customer data activation lives in the warehouse, not in yet another silo. For marketing, growth, and data leaders navigating increasingly complex stacks, that shift could simplify operations—and unlock faster, more reliable personalization at scale.

Whether this marks the beginning of the end for traditional CDPs or simply a new phase of competition, one thing is clear: the center of gravity in customer data is moving, and Gartner just confirmed it.

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The Drupal Association today unveiled Drupal CMS 2.0, marking a major evolution in the 25-year-old open-source platform. Once developer-first, Drupal now empowers marketers and content teams to build enterprise-grade websites visually, dramatically speeding time-to-launch while maintaining the platform’s open-source flexibility.

At the heart of the release is Drupal Canvas, a drag-and-drop visual page builder that combines power with simplicity. Marketing teams can now launch fully branded, professional sites in days—not weeks—while developers retain full control over scalability, governance, and performance. Complete site installs can be achieved in under three minutes using pre-built templates and optional AI tools.

“There has been a perception that ‘Drupal is hard,’ and CMS 2.0 debunks that notion,” said Dries Buytaert, Drupal founder and project lead. “Marketers and site builders can now create professional, on-brand sites without relying on developers or being locked into platforms that don’t adapt. CMS 2.0 puts enterprise-quality publishing tools directly in their hands.”

Key Features of Drupal CMS 2.0

• Visual-First Building: Drag-and-drop components, live preview, and integrated content management make site building intuitive—no Drupal expertise required.

• Pre-Built Site Templates: Complete starting points for specific use cases, including themes, content, layouts, and design systems. Initial installs are ready in under three minutes, with additional templates planned.

• Optional AI Tools: AI-assisted page creation from text prompts, admin chatbot guidance, and AI-generated alt text for accessibility—all with governance and human oversight.

• One-Click Integrations: Simplified automation for Mailchimp, Google Analytics, Google Tag Manager, and AI—no technical setup required.

• Drupal Core 11.3 Foundation: Delivers the largest performance improvements in a decade, scaling from startups to enterprise-level traffic.

Designed for mid-size and enterprise organizations, digital agencies, and developers, Drupal CMS 2.0 blends ease of use with robust capabilities, ensuring teams with limited technical resources can still deliver innovative digital experiences.

“This release represents a major step forward for the Drupal community,” said Tim Doyle, CEO of the Drupal Association. “CMS 2.0 makes Drupal more accessible and flexible, giving marketers the tools to manage sites with the robustness enterprises expect, all while remaining true to our open-source roots.”

By combining visual building, AI, templates, and one-click integrations on a stable Drupal Core foundation, Drupal CMS 2.0 aims to democratize enterprise web publishing, making the platform as approachable for marketing teams as it has historically been for developers.

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Collective OS officially launched today, introducing an AI-driven platform designed to help marketing agencies and consulting firms source trusted partners, fill service gaps, and capture new revenue opportunities.

As client expectations shift toward full-service solutions, small and mid-sized agencies face pressure to do more with fewer resources. Collective OS addresses this challenge by making inter-agency collaboration scalable, seamless, and efficient, enabling firms to grow without increasing headcount or overhead costs.

“In a world where agencies are leaner, distributed, and increasingly independent, collaborations are the next evolution for growth,” said Jason Flack, CEO and co-founder of Collective OS. “The era of the isolated agency is over. We built Collective OS to replace cold outreach with warm opportunity and turn partnerships into a repeatable growth engine.”

AI-Powered Collaboration for Agencies

Collective OS uses advanced algorithms to analyze each firm’s structure, services, skills, past work, and growth goals, connecting them with complementary partners. The platform helps agencies act more full-service, discover new opportunities, and streamline collaboration.

Key features include:

• Detailed member profiles and partnership goal definitions

• Algorithm-driven partner matching for complementary capabilities

• Tools to explore, connect, co-develop proposals, manage contracts, and process payments

• A vetted, application-based membership model to ensure trust and quality

“The market is shifting,” Flack added. “Clients want boutique agility with global capabilities. Collective OS bridges that gap—operationalizing trust so independent firms can compete with the largest holding companies without merging.”

