Different types of app localization

When expanding into new markets, marketers must consider the depth of localization needed to meet their goals effectively. There are two main approaches to app localization: the MVP (Minimum Viable Product) approach, and full localization. 

The choice between these options depends largely on the company’s growth strategy, resources, and specific market objectives. Let’s look at the unique benefits and challenges of each approach.

MVP app localization

The MVP approach aims to quickly and efficiently adapt the app to a new market with minimal resources and time investment. The thinking behind such an approach is often to enter a new market as soon as possible, with the aim of adapting and optimizing the experience on the fly. 

Key characteristics of this approach include: 

• Targeted translation. Translating essential text elements such as menus, calls to action, and product pages. 
• Limited scope. Localizing only the core features and functionalities
• Minimal cultural adaptation. Relying on basic language translation rather than adjusting fully for cultural nuances. 
• Quick launch. The aim is to achieve a quick market entry to test the app’s viability in the new region.
• Post-launch optimization. User feedback and analytics is used to guide further localization efforts and prioritize subsequent improvements.

There are several benefits to the MVP approach, including the speed of deployment to new markets, and a more cost-effective initial investment which allows brands to test the market before making further improvements. 

However, this approach restricts the user experience, and can limit the appeal of the app. By presenting an MVP version, businesses run the risk of  deterring users and stalling expansion plans. 

One example of an MVP app is the first Ubercab app, which launched in San Francisco using a prototype app to test the concept. In this case it worked, and the concept of MVP fits well for tech startups. For brands with more established apps, there may be drawbacks to this approach. 

Full app localization

This approach is designed to launch an app which has been fully adapted to meet all the linguistic, cultural, technical, and legal requirements of the target market, with a more relevant and seamless user experience.

Key characteristics of this approach include: 

• Full translation. All elements are translated into the local language, including help documentation, support content, and marketing content. 
• Cultural adaptation. Visuals, cultural references, and user interactions are carefully adapted to align with local  preferences. This may include redesigning areas such as imagery and copy to be culturally appropriate.
• Legal compliance. Ensuring that the app complies with local regulations and standards, including data protection laws and content guidelines.
• Pre-launch testing. Thorough testing with local users to ensure that the app functions smoothly and meets local expectations.
• Full localization of features. All features and functionality are localized to ensure a consistent and complete user experience.

This approach is more time consuming, with key processes such as testing and full translation potentially delaying the launch of the app, while a greater up-front investment is needed before launch. 

However, this investment in time and resources can produce a better, more relevant experience for users, which should translate into increased satisfaction and engagement, as well as faster growth. 

While the app may still need to be optimized on the go, full localization provides a stronger platform for success, and gives apps a competitive advantage.

How marketers should approach app localization

Effectively localization requires a comprehensive approach that accounts for language, cultural nuances, technical and legal aspects, and a process of testing and optimization.  

These are some of the main app localization challenges for marketers:

Here are some key areas to consider, which will help marketers to address these challenges above. 

Market research 

• Understand the target customer. Gain an understanding of local culture, user behavior, preferences, and competitors.
• Gauge demand. Evaluate the demand for your app in the new market. Consider factors such as market size, growth potential, and local economic conditions.

Define goals and prioritize key localization elements 

• Set clear objectives. Define your goals, whether this is user acquisition, engagement, revenue growth or a combination of the three. 
• Prioritize. Decide which parts of the app should be localized first, based on potential user impact and strategic importance.

Translation and cultural adaptation

• Focus on quality. Implement quality assurance processes such as proofreading and user testing to ensure translations are accurate and contextually relevant.
• Use machine translation. Machine translation combined with automated language quality assessment tools can improve the accuracy of translation, and speed up time to market, as well as subsequent content updates.  

Cultural adaptation

• Adapt visual content. Images, icons, colors, and graphics should  align with local cultural norms and preferences. Avoid content, colours and images that could be culturally insensitive or have any potential for misunderstanding. 
• Tailor content and messaging. Transcreation of marketing messages, slogans, and promotional materials ensures cultural sensitivity and alignment with what resonates with local audiences. This process demands thorough research into the local market. 

