As privacy regulations tighten and signal loss reshapes digital advertising, marketers face a growing paradox: they need more data to improve performance, but fewer ways to use it safely. transcosmos and Priv Tech, Inc. believe the solution lies at the intersection of privacy engineering and marketing execution.

The two companies have announced the joint launch of Privacy Consulting Services, set to roll out in December 2025. The new offering is designed to help companies leverage first-party data for digital marketing—particularly through Conversion APIs (CAPI)—while complying with complex privacy regulations in Japan and overseas.

The timing is deliberate. As platforms push server-side tracking and AI-driven optimization, outdated privacy policies have become a hidden bottleneck preventing marketers from fully activating their data.

Why privacy has become a growth constraint

For years, privacy compliance was treated as a legal checkbox. Today, it directly impacts marketing performance.

Regulations such as Japan’s Act on the Protection of Personal Information (APPI), the Telecommunications Business Act, GDPR, and CCPA have raised the bar for how companies collect, disclose, and activate user data. At the same time, advertising platforms increasingly rely on first-party conversion and event data—often transmitted via CAPI—to fuel AI learning and improve targeting accuracy.

The result is a growing gap. Many companies want to deploy CAPI to offset cookie loss and improve ROI, but their privacy policies, consent frameworks, and internal governance structures aren’t ready. Revising them has become slow, risky, and resource-intensive, especially for organizations operating across markets.

That’s the problem transcosmos and Priv Tech are aiming to solve.

A joint approach: privacy by design, performance by default

The new Privacy Consulting Services combine transcosmos’s strength in marketing execution and technology deployment with Priv Tech’s expertise in privacy protection and privacy-enhancing technologies.

Rather than treating privacy and performance as opposing forces, the partnership positions privacy as an enabler of modern digital marketing. By building compliant foundations first, companies can activate data with greater confidence and scale.

The service provides end-to-end support, spanning privacy strategy, policy revision, technology implementation, and marketing enablement. This approach is designed to help businesses move from regulatory uncertainty to operational readiness—without stalling their advertising initiatives.

What the service covers

At its core, the offering focuses on removing the friction that prevents companies from deploying CAPI and other data-driven marketing technologies.

Key service components include:

• Support for revising privacy policies to align with new and evolving regulations

• Compliance assistance for Japanese privacy laws, including APPI and the Telecommunications Business Act

• Cookie policy development, including site scans and policy template creation

• Consent management platform (CMP) deployment, ensuring proper user consent flows

• Regulatory risk mitigation, reducing exposure to compliance failures and public backlash

By addressing both legal requirements and technical implementation, the service aims to shorten the path from compliance to activation.

Why CAPI is central to the strategy

Conversion APIs have become a critical infrastructure layer for digital advertising. As browser-based tracking degrades, server-side data transmission allows platforms like Meta and Google to receive higher-quality conversion signals, improving attribution and optimization.

But CAPI only works if companies can lawfully collect, process, and transmit user data—and clearly disclose those practices. Without compliant privacy policies and consent mechanisms, CAPI adoption can expose businesses to regulatory and reputational risk.

This is where the partnership’s positioning is notable. Instead of selling CAPI deployment in isolation, transcosmos and Priv Tech are framing it as part of a privacy-first data utilization strategy.

Expected outcomes for marketers

According to the companies, businesses that adopt the new service can expect several tangible benefits:

• Stronger customer trust and brand value, driven by transparent data practices

• More effective use of marketing data, improving targeting precision and ROI

• Reduced privacy risk, including protection against compliance violations and online backlash

• A competitive advantage, built on privacy as a differentiator rather than a constraint

In an environment where consumers are increasingly sensitive to how their data is used, these outcomes carry strategic weight beyond short-term performance metrics.

A broader signal for the MarTech market

The launch reflects a broader trend across MarTech and AdTech: privacy is no longer a downstream concern—it’s becoming a core capability.

As AI-driven marketing depends more heavily on high-quality first-party data, companies that can operationalize privacy at scale will move faster than those stuck in legal and technical gridlock. Consulting models that blend compliance, technology, and performance may become more common, particularly in markets like Japan where regulatory complexity is high.

For transcosmos, the partnership reinforces its role as a full-stack marketing and technology partner. For Priv Tech, it positions privacy engineering not just as risk management, but as a growth enabler.

Building sustainable growth through trust

transcosmos says it remains committed to helping businesses achieve sustainable growth by addressing real-world client challenges. In today’s digital marketing environment, few challenges are as pressing—or as intertwined—as privacy compliance and performance.

By launching Privacy Consulting Services together, transcosmos and Priv Tech are making a clear statement: the future of data-driven marketing belongs to companies that can balance trust, compliance, and AI-powered optimization—at the same time.

Get in touch with our MarTech Experts.

