By: Rich Andes
When I talk with media organizations about AI, the conversation usually starts with something very practical. They want to generate metadata without someone watching every frame. They want to translate captions, find a particular person in hours of footage, or get a highlight out while an audience still cares about it.
Those are useful problems to solve. But once you start putting AI into everyday production, a few other questions come up pretty quickly. What will it cost when you run it across all your content? Where is that content going, and how do you keep it secure? And how do you control what the AI does within your workflow?
Telestream’s approach to practical AI is built around those questions. Automating a task is only part of the job. Our customers also need to be able to plan the cost, govern the process and retain control over their media and data.
Controlling Costs as AI Usage Grows
Cost is becoming one of the biggest considerations in putting AI into everyday media operations. We are hearing from customers who started using AI or machine learning a few years ago and are now feeling the burden of running those services at scale.
GPU resources are among the more expensive resources you can use in the cloud. When you need real-time performance across large volumes of media, those costs become a significant part of the economics. If costs grow beyond what an organization can justify, even a useful AI capability can become a dealbreaker.
Think about a live sports event. You might have 20 cameras capturing different views of the action. Applying AI across those feeds can help you find moments that would otherwise require operators to watch and mark each one. It can also help you create more content for different audiences, faster.
But you need to understand the economics of doing that throughout an event, across a season and across multiple productions. A useful capability has to make financial sense at the volume you actually intend to use it.
That’s one reason we built local deployment into our practical AI approach from the start. Customers can run AI within their own infrastructure as part of a hybrid environment. We support customers bringing their own GPUs, so they can plan compute capacity around the volume of media they need to process.
For locally deployed capabilities, customers pay for a perpetual or subscription license rather than for each use. They can run the licensed AI capabilities as much as their infrastructure supports, without software costs increasing with the volume of media processed. This allows them to plan licensing and compute capacity around their operational needs.
Hardware, power, and maintenance still need to be accounted for. The benefit is a clearer basis for budgeting as AI becomes a regular part of the operation and the volume of content being processed grows.
That changes the conversation from “Can we afford to try this?” to “How do we resource this so we can use it consistently?”
Governing AI Within the Workflow
AI can be very useful when a task involves interpreting content or finding information. Professional media workflows also contain steps that need to behave consistently and meet specific technical requirements.
We bring those together through Vantage, which provides the workflow logic around our AI capabilities. Today, custom scripting, along with robust and accessible APIs, allow us to connect customer-developed agents to Vantage workflows. Planned support for Model Context Protocol (MCP) will provide another way for agents developed by customers or partners to interact with Vantage and use its media processing capabilities.
One example is locating missing material. A workflow may receive a list of content that needs to go into a playout system, but some of that content is missing. Traditionally, you might look in a few known folders. Today, the material could be spread across different systems or still sitting with a distributor.
An agent integrated with Vantage can help cast a wider net, locate the material, and return a download link. That information can then feed back into the workflow so the established processing steps can continue.
The real value is in how they work together. The agent helps with a task that requires a more flexible search, and Vantage supplies the workflow logic for what happens next.
From a governance perspective, organizations need to decide where this level of automated flexibility is appropriate, what outputs and variances they will accept, and where human review or oversight is required. These decisions should be part of the core design of the workflow when incorporating AI and agentic processes. It is essential that the workflow be built with consideration for where humans do, or do not, gate the decisions of the AI.
There is also a difference between generating something that looks right and producing something that meets broadcast-grade requirements. Speech-to-text is a good example. Generating a general transcript of speech data versus creating ready-to-air closed caption data that meets delivery and accessibility requirements, involve very different challenges. We have specialists who have worked on those challenges for decades—experience that makes a big difference when you start putting AI into production.
Maintaining Sovereignty Over Media and Data
Sovereignty is becoming a much bigger part of the AI conversation.
The questions go well beyond which model you can use. Where is your content going? Who has access to it? What happens to the data once it leaves your environment? And what dependencies are you creating for the next five or ten years?
For media companies, content is their most valuable asset, and it needs to be treated as such. It might be an archive built over decades, an unreleased program, a live sports feed, or iconic news footage. Sending that material to an external service needs to be a deliberate decision.
