LiveRamp is making a clear statement about where advertising data is headed next—and it’s not just toward better targeting, but toward AI systems that can reason, predict, and act using real-world, permissioned data.

The data collaboration company today announced a major expansion of its Data Marketplace, adding support for AI training data, third-party AI models, and AI-powered applications and agents. The move effectively transforms the Marketplace from a data licensing destination into a centralized hub for building, deploying, and scaling AI across advertising and marketing workflows.

For marketers, data scientists, and developers, the promise is simple: easier access to high-quality data and AI capabilities—without sacrificing governance, privacy, or control.

Why LiveRamp Is Reframing the Data Marketplace Around AI

AI adoption in marketing has accelerated rapidly, but most teams face the same constraint: models are only as good as the data they can safely access.

Public data is limited. First-party data is siloed. And licensing third-party data or models often involves complex negotiations, technical integration, and compliance risk. LiveRamp’s expanded Marketplace is designed to remove that friction.

By enabling clients to license data for AI training, license partner AI models, and soon license AI-powered applications, LiveRamp is positioning its Marketplace as infrastructure for responsible AI—not just a transaction layer.

The shift reflects a broader industry reality: AI in advertising is moving from experimentation to production. And production-grade AI demands governed access to real consumer signals.

A Centralized, Governed Marketplace for AI

At the heart of the expansion is LiveRamp’s focus on trust and control.

Through a single, user-friendly interface, clients can now access premium datasets and AI intelligence for specific, auditable use cases. Every interaction in the Marketplace is authenticated, purpose-bound, and logged—an important distinction as regulators, brands, and consumers scrutinize how data is used in AI systems.

For data partners, the Marketplace offers something equally important: visibility.

Instead of losing control once data or models are licensed, partners retain clear insight into how their assets are being used, where they’re deployed, and by whom. That transparency lowers the barrier for data owners who want to participate in AI ecosystems without exposing sensitive assets.

For marketers, the result is a single destination to power AI initiatives—without stitching together point solutions or navigating legal gray areas.

Three Core AI Use Cases LiveRamp Is Targeting

LiveRamp’s expanded Data Marketplace is structured around three primary AI-driven use cases, each addressing a different layer of the marketing stack.

1. Licensing Data to Train and Tune AI Models

Clients can now securely discover and license AI-ready, permissioned datasets spanning consumer behavior, commerce activity, media engagement, and transaction signals.

These datasets help fill persistent blind spots in consumer intelligence—improving model accuracy, scoring, and real-time decisioning. For brands building predictive models or personalization engines, access to richer signals can mean the difference between generic automation and meaningful relevance.

Crucially, the data remains governed throughout the process, ensuring it’s used only for approved purposes.

2. Licensing Partner AI Models—Without Data Movement

In a notable shift from traditional data sharing, marketers can license a partner’s AI model and apply it to their own first-party data—without sensitive information moving or being exposed.

This model-first approach allows brands to tap into external intelligence while maintaining control of their data. It also opens the door for specialized modeling partners to distribute AI capabilities without handling raw customer records.

For enterprises concerned about data leakage or compliance, this architecture is likely to resonate.

3. Licensing AI-Powered Applications and Agents (Coming Soon)

Looking ahead, LiveRamp plans to enable direct access to AI-powered applications and agents within the Marketplace. These tools will support use cases such as audience building, measurement, and media optimization—effectively packaging AI outcomes, not just inputs.

If executed well, this could significantly lower the technical barrier for marketers who want AI-driven results without building or training models themselves.

Why Permissioned Data Is Becoming AI’s Competitive Advantage

As large language models become more commoditized, differentiation is shifting to data—specifically, data that is accurate, current, and permissioned.

“Just as language is now fuel for AI reasoning, consumers and their attributes help AI drive the most effective possible ad targeting,” said Adam Heimlich, CEO of Chalice AI. He pointed to LiveRamp’s role in enabling AI systems to operate privately within trusted environments like clean rooms.

That distinction matters. AI systems trained on ungoverned or low-quality data risk poor performance and regulatory exposure. By contrast, AI powered by permissioned, real-world signals can deliver more incremental lift—while staying on the right side of privacy expectations.

LiveRamp’s extensive network gives brands reach and scale that would be difficult to replicate independently, particularly as third-party cookies disappear and identity resolution grows more complex.

LiveRamp’s Broader AI Push Comes Into Focus

The Marketplace expansion doesn’t exist in isolation. It builds on a series of recent LiveRamp innovations designed to make AI usable across data collaboration workflows.

Those include multi-agent collaboration, AI-powered segmentation, and AI-driven search, all aimed at reducing friction between data, insights, and activation.

Taken together, the strategy is clear: LiveRamp wants to be the connective tissue between enterprise data and AI execution—especially in environments where privacy and trust are non-negotiable.

According to LiveRamp Chief Revenue Officer Vihan Sharma, safe access to premium data will fundamentally reshape enterprise intelligence. “By increasing access to the world’s most powerful data collaboration network, we can empower the ecosystem with the highest quality signals for superior, responsible performance,” he said.

Implications for Marketers, Data Scientists, and AI Builders

For marketers, the expanded Marketplace simplifies AI adoption. Instead of managing multiple vendors, contracts, and integrations, they can source data, models, and applications in one governed environment.

For data scientists and developers, it shortens the path from experimentation to deployment—especially for models that require real-world behavioral signals to perform well.

And for data partners, it creates a new revenue stream that aligns with modern expectations around transparency and control.

In a market where AI ambition often outpaces operational readiness, LiveRamp is betting that infrastructure—not algorithms—will decide who wins.

