As global markets change and consumers become selective, content marketers reevaluate localization strategies. With advancing technology and evolving consumer behavior, a key question emerges for leaders. Should content be created in one language and then localized for markets? Or should companies use generative AI (GenAI) to produce customized local content? This dilemma affects industries, including regulated ones like finance, life sciences, and law. GenAI could enable better growth by tailoring content creation for different markets.
The decision isn’t straightforward, particularly as AI technology rapidly evolves. GenAI presents impressive capabilities that challenge the traditional paradigms of content creation and localization. However, it also introduces substantial limitations and difficulties in measuring return on investment (ROI). Companies that fully embrace GenAI often find themselves disappointed with the outcomes or struggling to deliver on the promise of a completely automated content lifecycle.
The Dynamic Landscape of Content Creation
Currently, the best strategy for leveraging GenAI versus traditional localization methods hinges on the specific use case and the value of the transformed content. As GenAI continues to mature, its scope of applications will expand, and the quality of its outputs will improve. However, one thing remains certain: shortly, traditional localization will coexist with GenAI-generated custom content. The primary challenge will be to find the optimal balance between these two approaches to meet key performance indicators (KPIs) at scale consistently.
In essence, businesses strategically leveraging GenAI alongside traditional localization methods will likely gain a long-term competitive advantage.
Benefits of Generative AI
GenAI offers significant advantages in enhancing productivity for translation, review, and quality assurance processes. By improving machine-translated text and identifying more language issues, organizations can quickly craft and deliver high-quality, on-brand content creation across all digital channels, languages, and markets. This enables companies to target smaller customer segments with more personalized messages that resonate more effectively and convert at a higher rate. Achieving this higher volume and velocity of content allows businesses to capture a larger share of their total addressable market (TAM) and accelerate growth with fewer resources.
For the first time, fully automated, multi-variant copy testing has become not only feasible but also commercially viable, allowing companies to iterate rapidly and optimize performance in real time. This capability enables businesses to target and capture their TAM with unprecedented precision. By shifting from an output-focused approach to an outcome-driven one, companies can optimize content for performance at a fraction of the cost and with increased content velocity. GenAI helps businesses craft unique, multilingual digital experiences and buyer journeys to boost growth. For example, companies can create personalized support or sales agents capable of working across multiple languages and product lines.
Challenges of Generative AI
Despite its benefits, GenAI is currently less effective at translating content than purpose-built machine translation (MT) engines. In addition to lower quality, the costs associated with running GenAI are significantly higher than those for MT. Fully automating content generation with GenAI is also not yet possible; human input is still required to create prompts, provide essential information, and train the large language models (LLMs) with appropriate language data.
As organizations produce thousands of content deliverables each month, they must manage shorter content creation life-cycles. The increase in multi-variant copy testing further amplifies the number of content pieces requiring management. This surge in content can overwhelm existing marketing, content, and localization teams, as well as their budgets and infrastructure.
Modern Content Management Systems
Modern content management systems currently lack the capabilities to fully support this influx of content. Even translation management systems need to adapt more robustly to integrate LLMs and ensure the quality of deliverables. Furthermore, reallocating the entire localization budget to marketing teams and inundating customers with automatically generated content is not a sustainable strategy, as some companies have discovered.
Although GenAI can create highly readable and authoritative content, consistently meeting brand voice and quality standards is challenging. This requires a focused content strategy and meticulous preparation to configure the AI engines. Additionally, maintaining alignment and a disciplined approach to content and localization operations is essential.
GenAI also introduces uncertainty and risks that are not yet fully understood. Issues such as AI “hallucinations” mean that companies relying heavily on GenAI need to be vigilant to detect problems early and minimize potential damage to their brand. The impact of GenAI on search engine optimization (SEO) is still unclear, as search engines like Google continue to determine how to handle automatically generated content in search results.
The lack of a legal framework regulating intellectual property rights and defining plagiarism adds another layer of complexity. The ongoing debate over copyright violations is exemplified by The New York Times lawsuit against OpenAI and Microsoft, alleging that its content was used without authorization to train OpenAI’s LLMs.
A New Approach to Content Localization
The challenge of creating scalable content that resonates with global audiences while maintaining a consistent brand voice underscores the importance of language as a critical business problem today. To achieve sustained growth through multilingual content, a novel approach to localization that strategically incorporates GenAI is needed.
For marketers and content creators, this shift emphasizes strategy and research over execution. Language professionals will focus more on reviewing and curating language assets upstream. Style guides, glossaries, and translation memories are essential for high-quality GenAI content. Building language assets isn’t a one-time task but an ongoing process. It must integrate into content lifecycles to address feedback and KPI fluctuations. This new approach surpasses traditional localization paradigms, supporting businesses throughout the content lifecycle.
Anouk Perquin from Lean Localization, a localization strategy advisory company, discusses GenAI’s transformative potential. “GenAI disrupts by doing what should have happened a long time ago. We will move away from word-based pricing and include linguists in processes. Removing the need for intermediaries will streamline localization and content workflows. Additionally, GenAI will help executives see the value in local content. This awareness encourages more investment in making localized content effective and impactful.”
Localization Industry
While the localization industry has long understood the value of high-quality language data, it has struggled to convey that value to business leaders. The advent of GenAI offers an opportunity to make a stronger business case for high-quality data, as it is increasingly linked to financial performance. Translators and localization professionals can add significant value by guiding businesses on GenAI best practices and helping them develop new approaches to content operations.
Transforming marketing, content, and localization teams demands persistence and a clear sense of purpose. While this represents a substantial change-management effort, it is worthwhile. Companies that effectively reorganize their content operations will better engage their customers with more compelling content and enhanced digital experiences.
In conclusion, while language is inherently human and personal, paradoxically, GenAI may help us become even better at it. By combining the strengths of GenAI with traditional localization methods, businesses can achieve a more strategic, effective, and scalable approach to content creation and localization.