Technical and legal considerations

Ensuring a seamless and compliant user experience goes beyond translation and adaptation – technical and legal considerations that can make or break your app’s success in a new market. From supporting local formats to meeting regulatory standards, these elements are critical to delivering a fully localized app: 

• Formatting and design.  Ensure that the app supports local date, time, and number formats, as well as currency symbols and units of measurement. Aspects such as checkout forms should be adapted to input fields that match local conventions, such as address formats and phone number patterns.
• Understand local regulations. Ensure compliance with local laws and regulations, including data protection laws, content restrictions, and advertising standards.
• Integrate with local services. Where appropriate, your app should be compatible with local payment gateways, and other third-party services that are popular in the target market.

Testing and optimization 

• Conduct user testing. Test the app and gather feedback from local users, which helps to quickly identify any localization issues and areas for improvement. Monitor user reviews, ratings, and other feedback to address any emerging issues.
• Track performance. Monitor key performance metrics  such as downloads, user engagement, retention rates, and revenue in new markets. 
• Optimize continuously. Based on feedback, testing, and performance data, the app should be optimized on a regular basis. 

 

In the next part of this article, we’ll look at what marketers need to know about app store localization, and how they can choose the most effective app localization tools. 

Tackling misinformation in the age of AI

With the rise of AI-driven content, ensuring accuracy and combating misinformation has become a top priority. Several key sessions looked at AI quality and user experience, and shed some light on ways technical communicators can proactively safeguard against misleading content.

Countering fake news through better UX

In a session on misinformation, Ray Gallon from The Transformation Society argued that effective UX strategies can help counter fake news. He stressed that presenting accurate information alone is insufficient; communicators must appeal to users’ emotions. Techniques like user journey mapping and inferential learning help create engaging, accurate narratives without sensationalism.

Addressing AI errors at the source

Regina Preciado’s session “Your AI Isn’t broken, your content is” highlighted the integral role of content quality in enhancing AI-generated documentation. Looking in particular at AI hallucinations and inaccuracies, Regina honed in on poorly structured content, advising businesses to establish a single source of truth approach to all of their documentation and content – no matter its intended audience.

She emphasized how always referring to an original source helps AI systems provide consistent, reliable outputs, and how reliable AI can help secure trust and future investment internally.

Enhancing AI accuracy with ontologies

Prof. Dr. Martin Ley and Max Gärber from PANTOPIX emphasized the importance of ontologies—structured frameworks that define relationships between concepts—to improve the accuracy of generative AI. Product ontologies serve as a foundation for large language models (LLMs), ensuring they interpret product data more reliably.

For communicators, this means that using structured data frameworks can help mitigate errors in AI-powered chat applications, adding precision to user interactions and reducing misinformation risks.

Multiple sessions this year stressed the importance of structured, rigorous data inputs when training AI and building processes. From proactive content verification to UX design, it’s important that companies plan ahead in order to create and maintain credible, reliable AI-generated communication.

Maximizing ROI and quality in global communications

As localization becomes a more crucial component of internationalization strategies, ROI is increasingly on the agenda, with more financial eyeballs on the function than ever. Beyond typical localization metrics, several speakers discussed how to quantify ROI more effectively, and how financially savvy approaches can actually impact the user experience and the fortunes of an entire business. 

Linking localization to ROI

In an interesting session linking localization efforts directly to business goals. Translate.One’s Quality and Product Manager, Christiane Schaeffler highlighted the need for both quantitative metrics (like market expansion and customer retention) and qualitative indicators (such as enhanced user experience).

By aligning around customer satisfaction, rather than relying on siloed metrics like turnaround time, it becomes possible to build more compelling business cases for localization investments, and fine-tune strategies to improve content relevance and impact.

Enhancing accessibility with LangOps

Vivien Krämer and Jochen Hummel gave a wide-ranging intro to Language Operations (LangOps). LangOps combines machine translation, advanced text analytics, and multilingual knowledge systems to optimize communication efforts at scale. Exploring its recent adoption at Roche Diagnostics, they explained how LangOps has enhanced content accessibility across languages, and proven useful in standardizing regulatory compliance. By unifying language resources, LangOps supports product localization and also assists with search and customer support, making it a versatile enterprise tool. 