That is why we have built on-premises AI into Telestream’s approach. Customers can run these local capabilities on-premises or within their own data centers, independently of an internet connection. That gives them a practical way to apply AI while keeping media and data inside their own private, secure/airgapped environment.
Control also extends to the information those capabilities use. With customized face detection, for example, customers can build a private library of reporters, politicians, players or coaches they need to identify. They can apply that intelligence to their content locally.
There are workloads where public cloud and large external models make tremendous sense. For others, local processing may be a better fit because of security, compliance, latency or cost. Telestream gives customers choice by making local AI an option within their media workflows, alongside integrations with third-party technologies. They can decide where external services are appropriate and which content needs to stay under their control.
That flexibility matters over time, too. Organizational policies and regulatory requirements can affect which models are acceptable. Before launching our AI Vision capabilities, we replaced the underlying Qwen model with a Gemma model, adapting the prompting and integration in approximately three weeks. That was engineering work on our side, but it demonstrated the value of an architecture that can accommodate a model change without rebuilding the entire capability.
Sovereignty comes back to control over those choices. Our job is to give customers the technology and flexibility that will enable AI to make a real difference in their workflows, regardless of how requirements evolve – all while retaining control over their content and operations.
Putting Practical AI to Work
These considerations come together in the actual media workflow. During live capture, for example, we can apply AI to a growing file and generate metadata as the event unfolds. That information can feed connected systems so teams can find and use relevant content more quickly.
The operational value is clear: less manual logging and faster access to material. Making it sustainable also means planning the compute capacity, deciding where processing happens, and defining how the metadata is used.
That’s how I think about practical AI. Start with a real task, work out where intelligence can help, then build it into an operation whose costs and behavior you can manage.
The more useful AI becomes, the more frequently people will want to use it. We want customers to be able to do that with confidence in the economics, the workflow, and their control over the content.
Learn more about Telestream AI capabilities in Vantage and UP. To connect with us for a demo or to chat more about practical AI, visit: https://www.telestream.com/contact-us/.
Frequently Asked Questions
What is practical AI for media workflows?
Practical AI applies intelligence to specific production tasks, such as generating metadata, translating captions, identifying people in footage and creating highlights. Telestream’s approach combines those capabilities with workflow automation, deployment choices and cost planning so organizations can use AI consistently while retaining control over their media and operations.
How can media organizations manage AI costs at scale?
Organizations need to plan software and compute costs around the volume of media they intend to process and the performance they require. Telestream supports local deployment using customer-provided GPUs. For locally deployed capabilities, perpetual or subscription licensing offers an alternative to paying for every use. Infrastructure, power and maintenance remain part of the overall cost.
Can Telestream’s AI capabilities run on-premises without an internet connection?
Telestream’s local AI capabilities can run on-premises or within customers’ own data centers, independently of an internet connection. This allows organizations to process media inside their own environment. Customers can also use local AI within a hybrid environment, choosing external services where appropriate for their workloads.
What does data sovereignty mean when using AI in media production?
Data sovereignty means retaining control over where media and data are processed, who can access them and which services are used. Local processing gives media organizations a way to apply AI while keeping valuable or sensitive content within their own environment. Decisions to send content to external services can then impact their security, compliance, latency and cost requirements.
How does Vantage support governance of AI workflows?
Vantage provides the workflow logic around Telestream’s AI capabilities and integrations with customer-built or partner-developed agents. Organizations can design workflows that define where AI is used, which outputs are acceptable and where human review is required. This connects AI tasks with established media processing steps.
Is an AI-generated transcript enough to create delivery-ready captions?
A transcript is a starting point. Captions also need to meet applicable delivery and accessibility requirements. Telestream combines AI capabilities with specialist media expertise to address the requirements involved in turning speech recognition output into captions suitable for professional use.
Can AI generate metadata while a live event is being captured?
Telestream can apply AI to a growing file during live capture and generate metadata as the event unfolds. That information can feed connected systems, helping teams find and use relevant material faster while reducing manual logging. Deployment planning should account for compute capacity, processing location and how the metadata will be used.