Threat modeling has long been considered a “nice to have” in application security—valuable in theory, but hard to scale in practice. That’s changing fast. As AI accelerates software development and expands the attack surface, enterprises are being forced to rethink how security is embedded from day one.

Against that backdrop, ThreatModeler has announced the acquisition of IriusRisk, bringing together what the companies describe as the two leading enterprise threat modeling platforms. The deal positions ThreatModeler as a dominant force in a rapidly expanding $30 billion application security market, with ambitions to make secure-by-design practices continuous, scalable, and deeply integrated into modern development lifecycles.

The move is as much about timing as it is about technology.

Why this acquisition matters now

Enterprise security teams are under pressure from both sides. On one end, development velocity is increasing, driven by cloud-native architectures, microservices, and AI-assisted coding. On the other, cyber threats are becoming more automated, targeted, and sophisticated.

Threat modeling sits at the intersection of those forces. It helps organizations identify design-level risks before code is written or deployed—but only if it can be applied consistently and at scale. Historically, that’s been the challenge.

By acquiring IriusRisk, ThreatModeler is betting that consolidation, automation, and AI-native intelligence are the keys to unlocking threat modeling’s next phase.

“With the addition of IriusRisk, we’re building the global leader in the threat modeling market to meet rapidly expanding demand,” said Matt Jones, CEO of ThreatModeler. “Together, we deliver customers greater innovation, expanded support, and more scalable solutions that make secure-by-design a sustainable, continuous practice at enterprise scale.”

Two leaders, complementary strengths

While both companies operate in the same category, their strengths have historically been complementary rather than redundant.

ThreatModeler is known for its AI-driven threat modeling platform, designed to help security architects rapidly model threats across complex, enterprise-scale environments. Its focus has been on speed, automation, and consistency—critical for organizations managing hundreds or thousands of applications.

IriusRisk, by contrast, has built deep traction with development and architecture teams, emphasizing collaboration, education, and adoption. Over time, that approach has helped foster what is widely regarded as the industry’s most active professional threat modeling community.

Bringing these two approaches together creates a platform that spans both sides of the security equation: architectural rigor at the enterprise level and practical engagement at the developer level.

According to the companies, customers using the combined capabilities have already seen measurable gains, including building threat models twice as fast and scaling adoption by more than tenfold.

Scaling “secure by design” beyond theory

One of the most striking claims around the acquisition is its focus on democratization. Threat modeling has traditionally been the domain of specialized security experts—a bottleneck in organizations trying to move faster.

The combined ThreatModeler–IriusRisk organization says it is uniquely positioned to change that dynamic. With hundreds of customers, tens of thousands of threat models built, and the largest professional threat modeling communities, the goal is to make secure-by-design practices accessible across entire enterprises.

That matters because most breaches aren’t caused by obscure zero-days. They’re the result of architectural oversights, misconfigurations, and design decisions made early—and rarely revisited.

By embedding threat modeling across the software lifecycle, the combined platform aims to help enterprises “virtually scale” their security teams, applying expert-level analysis without requiring expert-level headcount.

The AI angle: data, intelligence, and speed

AI is a recurring theme in the deal, and not just as a buzzword.

ThreatModeler emphasizes that the acquisition accelerates its vision of an AI-native security platform, powered by what it calls the industry’s largest proprietary threat modeling dataset. That dataset—now expanded with IriusRisk’s models, patterns, and community insights—forms the foundation for deeper intelligence and more automated decision-making.

“This milestone accelerates our vision to protect customers with an AI-native platform powered by the industry’s largest proprietary dataset,” said Archie Agarwal, Founder and Chief Innovation Officer of ThreatModeler. “By combining our teams and technology, we’re enabling faster innovation, deeper intelligence, and a security partner built to scale with our customers.”

In practical terms, this means more automated threat identification, smarter recommendations, and less reliance on manual expertise—all critical as AI both empowers developers and lowers the barrier for attackers.

A market ripe for consolidation

The threat modeling space has historically been fragmented, with a mix of open-source tools, consultancy-led approaches, and niche platforms. That fragmentation made it difficult for large enterprises to standardize practices globally.

This acquisition signals a shift toward consolidation, mirroring what has already happened in adjacent security markets such as application security testing and cloud security posture management.

Investors appear to agree. The combined company is majority owned by Invictus Growth Partners, with Paladin Capital Group, a long-standing investor in IriusRisk, remaining a shareholder. That continuity suggests confidence in the long-term growth of threat modeling as a core security discipline.

“Cybersecurity is a nonstop arms race, now accelerated by AI,” said John DeLoche, Co-Founder and Managing Partner at Invictus Growth Partners. “Threat modeling is essential for teams that want to proactively protect enterprise systems and applications. This acquisition unites leading threat-modeling expertise and creates the industry’s largest dataset, giving enterprises a decisive advantage in the AI era.”

Implications for enterprise security teams

For CISOs and application security leaders, the deal highlights a broader trend: design-time security is becoming non-negotiable.