From Data Licensing to AI Infrastructure

LiveRamp’s expansion marks a meaningful evolution in how data marketplaces are defined. This is no longer just about buying and selling datasets—it’s about enabling AI systems to operate responsibly at scale.

As advertisers, agencies, and platforms race to embed AI deeper into planning, measurement, and optimization, access to trusted data will become a prerequisite rather than a differentiator.

By turning its Data Marketplace into a hub for AI data, models, and applications, LiveRamp is positioning itself at the center of that shift—where data collaboration meets AI execution.

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For all the promise of generative AI in marketing, many agencies are discovering an uncomfortable truth: more tools don’t automatically mean more efficiency. In fact, they often mean the opposite.

Cogzia, an AI-native enterprise application platform, is betting that the next phase of AI adoption isn’t about smarter models—it’s about better orchestration. The company has announced a strategic collaboration with Marketing Maven, a bicoastal integrated marketing agency, to unify the agency’s growing stack of AI tools into a single, secure, and automated system.

The partnership tackles a problem quietly plaguing modern marketing teams: AI tool sprawl.

When AI Innovation Becomes Operational Drag

Marketing Maven has been an early adopter of generative AI, integrating tools for SEO, content generation, and data analytics into its proprietary Marketing Maven Method. Like many forward-thinking agencies, it embraced best-in-class tools for specific tasks—copywriting, imagery, analysis—each excelling in isolation.

The downside emerged over time.

As AI agents multiplied, workflows became fragmented. Context had to be manually passed between tools. Data lived in silos. Strategists spent more time switching tabs than shaping campaigns.

“Marketing agencies today are drowning in tabs,” said Lindsey Carnett, CEO and President of Marketing Maven. “We have excellent tools for copy, distinct tools for imagery, and separate tools for analytics, but no connective tissue.”

That lack of “connective tissue” is what Cogzia is designed to provide.

Cogzia’s Pitch: The Last Mile of Enterprise AI

Cogzia positions its platform as the “Last Mile Infrastructure” for enterprise AI—less about building new models, and more about making existing ones work together securely and at scale.

By deploying Cogzia, Marketing Maven can connect disparate AI agents, internal databases, and workflows into a unified system. The result is orchestration without engineering bottlenecks.

Instead of relying on developers to stitch tools together, Cogzia enables non-technical users—so-called “citizen developers”—to build custom applications that automate complex, multi-step workflows.

In practice, that could mean turning market research directly into campaign briefs, syncing analytics outputs with creative tools, or automating reporting across clients—all without writing code.

“We aren’t just using AI tools anymore,” Carnett said. “We are building an integrated AI ecosystem that aligns perfectly with our client workflows.”

Why Orchestration Is Becoming the Real Differentiator

The collaboration highlights a growing realization across martech and professional services: AI capability is no longer scarce. Integration is.

Many agencies now use similar models for content, design, and analysis. What separates leaders from laggards is how seamlessly those models work together—and whether they can do so securely for enterprise clients.

Cogzia addresses this with three core pillars:

Unified Orchestration
The platform acts as a central nervous system, allowing Marketing Maven’s teams to trigger workflows that span multiple AI models and internal systems. Tasks that once required manual handoffs now run automatically, preserving context end to end.

Enterprise-Grade Data Security
Unlike public, web-based AI tools, Cogzia processes client data within a governed environment. This matters for enterprise brands that care deeply about compliance, privacy, and data ownership—areas where ad hoc AI usage often falls short.

The Citizen Developer Model
Strategists and marketers don’t have to wait in line for engineering resources. They can build and adapt “mini-apps” themselves, accelerating experimentation without sacrificing control.

This approach reflects a broader shift: AI is moving from experimentation to infrastructure. And infrastructure needs guardrails.

Built on Model Context Protocol (MCP)

Under the hood, Cogzia is built on the Model Context Protocol (MCP), an emerging standard designed to help AI tools and data sources share context consistently.

According to Cogzia co-founder and CEO Lana Feng, context—not intelligence—is the biggest bottleneck in enterprise AI.

“The biggest bottleneck in enterprise AI isn’t model intelligence; it’s the inability of tools to share context securely,” Feng said. “MCP provides the universal standard necessary for different tools and data sources to ‘speak’ to one another.”

By leveraging MCP, Cogzia allows Marketing Maven to chain together specialized AI agents—from data analysis through creative execution—without losing meaning or continuity along the way. That continuity is critical for workflows that span strategy, content, and performance measurement.

A Blueprint for the Next Phase of AI Adoption

While this partnership is specific to Marketing Maven, its implications are broader.

Across marketing, consulting, and professional services, firms are hitting the same wall: dozens of AI tools, each powerful, collectively chaotic. The next competitive advantage won’t come from adding yet another model—it will come from running core operations on unified AI infrastructure.

Cogzia and Marketing Maven are effectively offering a case study in what that future looks like:

• Fewer manual handoffs

• Faster campaign execution

• Better governance over client data

• AI systems that align with real-world workflows

For agencies serving enterprise clients, that combination could become a baseline expectation rather than a differentiator.

From AI Experiments to AI Operations

The Cogzia–Marketing Maven collaboration underscores a key inflection point for martech. The industry is moving past isolated AI experiments toward operationalized AI systems that power day-to-day business.

Firms that fail to address fragmentation risk slower execution, higher costs, and inconsistent results—no matter how advanced their individual tools may be.

By unifying AI agents, data, and workflows into a single platform, Cogzia is positioning itself not as another AI solution, but as the layer that finally makes them all work together.

And for agencies like Marketing Maven, that could mean less time managing tools—and more time delivering outcomes.

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As sports evolve into one of the world’s most powerful engines of culture, commerce, and fandom, brands are under growing pressure to show up with more than logos and sponsorship deals. Adcetera believes the moment calls for specialization—without sacrificing scale.

