Generative AI is now a daily reality in the streaming industry. And it’s changing the way streamers engage their subscribers on-service.

Netflix is integrating OpenAI-powered search to its interface. Prime Video is serving AI-generated season recaps of its original series to help viewers catch up on past action. And overall, the global market for AI in media and entertainment is expected to grow tenfold this decade. 

Generative AI is fast, cheap, and increasingly high quality. With its potential to help squeezed teams deliver high volumes of copy, it’s no surprise that a common conclusion is to harness it to generate as much on-service content as possible.

However, that’s an oversimplification. And one that could prove costly for streamers aiming to deliver the excellent local UX that underpins global success. 

Before diving headlong into the rush, it’s essential to consider the strengths and drawbacks of GenAI, and determine where and how it should fit into a scalable copy workflow that delivers global success. 

PRESERVING SECURITY WHEN USING LLMS

Security is the first issue to consider when implementing AI for on-service copywriting. For a significant number of organizations, this is a major concern when it comes to embedding AI in their copywriting processes. 

Globally, 22% of business and cyber leaders said they were most concerned about data leaks and exposure of personally identifiable information through GenAI. With LLM technology still in its infancy, many industry leaders have justifiable concerns around exactly how their IP will be processed, stored, and used by any AI algorithms they employ to generate copy. 

First and foremost, there’s the fundamental need to ensure that proprietary IP isn’t used to train the LLM by default. It’s a basic tenet of the creative industries that copyrighted material shouldn’t be used without direct permission. That presents a significant barrier if using LLMs to generate on-service copy means tacitly granting AI companies full rights to train their models with scripts, clips, and audio. 

At the same time, without complete transparency into usage and processing, inputting proprietary content into LLMs could present a leak risk. Security around pre-release IP must be watertight – streaming leaders may justifiably feel that inputting that IP into black-box third-party AI applications constitutes an unacceptable level of risk.

As a result, AI should only be used in trusted environments. And only trusted partners should be empowered to use it. Any LLM systems that are used should be able to provide concrete proof that sensitive IP will be restricted to an isolated environment and not used to train public-usage models. An untrusted freelancer or agency wouldn’t be given free rein over IP. Neither should an AI model.

HOW TO GET THE BEST OUT OF AI

Once security assurances are in place, the next question is how best to integrate AI into copywriting processes. The fundamental principle is to deploy AI as an assistance tool, rather than a wholesale replacement of human talent. There are a number of key reasons for this: 

1) KEEP IT SIMPLE

AI is at its best when dealing with simpler, more predictable content, with straightforward plotlines, identifiable genre conventions, and clear titles. Within those parameters, it can prove a helpful resource to writers, providing useful words and phrases, aiding with variation, and conquering the tyranny of the blank page. 

However, when the content is more complex, AI’s limitations become clearer. For example, when dealing with films or shows that contain multiple interwoven plotlines or genre-bending elements, AI risks missing the nuance, ignoring plot elements, or mis-describing the style of content.

2) CHECK FOR ACCURACY

When dealing with content that hasn’t been released yet, there’s a danger that AI models left to their own devices will output incorrect information.

For example, Claude Sonnet 3.5 was asked to write a 150-character synopsis about the upcoming Prime Video film Heads of State (releasing July 2025). The synopsis needed to reference the talent. Here are three different outputs it sent back:

1. Dwayne Johnson and Chris Rock star as elite agents forced to team up and protect America’s First Couple in this action-comedy from Amazon Studios, blending explosive thrills with sharp wit.
2. Dwayne Johnson and Chris Pratt star as rival security agents forced to work together protecting a VIP in this action-comedy from Amazon Studios.
3. “Heads of State” stars Idris Elba and John Cena as unlikely allies forced to team up on a high-stakes mission to protect a world leader.

Only the third synopsis includes correct talent and plot information. Using one of the other synopses on service could mislead viewers, negatively impact their experience, and potentially even cause issues with talent and filmmakers. 

As a minimum, human fact-checking should remain an essential part of the copywriting process to ensure everything from gender attribution to talent recognition is correct. Humans also have a key role to play in checking for localization quality, ensuring content delivered in multiple languages is not just technically correct but idiomatic and compelling.

3) PRIORITIZE LOCAL NUANCE

Industry leaders recognize that the key to global success is local UX that factors in cultural nuance and local preference. 

Netflix prioritizes delivering a joyful experience for subscribers when they’re exploring content, while YouTube aims to make its UX “fun.” The question to ask is whether AI can successfully capture local nuances to introduce those moments of joy. On its own, is it capable of delivering the tailored user experience that could make a difference in a highly competitive global marketplace? 

In most cases, the answer is likely to be “no” – human expertise remains critical to ensure that regional nuance is successfully captured and turned into winsome words.

THE BIGGER PICTURE

While AI can speed up workflows, help to deal with writer’s block, and empower writers’ creativity with suggestions and ideas, it’s not a full solution to scalable creative excellence in and of itself. 

Ultimately, global success is driven by four key components:

1. Retaining global creative talent that thinks differently about creative needs.
2. Embedding good processes engineered for both creativity and efficiency.
3. Developing systems that harness innovation to amplify human creativity.
4. Proactively evolving and continuously improving partnerships, ensuring the company is working with experts who can deliver excellent local UX on a global scale.

Seeing AI as the whole answer, rather than only part of the solution, risks diminished returns and limited effectiveness. Because no matter how good an AI-enabled process is, creative excellence at scale isn’t achievable without the combination of people, process, systems, and partnership.

FINAL THOUGHT

As streaming companies consider where to implement AI in their on-service copy workflows, it’s important to keep the big picture in mind. AI isn’t the whole answer to delivering quality on-service copy at scale. But with the right security assurances, quality control, and broader ecosystem in place, it can play a hugely valuable role in driving global streaming success.