As regulatory pressure increases and software supply chains grow more complex, organizations are being judged not just on how they respond to incidents, but on how well they prevent them. Threat modeling, once relegated to periodic reviews, is increasingly expected to run continuously alongside development.

By combining AI-driven automation with deep community adoption, ThreatModeler and IriusRisk are positioning themselves as a foundational layer in that shift.

Competitors will likely feel the pressure. Smaller vendors may struggle to match the scale, dataset depth, and enterprise reach of the combined platform, while larger security suites may look to strengthen their own design-time security capabilities through partnerships or acquisitions.

What comes next

While financial terms were not disclosed, the strategic intent is clear. ThreatModeler isn’t just expanding its footprint—it’s attempting to define what enterprise threat modeling looks like in an AI-first world.

“This is an exciting leap forward for the industry,” said Stephen de Vries, CEO of IriusRisk. “Both our companies share a passion for helping enterprises start left with their secure-by-design approach. By joining forces, we are better positioned to deliver on that shared mission.”

If successful, the acquisition could mark a turning point for threat modeling—from a specialist discipline practiced by a few, to an automated, AI-augmented capability embedded across every application and infrastructure layer.

In a security landscape where speed and foresight increasingly matter more than reaction, that shift could prove decisive.

Get in touch with our MarTech Experts.

Microsoft has announced a new suite of agentic AI solutions aimed at bringing intelligent automation across the entire retail value chain—from merchandising and marketing to fulfillment and store operations.

Designed to help retailers move faster and operate with greater precision, the new capabilities introduce a connected layer of intelligence that replaces fragmented workflows with coordinated, context-aware execution. Microsoft positions the offering as a foundation for a unified, intelligence-driven retail operating model built for speed, relevance, and resilience.

“The retailers that thrive will be the ones that unify their business with intelligence that reaches every corner of the value chain,” said Kathleen Mitford, Corporate Vice President of Global Industry at Microsoft. “With Microsoft’s agentic AI, retailers can automate what slows them down and amplify what sets them apart.”

Turning Conversations Into Conversions With Copilot Checkout

A central pillar of Microsoft’s retail push is Copilot Checkout, a new capability that allows shoppers to complete purchases directly within Copilot conversations—without being redirected to external websites.

The launch comes as AI-driven ecommerce traffic continues to surge. Adobe reports that AI-powered ecommerce visits during the 2025 holiday season increased 693% year over year, underscoring the growing importance of frictionless, intent-driven shopping experiences.

Copilot Checkout is now live in the U.S. on Copilot.com, with support from partners including PayPal, Shopify, and Stripe. Early participating brands include Urban Outfitters, Anthropologie, Ashley Furniture, and Etsy sellers.

For Shopify merchants, Copilot Checkout will be enabled by default following an opt-out period, allowing retailers to preserve their checkout experience while meeting customers inside AI-powered discovery flows.

Personalized Shopping Through Brand and Commerce Agents

Microsoft is also introducing Brand Agents for Shopify merchants and a personalized shopping agent template in Copilot Studio. These tools allow retailers to deploy conversational shopping experiences trained on their product catalogs and brand voice.

Brand Agents provide a turnkey option for answering product questions and guiding shoppers, while the customizable shopping agent template enables advanced experiences such as real-time recommendations, outfit building, and cross-channel discovery across web, mobile, and in-store environments.

Retailers including Kappahl Group are already exploring these tools to improve conversion rates and reduce returns by helping shoppers make more confident purchase decisions.

Catalog Enrichment and Smarter Product Discovery

To support discovery and personalization at scale, Microsoft is launching a catalog enrichment agent template in public preview. The agent automatically extracts product attributes from images, enriches listings with social and contextual insights, and streamlines onboarding, categorization, and error resolution.

Brands like Guess see catalog enrichment as a foundational layer for delivering real-time recommendations and cohesive shopping journeys across channels.

AI Agents for Store Operations and Frontline Staff

Microsoft is extending agentic AI beyond digital commerce into physical retail operations. The new store operations agent template, now in public preview, provides store associates and managers with natural-language access to inventory data, policies, and operational insights.

By combining internal data—such as sales trends and foot traffic—with external signals like weather and local events, the agent recommends staffing adjustments, flags exceptions, and suggests next-best actions in real time.

Retailers such as Strandbags are using the solution to empower frontline teams while improving decision-making speed and consistency.

A Broader Shift Toward Agentic Retail

With this launch, Microsoft is signaling a broader shift toward agentic AI as the backbone of modern retail operations. By automating routine workflows across commerce, catalog management, and store operations, retailers can redirect resources toward strategy, innovation, and customer experience.

Microsoft says its approach combines deep enterprise integration with responsible AI development—positioning intelligent agents not just as tools, but as long-term operational partners for retailers navigating an increasingly competitive and AI-driven market.

Get in touch with our MarTech Experts.

