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.

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