Industry Insights: The risks, governance and future of AI in broadcast workflows

Artificial intelligence is now moving from operational deployment to broader questions about trust, governance and long-term impact.

In this final installment of our three-part Industry Insights roundtable, broadcast vendors examine the risks and realities shaping the next phase of AI adoption across media workflows.

The discussion explores whether the current surge in investment represents a temporary bubble or the early stage of a deeper technological shift. Participants also examine how organizations are maintaining editorial standards as automation expands, what safeguards are required to monitor AI-driven systems and how teams are developing new skills to work alongside these tools.

Looking ahead, the conversation highlights where AI may influence the next generation of broadcast infrastructure, production workflows and content experiences across the industry.

Key takeaways from this Industry Insights roundtable

• Bubble vs. reality: While some contributors see signs of a market bubble around AI valuations, most expect the underlying technology to remain a long-term foundation for media workflows even if vendors consolidate.
• Human oversight remains essential: Across editorial and production environments, organizations continue to rely on human review loops to maintain accuracy, context and editorial standards.
• Operational safeguards: Confidence scoring, exception queues, audit trails and staged rollouts are emerging as common safeguards to detect errors and maintain control over automated processes.
• New skills emerging: AI adoption is driving demand for professionals who understand both media workflows and AI systems, creating new roles focused on automation oversight, data governance and workflow engineering.
• Next workflow frontier: Future AI impact is expected in areas such as supply chain automation, live workflow orchestration, vertical video production, archive exploitation and infrastructure-level optimization.

Is AI a bubble … and what happens if these workflows built on OpenAI and others, suddenly collapse?

Michael Chan, VP, delivery operations in corporate, Accedo: While there’s a narrative out there that companies like OpenAI, NVIDIA, AMD, etc can’t continue to climb with their valuations, I don’t think any of us can actually tell the future. From a personal delivery experience, working with customers, working with teams, and working with the technology itself, we’re only just scratching the surface on what AI can do, so I can’t see why companies wouldn’t continue investing in this technology. I think adoption is the leading indicator, and you will see an increasing focus on AI transformation this year.

Clara Aler, head of marketing, Knox Media Hub: Yes, there is a bubble that is already being compared with the early web era and the dot-com bubble: the technology stays, the landscape reshapes. The business and operating models are not settled yet, but the technology will not disappear. In media specifically, we can see there is a lot of fragmentation. My prediction is that in a few years, only a small percentage of AI vendors will remain relevant or independent.

Aitor Falcó, sales manager, Knox Media Hub: AI itself is not the bubble; dependency on monolithic, opaque providers is the risk. Media organizations mitigate this by abstracting AI services behind a modular API-first orchestration layer, allowing models or vendors to be swapped without redesigning workflows. When AI is treated as a replaceable service rather than a core system, operational resilience is maintained even if individual providers may change or fail.

Charlie Dunn, executive vice president, products, Telestream: The AI bubble refers to the market valuation of public and private companies that are creating AI. We believe that there is certainly the possibility for prices to get reset when the companies making the large investments have to start showing returns that match the speculation. We don’t believe that a reset will lead to an overall collapse of the use of AI based on the impact of the technology.

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Derek Barrilleaux, CEO, Projective: I’m utterly convinced that there is a bubble, but bubbles burst in unexpected ways. These new technologies should be seen as an augmentation of existing capabilities, not a replacement for staff — I know that’s a cliché now. However, trimming staff to save short-term costs only increases your long-term risk.

Miguel Coutinho, head, NDI: Speculative bubbles are a feature not a bug of new technological paradigms — this phenomenon is pretty well studied. Past technological driven bubbles always had an overcapacity building phase, for example. So yes, it might be considered a bubble but that doesn’t mean it’s useless or that it will never deliver the expected value.

How are editorial teams using AI without undermining accuracy, context or editorial judgment?

Derek Barrilleaux, CEO, Projective: We’re seeing a tightrope being walked, customers using tooling to accelerate getting started, but actual artists are required to finalize the work.