Snowflake has signed a definitive agreement to acquire Observe, an AI-powered observability platform built natively on Snowflake, as the data cloud provider deepens its push into enterprise-scale reliability, operations, and AI-driven application management.

The acquisition positions Snowflake to deliver a new generation of AI-powered observability, designed for the scale, complexity, and economics of modern AI-native enterprises operating across distributed systems, autonomous agents, and data-intensive applications.

“As our customers build increasingly complex AI agents and data applications, reliability is no longer just an IT metric—it’s a business imperative,” said Sridhar Ramaswamy, CEO of Snowflake. “By bringing Observe’s capabilities directly into the Snowflake AI Data Cloud, we’re enabling enterprise-wide observability with open architecture and AI-powered troubleshooting at massive scale.”

From Monitoring to Agentic, AI-Driven Troubleshooting

Observe was built on Snowflake from its inception, making the integration a natural extension of the Snowflake AI Data Cloud. Together, the companies aim to move observability beyond reactive monitoring toward proactive, automated operations.

At the core of the offering is Observe’s AI-powered Site Reliability Engineer (SRE), which correlates logs, metrics, and traces through a unified context graph. By combining this intelligence with Snowflake’s trusted enterprise data, teams can detect anomalies earlier, identify root causes faster, and resolve production issues up to 10 times faster, according to the companies.

This agentic approach is increasingly critical as enterprise systems become more distributed, autonomous, and AI-driven.

Open Standards and Telemetry at Scale

The acquisition also establishes a unified, open-standard observability architecture based on Apache Iceberg and OpenTelemetry, both of which Snowflake actively contributes to.

By treating telemetry as first-class data within the Snowflake AI Data Cloud, enterprises can manage terabytes to petabytes of logs, metrics, and traces using economical object storage and elastic compute. This approach allows observability data to be analyzed alongside business and operational data with consistent governance, analytics, and AI models.

Industry analysts see this as part of a broader shift away from specialized observability stacks toward lakehouse-style economics.

“Observability’s cost problem stems from treating telemetry as special-purpose data,” said Sanjeev Mohan, Principal Analyst at SanjMo. “Snowflake’s acquisition highlights how the lines between data platforms and observability platforms are blurring.”

Eliminating the Cost-Retention Tradeoff

As AI-driven applications generate unprecedented telemetry volumes, many organizations have been forced to rely on data sampling or short retention windows to control costs. Snowflake and Observe say their combined platform removes that tradeoff.

By unifying Observe’s AI-driven observability with Snowflake’s scalable data foundation, enterprises can retain high-fidelity telemetry data for longer periods while reducing overall observability costs—improving visibility across their entire data estate without sacrificing economics.

“Observability is fundamentally a data problem,” said Jeremy Burton, CEO of Observe. “By combining our AI-powered SRE with Snowflake’s AI Data Cloud, we can deliver faster insights, greater reliability, and dramatically better economics for operating the next generation of AI applications.”

Expanding Snowflake’s IT Operations Footprint

Beyond product integration, the acquisition expands Snowflake’s presence in the fast-growing IT operations management (ITOM) market. Gartner estimates the ITOM software market reached $51.7 billion in 2024, growing 9% year over year.

Following the close of the transaction—subject to regulatory approvals—Snowflake plans to deepen its focus on helping enterprises operate reliable AI agents and applications at scale. Observe’s developer-friendly approach is expected to complement Snowflake’s existing workload engines with real-time enterprise context, faster root-cause analysis, and AI-assisted troubleshooting.

Together, Snowflake and Observe aim to redefine observability as a core capability of the modern data platform—one built for AI-native systems where reliability, cost efficiency, and speed are inseparable.

Get in touch with our MarTech Experts.

Amperity is taking aim at one of marketing’s most persistent problems: the gap between insight and action.

The AI-powered Customer Data Cloud company has introduced its Customer Data Agent, positioning it as the first enterprise AI agent designed not just to analyze customer data, but to act on it. Built on unified, trusted customer profiles, the agent allows marketers to move from questions to live segments and journeys in hours instead of weeks.

In an era where AI tools are plentiful but impact is uneven, Amperity’s message is clear: AI only works in marketing when it’s deeply connected to customer data and embedded directly into daily workflows.

From AI Insight to Activation—Without the Wait

For many marketing teams, access to customer insights remains a bottleneck. Data lives across systems, insights require specialized queries, and activation often depends on engineering backlogs. The result is slow decision-making and missed opportunities.

The Customer Data Agent is designed to eliminate that friction. Using conversational, natural-language prompts, marketers can explore unified customer data, generate insights, and immediately turn those insights into activation-ready segments and journeys—without translating questions into SQL, tickets, or dashboards.

“This marks an important step forward for the category,” said Tapan Patel, Research Director at IDC. “It shows how AI can move from simply providing customer insights to actually helping marketers decide what to do next.”