The Houston-based integrated agency has unveiled A82 Sports Marketing, a newly formalized division designed to help brands activate, perform, and differentiate across the global sports landscape. Alongside the launch, Adcetera debuted A82SportsMarketing.com, outlining the group’s services and its belief that sports, when done right, can forge deeper and more culturally resonant brand connections.

While the A82 name is new, the work behind it is not. Adcetera has quietly delivered sports marketing strategy, production, sponsorship support, and live event services for years. The difference now is focus—and a dedicated structure built specifically for sports.

Why Adcetera Is Doubling Down on Sports

Sports marketing has changed. What once centered on broadcast ads and jersey logos has expanded into a complex ecosystem spanning social platforms, live events, streaming, creator-led storytelling, and immersive fan experiences.

For brands, that means higher stakes and fewer second chances.

A82 Sports Marketing is Adcetera’s answer to that shift: a specialized team that blends deep sports expertise with the agency’s full-service creative, digital, and analytics capabilities. The goal is to help brands not just appear in sports, but perform within sports culture—authentically and at scale.

“Sports has become one of the most powerful storytelling platforms in the world,” said Thomas King, Vice President of Motion Services at Adcetera. “A82 Sports Marketing allows us to take Adcetera’s creative, digital, and strategic core and apply it with precision to the sports landscape.”

That precision matters as brands look to connect with fans across multiple touchpoints—from arenas and stadiums to TikTok feeds and streaming platforms.

From Sponsorships to Storytelling Systems

Unlike boutique sports agencies that operate in silos, A82 is positioned as a fully integrated extension of Adcetera. That means sports investments don’t live on an island—they plug directly into broader brand, media, and campaign strategies.

The division offers end-to-end support across teams, leagues, events, and sponsor brands, covering everything from early-stage planning to live execution and post-campaign measurement.

Its core capabilities include:

Sports Strategy and Planning
A82 helps brands enter or expand in sports with clear playbooks rooted in audience insight, cultural alignment, and measurable business outcomes—rather than one-off activations.

Sports Production and Content Creation
Backed by Adcetera’s award-winning Motion Services team, A82 produces broadcast-ready, social-first, and documentary-style content designed to elevate moments and energize fans with cinematic execution.

Partnership and Sponsorship Consulting
From evaluating opportunities to negotiating deals, A82 works to ensure sponsorships align authentically with brand values, audience expectations, and long-term growth goals.

Sports Events and Activations
The division develops immersive brand experiences—on-site, on tour, or digital—that give fans meaningful reasons to engage, not just observe.

What ties it all together is integration. Creative, media, digital, analytics, and campaign orchestration all sit within Adcetera’s broader ecosystem, allowing sports marketing efforts to scale consistently across channels.

A Response to Fragmentation in Sports Marketing

The launch of A82 reflects a broader trend in the marketing industry. As sports become more global and digitally native, brands are struggling to manage fragmented partner ecosystems—one agency for creative, another for sponsorships, another for events.

Adcetera is betting that consolidation wins.

“Our clients don’t need to piece together disconnected partners,” said Rowan Gearon, Chief Creative Officer at Adcetera. “Formalizing A82 simply gives a name to the expertise we’ve already built, and ensures clients benefit from both specialized sports leadership and the full creative and strategic depth of Adcetera.”

For brands, that could mean fewer handoffs, clearer accountability, and stronger alignment between sports initiatives and overall marketing performance.

Sports as a Growth Engine, Not a Side Bet

The timing of A82’s launch is deliberate. Global sports fandom continues to surge, driven by streaming access, social amplification, and the rise of athlete-led media. At the same time, sponsors are demanding more measurable returns on often sizable investments.

That combination is pushing sports marketing toward performance-minded creativity—where storytelling, data, and experience design must work together.

A82 positions itself squarely at that intersection, offering brands a way to treat sports not as a passion project, but as a disciplined growth engine.

Whether that approach resonates will depend on execution. But the message from Adcetera is clear: sports marketing has matured, and brands need partners built for its complexity—not just its spectacle.

Get in touch with our MarTech Experts.

Reshift Media, one of the most prominent digital marketing agencies serving franchise brands, is making a clear push into the U.S. market. The company has appointed seasoned sales and marketing executive Ryan Arcoraci as Sales Director, tasking him with leading Reshift’s growing U.S. presence and supporting franchisors navigating an increasingly competitive digital landscape.

Based in Las Vegas, Arcoraci will work directly with U.S.-based franchisors to deploy Reshift Media’s mix of digital marketing, website development, and proprietary software solutions—tools designed to help franchise systems scale without losing local relevance.

The hire signals more than a routine leadership addition. It reflects where franchise marketing is headed: toward data-driven execution, tighter alignment between marketing and sales, and technology stacks built to support growth across dozens—or hundreds—of locations.

Why This Hire Matters for Franchise Marketing

Franchise brands face a unique set of marketing challenges. They must balance national brand consistency with local market performance, manage complex lead flows, and prove ROI to both corporate teams and individual franchisees. As ad costs rise and platforms fragment, those challenges have only intensified.

Arcoraci brings more than a decade of experience at the intersection of digital advertising, SaaS sales, and franchise consulting, having worked closely with franchisors, business coaches, and franchise consultants. His background centers on helping multi-location brands turn marketing data into operational and revenue gains—a skill set Reshift Media sees as critical for its U.S. expansion.

“Ryan has a unique, end-to-end understanding of how technology, marketing, and sales intersect to fuel scalable growth for franchises,” said Steve Buors, co-founder and CEO of Reshift Media. “He has positioned himself as a trusted expert within the industries we serve, making him an ideal choice to lead our expansion into U.S. markets.”