Tracking localization ROI, leveraging AI-powered LangOps, and fostering cultural competence are crucial for ensuring effective global comms. This approach ensures that localized content meets quality standards while resonating with diverse audiences, maximizing both engagement and ROI.

Optimizing documentation processes and efficiency

As content grows more complex, documentation teams face the challenge of managing reviews efficiently. These sessions shared techniques to streamline review cycles, embed quality assurance early, and prioritize sustainable content design.

Tackling review challenges in technical documentation

Dr. Saul Carliner from Concordia University identified five common issues in documentation reviews, such as contradictory feedback and unresponsive reviewers. He recommended solutions like setting clear review guidelines and holding pre-review meetings to clarify expectations, creating more relevant and timely feedback. Carliner also highlighted the importance of cultural competence, helping communicators assess cultural sensitivity, ethical considerations, and adaptability for inclusive, globally resonant content.

Embedding quality assurance early: The shift-left approach

Valentina Turra and Daniela Fleck from Philips shared their “shift-left” approach to localization, which emphasizes proactive quality checks early in content development. This strategy allows teams to resolve issues early, producing clear, consistent documentation that meets user needs. At Philips, this approach has improved documentation quality and streamlined user experience.

Promoting sustainable UX in documentation

Nolwenn Kerzreho from Madcap Software advocated for minimalist, sustainable content design to reduce information overload. By focusing on essential information, technical communicators can create clearer, more user-friendly documentation, enhancing engagement through simplicity.

All of these sessions underscored the value of refined review processes, early quality checks, and sustainable design principles, enabling teams to produce efficient, user-focused documentation that improves workflows and user experiences.

Enhancing user experiences with data

Long used for customer-facing applications, data-driven insights are now changing the game for technical documentation. Several sessions illustrated how technical communicators can use data, JavaScript, and tailored workflows to create content that resonates with today’s users.

Enhancing documentation through data

Rachael Hewetson and Sophie Sofce Kohl from SAP highlighted how data-driven insights guide documentation improvements. By analyzing metrics like page views and search trends, SAP identifies content gaps and reduces support requests. This approach enables communicators to prioritize updates that directly address user needs, resulting in documentation that is both relevant and user-friendly.

Empowering writers with JavaScript

Collibra’s Ken De Wachter encouraged technical writers to learn basic JavaScript to add interactive elements such as tabs and dropdowns to documentation. These elements make content more engaging and user-friendly. De Wachter emphasized that even a small amount of coding knowledge can enable writers to meet modern expectations for dynamic, interactive documentation.

Customizing DITA DocOps for corporate efficiency

Dia Daur from A-Jour-Net Inc. discussed how corporate teams can optimize their DITA (Darwin Information Typing Architecture) workflows. Customizing DITA DocOps allows for efficient content management, enforcing taxonomy standards, simplifying navigation, and facilitating mass updates. This approach ensures content consistency and adaptability across various channels, which is essential for large teams.

Combining data insights, interactivity, and optimized workflows empowers technical writers to deliver engaging, user-centered content. These methods help meet the high expectations of today’s users by enhancing the usability, relevance, and accessibility of documentation.

Leveraging simplified language standards

Clarity in technical documentation is especially important when dealing with multilingual operations teams. A pair of useful sessions highlighted how language standards like Simplified Technical English (STE) support clearer communication and reduce translation errors.

Writing procedures in Simplified Technical English (STE)

Daniela Zambrini and Orlando Chiarello from eXeL8 and STEMG introduced ASD-STE100, a controlled language standard designed to simplify technical English. They outlined STE’s structure and vocabulary limitations, showing how it eliminates ambiguity for readers. Through practical exercises, attendees saw how STE enhances understanding for non-native speakers and translators, enabling them to convey technical details accurately.