A Different Model for Enterprise AI

Unlike generic AI assistants layered onto fragmented datasets, Amperity’s Customer Data Agent is built on the company’s patented identity resolution technology, giving it a complete and accurate view of the customer across channels and touchpoints.

That foundation enables a different kind of enterprise AI—one that can be trusted to support decisions, not just surface patterns.

With this release, the Customer Data Agent:

• Operates on unified, real-time customer profiles, not system-level records

• Orchestrates specialized AI agents for segmentation, journey design, and analytics

• Compresses the timeline from insight to revenue impact from weeks to hours

This approach reflects a broader industry shift: AI is no longer judged by how impressive its outputs look, but by how effectively those outputs translate into measurable business results.

AI That Matches How Marketers Actually Work

Rather than forcing teams to adapt to new technical interfaces, the Customer Data Agent is designed to work the way marketers already think and communicate.

Teams can ask plain-language questions such as:

• “Build a segment of high-value customers likely to repurchase this quarter.”

• “Design a journey for first-time buyers with declining engagement.”

• “Show which customer groups are driving the most incremental revenue.”

The agent delivers answers and can route them directly into activation, measurement, or optimization workflows—closing the loop between understanding and execution.

“Most AI value gets stuck between the model and the workflow,” said Derek Slager, Co-Founder and CTO at Amperity. “The Customer Data Agent closes that gap by delivering outputs that are immediately usable.”

Why This Matters Now

AI has become central to enterprise marketing strategies, but many organizations still struggle to operationalize it. The challenge is rarely model performance—it’s the lack of clean, unified data and the operational layers needed to act on AI outputs.

Amperity’s Customer Data Agent brings those pieces together: trusted data, applied intelligence, and built-in activation. For consumer brands under pressure to personalize faster, improve ROI, and reduce dependency on technical teams, that combination could be a meaningful competitive advantage.

As AI continues to move from experimentation to expectation, tools that directly connect intelligence to execution may define the next phase of marketing technology.

Get in touch with our MarTech Experts.

Domaine Worldwide is doubling down on experience as the next battleground in ecommerce.

The global Shopify design and development specialist has acquired Pattern, one of the most decorated digital design agencies in the commerce ecosystem, in a move that underscores a broader shift in digital commerce: design is no longer a finishing touch—it’s a growth lever.

The acquisition brings together Pattern’s brand-led, experience-first design capabilities with Domaine’s Shopify engineering, platform migration, and global delivery scale. The result is a combined offering aimed squarely at brands that see digital experience as a competitive differentiator, not a cosmetic upgrade.

Why This Deal Matters

Ecommerce has matured. Performance marketing is more expensive, platform features are increasingly commoditized, and AI-driven personalization is raising customer expectations. In that environment, brands are competing less on who has the fastest site—and more on who delivers the most cohesive, emotionally resonant experience across channels.

That’s where this acquisition lands.

“Experience design has become a defining competitive advantage for commerce brands,” said Marko Bon, President of Domaine. Bringing Pattern into the fold, he noted, expands Domaine’s ability to help clients reimagine their brands while delivering scalable, technology-driven experiences.

In practical terms, Domaine is positioning itself not just as a Shopify systems integrator, but as a full-spectrum commerce partner—from brand identity and UX to platform engineering and AI-ready infrastructure.

Pattern’s Role: Design at Critical Moments

Pattern has built its reputation by partnering with brands at inflection points: rebrands, digital redesigns, and major platform migrations where the stakes—and expectations—are highest.

Its client roster includes digitally native and enterprise brands such as Marine Layer, Framebridge, Bobbie, Image Skincare, and Rothy’s, and its work has earned more than 200 industry awards, including repeated recognition from the Webbys, W3, Glossy, and ADWEEK.

Notably, Pattern and Domaine have already worked together on high-profile projects, including Rothy’s—making this acquisition more of a formalization than a leap into the unknown.

A Unified, Experience-First Commerce Stack

The combined company is targeting brands across the growth spectrum:

• VC-backed startups building digital-first identities

• High-growth brands evolving their ecommerce experience

• Enterprises needing scalable design systems, complex workflows, and AI-enabled efficiencies

Pattern brings deep expertise in brand systems, UX, and storytelling. Domaine adds global delivery, Shopify Plus engineering, and operational scale. Together, they aim to offer something many agencies struggle to balance: craft and scale.

“As Pattern experienced record-breaking growth, finding the right partner to scale with became critical,” said Isaac Newton, Co-Founder of Pattern. Cultural alignment and shared ambition, he added, made Domaine a natural fit.

Shopify, AI, and the Experience Arms Race

The timing of the deal is telling. Shopify is increasingly positioning itself as an operating system for modern commerce, with AI-driven features, composable architectures, and personalization tools baked into the platform.

But technology alone doesn’t differentiate brands.