In practical terms, that means helping franchise systems improve lead quality, streamline marketing operations, and connect digital performance to real business outcomes—areas where many brands still struggle.

A Track Record Built Around Data and Growth

Arcoraci’s career has focused on applying data-driven strategies to franchise marketing and sales ecosystems. His work has spanned lead generation optimization, funnel performance, and the operational systems needed to support growing franchise networks.

That experience aligns closely with Reshift Media’s positioning. The agency has built its reputation around helping franchises scale marketing programs that work at both the corporate and local levels, combining paid media, web development, and technology platforms designed for multi-location complexity.

Beyond client work, Arcoraci is also the host and producer of the Business Stories with Ryan Arcoraci podcast, where he interviews business leaders about growth, operations, and decision-making. The podcast has helped him build visibility within entrepreneurial and franchise communities—an added advantage as Reshift looks to deepen its footprint in the U.S.

Reshift Media’s Expanding Global Influence

Reshift Media is no newcomer to franchise marketing. The agency represents more than 200 major franchise brands across 22 countries, making it one of the most visible players in the space. Its client roster and global reach have earned consistent industry recognition.

Most recently, Reshift was named to Entrepreneur magazine’s Top Franchise Suppliers list for the third consecutive year, a distinction closely watched by franchisors evaluating agency partners. The company also picked up two 2025 Stevie® Awards, including:

• Silver for Company of the Year – Advertising, Marketing and Public Relations

• Gold for Marketing Disruptor of the Year

In addition, Reshift Media played a central role in launching the first World Franchise Day, reinforcing its influence beyond client services and into broader franchise industry initiatives.

The Arcoraci hire builds on that momentum, adding U.S.-focused leadership to a company that has historically grown through international markets.

The U.S. Opportunity—and the Stakes

The U.S. franchise market is both massive and fiercely competitive. From QSR and fitness to home services and education, franchisors are under pressure to deliver predictable growth while managing rising acquisition costs and platform volatility.

Digital marketing agencies serving this space are increasingly expected to do more than run ads. They must integrate technology, analytics, and process—often acting as an extension of internal marketing teams.

Reshift Media appears to be positioning itself squarely in that role.

By bringing on a sales leader with deep franchise-specific experience, the company is signaling its intent to compete aggressively in the U.S. market, not just as a service provider, but as a strategic partner for growth-stage and enterprise franchisors.

What Arcoraci Brings to the Role

According to Arcoraci, Reshift’s reputation within the global franchise community was a key factor in his decision to join.

“The Reshift name carries significant weight within the international franchising community,” he said. “It stands for innovation and sophistication, scaled to help franchise businesses build trust at both the local and national level. Reshift is poised to transform franchise marketing here in the U.S.”

His mandate will likely extend beyond traditional sales leadership. As franchisors demand clearer ROI, better reporting, and tighter integration between marketing and operations, sales leaders increasingly act as strategic advisors—guiding brands toward solutions that fit their growth models.

That advisory role aligns with Reshift’s broader positioning as franchise marketing becomes more complex and more accountable.

A Broader Signal for the Franchise MarTech Space

This move reflects a broader trend in franchise-focused martech and agency services. As technology platforms mature and AI-driven optimization becomes standard, differentiation is shifting toward execution, expertise, and industry fluency.

Agencies that understand franchise economics—and can translate marketing performance into business outcomes—are gaining an edge. Leadership hires like Arcoraci’s suggest Reshift Media sees that shift clearly and is investing accordingly.

For U.S. franchisors evaluating partners, the message is straightforward: Reshift Media is no longer just an international success story—it’s building the leadership and infrastructure to compete head-on in the American franchise market.

Get in touch with our MarTech Experts.

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As media channels splinter, buying cycles compress, and AI becomes central to marketing operations, one long-standing problem continues to slow the industry down: advertising intelligence lives in too many places, in too many formats, and rarely where decisions actually happen.

MediaRadar thinks it has a fix.

The marketing intelligence firm today introduced MediaRadar Data Cloud, a new platform designed to make advertising data immediately usable across analytics tools, activation platforms, and AI workflows. Rather than forcing teams to extract insights from dashboards and manually push them downstream, the Data Cloud embeds MediaRadar’s intelligence directly into the systems where marketers, publishers, and adtech teams already work.

It’s a clear response to a market reality: insight delayed is insight wasted.

Why MediaRadar’s Data Cloud Matters Now

Marketing intelligence hasn’t kept pace with how modern teams operate. While AI adoption accelerates and media fragmentation deepens, advertising data often remains siloed—locked inside reporting tools, disconnected from planning systems, or unusable by AI models that need clean, structured context.

MediaRadar’s Data Cloud is positioned as a shift from “intelligence as a destination” to “intelligence as infrastructure.” Instead of asking users to come to the data, the platform pushes intelligence into analytics environments, planning tools, and AI systems in real time.

That’s increasingly table stakes.

CMOs and revenue leaders now expect competitive insights to inform everything from media mix decisions and budget allocation to automated recommendations generated by large language models. Data that can’t be activated quickly—or safely—loses value fast.

From Insight to Action, Without the Friction

At its core, the Data Cloud makes MediaRadar’s advertising intelligence interoperable across the modern data stack.

Clients can work with mission-critical datasets—such as competitive ad spend, creative trends, and media mix analysis—inside their own environments rather than toggling between platforms. That means teams can analyze markets, shape strategy, and activate insights across planning, measurement, and optimization workflows without breaking momentum.

More notably, MediaRadar is positioning the Data Cloud as AI-native, not AI-adjacent.