STE’s role in AI-assisted content creation

Orlando Chiarello also joined Jennifer Bennor to discuss the importance of STE in AI-driven content creation. As AI increasingly supports translation and content generation, standards like STE are essential to maintain clarity. By minimizing vocabulary and simplifying structure, STE helps prevent AI misinterpretations, ensuring content remains accurate and consistent across languages.

Simplified Technical English (STE) plays a vital role in creating clear, translatable documentation. Controlled language standards like STE are critical for ensuring accuracy, particularly as AI becomes more involved in multilingual content delivery.

This year’s tcworld conference emphasized the need for structured frameworks, from UX design to STE, in managing misinformation and leveraging AI effectively. Sessions underscored the importance of measuring localization ROI, adopting LangOps, and nurturing cultural awareness to ensure content resonates with diverse audiences. Additionally, prioritizing documentation efficiency and integrating data-driven insights and interactivity were highlighted as essential steps for technical communicators aiming to meet the demands of a globalized digital landscape.

When we talk about artificial intelligence (AI) in localization, there’s a natural tendency to speak about it from an operational angle.

After all, the right use of AI and Large Language Models (LLMs) has been busily revolutionizing high-volume machine translation (MT) for some time. Companies can now handle and translate vast amounts of content quickly and efficiently.

However, there is another major impact that we’re perhaps guilty of dancing around – how exactly will AI impact jobs in our industry.

As more automation and machine-assisted processes become the norm, there’s a natural tendency to worry about people being replaced.

This then leads to other questions: If people are removed from the loop, can we trust AI to handle things? Will the translation quality meet our standards and those of our customers? Should we be worried about AI ‘hallucinations’ in our content?

And the questions don’t stop there.

As AI becomes more prominent, the roles of localization professionals will inevitably shift, but in the majority of cases, this might be the best thing that could happen.

In this article, I’d like to look at how those roles are adapting. More importantly, why the role of humans is so crucial if we’re going to ensure that these technologies are applied ethically, safeguarding both the integrity of translations, and maintaining cultural sensitivity across incredibly diverse and complex markets. Let’s dive in…

The power (and pitfalls) of AI in localization

Efficiency and volume – how AI and LLMs are revolutionizing high-volume translation tasks

Let’s start by thinking about how and why AI and LLMs are being applied currently. One of their main advantages over human translation is the ability to translate very large volumes of content efficiently.

Coupling speed with cost-effectiveness means companies can scale their translation efforts and stick within their budgets. AI also provides a fairly straightforward path to new, often less widely spoken, language pairs, meaning that internationalization is quicker and easier.

Despite these advantages, AI comes with several ethical challenges that need to be addressed so that the systems can be used responsibly:

• Bias in Training Data: Because LLMs utilize such large datasets, any biases in those sets can easily be amplified. In many cases, AI is looking for the most common, repeatable uses of certain terms, so it’s easy to perpetuate bias. This can skew translations, and provide outputs that marginalize or exclude certain groups. Considering the lengths businesses go to ensure cultural sensitivity and relevance, it’s easy to see how this could have a significant impact on brand reputation, and the bottom line. 
• Human Bias in AI Use: With the best will in the world, everyone carries conscious and unconscious biases, and it’s all too easy to introduce these into our AI processes. From inadvertently favoring a particular dataset to interpreting outputs based on our own beliefs or cultural background, none of us are immune. If outputs aren’t scrutinized properly, or are applied inappropriately, AI can easily be misapplied. 
• Bias For and Against AI: Perhaps most importantly here, there is also a notable bias for or against the use of AI in translation. At one end, company leadership may be clamoring for cost reduction, efficiency and scalability. At the other, localization managers might be concerned about their job and their team being sidelined by new technology, leading to slow adoption and a reluctance to apply AI capabilities in certain areas.

As with most business decisions, it’s all about balance. AI is not, and never has been, designed to fully replace human expertise; rather to augment it and allow localization professionals to focus on more strategic, high-impact tasks. 

By addressing these biases and ensuring that AI is used ethically, localization managers can play a vital role in guiding the responsible integration of AI into the industry.

Of course, this is easier said than done. Localization professionals will need to consider which of their existing skills can most readily be employed in AI-led translation processes. 