From Beta to Revenue Generation

Following its public beta, Collective OS secured $2.5 million in funding from investors including Early Light Capital, Team Ignite Ventures, and The Band. During the beta, the platform onboarded nearly 1,000 agencies and facilitated more than $5 million in deals between partners.

“Partnerships have long been treated as a buzzword rather than a strategy,” said Freddie Laker, Co-founder and Chief Growth Officer. “We don’t just make introductions. We remove friction from growth, helping agencies activate opportunities and generate predictable revenue. Collective OS turns trust into a scalable asset.”

Collective OS is now available for creative, marketing, and advertising agencies, as well as consulting firms looking to modernize how they find and leverage business partnerships. More information is available at joincollectiveos.com.

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Onapsis, the global leader in SAP cybersecurity and compliance, has strengthened its go-to-market leadership team with three key executive appointments: Mark Francetic as Global Head of Partners & Alliances, John LoVerme as Head of North America Sales, and Nadine Rahman as Head of International Go-To-Market.

The hires come amid growing enterprise demand for proactive SAP security, fueled by an unprecedented wave of SAP zero-day vulnerabilities and heightened risk exposure across manufacturing and other critical industries in 2025.

“After a year that fundamentally reshaped how organizations view the risk of enterprise applications, it’s clear that SAP security is no longer optional—it’s foundational,” said Mariano Nuñez, CEO and co-founder of Onapsis. “With Mark, John, and Nadine joining our team, we’re doubling down on global execution, partner-led scale, and helping more customers protect the systems that run their businesses.”

Accelerating Partner-Led Growth

Mark Francetic, as Global Head of Partners & Alliances, will oversee Onapsis’ global partner strategy, including SAP, global system integrators (GSIs), value-added resellers, and strategic technology partners. He brings 25+ years of experience building alliances across cloud, identity, and cybersecurity markets.

Mark previously served as SVP of Global Alliances at Saviynt, where he led the company’s GSI strategy and drove large partner-influenced deals with Accenture, Deloitte, and PwC. Earlier, he guided global alliances at ForgeRock during its path to IPO. At Onapsis, Mark will accelerate partner-led growth and joint go-to-market execution, positioning Onapsis as a preferred partner for securing digital transformations.

Expanding Regional and International Sales

John LoVerme, Head of North America Sales, brings deep experience scaling security software businesses. He began his career at Rapid7, leading enterprise sales through an IPO, and spent over a decade at Prevalent, guiding the company’s global sales and its successful acquisition by Mitratech in 2024. John will focus on new logo acquisition and partner-driven revenue growth in North America.

Nadine Rahman, Head of International Go-To-Market, will lead global field sales, go-to-market strategy, and ecosystem development. With 20+ years of international leadership experience across over 30 countries—including CXO roles in industrial automation, SAP, and the SAP ecosystem—Nadine brings a foundation as an SAP project consultant and ABAP developer. She will focus on scaling international revenue, expanding customer acquisition, and driving adoption of the Onapsis Platform.

“Together, we will leverage our rich partner ecosystem to accelerate growth and deliver unparalleled value to our customers,” said Paul “PK” Kleinschnitz, Chief Revenue Officer at Onapsis.

Momentum in SAP Security and Ecosystem Partnerships

These executive additions align with Onapsis’ continued investment in strategic ecosystem integrations, including collaborations with CrowdStrike and Microsoft, enhancing its ability to detect, respond to, and recover from advanced SAP-focused cyber threats.

As enterprises grapple with increasing SAP vulnerabilities and regulatory scrutiny, Onapsis is positioning itself as the go-to platform for proactive security, compliance, and risk management, supported by a strengthened global leadership team and partner-driven growth strategy.

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