For all the hype around AI in go-to-market teams, much of today’s “AI” still amounts to smarter chat interfaces, better copy generation, or faster dashboards. FlashLabs is aiming higher—and riskier.

The company has launched FlashLabs SuperAgent, positioning it not as an assistant or copilot, but as a fully hosted, enterprise-secure AI Revenue Worker that operates 24/7 across sales, marketing, and revenue operations. The pitch is blunt: SuperAgent doesn’t just suggest actions. It executes them.

In a market increasingly saturated with AI copilots, FlashLabs is betting that the next phase of enterprise AI is less about conversation—and more about autonomous work.

From conversational AI to execution engines

SuperAgent is designed to handle revenue workflows end to end, operating with persistent memory, business context, and multi-step autonomy. Rather than waiting for prompts inside a UI, it continuously runs in the background, monitoring systems, data, and performance—even when teams are offline.

According to FlashLabs, SuperAgent can:

• Automate email, calendar, CRM, invoicing, and RevOps workflows

• Execute browser-level actions across the web

• Identify and qualify customers by scanning multiple data signals

• Generate decks, proposals, images, videos, research, and GTM plans

• Manage pipeline hygiene, forecasting, deal QA, and follow-ups

• Integrate with thousands of systems, including CRMs, ERP, finance tools, email platforms, and social networks

• Monitor business systems continuously for changes, risks, and opportunities

This positions SuperAgent closer to an autonomous digital operator than a traditional AI tool—more RPA meets agentic AI than chatbot meets analytics.

Messaging becomes the command line

One of the more unconventional aspects of SuperAgent is how it’s controlled.

Instead of requiring users to log into a proprietary interface, FlashLabs turns messaging platforms into the control plane. Teams can operate SuperAgent through:

• Telegram

• iMessage

• SMS

Additional channels are planned, but the idea is already clear: a single message can trigger complex, multi-system workflows.

In practice, that means a sales leader could request pipeline cleanup, forecasting updates, or deal follow-ups via a simple message—while SuperAgent handles the orchestration behind the scenes. It’s a sharp contrast to the dashboards and workflow builders that dominate today’s RevOps stacks.

Built for enterprises, not experiments

FlashLabs is also leaning hard into enterprise-readiness, an area where many agentic AI projects stall.

SuperAgent is fully hosted and production-ready, requiring:

• No hardware deployment

• No infrastructure management

• No exposed credentials

• No complex authentication flows

By abstracting away infrastructure and security concerns, FlashLabs is clearly targeting organizations that want outcomes without adding operational burden—or risk—to already complex tech stacks.

This matters because autonomous AI raises uncomfortable questions for security, compliance, and governance. FlashLabs’ approach suggests it wants to remove friction not just from usage, but from approval.

AI as a revenue workforce

The most provocative framing around SuperAgent is how FlashLabs describes its role: not software, but labor.

Early adopters report SuperAgent autonomously progressing deals, updating pipelines, managing follow-ups, and delivering revenue insights around the clock. In effect, it behaves like a tireless revenue operations employee—one that doesn’t log off, forget tasks, or drop handoffs between systems.

That framing aligns with a broader industry shift. As AI agents mature, vendors are increasingly positioning them as digital workers rather than productivity tools. Microsoft, Salesforce, and a wave of startups are racing to define this category—but most still rely on human-in-the-loop execution.

FlashLabs is attempting to push past that boundary.

Why this matters for MarTech and RevOps

Revenue teams are under pressure from both sides: rising expectations for personalization and speed, and shrinking tolerance for headcount growth. At the same time, RevOps stacks have become notoriously fragmented, with automation spread across CRMs, sales engagement tools, finance systems, and analytics platforms.

SuperAgent’s promise is to sit above that stack, coordinating actions across systems without requiring teams to stitch workflows together manually.

If it works as advertised, this could signal a shift away from tool-centric RevOps toward agent-centric execution layers—where AI handles the operational glue and humans focus on strategy, relationships, and judgment.

The competitive landscape

SuperAgent enters a crowded but unsettled space. Established players like Salesforce and HubSpot are embedding AI deeper into their platforms, while startups push agentic automation, browser control, and multi-step reasoning.

What differentiates FlashLabs is its insistence on full autonomy and messaging-first control, combined with enterprise hosting and security. That combination may appeal to teams frustrated by AI tools that still require heavy configuration and constant supervision.

The risk, of course, is trust. Autonomous execution demands confidence that the AI understands context, priorities, and boundaries—especially when revenue, compliance, and customer relationships are on the line.

The bigger picture

FlashLabs SuperAgent reflects a growing belief in B2B tech: the future of AI isn’t more suggestions—it’s more execution.