The ethical imperatives in AI-driven localization

Data quality and bias mitigation

As I mentioned earlier, the quality of training data for any LLM is paramount. High quality, and crucially, highly representative datasets are essential to combat bias. 

For instance, neural machine translation (NMT) systems often struggle with less widely spoken languages due to the limited availability of high-quality data. This can result in errors and mistranslations that misrepresent these languages and their cultural nuances. This has traditionally been particularly prevalent with many African languages. 

Consider for example, that Nigeria alone has more than 500 languages and dialects, each with a high level of cultural and geographical specificity (and if you want to dive deeper, you might also consider the bias I’m displaying by choosing Africa as a  hub of diverse languages).

Culturally sensitive translation is something that is particularly challenging for AI. Context or cultural applications of certain terms change much more frequently than most datasets, leading to translations and applications that might be technically perfect, but could also be inappropriate or even offensive.

As an example, consider a hypothetical situation where AI is being used as part of the content moderation process on a gaming platform.

In the UK, the word ‘Bloody” can be used as a mild curse word for emphasis, but if our user is playing a first-person shooter game, are they actually cursing, or commenting on the amount of pixelated blood that’s spraying around on-screen?

It’s a very minor, and very specific usage, but could well have a compound effect on auto-moderated in-game conversation, blocking content or players for perfectly innocent interactions.

The human element is crucial here, and equipping decision-makers with more culturally aware training processes can make a huge difference.

Culturally diverse businesses often outperform their contemporaries, so it makes sense to build this approach into our AI data as well.

Because we’re talking about a technological problem, we might want to apply a technological solution. In the past, this would have meant “better datasets”, But there is a fundamental difference between earlier neural models and the emerging way in which LLMs are being used.  

Neural models were trained from scratch on large data sets.  So controlling and manipulating these data sets was very important and consequential.  LLMs however,  are “pretrained” by the large organizations that create them, so the end user often has little or no control over what information was used for initial training.  

However, users do now have much richer methods of control over refining outputs, by interacting with them and adding nuanced instructions and prompts. This instruction could include formal or informal language, changing the tone of voice, avoiding certain words, and more.  

This level of control and interactivity is new, and wasn’t possible until quite recently, but again, it very much requires human experts to drive the changes and to evaluate and ensure the results are satisfactory.

 

Transparency and accountability

The types of AI systems at work in high-volume machine translation can often seem a mystery if you aren’t an expert. If managers and users can’t understand why their AI system is making the choices it makes, they aren’t going to feel comfortable using it – or passing those translations onto users.

Localization professionals are quite right to demand clear, exact explanations from LLM engines and vendors around which datasets and algorithms are being used, and why decisions are being made by these systems.

While training to help understand what is happening ‘under the hood’ can help here, there also needs to be a focus on who trains AI systems, and in particular, how we train them so that they can check and evaluate the output of other AI systems, as well as how users can regularly audit for changes in the levels of bias, and quality rate measurement to spot dips or peaks in accuracy.

If you’re dealing with legal, medical, or indeed any type of personal data, the risks are obviously much higher, and while this is chiefly seen as an engineering issue, any vendors should have clear guidelines around how personal data is anonymized and removed from AI memory. The legal costs of misuse or misapplication can be enormous. 

Cultural and contextual sensitivity

It’s hard to underline this enough. AI translation is efficient, but without very clear guidelines and refinements, it is not subtle. Without careful consideration of context, your translations are going to miss the mark in many ways – and not all of them will be expected. 

The most obvious example of this might be the literal translation of an idiomatic expression:

 

“It’s still up in the air”. 

Is your project literally on board an airliner right now, or do you simply mean you’re waiting for something to be resolved?

It’s easy to become overly concerned about these things, but on the flipside, getting it right offers huge opportunities to raise engagement and foster loyalty, and this means combining human oversight with large-scale AI powered translation.

(As a delightfully silly example, the French word for “choice” is “Choix”. In the French translation of the Harry Potter books, the famous Sorting Hat is referred to as a “Choixpeau” – a take on “Chapeau”,  the French word for hat. It’s often the smallest examples that showcase why and how a human’s insight and ability to interpret can make all the difference.) 

