Comment l’IA générative transforme la production de contenus pour la télévision et le web -

As I watch the rapid rise of generative AI in television, streaming and online video, I can clearly see that we are at the beginning of a major shift in how content is conceived, produced and distributed. From TV broadcasters and OTT platforms to independent YouTube creators, generative AI tools are quietly integrating into daily workflows. They are speeding up pre-production, automating repetitive tasks in post-production and opening up entirely new ways to personalise content for audiences on the web.

In this article, I want to share how generative AI is already transforming content production for television and online platforms, what concrete use cases I see emerging in news, entertainment and branded content, and which tools and workflows are becoming essential for professionals who want to stay competitive.

What generative AI really changes in content production

Generative AI is not just another buzzword in media and broadcasting. It refers to a class of models capable of creating text, images, audio and video based on training data. In practical terms, this means a model can generate a script draft, design a virtual set, invent a character’s voice or automatically assemble a rough cut of a video.

For television and web content production, generative AI mainly transforms three layers of the workflow:

Idea generation and pre-production (research, synopsis, script outlines, moodboards).

Production and virtual production (AI-assisted cameras, virtual sets, synthetic actors).

Post-production and publishing (editing, localisation, thumbnails, metadata, social adaptations).

Each of these layers used to be time-consuming and labour-intensive. Today, with the right generative AI tools and a clear editorial vision, a lot of these steps can be accelerated, semi-automated or enhanced.

How TV broadcasters use generative AI in the newsroom

In traditional television, the newsroom is often the first place where I see AI adopted at scale. The need to produce fast, accurate and multi-platform content fits very well with what generative models can do.

Typical uses I encounter include:

Assisted scriptwriting for news anchors: Generative AI can transform agency wires, press releases and live feeds into structured, on-air ready scripts. Journalists then revise and validate the final version, but the drafting time is dramatically reduced.

Automatic generation of web articles from TV reports: A report that goes on air can be transcribed and turned into a long-form article for the website, with SEO-friendly headlines and subheadings suggested by AI.

Summaries and explainers: For big stories, AI models can generate short explainers, FAQs or contextual boxes that are adapted for mobile apps, connected TV interfaces or social media carousels.

Beyond text, generative AI is starting to touch the visual side of news production as well. Some channels experiment with:

AI-generated infographics and data visualisations, based on structured data fed directly from news agencies or government APIs.

AI-assisted localisation: automatic dubbing, subtitling and voice cloning to adapt a news segment into multiple languages at a fraction of the traditional cost.

For broadcasters who want to maintain a strong editorial line, the key is not to let AI decide the angle or the priorities, but to use it as a powerful assistant for formatting and distributing journalism across platforms.

Generative AI in entertainment and scripted TV content

On the entertainment side, I observe a very different use of generative AI. Here, the priority is creative exploration, visual experimentation and cost optimisation for high-end productions.

In scripted series, talk shows, game shows and reality formats, generative AI is deployed to:

Create concept art and previsualisations of sets, costumes and atmospheres before going into full production. AI image generators help directors and showrunners align on a visual language early in the process.

Design virtual sets for LED walls and volume stages in virtual production environments. AI can generate variations of backdrops and environments based on a few prompts from the production designer.

Generate alternative lines, jokes or dialogues during writers’ rooms. The AI does not replace screenwriters, but it provides a large pool of ideas and variations that can be refined.

For unscripted entertainment, game shows and late-night talk shows, some producers use generative AI to simulate games, create fake social media posts for sketches, or rapidly design graphics packages and idents tailored to specific themes or guests.

In all these cases, what matters most for me is the collaboration between humans and AI. When directors, writers and designers remain in control, generative tools become accelerators, not substitutes.

How YouTube creators and web video producers leverage generative AI

If there is one area where generative AI adoption is exploding, it is independent creation on YouTube, TikTok and other web platforms. I see creators with small teams — sometimes working alone — using AI to achieve production values that used to require a full agency or post-production house.

For web and YouTube content, the most common use cases I see are:

AI scriptwriting and video outlines: Creators feed the AI with their topic, target audience and preferred tone. The AI proposes chapter structures, hooks for the first 30 seconds, and transitions between segments.

Automatic B-roll generation or selection: Some tools can suggest stock footage or even generate synthetic B-roll for explainer videos and tutorials.

Voiceover and speech enhancement: Text-to-speech models produce high-quality voiceovers in multiple languages, while AI audio tools remove background noise, equalise levels and improve clarity.