The platform is designed to connect trusted advertising data to AI models and agents running on platforms like ChatGPT, Anthropic, and Gemini. That opens the door for AI-driven workflows that don’t just summarize data, but reason over it—spotting shifts in spend, identifying emerging competitors, or flagging whitespace opportunities before rivals react.

Future support for the Model Context Protocol (MCP) suggests MediaRadar is thinking ahead to a world where advertising intelligence must move seamlessly across multiple AI agents, tools, and teams without losing consistency or governance.

A Data Foundation Measured in Billions, Not Dashboards

MediaRadar’s confidence in launching a data-first platform comes from the scale of its underlying intelligence.

The company’s data foundation spans:

• $280 billion in tracked media spend

• 35 million-plus creative assets

• 30+ media channels, including social, digital video, programmatic, CTV, AVOD, linear TV, and retail media

That breadth matters. As advertisers diversify spend across channels and formats, intelligence that only covers part of the ecosystem becomes less reliable. MediaRadar’s pitch is that comprehensive coverage enables cleaner benchmarking, stronger competitive analysis, and more trustworthy AI outputs.

In practical terms, brands and agencies using the Data Cloud can:

• Detect shifts in competitor spend and creative strategy in near real time

• Adjust media plans faster as market conditions change

• Benchmark share of voice across channels rather than in silos

• Identify underutilized channels or formats before they become crowded

This is especially relevant as retail media and CTV continue to blur traditional planning boundaries.

Built for Advertisers—and the Companies Selling to Them

While advertisers and agencies are an obvious audience, MediaRadar is also aiming squarely at publishers and adtech platforms.

Selling ads has become harder, not easier. Buyers are more selective, sales cycles are longer, and proof of value matters earlier in the conversation. The Data Cloud is positioned as a commercial intelligence engine that helps publishers and platforms compete more effectively for advertiser budgets.

With access to brand- and product-level advertising activity, sales teams can:

• Identify advertisers most likely to spend, not just those already in-market

• Tailor pitches based on actual media behavior, not assumptions

• Align outreach with emerging trends before budgets are fully allocated

In theory, this shortens sales cycles and increases win rates—two metrics publishers care about deeply as competition intensifies.

What’s Under the Hood: Key Capabilities Explained

Rather than positioning the Data Cloud as a single feature, MediaRadar is rolling it out as an integrated ecosystem built around interoperability, consistency, and AI-readiness.

AI-Enabled Brand Identity System
At the foundation is a parent-child taxonomy that acts as a single source of truth for brands, sub-brands, products, and co-ops. This structure ties together media spend, creative assets, and campaigns across channels. The payoff is cleaner analysis, better benchmarking, and fewer errors when AI systems interpret brand relationships.

Accessible Wherever Teams Work
The Data Cloud is fully cloud-native, delivering creative, competitive, commercial, and market intelligence directly into analytics platforms, planning tools, and AI environments. The goal is to eliminate lag between insight and action—especially in revenue and optimization decisions.

Context-Rich Semantics
Standardized metadata for brands, creatives, and campaigns ensures consistent meaning across datasets. That consistency is critical for AI systems, which are only as reliable as the context they’re given.

AI-Ready by Design
By harmonizing spend, creative, and campaign data into a single interoperable framework, MediaRadar aims to provide AI systems with the structured inputs they need to generate accurate insights, recommendations, and forecasts at scale.

Why AI-Ready Advertising Data Is the Real Differentiator

AI adoption in marketing has moved past experimentation. Teams now expect models to support planning, forecasting, competitive analysis, and even sales enablement. But many AI initiatives stall because the data feeding them is fragmented or poorly structured.

MediaRadar is betting that data readiness—not algorithms—will be the limiting factor for most organizations.

With clean, consistently classified datasets, clients can:

• Train AI models on higher-quality advertising intelligence

• Improve prediction accuracy for spend shifts and competitive moves

• Accelerate go-to-market and product decisions

• Identify whitespace opportunities before competitors see them

In other words, the Data Cloud isn’t just about better reporting—it’s about making AI outputs more reliable and more defensible.

A Signal of Where Marketing Intelligence Is Headed

MediaRadar’s launch reflects a broader shift across martech and adtech: intelligence platforms are becoming infrastructure layers rather than standalone tools.

As AI agents take on more decision support, data providers must ensure their insights are portable, interoperable, and governed. Platforms that remain closed or dashboard-centric risk becoming irrelevant—no matter how good their data is.

By focusing on interoperability, AI integration, and real-time activation, MediaRadar is aligning itself with how modern marketing teams actually operate. The challenge, as always, will be execution—particularly as more vendors make similar claims.

Still, the direction is clear. In an AI-driven advertising market, the winners won’t just have the most data. They’ll have the data that moves fastest, travels farthest, and makes the most sense to both humans and machines.

Get in touch with our MarTech Experts.

Zeta Global is betting that the next era of enterprise marketing won’t be driven by dashboards, but by agents that listen, reason, and act. The AI marketing cloud company announced a strategic collaboration with OpenAI to power the conversational intelligence and agentic capabilities behind Athena by Zeta™, its superintelligent marketing agent—and expanded beta access amid growing enterprise demand.

The partnership brings OpenAI models deeper into Athena’s core, shaping what Zeta calls the platform’s “next phase of development.” In practical terms, it means more natural conversations, more reliable reasoning, and more automation embedded directly into marketers’ daily workflows. It also signals how quickly agentic AI is moving from experimentation to operational reality in enterprise marketing.

From Insights to Action: Why Athena Matters Now

Athena was first unveiled at Zeta Live as an answer-driven interface for marketers frustrated by data overload. Instead of navigating reports or stitching together insights across tools, Athena allows users to ask questions in natural language and get decision-ready answers instantly.