As AI accelerates content creation and personalization, experience design becomes the filter that determines whether those capabilities feel coherent—or chaotic—to customers. Domaine and Pattern’s shared “one site per customer” philosophy reflects this shift toward deeply personalized, brand-consistent experiences powered by data and automation.

This acquisition also mirrors a broader trend in the MarTech and commerce services market: agencies are consolidating to meet rising client demands for end-to-end execution, rather than piecemeal design or development work.

Competitive Implications

Domaine’s move raises the bar for Shopify-focused agencies, many of which specialize either in design or engineering, but not both at enterprise scale. It also puts pressure on consultancies and systems integrators that have historically deprioritized brand and experience in favor of implementation speed.

For brands, the message is clear: digital commerce partners are expected to deliver strategy, experience, technology, and scalability—all under one roof.

What Comes Next

With Pattern fully integrated, Domaine plans to expand its global footprint and deepen its role in shaping how commerce brands adapt to rapid change across AI, personalization, and platform evolution.

For clients, the promise is simpler: fewer handoffs, tighter alignment between brand and technology, and digital experiences designed to scale—not break—as expectations rise.

In a crowded ecommerce landscape, Domaine is betting that experience-first commerce isn’t just a design philosophy. It’s the next phase of competitive advantage.

Get in touch with our MarTech Experts.

For decades, buying premium video—especially live sports—has been one of advertising’s most manual, time-intensive, and human-dependent processes. It’s also one of the most valuable. Now, NBCUniversal and a group of technology and agency partners are making a bold case that artificial intelligence is finally ready to take on the hardest job in media buying.

NBCUniversal, independent agency RPA, FreeWheel, and Newton Research have announced a new partnership that introduces agentic AI into premium video buying across both linear television and digital platforms. In a first-of-its-kind proof of concept, AI agents can execute and optimize a single premium video investment across NBCUniversal’s linear TV and streaming inventory in seconds—without removing humans from the loop.

The demo may look futuristic, but the implications are immediate: faster execution, fewer manual handoffs, and a fundamentally new way to transact high-value video advertising at scale.

And notably, this isn’t happening in long-tail inventory or test environments. The first real-world execution will include live football playoff games in Q1 2026—marking the first time AI agents have automated live sports inventory on linear television.

From fragmented buying to agent-led orchestration

At the center of the announcement is a shift away from siloed buying workflows. Traditionally, advertisers and agencies plan, negotiate, activate, and optimize linear TV and streaming video through separate systems, teams, and timelines. Even as “converged TV” has become a buzzword, execution has remained stubbornly fragmented.

The new model flips that dynamic.

Using agentic AI, buy-side and sell-side agents communicate directly with one another to orchestrate cross-platform video buying and optimization in real time. These agents span NBCUniversal’s linear networks and streaming properties, with FreeWheel and NBCUniversal deploying AI sales agents on the sell side, while Newton Research—working with RPA—has designed and implemented buy-side agents.

The result is a single, unified investment that can be planned, executed, and optimized across platforms almost instantly.

This is not simply automation of existing steps. It’s a reengineering of the workflow itself—one that replaces sequential, manual processes with parallel, machine-driven intelligence that still defers to human judgment on strategy and nuance.

Why “agentic AI” matters here

The term “agentic AI” is quickly becoming one of the most important—and misunderstood—concepts in enterprise technology. Unlike traditional AI tools that respond to prompts or automate narrow tasks, agentic AI systems can act independently within defined constraints, coordinating with other agents to achieve specific goals.

In this case, those goals include:

• Translating campaign objectives into actionable media decisions

• Negotiating and allocating inventory across linear and streaming

• Optimizing delivery in real time based on performance signals

• Preserving brand, pricing, and placement guardrails set by humans

The agents operate using Model Context Protocol (MCP), enabling agent-to-agent collaboration across different organizations’ systems—a critical requirement for media transactions that involve buyers, sellers, data providers, and measurement partners.

What makes this noteworthy is not just the speed, but the interoperability. Historically, media buying technology has struggled to connect across vendors and platforms. Agent-based systems, if widely adopted, could finally provide a common intelligence layer across the ecosystem.

Live sports: the ultimate stress test

If there’s one category that exposes the limits of automation, it’s live sports.

Live sports inventory is scarce, expensive, time-sensitive, and operationally complex. Ads must be delivered flawlessly, at scale, often during unpredictable moments. That complexity is precisely why sports have remained one of the last strongholds of manual media buying.

By applying agentic AI to live football playoff inventory, NBCUniversal and its partners are signaling confidence that AI can handle the most demanding use cases—not just remnant or digital-only placements.

Mark Marshall, Chairman of Global Advertising & Partnerships at NBCUniversal, framed the move as both symbolic and strategic.

“NBCUniversal is proud to introduce agentic AI into the future of media buying alongside our partners,” Marshall said. “This step forward will redefine how inventory is bought and sold, and what better place to start than within our live sports inventory.”