Phil Petitpont, co-founder and CEO, Moments Lab: AI-powered video discovery gives teams far better access to existing, human-created content. At Banijay, creatives can search and reuse footage across 120 plus production companies, while humans retain final editorial control. At Asharq News, multi-language media indexing enables fast discovery of thousands of hours of interviews, overseen by librarians to ensure accuracy and consistent editorial voice.

What safeguards are in place to catch errors or unintended consequences from automation?

Michael Chan, VP, delivery operations in corporate, Accedo: First and foremost, there has to be a human-in-the-loop design. It’s also critical not to replace everything with AI at once in the same workflow. From a coding perspective, you shouldn’t implement AI-generated coding or AI-assisted coding as well as AI quality assurance in the same place otherwise you have AI checking AI. This can very quickly lead to bad outputs and too many hallucinations.

Josh Rosen, VP, production and content, North America, Gravity Media Group: For anything affecting editorial integrity or external release, human-in-the-loop review remains essential. Common safeguards include confidence thresholds, exception queues, spot checks, and clearly defined rollback paths. The goal isn’t to eliminate errors entirely, but to detect and correct them quickly and predictably.

Scott Goldman, general manager, U.S., Qibb: Human‑in‑the‑loop review, confidence scoring, and policy‑based automation help validate AI outputs before they affect live workflows. Low-confidence tags, compliance-sensitive segments, and key editorial decisions can be flagged for review rather than automatically published. Audit trails and governance frameworks ensure transparency and accountability.

Jacob Arends, senior product manager, playback and AI, Bitmovin: If you think back to pre-AI, when media companies automated tasks using rule-based algorithms and digital tools, this required rigorous initial testing and iteration until the failure rate was below a defined level, as well as ongoing manual oversight and rigorous QC processes. All of these mechanisms were critical to avoid errors and undesirable outcomes, and similar precautions are needed with AI-driven automation. The importance of human set parameters and human oversight cannot be understated.

Charlie Dunn, executive vice president, products, Telestream: At Telestream, we are focused both on how we automate workflows and how we can validate that this automation is correct. In the world of AI, this is especially important. As a result, we are making equally large investments in how we can test content for accuracy through our flagship QA service, Qualify.

Zoe Liu, CEO and founder, Visionular: AI-driven optimization operates strictly within established video coding standards, ensuring that all produced bitstreams remain fully standard-compliant and can be decoded by existing standardized decoders and players without requiring any special playback infrastructure. Operational controls typically include side-by-side bitrate and quality comparisons against baseline encoder presets, controlled A/B testing in live production traffic, and staged rollout strategies before enabling AI optimization globally. In addition to traditional full-reference metrics such as VMAF, PSNR, and SSIM, we have also developed no-reference quality monitoring methods that track quality signals throughout the entire video processing chain, enabling continuous visibility even in real-time and large-scale environments.

How are organizations balancing speed gains with the need for human oversight?
Where have AI-driven editorial tools required adjustment after real-world use?

Santiago Miralles, founder and CEO, Knox Media Hub: Organizations are gaining speed by using AI to automate high-volume, repetitive tasks while keeping humans in the loop for review, approval, and exception handling. In workflows such as localization, editing, and versioning, having one or two people validate AI-generated outputs is significantly faster than fully manual processes, without removing editorial accountability. Most real-world adjustments occur around defining where human oversight is mandatory — particularly for high-risk decisions — ensuring AI accelerates execution rather than replacing judgment.

Sam Peterson, COO, Bitcentral: AI is taking on routine, time-consuming tasks, while humans remain responsible for editorial judgment and final approval. By reducing manual work like transcription and tagging, AI gives producers more time to focus on creative decisions, context, and storytelling. Well-designed workflows use AI to accelerate production without compromising accuracy or editorial standards.

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Simon Wocka, regional sales manager, Pixitmedia: Most teams are comfortable using AI to accelerate discovery, tagging, and preparation, but they keep editorial decisions firmly with people. Storytelling is still a true craft that relies on experience and human nuance to entertain and inform audiences. That balance protects creative judgement while still delivering meaningful efficiency gains.