The evolving role of localization managers

From tactical to strategic

Localization managers are now strategic business assets. Certainly, there’s still the need to coordinate assets and make sure projects are delivered on time, but the bigger picture is all about balancing the gains offered by AI with the need for high quality, consistent and sensitive translations and transcreation.

Localization managers should ideally be looking at ways to use AI to create more efficient workflows and drive scale, but will also need to be sharing that knowledge by setting operational guidelines for its use.

In practice, this means that a specialist localization professional is in charge of keeping humans in the loop. 

In many cases, this means reconfiguring processes and ensuring that translation teams are able to review and optimize AI-generated content regularly.

It also means developing new methods to assess how effectively teams improve or correct flagged content. In this new era, monitoring dashboards and analysis tools become crucial.

Similar to the ways Engineering and Marketing teams work, the modern localization team needs a set of tools and processes that adopt the ‘kaizen’ way of working – a method that consistently looks for ways to optimize both processes and outputs. 

Localization managers as educators

The localization team is now a hub of knowledge, and education is fast becoming one of their primary responsibilities.

While it may seem we’re reaching a saturation point with AI, in practice we are far from it, and knowledge is often very unevenly distributed across the business. Localization managers are perfectly positioned to inform and educate decision-makers on the capabilities and limitations of AI.

This may seem like yet more work being heaped on to localization managers’ plates, but luckily, there are standard approaches that can help here. 

Organizing training programs and ‘lunch and learn’ sessions for internal teams, working with marketing to develop more informative assets for clients, and sharing reporting to help set reasonable, contextual expectations for AI performance, backed up by solid numbers.

This increased engagement with the broader business can also help localization teams emphasize just how important it is to keep human oversight and use it in conjunction with AI to get better results. After all, if you are the ones sharing all the knowledge, how can you be replaced?

Beyond being the go-to source of knowledge, there are also ways localization teams can showcase their value during the initial set-up and ongoing running of AI technologies.

Three ways localization teams can make sure AI is used ethically

Build ethical guidelines

Every new technology needs a set of robust guidelines before rollout, and while a good vendor will have these in place, it pays to pay close attention and plot out clear guidance for areas like data quality, bias reduction, and translation training to accommodate cultural nuance.

Strong frameworks should always include the need for human oversight. While this oversight will need to be more robust for materials and translations where more nuance is required, it’s important to have regular checks and balances in place wherever you employ AI to scale translations. In the past, there have been several high-profile examples of seemingly simple applications going rogue, or being open to interference from outside actors. Always remember that AI is never a ‘set and forget’ solution, but an active and evolving system. 

By setting clear rules, localization managers can ensure that AI supports, rather than undermines, industry ethics.

Ongoing monitoring and refinement 

Speaking of continuous involvement, remember to plan in regular audits of your AI input and outputs. These are absolutely key to spotting and addressing biases and errors that can creep in. As more information is provided to an LLM, so the risk of bias increases, so make sure you are performing regular checks. 

Localization teams can use both AI and human experts to review translation processes regularly. Automated tools can flag issues in AI-generated translations, while human experts fine-tune for accuracy and cultural fit. This approach will also keep improving AI, making it more reliable over time.

Transparency in AI 

Finally, let’s think about transparency in more detail.

From a customer-facing perspective, companies and organizations need to be clear about how and where they use AI, and to be honest about the potential for bias and errors in results. As in other areas of the business, being open about risks and the steps being taken to limit them is a key part of building trust with stakeholders and customers. This also provides an opportunity where the localization team can showcase their expertise in both the benefits and risks of AI—key strategic areas where the team should have a voice.

As an example, including being transparent about where AI is used can help set clear expectations for users. It’s equally important to communicate  internally about which data is used and how it has been prepared before it is used to train AI systems. 

Clean data is an imperative, and while it means more work up front, it will always lead to better results. By focusing on transparency, localization teams can promote a more ethical and responsible use of AI.

