How AI has changed the economics of content localization.

Could a show that performed well in North America find an audience in Brazil? What about Germany or Japan?

For media companies with large content libraries, the opportunity is easy to see. A title you’ve already paid to produce could have an untapped audience somewhere else in the world. Finding that audience, however, has traditionally required a meaningful upfront investment in localization. You want some evidence that the opportunity exists before committing resources, but reaching the audience may require spending money on localization first.

But AI could make that bet a lot less expensive. New tools are making it faster and cheaper to adapt existing content for different languages and markets, giving media companies more room to experiment before committing to a full localization effort. Instead of placing a big bet on one market, content owners can potentially test several opportunities on a smaller scale and see where audiences respond. For companies sitting on years of existing content, that could open up international opportunities that were previously too expensive to explore.

The problem with localization.

Localization can open existing content to entirely new audiences, but preparing a title for another market can quickly become a substantial production workflow. Dialogue may need to be transcribed and translated before teams can produce new subtitle or audio tracks, all of which have to stay synchronized with the original video and pass quality assurance before distribution. The work can extend further when humour, cultural references or other elements of the original don’t translate cleanly. Suddenly, exploring a new market involves considerably more than converting a script from one language to another.

Then there are the expectations of the audience itself. Viewers in France, for example, may be more receptive to subtitles, while audiences in Quebec may be more likely to expect dubbed content. A market with greater cultural differences could require closer scrutiny of local expectations or restrictions before a title is ready for release. The same piece of content may therefore require a different localization approach depending on where you’re trying to distribute it.

For a major release with obvious international appeal, that investment may be relatively easy to justify. The calculation gets harder for the rest of the catalog. A content owner might suspect that an older title has an audience overseas, but finding out requires investing in the localization needed to reach that audience. That leaves teams with a tricky question: How much are you really willing to spend to find out if a market exists?

How AI changes the math.

AI, however, could bring that upfront investment down considerably. As more of the localization workflow can be accelerated or automated, content owners may be able to prepare a title for another market without committing the resources required for a traditional localization project. That makes smaller experiments economically viable, including titles and markets that might otherwise have been passed over. And when the cost of an experiment comes down, you don’t need the same level of certainty before deciding to run it.

Think of traditional localization like opening a retail store in a new city. Leasing the space and staffing the location require enough upfront investment that you’d probably want strong evidence of local demand before opening the doors. A pop-up shop requires far less commitment, so you can put your products in front of actual customers and see how they respond. AI-assisted localization could give content owners a similar way to approach new markets.

Here’s where things get interesting. Instead of committing a full localization budget to the market you think has the most potential, you could run smaller tests across several promising markets and see where the content actually finds an audience. In a recent webinar, REDspace Director of Client Engineering Mark McKenna suggested using AI to help content owners explore these opportunities at a scale that previously would have been difficult to justify. Localization can then provide market intelligence that helps determine where a larger investment makes sense.

There is one important catch. A cheaper test still needs to give the content a fair shot. If an automated translation misses important context or a dub sounds unnatural, weak engagement could tell you more about the localization than the underlying demand for the title. Worse, a poor viewing experience could damage the audience’s perception of your brand. Human review is essential to maintaining the creative quality audiences expect while ensuring the experiment produces a useful signal.

How to test a new market without betting big.

So, what could this look like in practice? McKenna recommends approaching localization as a series of smaller experiments. “Do probes in a few different areas. Do a small rollout and see how things land,” he says. A practical test could look something like this:

• Start with content that has already shown potential. Look through the existing catalog for titles that have demonstrated audience interest and could have appeal beyond their original market. Existing viewing data can help narrow the field, while the creative team can provide context that won’t necessarily show up in an analytics dashboard.
• Identify a few plausible markets. Use what you already know about the content and its audience to narrow down the possibilities, then test several reasonable candidates. At this stage, you don’t need to predict which market will produce the strongest response.
• Match the localization approach to the audience. Automated subtitles may provide a useful test in a market where viewers commonly consume subtitled media, while another audience may require dubbing before they’ll give the content a fair chance. The test should reflect how people in that market actually consume foreign-language media.
• Run a limited distribution test and watch the signals. Platforms such as YouTube can provide an inexpensive way to put localized content in front of international audiences. If one language or region begins generating disproportionate engagement, you have evidence to support a larger investment in localization or distribution.

There are still some guardrails. Content owners need to understand the rights they hold before using AI to manipulate an actor’s voice or likeness, along with any licensing implications introduced by third-party AI platforms. The localized version also needs enough quality to give the content a fair shot with its intended audience. But get those pieces right, and a relatively small experiment can provide real viewing behaviour to inform the next investment decision.

What if localization came before the business case?

Localization has traditionally followed the decision to pursue a new market. You identified the opportunity, built the business case and then invested in adapting the content for that audience.

AI-assisted workflows could change that sequence. If localization becomes inexpensive enough to support smaller experiments, content owners can use those experiments to gather the audience signals that help build the business case in the first place.

For companies sitting on years of existing content, that’s a compelling possibility. Your next international audience could already be out there. Now it may be considerably cheaper to go looking for them.

Interested in learning more? To hear Mark McKenna explore how media companies can use AI to unlock more value from their existing content libraries, including localization and other opportunities for repurposing content, watch Your Next Revenue Stream May Already Exist.