Agentic AI: A New Path to Efficiency and ROI
For much of the past two years, media and entertainment leaders have wondered whether agentic AI had a place in their organizations. That hesitation is giving way to pragmatism. By this time next year, agentic AI will sit inside everyday operations no longer as an experiment, but as part of the connected backbone that powers how content is created, managed, and delivered. The question has shifted from if to how, and specifically, how to make it usable across the whole business.
Agentic AI extends automation beyond basic tasks. It introduces systems that understand intent, coordinate across tools, and execute steps on behalf of teams while keeping people in control. The impact isn’t theoretical anymore; it’s showing up in how media organizations design, run, and scale their workflows.
From Pipelines to Adaptive Worknets
Most media workflows were built for predictability: linear stages, handoffs, and specialist roles. Even in the cloud, they often run like assembly lines that are efficient, but rigid and hard to change.
Agentic AI enables adaptive worknets: networks of intelligent agents that interpret goals, negotiate dependencies, and act across systems. Instead of scripting every action, teams describe outcomes. A producer might ask, “Prepare localized versions for next week’s campaign,” and the system handles the sequence, sourcing approved assets, validating rights, coordinating translations, and packaging deliverables for review. If priorities change or a live segment runs long, the plan adjusts automatically.
Crucially, this approach doesn’t demand a rip-and-replace. It wraps what’s already in place - from asset management to scheduling and publishing - with an orchestration layer that can reason about tasks and timing. Its strength lies in connecting the spaces between systems and turning hand-offs into continuous flow.
Media Operations, Open to All
Professional media stacks have traditionally required specialist knowledge to execute specific workflows. That creates bottlenecks: producers, marketers, and communications teams rely on complex, often inflexible, custom scripts or specialized operators to retrieve material, trigger jobs, or route approvals. The next phase is conversational and collaborative.
Instead of navigating folder hierarchies or workflow menus, any authorized user can drive media workflows using natural language commands from a simple to use dashboard. Instructions like “Find the last two interviews with our CEO,” or “Update the trailer with the new logo and schedule it for Friday” can be facilitated by agents or a framework of agents with ease. There is no cumbersome searching through endless archives or opening of editing applications.
Agents are far more than chatbots.
Under the hood, agents translate requests into concrete actions across the tools already in use. The shift isn’t only technical, it’s cultural. The agent is continuously learning, trained on the media engine, and equipped to offer suggestions to improve outcomes. Its ease of use enables wider participation, in addition to a much faster turn around. Going forward, storytelling can flow without such reliance on deep technical expertise.
Embed, Not Replace
A persistent myth is that you have to replace legacy systems to benefit from agentic AI. In practice, the fastest wins come from integrated orchestration.
Instead of asking teams to learn new tools or abandon existing investments, agentic systems connect what’s already there from production platforms, asset management, planning tools, to publishing and distribution and make them work together. Conversational commands describe what is needed, and the system determines which applications, data, and resources to use. Agents can locate the right material, launch the appropriate workflow, and coordinate the process securely across departments.
This approach significantly optimizes what works by removing manual effort and friction. Securely integrating AI agents across existing solutions significantly enhances accessibility and efficiency.
Real Results
Turnaround times shrink as orchestration fills idle gaps between steps. Versioning scales effortlessly as agents generate the right renditions, apply local requirements, and package for each endpoint. Consistency strengthens as rules travel with the work. For example, if a logo changes or a disclaimer is required, updates cascade automatically. Cross-department access expands, enabling teams across marketing, corporate, and live-event operations to self-serve routine tasks from the same trusted catalog, reducing queues and re-exports.
The outcome is less about speed for its own sake, and more about agility and the ability to respond quickly to opportunity or change without disruption.
2026 and Beyond
Over the next 18 months, agentic AI has the potential to move from test pilots to day-to-day assistants that drive media workflows, delivering the efficiency and creative freedom we are all looking for.
As we progress forward, the question is no longer, “Can we automate this?” Rather, “Can we trust it?” That trust will define the next phase of adoption.
As a community, we need to ensure transparency in the data and policies behind the agentic systems we adopt. When teams can see and understand the reasoning behind results, confidence grows naturally. The technology is ready. The challenge now is cultural, building trust, defining shared standards, and helping teams collaborate with systems that can plan and act. Once trust in technology has been established, agentic AI can empower users to leverage their creative muscle, driving workflows and operations at the speed of business.
The organizations that succeed will not simply automate faster; they will adapt faster, evolving their media operations as quickly as audiences, platforms, and stories themselves.
[Editor's note: This is a contributed article from Dalet. Streaming Media accepts vendor bylines based solely on their value to our readers.]
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