This latest announcement pushes Athena beyond conversational analytics and into what Zeta sees as the future of marketing operations: agentic systems that don’t just surface insights, but recommend—and in some cases execute—the next best action.

“AI is moving from the edges of marketing to the center of how enterprises operate,” said David A. Steinberg, Zeta Global’s Co-Founder, Chairman, and CEO. “Athena transforms the Zeta Marketing Platform into an intelligent operating system for growth—one that can listen, reason, and act on behalf of marketers.”

That framing aligns with a broader industry shift. As marketing stacks grow more complex and data volumes explode, enterprises are looking for AI that reduces friction, not adds another interface. Athena’s promise is speed: fewer handoffs, less manual analysis, and faster movement from question to outcome.

Deepening the OpenAI Connection

Under the expanded collaboration, Zeta will align Athena’s product roadmap with advances in OpenAI’s models, allowing the platform to evolve alongside improvements in reasoning, conversation, and agentic behavior. Zeta will also have opportunities for early access to new OpenAI models and features, giving Athena a faster path to adopting cutting-edge capabilities.

“Zeta shows how advanced AI moves beyond insight and into action,” said Giancarlo “GC” Lionetti, Chief Commercial Officer at OpenAI. “By working together, we are bringing agentic intelligence directly into everyday marketing workflows, helping enterprises move faster and act with confidence.”

This is a notable step in how OpenAI is showing up in enterprise software. Rather than being positioned as a generic layer or add-on, OpenAI models here are embedded as a core engine inside a verticalized platform—one designed specifically for marketing use cases like audience insights, campaign optimization, and revenue growth.

Inside Athena’s First Agentic Apps

Alongside the OpenAI news, Zeta announced that Athena’s first two agentic applications—Insights and Advisor—have entered beta.

Insights with Athena is positioned as a conversational analytics engine. Executives can ask a single question and receive an immediate, usable answer, complete with performance drivers and ready-to-share dashboards. The goal is to eliminate the lag between curiosity and clarity that often slows decision-making in large organizations.

Instead of waiting on analysts or digging through reports, a CMO can ask Athena about emerging growth segments, audience trends, or campaign performance and get an answer in seconds. It’s analytics reframed as a conversation, not a task.

Advisor with Athena goes a step further. Designed as a goal-driven optimization agent, Advisor continuously scans campaigns and recommends—or automatically executes—next best actions based on objectives like revenue growth, efficiency, retention, or engagement. This is where Athena begins to resemble an always-on marketing operator rather than a passive assistant.

Together, the two apps reflect a shift from descriptive analytics (“what happened”) to prescriptive and autonomous marketing (“what should we do next”).

Early Results from the Field

TKO Group Holdings, the parent company of UFC and WWE, participated in Athena’s Early Access Program and has already put the platform to work.

“Athena is already transforming how our team works,” said Deborah Cook, Vice President of Data Intelligence at TKO Group Holdings. “Generating segment-based reports from a simple prompt and running ad hoc analysis in seconds has been a game-changer.”

Cook noted that tasks once requiring significant manual effort—like comparing performance across segments or identifying creative optimization opportunities—now happen almost instantly. As Athena expands into deeper geographic and performance insights, TKO sees potential for broader adoption across the organization.

That kind of testimonial underscores why agentic AI is gaining traction. Enterprises aren’t just looking for smarter tools; they’re looking for leverage—ways to compress time, reduce labor, and move faster without sacrificing control.

The Bigger Picture: Agentic AI Comes to Marketing

Zeta’s move reflects a broader trend across enterprise software: the rise of agentic AI as a new interaction model. Unlike traditional AI features that assist with specific tasks, agents are designed to operate continuously, adapt to goals, and take action across systems.

In marketing, the appeal is obvious. Teams are under pressure to deliver more personalized, data-driven experiences while managing sprawling media, CRM, and analytics stacks. An agent that can unify data, reason over it, and act autonomously could fundamentally change how marketing organizations operate.

Competitors across the MarTech landscape are racing in the same direction, from AI copilots embedded in CRM platforms to autonomous media optimization tools. What differentiates Athena is its positioning as a centralized, answer-driven operating layer—one designed to sit on top of Zeta’s broader marketing platform rather than function as a point solution.

What’s Next for Athena

Driven by what Zeta describes as “unprecedented demand” from brands and agencies, the company plans to make Athena generally available to all customers by the end of Q1 2026. Between now and then, expanded beta access will allow more enterprises to test how agentic applications fit into real-world marketing workflows.

If Athena delivers on its promise, it could mark a turning point for enterprise marketing AI—from tools that inform decisions to systems that help make them. And with OpenAI models now embedded at its core, Zeta is positioning Athena as a front-line example of how agentic intelligence moves from theory into day-to-day business impact.

For marketers navigating increasing complexity, the message is clear: the future may not be another dashboard, but an AI that already knows what you’re trying to achieve—and helps you get there faster.

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For decades, brand growth followed a familiar playbook: build awareness through advertising, reinforce memory structures, and ensure products are easy to buy. Omnicom Media’s latest research suggests that model is no longer sufficient—and may already be outdated.

In a new report, The Future of Brand Influence, Omnicom Media argues that influence today is no longer linear, predictable, or dominated by advertising. Instead, it is shaped by a fragmented ecosystem where influencers, peers, retail environments, and increasingly AI-driven recommendations play a decisive role in how consumers form opinions and make decisions.

Backed by research conducted by Omnicom Media Intelligence, the study introduces a critical evolution of classic marketing theory. Physical and mental availability still matter, but they are no longer enough. Brands must now compete on emotional availability—their ability to earn trust, relevance, and resonance across a growing web of human and machine-driven touchpoints.

Advertising Has Lost Its Monopoly on Influence

One of the clearest signals from the research is that advertising is no longer the primary driver of brand perception.

