Is 2026 the year agentic AI moves from theory to operations in media production?

The broadcast industry has spent years testing AI in pilot programs. In 2026, the question is whether organizations can deploy autonomous systems at scale.

The urgency stems from a shift in what AI can actually accomplish.

Earlier generations of AI tools required extensive manual tagging and metadata to function. Agentic AI systems can understand video content directly, work across multiple platforms simultaneously and make decisions without constant human input. This creates opportunities that weren’t previously viable, particularly in monetizing archived content that has been inaccessible or underutilized.

At the same time, traditional broadcasters face pressure from the creator economy and streaming platforms, which are diverting advertising revenue from legacy media. Organizations need to extract more value from existing assets and produce content faster across multiple platforms. The technology to do this exists, but deployment has lagged.

The trends shaping broadcast and media production in 2026

Fred Petitpont, co-founder and CTO at Moments Lab, described the current situation as an “implementation gap” between AI’s potential and its practical use in daily production. He cited Jon Roberts, CTO of ITN, who noted a disconnect between AI’s “genuinely transformational” potential and “the day-to-day practical experience” in production.

“The winners in 2026 will be those who jump in with both feet and deploy AI with clear human-in-the-loop frameworks rather than endlessly debating in committee rooms,” Petitpont said.

Why agentic AI differs from earlier tools

Several technology executives identified agentic AI as a focal point for operational change this year. These systems use AI agents to automate tasks such as metadata verification, content routing and archive management without constant human intervention. Unlike earlier AI tools that assisted with specific tasks, agentic systems can coordinate across multiple platforms and make autonomous decisions within defined parameters.

Steph Lone, global leader of solutions architecture for media, entertainment, games and sports at AWS, said the shift to agentic AI enables capabilities that weren’t possible with previous generations of tools.

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“M&E companies will rely on AI agents to automate tasks across video understanding, metadata generation, ad operations, creative development and even natural-language-driven media workflows,” Lone said.

Jonas Michaelis, CEO of qibb, said the biggest operational changes will come from agentic AI handling background tasks that humans cannot manage at scale, such as verifying metadata, checking rights windows and optimizing cloud resources in real time.

“The missing guardrails are the unsexy ones, including clear auditability, versioned decision logs and strict boundaries on what agents are not allowed to do without humans in the loop,” Michaelis said. He warned that without these controls, companies risk creating systems they cannot debug when problems occur.

Why deployment remains difficult

Organizations face structural barriers to implementing agentic AI despite its capabilities. Petitpont outlined three technical reasons why most broadcasters may fail to incorporate AI tools into their workflows despite investment.

First, teams may expect AI to perform better with the same limited data as before, such as video search that still relies on keywords and metatags rather than understanding video content directly. Second, some may choose AI tools that require significant alterations to their media asset management systems or workflows. Third, broadcasters may fail to provide adequate gateways for moving on-premises content into the cloud for AI processing.

“Failure to address all of these three concerns will cause the downfall of many AI projects in 2026,” Petitpont said.

Craig Wilson, principal enterprise specialist for broadcast at Avid, said organizations also face a cultural challenge around oversight. He identified agentic AI as a major area for development, particularly in how different technologies and systems interact, but emphasized that automation requires human supervision.

“One of the myths with AI is that it will just do stuff automatically, there still needs to be oversight,” Wilson said.

Ivan Verbesselt, chief strategy and marketing officer, Mediagenix, noted that deployment paradigms need refinement to achieve production-level reliability at scale, particularly in multi-vendor environments. He said a human-in-the-loop approach remains necessary to manage the level of autonomous decision-making granted to these systems.

Why production companies may move faster

Petitpont predicted that production companies, unburdened by legacy IT infrastructure and compliance committees, will monetize archives faster than traditional broadcasters. This matters because the business case for agentic AI has shifted from efficiency to revenue generation.

“The companies we work with used to ask ‘How many hours will this save?’ Now their focus is ‘How do we monetize our archive?’ and ‘What new revenue streams does this enable?'” Petitpont said.

The shift reflects a change in what’s economically viable. Archives that were previously too costly to search and repurpose can now be made accessible through AI that understands content without extensive manual tagging. Production companies can move faster because they don’t need to integrate new systems with decades-old infrastructure or navigate enterprise compliance processes.

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“The surprise won’t be who adopts AI fastest, it will be who monetizes it most effectively,” Petitpont said.

Petitpont noted that workers may self-adopt AI tools even as IT departments fail at company-wide implementations, citing an MIT report on a “shadow” AI economy. This creates a situation where individual teams use AI successfully while institutional deployment stalls.

New revenue models beyond automation

The focus on monetization rather than efficiency reflects a shift in how organizations view AI deployment – moving from workflow to bottom line. Operative’s Dave Dembowski, CRO, predicted that media companies will use agentic AI to buy and sell media in new ways, with agents eventually having access to entire product catalogs to provide recommendations for pricing, packaging and campaign delivery.

Petitpont said automation will move from “doing things faster” to “doing things previously impossible,” though he noted that fully autonomous AI video creation is overhyped.

“If you automate everything, you get repetitive content, and audience engagement will drop,” Petitpont said. He identified areas where AI is transforming workflows as agentic content discovery that understands narrative context, multi-agent coordination in live production, archive-to-air automation and metadata generation that makes video searchable at scale.

Michaelis said automation will shift from cost reduction to value creation, which changes the economic justification for deployment.

“The real competitive advantage comes from generating new content, tapping unused distribution channels and personalizing existing content for the right audience,” Michaelis said.

The distinction matters because efficiency gains compete for budget with other cost-saving measures, while revenue-generating capabilities represent new business opportunities. Organizations that frame AI deployment as a monetization strategy rather than an efficiency initiative may find it easier to justify investment and overcome implementation barriers.

So, is 2026 the year of agentic AI in broadcast?

The answer depends on how the question is framed. If the question is whether agentic AI technology is ready for production use, the executives interviewed believe so. If the question is whether the industry will successfully deploy it at scale, the consensus is more cautious.

The technology exists. The business case has shifted from cost savings to revenue generation, which changes the economics of deployment. Organizations have moved past the evaluation phase and are now focused on scaling rather than on whether the technology works.

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But the structural barriers remain significant.

Legacy infrastructure, organizational inertia and the complexity of integrating AI with existing media asset management systems will prevent many organizations from successful implementation, according to Petitpont. Production companies without these constraints may capture value faster than traditional broadcasters.

The industry will likely see a split between organizations that deploy production-ready systems with clear governance frameworks and those that remain stuck in pilot programs or struggle with failed implementations. Workers will continue to use mass-market AI tools independently, even as institutional efforts stall.

Whether 2026 becomes known as the year agentic AI transformed media production will depend less on the technology itself and more on organizational execution. The tools are available. The question is who uses them effectively.