AI from Greenlight to Glass: The implications for broadcasters

As social platforms and the advertising world race ahead with AI, broadcasters and producers should embrace the benefits of AI. AI has come of age at a time where broadcasters are under pressure: Pay‑TV is soft, ad budgets are fragmenting, and attention has been leaking to social and streamers for years.

Whilst ethical and legal factors must be considered, there are significant opportunities to use AI to augment output and revenues, and streamline costs. An initial approach of leaning into AI tools which streamline processes and unlock productivity gains will improve AI knowledge and equip broadcasters and producers to address longer term, existential questions around displacement by AI generated content curated by AI agents.

AI is redefining the game around TV

AI is already reshaping adjacent parts of the media ecosystem in ways that directly affect broadcasters. On the creator side, powerful, accessible tools are lowering costs and lifting production values. YouTube’s AI auto‑dubbing and multi‑language audio make multilingual publishing close to a default for many creators, compressing localisation timelines without studio‑scale budgets. Indeed, YouTube reports that Jamie Oliver’s MLA‑dubbed tracks have achieved three times the views versus English‑only uploads. In video generation, TikTok’s Symphony toolset (text‑to‑video, image‑to‑video, dubbing and creator avatars) speeds up ideation and production while keeping output platform‑native; and Open AI’s Sora2 is raising questions around AI replacing social media creators.

On the advertising side, digital platforms are embedding AI deep into delivery and measurement. Beyond targeting, attribution and optimisation, Google, Meta and TikTok are all using Gen AI to automate creative asset production and DoubleVerify’s AI‑powered content‑level controls on TikTok and Meta bring broadcast‑style suitability and verification to user‑generated environments.

As the substitutes to television embrace AI, broadcasters must evolve to compete to attract viewers, subscribers and ad dollars.

What to do now: A broadcaster’s playbook

1) Commission & schedule smarter. Use AI‑enhanced social listening, focus groups and predictive models to simulate audience response, demographic appeal and likely ratings before greenlighting; then let scheduling algorithms test slot, platform and repeat pattern intensity. The aim isn’t to replace judgement—it’s to give you more tools and insight to raise the hit‑rate and maximise audiences.

2) Produce faster – whilst preserving creativity. Accelerate prep and post with AI logging, transcripts and diarisation; accelerate edits with searchable footage; use audio clean‑ups and automated ‘franken-byte’ smoothing to shrink turnaround. Reserve human craft for what audiences notice; let machines chew through the volume work, and unlock the ability to ride zeitgeist moments with short time from script to screen.

3) Monetise better. Bring AI into forecasting and yield; enable ad product innovation and delivery optimisation across linear and VOD. Provide low-cost personalized ad creation to extend into SMEs. Streamline and augment existing processes with AI – from ad scheduling, agency reporting - to manage the operational costs of delivering TV and VOD ads.

4) Automate localisation and compliance. Turn what used to be bespoke edits into a factory: blur what needs blurring, bleep what needs bleeping, and localise fast with AI subtitling and neural dubbing. The payoff is reach and speed—more markets, sooner, at a lower cost per hour.

5) Personalise and streamline discovery. Use AI to power better recommendations and search, generate and test artwork and thumbnails, and auto‑enrich metadata and synopses to improve discovery. The result: more starts, longer sessions, greater loyalty.

Managing Risks: Importance of Guardrails and Governance

Near‑term execution risks.

The immediate danger is using tools that aren’t fit for purpose or diluting quality where it matters. The wrong AI in the wrong place can contravene copyright, leak confidential information or compromise existing IP and brand safety. Rigorously (but quickly) assess AI tools for legality and confidentiality as well as quality before rubber-stamping them for use. Keep humans in the loop where quality defines the brand, and identify clear “no‑compromise” areas where efficiency is not allowed to trump standards.

Finally, manage reputation and talent risks by being clear where it counts: for example, set consent/credit/compensation policies for AI use of likeness and voice, respect IP ownership, and maintain open lines of communication to avoid unexpected backlash.

Longer‑term strategic risks.

The structural risk starts with IP being used against you: third‑party models trained on your - and others’ - catalogues can generate competing IP. One hold‑out won’t fix it; the industry needs a position. That means coordinated regulation and lobbying, plus building your own AI‑powered IP factory trained on rights‑cleared libraries, alongside clear licensing policies for any external training or inference.

There’s also the risk of broadcasters losing the curator role – will audiences turn to their Chat GPT agent and ask ‘what should I watch tonight’? Minimise this by maintaining relevance by doubling down on brand, product and first‑party data; irreplaceable content – marquee sports and culture-defining moments; or indeed collaborating with others to enable cross-broadcaster, agentic content discovery.

And beware the slow fade of talent pipelines: over‑automating entry‑level work reduces on‑ramps for the producers, writers, directors, VFX producers and on‑screen talent you’ll need in ten years.

With all this – keep in mind that the biggest risk is simply being left behind—failing to capture AI’s upsides while platforms and creators set the bar on personalisation, accountability and speed, eroding broadcaster relevance and revenue.

Planning for future success

TV is facing another period of intense change – like analogue to digital, linear to OTT. Whilst complexity has mushroomed, we still have live TV, broadcast advertising, alongside SVOD, AVOD and social media. Broadcasters and producers must lean into change while maximising what still works.

Every broadcaster must reassess its strategy in a world of AI, by answering three questions:

1. Who wins the premium‑content game in a Gen‑AI world? Define the roles you’ll play (creator, curator, platform), where you have right‑to‑win, and where partnerships beat ownership.

2. What assets and capabilities will matter - and what gaps do you have today? Think rights‑cleared IP and data, direct audience relationships, an AI‑powered IP factory, measurement-ready ad products, and product/engineering muscle.

3. What operating model gets you there while protecting the core? Organisation structure eg innovation skunkworks vs integrated teams, rebalancing towards critical capabilities and talent, culture and incentives. Change needs to be phased so you build the future capabilities without starving the current business.

The industry has reinvented itself before; it can do so again.

Kim Chua is a partner at OC&C Strategy Consultants, specialist in media, broadcasting, and digital transformation