As agentic systems mature, the line between software and workforce continues to blur. Whether SuperAgent becomes a blueprint or a cautionary tale will depend on how well it balances autonomy with control.

Either way, it’s a clear signal that the era of “AI that helps” is giving way to AI that works.

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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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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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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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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.

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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Rural hospitals are under strain from nearly every direction: declining patient volumes, workforce shortages, tighter reimbursement, and growing competition from large regional health systems with far deeper marketing budgets. Yet visibility, trust, and service-line growth have never been more critical to survival.

Against that backdrop, 121G Marketing (121GM) is making a clear bid to become the marketing partner of record for rural healthcare systems, and it’s backing up that ambition with data.

The company has released a new case study detailing its partnership with Russell Medical, a rural hospital that overhauled its marketing performance and community reach in just seven months—without building a costly internal marketing department or relying on fragmented agency vendors.

The results point to a larger shift underway in rural healthcare marketing: from ad hoc tactics to embedded, accountable, performance-driven partnerships.

The Rural Hospital Marketing Gap

Marketing has long been a weak spot for rural hospitals—not because leadership doesn’t value it, but because the economics rarely work in their favor.

Many rural systems face a familiar set of constraints:

• Limited or nonexistent in-house marketing teams

• Reliance on small vendors handling isolated tasks (web, email, social)

• Inconsistent branding across service lines

• Minimal access to real-time performance data

• Pressure to justify every dollar spent

At the same time, patient expectations have evolved. Consumers now research providers online, expect clear digital communication, and increasingly choose care based on trust, convenience, and perceived expertise—not just proximity.

This gap between expectations and capabilities is where 121G Marketing is positioning itself.

A Different Agency Model—Built for Rural Healthcare

121GM describes itself not as a traditional agency, but as an embedded marketing partner designed specifically for rural hospitals. Rather than delivering isolated campaigns, the firm operates as an extension of the hospital—handling strategy, execution, analytics, and service-line growth under one roof.

“Rural hospitals don’t need cookie-cutter agencies or fragmented vendors,” said Alex Hoskins, Managing Partner at 121G Marketing. “They need a true partner—one that understands their communities, operates with transparency, and delivers measurable results.”

That philosophy guided the firm’s engagement with Russell Medical, which had reached a turning point in its marketing maturity.

Russell Medical’s Challenge: Visibility Without Overhead

Like many rural hospitals, Russell Medical was navigating growing competition from larger systems with more sophisticated marketing operations. Its leadership team recognized the need for a more strategic, data-driven approach—but faced two hard realities:

1. Building a full internal marketing department was cost-prohibitive

2. Piecing together vendors had already led to inconsistent messaging and limited accountability

What Russell Medical needed wasn’t more tools—it was a cohesive marketing function aligned with clinical, operational, and community priorities.

That’s where 121GM stepped in.

From Fragmented Efforts to an Embedded Marketing Function

Rather than acting as an external agency, 121GM assumed the role of Russell Medical’s marketing department of record.

The engagement spanned the full marketing lifecycle, including:

• Developing a unified brand and messaging framework across the hospital

• Launching integrated digital and community-focused campaigns

• Implementing real-time dashboards to track performance and ROI

• Aligning marketing priorities directly with service-line and operational goals

• Reducing vendor overlap and unnecessary spend

This approach replaced fragmented execution with a single, accountable team—while preserving institutional knowledge and community context.

The emphasis wasn’t just on visibility, but on sustainable, measurable growth.

Measurable Gains in Just Seven Months

Between April 1 and October 31, 2025, Russell Medical recorded significant improvements across digital reach, engagement, and operational efficiency.

Key outcomes included:

• 1.5 million Facebook impressions, reaching more than 204,700 users

• 238,757 email sends with a 39% open rate, exceeding healthcare benchmarks

• 78,000 active website users and 79,000 new users, with organic search as the top traffic driver

• 34% growth in LinkedIn followers, supporting recruitment and provider visibility

• Approximately $40,000 in vendor cost savings through strategic optimization

• The hospital’s first-ever unified brand and messaging system

These weren’t vanity metrics. Increased engagement translated into stronger awareness of specialty services, more consistent community communication, and a modernized marketing infrastructure that Russell Medical could sustain.

Crucially, all of this happened without adding internal headcount.

Why the Results Matter Beyond One Hospital

While the case study focuses on Russell Medical, its implications extend far beyond a single organization.

Rural hospitals nationwide face similar pressures:

• Declining inpatient volumes

• Greater reliance on outpatient and specialty services

• Heightened competition for clinicians and staff

• Increased scrutiny of marketing spend and ROI

The Russell Medical engagement suggests that enterprise-level marketing capability doesn’t have to come with enterprise-level costs—if the model is built for rural realities.