What new skills or roles are emerging as AI becomes embedded in workflows?

Jacob Arends, senior product manager, playback and AI, Bitmovin: There’s a huge appetite to use AI to bring improvements and create efficiencies across the entire media supply chain, and as such AI is already reshaping skills requirements across the video industry. There’s going to be a huge demand for skilled AI professionals, which is likely to lead to a skills gap because there just isn’t enough talent out there with the right AI knowledge and understanding. This applies to technical professionals understanding AI system engineering, as well as non-technical individuals being able to comprehend what each new wave of AI tools can do for them and their work to allow them to achieve new things.

Ken Kobayashi, business manager, remote cameras, Sony Electronics: AI always requires appropriate “input” to have the desired outcome. If the process or intention are not clear at the onset of a production, AI cameras will not always capture or follow the subject with expected framing and timing, so it’s important to provide clear direction. Also, someone needs to change or modify AI parameters by reviewing the expectations and results. In the same way an operator looks back at their performance and prepares for the next time, manual intervention should be used to monitor and improve AI settings to ensure accuracy and precision.

How are teams being trained to work effectively alongside AI-driven tools?

Rich Welsh, president, SMPTE: It is important for organizations to lean in to how their teams use AI and ensure they are doing so in a responsible manner. AI capabilities are evolving at such a rapid pace, making it essential that organizations keep up, and that means enabling their teams with knowledge and skill development, as well as tools. AI offers great benefits, but used in an uncontrolled manner, it can pose huge challenges for businesses.

What governance frameworks are proving necessary as AI use expands?

Jacob Arends, senior product manager, playback and AI, Bitmovin: As AI adoption becomes widespread, it is of course critical that governance and risk management are prioritized. This is particularly true when it comes to the deployment of AI agents that are given license to access separate systems and act autonomously to reach an objective. These agents need to be deployed safely and responsibly with clearly defined acceptable use policies, and human-in-the-loop oversight and monitoring.

Rich Welsh, president, SMPTE: Within organizations, data sovereignty is at the core of AI governance. Uncontrolled use of AI tools can lead to not only IP leakage but also direct business impacts such as weakened security or compromised business processes. On a wider industry level, protecting creative works and individual likeness is an area of utmost importance. The creative community is one of the most heavily impacted from IP theft in training models and they are acutely aware of this. Copyright and IP governance on a legislative level is unlikely to help since global differences in laws and jurisdiction will weaken any legal framework. As an industry, we must act to protect IP ourselves through data provenance, chain of custody, fingerprinting, ethical data sourcing and other such protective frameworks.

Looking ahead, what workflow areas are most likely to see meaningful AI impact next?

Yang Cai, CEO and president, VisualOn: AI will increasingly influence end-to-end optimization across encoding, delivery, and playback by adapting decisions in real time based on content type, network conditions, and device capabilities. The next phase is less about replacing tools and more about connecting intelligence across the media chain.

Josh Rosen, VP, production and content, North America, Gravity Media Group: In XR and virtual production, AI is already collapsing timelines for environment, prop, texture, and lighting generation — turning months of build work into minutes — while humans retain responsibility for creative judgment. We’re also seeing early but promising movement in AI-assisted and, in limited cases, AI-operated multi-camera production, particularly for rule-based or highly repeatable formats. Across both areas, the pattern is consistent: AI accelerates execution, but humans remain responsible for intent, judgment, and quality control.

Jacob Arends, senior product manager, playback and AI, Bitmovin: Vertical video is a rapidly expanding area for video providers and broadcasters who are increasingly leveraging vertical short-form video to drive engagement and aid discovery. However, vertical video only works for broadcasters if they can identify the right highlights that will engage users, apply subject-aware reframing, and generate vertical short-form clips in the right format quickly and at scale. AI tools allow video services to largely automate this process so it can be done almost instantaneously, so this could well be a game changer for broadcasters.