It’s a calculated bet: if AI can work here, it can work anywhere.

Preserving human expertise, not replacing it

One of the recurring concerns around AI in advertising is the fear of removing human judgment from decisions that require creativity, context, and brand sensitivity. The partners behind this initiative are eager to emphasize that this is not a “hands-off” system.

Instead, agentic AI is positioned as an operational layer—handling executional complexity so humans can focus on strategy.

RPA CEO Jim Helberg described the approach as a way to “hyper-streamline strategic media intelligence and transactions in service of business outcomes,” while freeing teams to focus on higher-value work.

By reengineering manual processes, Helberg said, agencies can redirect human expertise toward strategic planning, marketplace dynamics, and client-specific nuance—areas where AI still struggles.

This framing mirrors a broader trend across marketing technology: AI as a multiplier of human capability rather than a replacement.

What this means for agencies

For agencies, the promise is clear: fewer bottlenecks, faster activation, and greater control over cross-platform investments.

Today, executing a premium video campaign across linear TV and streaming often involves multiple teams, systems, and reconciliations—each introducing delays and inefficiencies. Agentic buying compresses that timeline dramatically.

Newton Research CEO John Hoctor highlighted how intelligent agents can support the full campaign lifecycle, from planning through measurement.

“Alongside humans, Newton’s agents interoperate and collaborate with other agents, data and technology companies to create a cohesive intelligence standard,” Hoctor said—one that could eventually power end-to-end campaign execution and optimization.

If that vision holds, agencies could see meaningful productivity gains at a time when margins are under pressure and clients are demanding more transparency and accountability.

Implications for publishers and broadcasters

For publishers like NBCUniversal, agentic AI represents more than operational efficiency—it’s a competitive differentiator.

As buyers push for faster, more flexible transactions across screens, publishers that can offer unified, intelligent access to premium inventory stand to gain. Automating sales-side workflows could also improve yield management, reduce friction in negotiations, and enable more dynamic pricing strategies over time.

FreeWheel General Manager Mark McKee pointed to the broader impact on connected TV, calling agentic buying a milestone in CTV’s evolution toward automation and outcomes.

“Historically, delivering ads live isn’t easy, especially with large-scale events like sports,” McKee said. “Now…something that seemed unimaginable just a short time ago is real.”

That statement underscores a key industry tension: as CTV grows, expectations around automation and measurement increasingly resemble digital—but premium content still demands TV-grade reliability. Agentic AI could be the bridge between those worlds.

How this compares to existing automation efforts

Automation in media buying is not new. Programmatic advertising has been around for more than a decade, and broadcasters have steadily introduced automation into linear TV through addressable ads and advanced planning tools.

What’s different here is scope and autonomy.

Programmatic systems typically automate bidding within predefined marketplaces. Agentic AI, by contrast, operates across systems, negotiating and optimizing holistically rather than transaction by transaction.

In that sense, this initiative aligns more closely with emerging trends in AI-driven enterprise software than with traditional ad tech. It’s less about auctions and more about orchestration.

Competitors are watching closely. Other major broadcasters and platforms are experimenting with AI-powered planning and optimization, but few have publicly demonstrated agent-to-agent transactions spanning linear and streaming—let alone live sports.

The road ahead: opportunity and caution

As groundbreaking as this announcement is, it’s still an early step.

The current implementation is described as a proof of concept, with a limited number of executions planned. Scaling agentic buying across more advertisers, inventory types, and publishers will raise new challenges around governance, transparency, and trust.

Questions remain about:

• How pricing controls and brand safety guardrails are enforced

• How agencies audit and explain AI-driven decisions to clients

• How measurement and attribution adapt to real-time agent optimization

There’s also the matter of standardization. For agentic AI to truly reshape the industry, more participants will need to adopt compatible protocols and data frameworks—a nontrivial task in a fragmented ecosystem.

Still, the direction is clear. As media operations grow more complex, manual workflows are becoming unsustainable. Agentic AI offers a plausible—and increasingly compelling—alternative.

Why this matters now

This announcement arrives at a moment when the advertising industry is searching for its next operational leap. Linear TV and streaming continue to converge, live sports remain the crown jewel of premium video, and marketers are demanding both speed and accountability.

By applying agentic AI to the hardest problem first, NBCUniversal and its partners are making a statement about where media buying is headed.

If successful, this approach could redefine not just how premium video is bought, but how agencies, publishers, and platforms collaborate in an AI-driven future.

For an industry long weighed down by complexity, that’s a future many are eager to test.

Get in touch with our MarTech Experts.

Independent insurance agencies have spent years modernizing back-office systems, yet the front office—where calls are answered, requests triaged, and service experiences formed—has largely remained manual. HawkSoft and Sonant are betting that voice AI is finally ready to change that.

