Using AI Tools in Multimedia Localization Workflows: a Productivity Evaluation (2025)

Abstract: Multimedia localization workflows are inherently complex, and the demand for localized content continues to grow. This demand has attracted Language Service Providers (LSPs) to expand their activities into multimedia localization, offering subtitling and voice-over services. While a wide array of AI tools is available for these tasks, their value in increasing productivity in multimedia workflows for LSPs remains uncertain. This study evaluates the productivity, quality, cost, and time efficiency of three multimedia localization workflows, each incorporating varying levels of AI automation. Our findings indicate that workflows merely replacing human vendors with AI tools may result in quality degradation without justifying the productivity gains. In contrast, integrated workflows using specialized tools enhance productivity while maintaining quality, despite requiring additional training and adjustments to established practices.

## introduction the demand to provide culturally and linguistically relevant content to global markets is at an all-time high. to remain competitive, businesses are pressured to produce broad-scale localized multimedia content faster and cheaper than ever before. as a result, language service providers (lsps) must find more efficient ways to provide multimedia localization services to meet these evolving client expectations. the evolution of artificial intelligence (ai) has introduced a plethora of tools designed to solve efficiency challenges for complex multimedia workflows. existing research on ai tools in multimedia workflows has focused mainly on subtitling productivity, with studies investigating postediting of machine-translated subtitles @xcite @xcite or ai-enhanced subtitling workflows @xcite . research on ai-enhanced voice-over (vo) workflows is even scarcer, mainly focusing on quality assessment models @xcite . in a recent survey, @xcite evaluated several categories of multimedia ai tools for their suitability in lsp business operations. the categories evaluated were transcription, translation, subtitling, and vo, with tools ranging from modular task-specific applications, which proved to be most suitable for lsps with low workloads, to fully integrated multimedia platforms, which demonstrated suitability for lsps with high-volume workloads. however, the effectiveness of ai tools in enhancing productivity in real world multimedia workflows and the impact to end product quality have been largely unexplored. moreover, productivity gains must be weighed against the costs of leveraging ai. incorporating ai in traditional workflows often requires additional computational power, specialized technical skills, training project managers and linguists in using new tools, and restructuring well-tested existing workflows. thus, the questions for lsps become: are the productivity gains of leveraging ai worth the upfront cost and effort? is the potential risk to end product quality worth the productivity gains? in this paper, we address these questions by conducting a productivity study, comparing quality, time and cost gains in different ai localization workflows. this study focused on localizing two videos for subtitling and voice-over into spanish-us and simplified chinese. to evaluate the gains and quality impact of ai tools on multimedia localization, we compared three different workflows: i) manual, where subtitling and vo were performed without the support of any ai tools, ii) cascaded, where the existing manual workflow was enhanced using automatic transcription, machine translation, and voice synthesis, and iii) integrated, where dedicated subtitling and vo platforms incorporating ai were used to execute the workflow end-to-end. our findings compare total time and cost, end product quality, and challenges associated with each workflow. through the comparison of the traditional workflow against ai-augmented workflows for impact on quality, cost and time savings, our goal is to provide guidance to lsps and other stakeholders on the implementation of ai automation in multimedia workflows. ## multimedia workflows and lsps localizing multimedia content, such as videos, consists of projects focused on adapting audiovisual materials into different languages, in order to make them applicable and accessible to different linguistic and cultural audiences. these projects have traditionally been complex, time-consuming, and costly for lsps, due to the fact that they require the involvement of a myriad of different specialized human resources to complete several different tasks, such as transcription, translation, subtitling, voiceover recording, and others. some of the resources involved include: desktop publishing specialists, native-speaking and subject-matter expert linguists, video editors, subtitlers, voice-over artists, localization engineers, and quality assurance (qa) resources. the nature of these workflows poses further challenges for lsps. the fact that each step requires specialized and highly-trained resources not only increases the operational cost and execution time, but it also requires dedicated, proficient, and meticulous planning and resource allocation. meeting tight deadlines becomes challenging, especially when handling large volumes of content or multiple language pairs simultaneously. additionally, quality control entails ensuring consistency and quality across all stages and, since each step involves human intervention, this can introduce variability in the output quality. maintaining high standards requires rigorous qa processes, further adding to the time and cost. the challenge of sourcing specialized subtitlers and voice-over artists to cover the diverse range of languages required by lsps serves as a key motivation for this article. unlike dedicated multimedia providers or streaming services whose main revenue comes from multimedia projects, lsps have distinct needs and workflows that may differ from those in the audiovisual industry. this distinction underpins our decision to test these workflows in this context. ## recommendations the goal of this experiment was to identify potential strategies of making the process of localization of multimedia products leaner and more cost effective. we think we have achieved that. here are some recommendations to lsps who want to test ai for such workflows: ## conclusion our productivity and quality analysis showed that ai technologies can be used successfully in the localization of multimedia products. amongst all the tasks analysed (transcription, translation, subtitle generation and voice-over), the one that is still lacking finesse and human quality is artificial voice generation. having said that, there are a large range of voice generation providers that were not tested during this exercise. a key observation from this experiment is that most ai tools, especially those offering ai dubbing/vo, are not designed with post-editing in mind, as they lack fundamental functionalities commonly found in cat tools. at the end of the day, companies need to strike a balance between quality of the end ai product, cost, learning curve and experience. the human element is still important in the form of post-editing (the transcribed source and the translation) and qa (subtitles and voice-over). the integrated workflow, with the use of platforms designed for the specific tasks, is the real winner in terms of quality and productivity, especially for subtitle generation. but it implies a steep learning curve, as language workers need to learn how to work in an alien environment. one of the clear conclusions of this experiment is that there is a need for training language providers workforce on the use of ai technologies; not only on the physical use of the various interfaces, but on the fundamentals of ai. by doing that, production teams will understand the possibilities of ai on their day to day tasks.