121GM’s approach replaces the traditional agency-client dynamic with something closer to operational integration, where marketing decisions are tied directly to clinical and organizational priorities.

A Repeatable Playbook for Rural Healthcare

121G Marketing is explicit about its ambitions. The firm isn’t positioning Russell Medical as a one-off success story, but as proof of a repeatable model.

“Our success with Russell Medical isn’t an exception—it’s a repeatable model,” Hoskins said. “We’ve built a playbook specifically for rural hospitals, allowing them to gain enterprise-level marketing capabilities without enterprise-level costs.”

That playbook is grounded in a few core principles:

• 100% in-house execution, avoiding vendor sprawl

• Senior-led strategy, rather than junior account handoffs

• Custom engagements, not templated packages

• Performance transparency, with real-time reporting

In an industry where trust is paramount, that level of accountability is increasingly attractive.

Marketing as a Trust-Building Function

One of the less discussed—but most important—outcomes of Russell Medical’s transformation was its impact on community trust.

For rural hospitals, marketing isn’t just about growth. It’s about reinforcing the hospital’s role as a community anchor. Consistent messaging, clear service-line communication, and accessible digital channels all contribute to patient confidence.

By unifying Russell Medical’s brand and improving how it communicates across platforms, 121GM helped modernize the hospital’s presence without alienating its core audience.

That balance—modernization without corporatization—is a delicate one for rural systems, and a key differentiator for partners that understand local dynamics.

The Competitive Landscape Is Shifting

As healthcare marketing becomes more data-driven and consumer-centric, rural hospitals risk falling further behind if they rely on outdated or under-resourced approaches.

Large health systems continue to invest heavily in digital acquisition, brand building, and recruitment marketing. Without comparable capabilities, rural providers may struggle to compete for patients, clinicians, and partnerships.

Embedded marketing models like 121GM’s offer a potential path forward—one that scales expertise without scaling overhead.

What This Signals for Rural Healthcare Leaders

For hospital executives and boards, the Russell Medical case study highlights several strategic takeaways:

• Marketing performance can improve rapidly with the right structure

• Unified strategy beats fragmented execution

• Data transparency is essential for trust and sustainability

• Outsourcing doesn’t have to mean losing control

As margins tighten and expectations rise, rural hospitals will increasingly be forced to rethink how marketing fits into their broader growth and sustainability strategies.

Bottom Line

121G Marketing’s partnership with Russell Medical underscores a growing realization in rural healthcare: marketing is no longer optional, and it can’t be an afterthought.

By acting as an embedded, accountable partner rather than a traditional agency, 121GM helped a rural hospital achieve measurable gains in visibility, engagement, and efficiency—without the burden of building an internal department.

For rural systems searching for a viable path to growth in an increasingly competitive healthcare landscape, the model offers a compelling alternative—and one that may soon become harder to ignore.

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InMarket is making a clear statement about where it believes the advertising industry is headed—and how it plans to compete there. The real-time marketing and measurement company has appointed Natalie Bastian as Chief Marketing Officer, tasking her with sharpening InMarket’s market position as advertisers push harder for measurable outcomes over traditional reach-based metrics.

The hire comes at a pivotal moment for InMarket, which has been doubling down on AI-powered measurement, real-world outcomes, and full-funnel attribution. With brands under increasing pressure to justify every dollar of media spend, InMarket is betting that strong product innovation must be matched with equally strong storytelling, market education, and go-to-market execution.

Bastian will lead InMarket’s global marketing organization, overseeing public relations, product marketing, brand, content, events, creative, and inside sales—effectively owning how the company shows up across the broader marketing, commerce, and data ecosystem.

A Strategic Hire for an Outcomes-Driven Market

InMarket has long positioned itself around real-world measurement—connecting digital media exposure to physical-world outcomes such as store visits, conversions, and incremental lift. That value proposition is gaining urgency as marketers grapple with signal loss, fragmented identity, and rising scrutiny from finance teams.

CEO Todd Morris framed Bastian’s appointment as a growth accelerator rather than a brand refresh.

“Natalie brings a proven track record of driving business growth at scale that will build on our double-digit growth trajectory and our recognition as one of the fastest-growing technology companies in North America,” Morris said. “As InMarket continues its mission to help move advertising from impressions to outcomes, Natalie’s leadership will drive the expansion of our market presence and strengthen the value we deliver to our clients.”

The emphasis on outcomes is deliberate. As cookies fade and probabilistic attribution becomes less reliable, platforms that can tie media exposure to verifiable business results are moving from “nice to have” to essential.

Why Natalie Bastian—and Why Now

Bastian’s background reads like a roadmap through modern ad-supported media and platform growth.