Only 32% of respondents say advertising most affects their overall opinion of a brand. By contrast, 40% point to what people are saying online, and a striking 71% say peer and influencer commentary matters more than brand advertising itself.

AI is also emerging as a powerful influence layer. Nearly half of respondents (45%) say AI-generated recommendations matter more than advertising when shaping their perceptions, putting machines on roughly equal footing with influencers (43%). For Gen Z, the shift is even more pronounced: 67% trust people on social platforms more than institutions or publications.

“Influence used to be relatively linear and predictable,” said Joanna O’Connell, Chief Intelligence Officer at Omnicom Media North America and lead author of the report. “Today, brand messaging exists alongside everything from influencer opinions to AI-generated answers—and that means brands must earn emotional relevance and trust across a much broader set of touchpoints.”

The implication is stark: brands can no longer assume that reach and frequency will do the heavy lifting. Influence is now negotiated in public, distributed spaces where brands have less control—and where credibility must be earned repeatedly.

Generative AI Is Compressing the Consumer Journey

If influence has become fragmented, it has also become faster. The rise of generative AI is dramatically compressing the path from curiosity to decision.

Seven in ten respondents say GenAI enables them to become an “expert” in almost any product or service category, helping them research pros and cons, compare brands, and validate choices in minutes rather than days. That acceleration reduces the window in which brands can shape consideration—and raises the stakes for how they show up in AI-mediated environments.

At the same time, attention is under unprecedented strain. Sixty-three percent of respondents describe their attention span as “just OK” or “not great,” while nearly four in ten say they don’t even notice ads on social platforms, despite high ad loads. Ad blockers, ad-free subscriptions, VPNs, and signal loss continue to chip away at traditional reach.

Together, these forces are creating what the report describes as a system where brand influence is frequently blocked, deprioritized, diluted, or even self-sabotaged.

Economic Pressure Is Undermining Brand Loyalty

The research also highlights a growing disconnect between how brands think they build loyalty and how consumers experience them.

More than 30% of respondents say they are now buying cheaper alternatives to their usual brands, up sharply from 19% earlier this year. While 75% say brand relatability is essential to purchase decisions, 72% believe brands care more about making money than building genuine loyalty. More than half feel brands no longer try to connect with them the way they once did.

This tension places emotional availability front and center. Consumers want brands that understand them, reflect their values, and show up with relevance—not just promotions. Yet many brands are perceived as prioritizing short-term revenue over long-term relationships, weakening trust at precisely the moment when trust has become the most valuable currency.

“Trust is migrating from institutions to individuals, and increasingly to machines as well,” O’Connell said. “That shift fundamentally changes how brands need to show up if they want to remain relevant and influential.”

Rethinking Availability in a Fragmented Ecosystem

A core contribution of the report is how it reframes the classic pillars of brand growth.

Physical availability now means more than shelf presence or distribution. Brands must ensure frictionless access across digital and physical channels, from retail media networks to e-commerce platforms and last-mile delivery.

Mental availability is no longer guaranteed by awareness alone. In an environment defined by noise, disintermediation, and AI-mediated discovery, brands must fight to remain salient when consumers are searching, scrolling, or asking machines for advice.

Emotional availability has emerged as the differentiator. It reflects a brand’s ability to connect authentically, build trust, and feel relevant in moments that matter—whether that moment occurs in a creator’s video, a retail environment, or an AI-generated response.

These shifts, Omnicom argues, point to a new marketing reality where influence is achieved by balancing machine efficiency with human connection.

From Research to Action: What Brands Should Do Next

The report doesn’t stop at diagnosis. It also outlines practical recommendations for brands navigating this evolving influence ecosystem.

On the human side, Omnicom Media advises brands to market to emotion at scale, using storytelling, live experiences, and influencer partnerships to create moments of elevated attention. Influencers, in particular, are positioned not as tactical add-ons, but as authentic brand ambassadors and scalable media channels.

Retail media also plays a central role, offering opportunities to surprise and delight shoppers closer to the point of purchase. Search, meanwhile, should be treated as a behavior rather than a channel—meeting consumers wherever and however they choose to look for answers.

On the machine side, the report urges brands to prepare for AI-driven discovery by adopting Generative Engine Optimization (GEO) strategies. As AI becomes a primary interface between consumers and information, brands must ensure their products, values, and differentiators are understood and accurately represented by machines—not just humans.

“The future of brand influence isn’t about choosing between humans and machines,” O’Connell said. “It’s about designing systems that serve both.”

Why This Matters for Marketers Now

What makes The Future of Brand Influence particularly timely is its alignment with broader industry shifts. Retail media networks are booming. Influencer marketing is maturing into a performance-driven discipline. Generative AI is reshaping search, discovery, and recommendation engines at speed.

Against that backdrop, Omnicom’s research reframes influence not as a single lever, but as a system—one where discovery, consideration, purchase, and loyalty feed into a self-reinforcing growth loop when executed well.

For marketers, the takeaway is clear: relying on advertising alone is no longer just insufficient, it’s risky. Influence today must be earned across human conversations, machine-generated answers, and moments of emotional relevance that cut through economic and attention pressures.

Brands that adapt may find themselves more resilient, more trusted, and better positioned for growth. Those that don’t risk fading into the background noise—seen by fewer people, trusted by fewer still, and increasingly invisible in a world mediated by both humans and machines.

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For decades, school websites have been treated as the digital front door for parents, students, and staff. GPT AI Corporation, Inc. is now arguing that door is effectively broken—and it has data to back up the claim.

The company has launched EdGPT.ai, an AI-powered conversational platform built specifically for educational institutions, from preschools to universities. Its premise is bold: traditional school websites no longer meet modern expectations for accessibility, usability, or responsiveness, and conversational AI should replace them as the primary communication layer.