Ken Kobayashi, business manager, remote cameras, Sony Electronics: We expect AI-based camera technology to become more widespread throughout the capturing process to help streamline and provide additional footage. For engaging and attractive content, using multiple camera angles is an effective way to generate richer immersion and greater variety, without a lot of time, monetary investment or strain on existing operators and staff.

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Steph Lone, global leader, solutions architecture, media and entertainment, games, and sports, Amazon Web Services: For agentic AI, the areas that might benefit the most in the near term include media supply chain automation, orchestrating content from ingest through metadata extraction, QC, versioning, and delivery. Generative AI can be applied for localization at scale, applying contextually appropriate voices and captioning. In post production, we’ll continue to see workflow acceleration with AI-assisted editing tasks, such as making scene selects or rough cuts, shot matching/continuity, creating alternate versions for platforms and assembling trailers or recaps.

Zoe Liu, CEO and founder, Visionular: The next major impact areas are: real-time video understanding inside live workflows; intelligent bitrate adaptation based on content complexity and varying network conditions; automated quality anomaly detection during live streaming; and AI-assisted video enhancement for low-bandwidth environments. As live streaming volumes increase, intelligent decision-making at the infrastructure level will become essential. The future is not AI as a feature. It is AI as a performance layer across the entire video pipeline.

Russell Trafford-Jones, industry engagement manager, Techex: AI’s not just going to be inside live workflows, it’s going to be making them. It’s no quick problem to solve, but bringing AI into the MCR is no longer on the horizon — it’s fast approaching. With ever-increasing scale of operations, a need to cover all tiers of sport and pop-up channels being ever more-useful, many organizations are skilling up their AI solutions to be able to create, manage and monitor live broadcast infrastructure with oversight from MCR staff.

Miguel Coutinho, head, NDI: The next step is better coordination across multiple devices and locations rather than smarter behavior in a single product. Cameras, switching, and collaboration tools will react to the same live context whether processing happens locally or in the cloud. That shared awareness is what makes distributed and hybrid production easier to manage. But we are still a long way from this as today there is no clear context layer that these AI system can learn from and add to in real-time.

Simon Wocka, regional sales manager, Pixitmedia: We expect growth in archive exploitation, compliance checking, rights management, and smarter reuse of content across platforms. All of these rely less on creative generation and more on how well data is organized, moved, and surfaced across systems. The strongest impact will keep coming where AI connects tightly to data management and workflow foundations, not where it operates in isolation.

What are we not discussing that we should be?

Jacob Arends, senior product manager, playback and AI, Bitmovin: Generative AI tools are advancing rapidly, making it even easier to create convincing fake videos and manipulate existing content. As a result, there’s a growing need for effective ways to verify content authenticity. The open technical standard C2PA (Coalition of Content Provenance and Authenticity) was developed to tackle this by recording digitally signed information about the provenance of the content. However, for it to reach its full potential, there needs to be large-scale implementation across the media ecosystem, which is some way off at the moment.

Rich Welsh, president, SMPTE: World models will dominate the next phase of AI development and shape the next generation of AI tools. There are numerous questions about how we safely and ethically build these models, but the outcome is the same: AI being more deeply woven into our everyday lives, and specifically for media, more immersive, interactive and deeply personal experiences that will transcend the current media experience. We should be discussing the implications of this now and understand what it means for us as an industry, and as a society.

Steph Lone, global leader, solutions architecture, media and entertainment, games, and sports, Amazon Web Services: As AI makes high-quality output more accessible, the real advantage shifts to creative teams that can present clear but unique points of view. AI supports strong editorial judgment at scale so teams can then turn their voices into defensible IP. Teams that treat restraint, ethics, and audience trust as creative inputs will stand out as premium and principled.

Russell Trafford-Jones, industry engagement manager, Techex: AI is here to stay in all aspects of life. For some things, AI is like a superhuman, for others it’s like a child. The most productive teams will be skilled at proficiently using AI in the right place at the right time. Whether to generate or analyze broadcast content, to manage infrastructure or just to help individuals work, companies would be wise to look at AI as a skill to give their staff rather than to a risk to be managed via policies.