Thumbnails and titles optimisation: Generative image models create multiple thumbnail options based on the video subject, and AI text tools help refine titles and descriptions for better click-through and SEO.

For creators who want to monetise their channels, generative AI also simplifies the production of derivative content: shorts, vertical clips, teasers and formatted snippets adapted to Instagram Reels, YouTube Shorts or Facebook.

Some all-in-one platforms now propose “AI video editors” that can cut long live streams or podcasts into dynamically captioned highlight clips, detecting key moments automatically based on audio and engagement data.

Key generative AI tools reshaping TV and web workflows

Because my readers often ask what specific software to look at, I tend to group the current AI tools into categories instead of focusing on brand names only. This also helps when comparing options or planning a purchase.

Text and script generation tools: These are large language model-based assistants that help with research, outlines, interview questions, news scripts and dialogue options. They integrate into newsroom systems, Google Docs or dedicated writing platforms.

Image and design generators: Used for concept art, storyboards, thumbnails, virtual sets and graphic design. Many integrate directly with tools like Photoshop or with virtual production workflows.

AI video tools: From automated editing assistants and highlight detection to full text-to-video generation, these products cover a wide spectrum. In television, they are often used for promo creation, trailers and social formats.

Audio and voice technologies: Voice cloning, text-to-speech, automatic mixing and restoration. For broadcasters, these tools are especially powerful for localisation and accessibility (audio descriptions and automatic captions).

When choosing tools, I look at several criteria relevant to TV and online video professionals:

Integration with existing NLEs and newsroom systems (Adobe Premiere Pro, Avid, EDIUS, Dalet, etc.).

Licensing and rights for commercial use, especially for AI-generated images and music.

Data security and compliance, which is essential for newsrooms working with embargoed or sensitive materials.

Quality and consistency of outputs across languages, since multilingual workflows are now standard.

Ethical and editorial challenges raised by generative AI

Even though I am impressed by the efficiency gains offered by generative AI, I also see major challenges that broadcasters and creators must address. The first is transparency: when a voice has been cloned, when an image or video is synthetic or heavily AI-generated, audiences should not be misled.

Several broadcasters are already adopting internal guidelines such as:

Mandatory on-screen labels for AI-generated or AI-altered visuals in news segments.

Internal review processes for all AI-assisted scripts before they go on air.

Restrictions on the use of deepfakes and voice cloning, especially for public figures and politicians.

For independent web creators, the responsibility is similar, even if there is no central editorial board. Viewers expect authenticity, and overuse of synthetic personalities, AI-generated testimonials or fake environments can damage trust and long-term audience loyalty.

There is also the question of jobs in media and production. From my perspective, the roles are more likely to evolve than disappear. Scriptwriters become “prompt designers” and narrative supervisors. Editors spend less time on basic cutting and more on creative decisions. Graphic designers move toward art direction and curation of AI proposals rather than manual production of every asset. But this shift requires training, experimentation and a clear communication strategy within teams.

How to prepare your TV or web production for an AI-driven future

For TV channels, streaming platforms and independent creators, adopting generative AI is not simply a matter of testing a few tools. It is a strategic choice that affects workflows, budgets and editorial identity.

From what I see across the industry, the most effective approach consists of several steps:

Audit existing workflows: Identify repetitive, low-value tasks in scriptwriting, editing, localisation and content repurposing. These are often the best entry points for generative AI.

Start with small, well-framed pilots: For example, use AI only to create social media variants of a flagship show, or to generate summaries for the VOD catalogue.

Involve editorial teams early: Journalists, producers and creators must understand how the tools work, where the risks are, and how their expertise remains central.

Define clear rules and communication: Decide how you will label AI-generated content, how you store and protect data, and how you manage rights on generated assets.

On the equipment and software side, I recommend keeping a close eye on NLE plugins, AI-powered newsroom add-ons and cloud services that can be easily plugged into existing infrastructures. For YouTube and small web teams, all-in-one platforms that combine scriptwriting, editing and thumbnail generation are often the most efficient way to start.

Generative AI is not replacing the need for strong editorial judgement, distinctive creative vision or journalistic integrity. Instead, it is changing the tools that television channels, streaming platforms and online creators use every day. For those who manage to align these tools with their brand identity and audience expectations, the result can be faster production cycles, richer formats and a much more agile content strategy across both linear TV and the web.