HawkSoft, a widely used agency management system (AMS) for independent insurance agencies, has announced a new integration with Sonant, a voice AI platform built specifically for the insurance industry. The partnership brings 24/7 conversational AI directly into HawkSoft, automatically logging calls, creating tasks, and writing notes back into the system of record.

The promise is straightforward but significant: fewer missed calls, faster service, less manual data entry, and measurable productivity gains—without forcing agencies to overhaul their existing workflows.

Turning phone calls into structured work

For most independent agencies, phone calls remain the primary entry point for customer interactions. Policy changes, billing questions, certificate requests, and quote inquiries still arrive by phone—often in bursts that overwhelm staff.

Sonant’s voice AI acts as a virtual receptionist designed specifically around property and casualty (P&C) insurance workflows. Unlike generic call bots, Sonant’s agents are trained to understand insurance terminology and intent out of the box. Calls are answered around the clock, triaged in real time, and converted into structured tasks inside HawkSoft.

That means no more scribbled notes, copy-pasting between systems, or delayed follow-ups when staff are busy or unavailable. Each call becomes an actionable item tied to the correct client and policy record.

For agencies already standardized on HawkSoft, the integration keeps everything in one place—arguably the most important factor for adoption.

Why this matters for independent agencies

The insurance labor market remains tight, particularly for service and customer support roles that see high turnover. At the same time, customer expectations are rising, shaped by always-on digital experiences in banking, retail, and healthcare.

This integration directly targets that pressure point.

According to the companies, agencies using Sonant within HawkSoft can reduce hold times, eliminate missed calls, and free staff to focus on higher-value work such as advising clients, handling complex cases, and driving retention.

The efficiency gains are not just about speed. Manual call logging and note-taking are error-prone, often leading to dropped context and inconsistent service. Automating those steps improves accuracy while creating a cleaner audit trail—an increasingly important consideration in regulated industries.

What Sonant brings to the table

Sonant positions itself as insurance-native voice AI, rather than a general-purpose conversational platform. Its feature set reflects that focus:

• Insurance-trained AI agents capable of understanding common P&C service requests

• Warm transfers that route complex or sensitive calls to human staff

• VIP bypass for priority clients

• Past-call memory that preserves context across interactions

• Real-time lookups that connect callers to the correct client and policy records

• Automatic task routing directly into HawkSoft

From an operational standpoint, the key differentiator is that Sonant writes everything back into HawkSoft. Agencies don’t have to manage a separate inbox, dashboard, or CRM just to see what the AI handled.

Practical AI, not experimental tech

One of the most notable aspects of this announcement is its positioning. HawkSoft and Sonant are not framing the integration as a futuristic experiment or innovation lab project. Instead, they emphasize time-to-value.

The solution is designed to show measurable results in weeks rather than months, with governance guardrails and SOC 2 Type II compliance built in. That focus reflects a broader trend in enterprise AI adoption: buyers are increasingly skeptical of abstract “AI transformation” promises and want practical, operational improvements.

For agencies, the value proposition is easy to quantify. Fewer missed calls translate directly into better service and revenue protection. Reduced manual work lowers staffing pressure. Faster resolution improves customer satisfaction.

Keeping humans in the loop

Despite the automation, the system is not designed to replace agency staff. Simple, repetitive requests can be handled end to end by the AI, while more complex scenarios are routed to humans with full context attached.

Rushang Shah, CMO of HawkSoft, framed the integration as a way to balance automation with service quality.

“When Sonant’s virtual receptionist answers calls, it can handle simple tasks while routing more difficult ones to a person, all while documenting the client and policy in HawkSoft,” Shah said.

That hybrid approach mirrors how AI is being deployed across other professional services sectors: machines handle volume and structure, while humans handle judgment and relationship-building.

A signal of where insurtech is headed

The HawkSoft–Sonant partnership also reflects a broader shift in insurance technology. Rather than standalone AI tools, the market is moving toward embedded intelligence within core systems.

Agency management systems like HawkSoft sit at the center of daily operations. Integrating AI directly into those platforms reduces friction and increases trust—two major barriers to adoption in insurance.

Voice AI, in particular, is gaining traction as speech recognition and natural language understanding improve. What once felt unreliable or gimmicky is now being deployed in high-stakes customer interactions, provided it’s trained on domain-specific data.

Competitors across the AMS and insurtech landscape are watching closely. Expect more integrations that bring AI directly into systems of record, rather than forcing agencies to bolt on separate tools.

The bigger takeaway

At a time when “AI” is often overused and under-delivered, this announcement stands out for its specificity. It addresses a real operational bottleneck, integrates into an existing workflow, and targets measurable outcomes.

For independent insurance agencies, the front office has long been a productivity sink. HawkSoft and Sonant are making the case that voice AI—when designed for the industry and embedded correctly—can finally turn phone calls from interruptions into structured, actionable work.

If adoption follows, this may mark a quiet but meaningful shift in how agencies handle customer service in an always-on world.

Get in touch with our MarTech Experts.