Most recently, she served as Global CMO at Teads, where she played a central role during the company’s nearly $1 billion acquisition by Outbrain. During that transition, she helped reposition Teads from a premium video player into a global omnichannel platform, aligning brand, product narrative, and sales enablement under a single strategy.

Before Teads, Bastian was SVP, Head of Marketing at Tubi, where she helped scale the free streaming service during a period of rapid growth that culminated in its acquisition by FOX. There, she focused on integrated marketing and sales strategies that expanded brand awareness while unlocking new revenue streams—experience that translates directly to InMarket’s enterprise ambitions.

Her earlier roles at Roku, DISH Media, and A&E Networks further anchor her expertise at the intersection of media, advertising, and data—an increasingly crowded and competitive space.

For InMarket, that mix matters. The company isn’t just selling technology; it’s selling a new way of thinking about media performance.

From Product Innovation to Market Education

In the past year, InMarket has rolled out a series of major product updates aimed squarely at enterprise advertisers:

• Predictive Moments, designed to identify high-intent consumer behavior in real time

• Unified Measurement, bringing together media exposure and real-world outcomes

• Lift Conversion Index for CPG, focused on incremental impact in retail and commerce

These launches signal a broader strategy: positioning InMarket as a platform that doesn’t just report what happened, but helps advertisers predict, optimize, and prove impact across the funnel.

Bastian’s role will be to translate that technical sophistication into clear, compelling narratives that resonate with CMOs, performance marketers, and analytics leaders alike.

That includes evolving InMarket’s go-to-market strategy, sharpening its brand voice, and increasing visibility with brands, agencies, and ecosystem partners who are reevaluating their measurement stacks.

Marketing Leadership as a Growth Lever

Unlike many CMO appointments that focus narrowly on demand generation, InMarket’s description of the role underscores its strategic weight.

Bastian will be responsible for:

• Refining InMarket’s brand and product narrative

• Driving go-to-market and growth strategies

• Elevating InMarket’s relevance across marketing, commerce, and data ecosystems

• Aligning marketing more tightly with sales and enterprise value creation

That scope reflects a broader trend in adtech and martech: marketing leaders are increasingly expected to shape category definition, not just pipeline.

As the line between data platforms, measurement providers, and media execution continues to blur, companies that articulate a clear point of view tend to win mindshare—and budgets.

Industry Context: Measurement Is the Battleground

Bastian’s appointment comes as advertisers are reassessing long-held assumptions about measurement.

With privacy changes limiting deterministic attribution and walled gardens controlling their own metrics, marketers are searching for independent, outcome-based measurement frameworks that can withstand scrutiny.

InMarket’s approach—grounded in real-time location intelligence, AI-powered insights, and closed-loop measurement—positions it at the center of that shift. But standing out in a crowded field requires more than technical credibility; it requires trust, clarity, and consistency.

That’s where Bastian’s experience scaling brands through inflection points becomes particularly relevant.

Bastian’s Perspective on the Opportunity

For her part, Bastian sees InMarket as well-positioned to capitalize on a fundamental change in how advertisers evaluate performance.

“As marketers increasingly seek better ways to attribute media investment and understand its impact on their business, InMarket sits at the center of this inflection point—delivering forward-looking solutions that close the gap and prove real, meaningful outcomes,” she said. “I’m excited to step into this role and build on the momentum of the brand’s transformation.”

Her focus on meaningful outcomes speaks to a growing frustration among marketers with metrics that look impressive but fail to move the business forward.

Recognition and Industry Credibility

Bastian also brings significant industry credibility. She has been recognized as:

• Chief Marketer’s Top Woman in Marketing

• Winner of She Runs It’s Changing the Game Award

• Finalist for AWNY’s Future is Female

She currently serves on the board of IRTS, is an active member of She Runs It’s Executive Class, and has previously served on the board of the Ad Council.

That visibility matters in an industry where relationships, trust, and thought leadership influence buying decisions as much as feature sets.

What This Signals for InMarket’s Next Phase

InMarket’s leadership move suggests the company is entering a new phase—one focused on scale, category leadership, and long-term enterprise value.

With double-digit growth already in place and a steady drumbeat of product innovation, the next challenge is differentiation in a market crowded with measurement claims. By investing in senior marketing leadership now, InMarket is signaling that it intends to define the conversation around outcomes-based advertising, not just participate in it.

For advertisers navigating a complex, privacy-constrained ecosystem, that clarity could be decisive.

Bottom Line

Natalie Bastian’s appointment as CMO is more than a personnel update—it’s a strategic bet on where advertising is going next.

As marketers move away from impressions and toward provable outcomes, InMarket is aligning leadership, product, and positioning to meet that demand head-on. With Bastian at the helm of marketing, the company is sharpening its voice at a moment when the industry is listening more closely than ever.

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