It’s a sharp challenge to long-held assumptions in education technology—and one that taps directly into mounting frustrations felt by administrators, families, and students alike.

Schools Are Overloaded With Repetitive Questions

At the heart of the problem is an administrative burden that rarely shows up on balance sheets but quietly drains time and resources. According to research cited by GPT AI Corporation, school administrative staff spend 15 to 20 hours each week answering the same questions about schedules, policies, lunch menus, admissions, and procedures—information that technically already exists online.

In practice, that information is often buried behind confusing navigation, outdated pages, or poorly designed search functions. The result: educators and staff repeatedly field phone calls and emails instead of focusing on student support.

The issue is amplified outside office hours. Roughly 68% of school-related information requests go unanswered for more than 24 hours, leaving parents and students stuck waiting for basic answers about homework rules, athletic schedules, or upcoming events. Over time, those delays erode trust and engagement.

Parents Have Stopped Using School Websites

The more troubling signal may be behavioral. Data highlighted in the announcement suggests that 73% of parents won’t return to a school website after a poor usability experience.

That statistic reflects a broader shift in expectations shaped by consumer technology. Families are accustomed to instant answers from search engines, messaging apps, and voice assistants. When school websites require multiple clicks, dense menus, or trial-and-error searching, users simply abandon them.

Instead, parents turn to Google, social media, or direct phone calls—ironically increasing the communication load schools were trying to reduce with websites in the first place. In this context, EdGPT.ai positions itself not as a website enhancement, but as a replacement for a model that no longer aligns with how people seek information.

Accessibility Is the Breaking Point

Usability frustrations are only part of the story. Accessibility failures are more systemic—and more serious.

The WebAIM Million 2025 study, which analyzed the top one million websites worldwide, found that 94.8% of home pages contain WCAG accessibility failures. Across those sites, researchers identified more than 50 million distinct errors, averaging 51 accessibility issues per page.

For educational institutions, which serve diverse populations including students with disabilities, these numbers are particularly alarming. Common failures include low-contrast text on 79.1% of pages, missing alternative text for images on 55.5%, and missing form labels on 48.2%.

“Traditional websites systematically fail users,” said Aftab Jiwani, founder of GPT AI Corporation. His conclusion is blunt: when nearly every website contains accessibility barriers, the model itself is flawed.

EdGPT.ai is designed as a direct response to that reality, promising a fully accessible, conversational interface that removes navigation and visual design barriers entirely.

How EdGPT.ai Works

Instead of clicking through pages, users interact with EdGPT.ai by asking questions in natural language. The platform delivers instant responses around the clock, covering everything from school policies and schedules to admissions requirements and campus services.

From an implementation standpoint, the barrier to entry is intentionally low. Schools provide their existing website URL, and EdGPT.ai automatically ingests publicly available information. Administrators can then upload additional documents—handbooks, calendars, policies, staff directories—to expand the platform’s knowledge base. According to the company, institutions can be operational in minutes.

The AI is configured specifically for educational contexts, understanding the nuances of school operations rather than relying on generic chatbot logic. It’s designed to work seamlessly with screen readers, voice commands, and assistive technologies, addressing many of the accessibility shortcomings baked into traditional websites.

Early Results Point to Measurable Gains

GPT AI Corporation says early adopters are already seeing tangible benefits. Schools piloting EdGPT.ai report a 65% reduction in administrative phone calls and a 75% improvement in engagement from prospective families. Some institutions have reclaimed hundreds of staff hours previously lost to repetitive inquiries.

The always-on nature of the platform is a key factor. Parents checking field trip requirements late at night or students reviewing assignment details on weekends no longer have to wait for office hours. That immediacy aligns more closely with how families actually operate—and reduces friction at critical touchpoints.

Administratively, schools report up to an 80% reduction in repetitive internal inquiries, freeing staff to focus on higher-value tasks like student support and program development.

One Platform, Many Education Levels

EdGPT.ai is pitched as adaptable across the entire educational spectrum.

Preschools and early learning centers use it to answer routine parent questions about daily schedules, pickup procedures, and meal programs. Elementary schools deploy it for homework policies, lunch menus, and after-school activities. Middle and high schools lean on it for more complex scheduling, extracurriculars, graduation requirements, and college preparation.

In higher education, the platform addresses a broader audience—prospective students, current students, parents, and faculty—covering admissions, financial aid, course catalogs, and campus services. For colleges and universities facing intense competition for enrollment, faster, clearer communication can translate directly into better recruitment outcomes.

Privacy and Compliance Considerations

In education, technology adoption often hinges on compliance as much as capability. GPT AI Corporation emphasizes that EdGPT.ai uses only publicly available information and approved school materials, maintaining alignment with FERPA requirements.

The platform is positioned as a communication layer rather than a student data system, reducing risk while still delivering meaningful improvements in access and responsiveness.

A Broader Shift in EdTech Communication

EdGPT.ai’s launch reflects a wider trend in enterprise and public-sector technology: moving away from static information repositories toward conversational, intent-driven interfaces. Similar shifts are already underway in customer support, healthcare, and government services.

What makes education different is the scale of accessibility and equity implications. When nearly all websites fail basic accessibility standards, conversational AI isn’t just a convenience—it may be a corrective measure.

Whether EdGPT.ai truly signals “the end of school websites” remains to be seen. Websites are deeply embedded in institutional workflows and compliance requirements. But as a primary interface for everyday questions, the model EdGPT.ai promotes feels aligned with how users already behave.

For schools under pressure to do more with less, the promise of reclaiming time, improving accessibility, and meeting families where they are may be difficult to ignore.

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