Ways Phrase can help address ethical issues

One of the main drivers behind Phrase’s own Quality Performance Scores (QPS) is to make translation quality more transparent. By breaking down how scores are calculated using the MQM 2.0 framework, it gives localization managers greater insight into the inner workings of AI translations, and is an opportunity to spot changes in quality, accuracy and bias.

Similarly, Auto LQA acts like automatic pre-translation, handling the initial quality check so that linguists can focus on refining and validating the results—ultimately saving time and effort.

For dispersed localization teams, centralizing translation tasks through a single platform like Phrase can help promote transparency, helping teams across different locations stay aligned and informed. This visibility reduces errors and miscommunication, ensuring projects remain consistent and high-quality.

Conclusion

The integration of AI and large language models (LLMs) into localization has brought both exciting opportunities and important ethical questions. As AI reshapes high-volume translation tasks, it’s crucial to keep standards high in areas like data quality, transparency, and cultural sensitivity. Localization managers now play a bigger role, moving from day-to-day tasks to overseeing how AI is used responsibly and effectively.

Key ethical issues—like bias in AI training data, the need for human oversight, and the importance of being transparent—are critical for the ethical use of AI in localization. Using AI responsibly means recognizing that it should enhance human expertise, not replace it. AI should be a tool that supports localization, without compromising quality or cultural respect.

Localization managers are leading this change. Their active role in shaping AI practices, educating teams, and setting ethical standards will ensure that AI remains a helpful resource in the industry. By taking these steps, they’ll help create a future where AI-powered localization not only meets the needs of global communication but also respects the diverse cultures and languages involved.

1. AI: Revolutionizing Localization Processes

It should be no surprise that the bulk of the LocWorld52 sessions covered AI in some capacity.

At Phrase, we’ve spoken extensively on the topic recently, and as the technology advances, it should be no surprise that it is set to affect the role of localization professionals ever more acutely.

While many talks touched on AI, several stand-out sessions looked at more niche areas where automation and AI can assist. 

• • AI for Low-Resource Languages: In a standout session, Sheriff Issaka and Tumi Samuel-Ipaye from the African Languages Lab shared how AI and NLP are bridging language gaps across Africa.

    They emphasized that high-quality AI translation for under-resourced languages requires a balanced combination of machine learning and human expertise.As an example, the speakers looked at the dialectical range in two languages – Igbo and Swahili.

    With so many small incremental changes to languages across a relatively small geographical area, AI is able to apply data points at a scale which previously would have been impossible.

    Techniques like transfer learning, taking a pre-trained model and adapting it to a new, related task or dataset and human-in-the-loop learning, where humans actively participate in the model training process, labelling data, verifying predictions, and providing feedback, are crucial. These approaches help ensure that digital transformation in Africa respects linguistic and cultural diversity.

• • Generative AI’s Impact on Project Management: Marilyn Dickinson and Aki Hayashi from Dell Technologies spoke about the ways AI is transforming project management.

    Using AI-driven frameworks, Dell has enabled managers to predict delays, allocate resources efficiently, and automate workflows, fostering innovation and efficiency in complex, multilingual projects.

    By optimizing workflows, Dell has been able to significantly increase its language offerings – from three to 50 – so that it can communicate more effectively with more customers.

  • Integrating LLMs with Existing Systems: In a session by Jonas Ryberg from Centific, the focus was on integrating Large Language Models (LLMs) into established localization systems.

    Instead of replacing current tools, companies are strategically augmenting them with LLMs to enhance functionality while maintaining consistency across existing workflows.

These discussions collectively underscored the importance of implementing AI thoughtfully—leveraging its capabilities without overlooking quality, cultural relevance, and the irreplaceable human touch.

2. Multimedia Localization in the AI Era

Multimedia localization, particularly in gaming and children’s content, received special attention this year.

As multimedia experiences grow more complex, the stakes for cultural accuracy and ethical responsibility have risen significantly, with greater rewards for getting it right, and harsher penalties when things go awry.

• • AAA Game Localization: Patrick Görtjes from Massive Entertainment gave an insightful look into the localization process behind Avatar: Frontiers of Pandora. 
    Embedding localization teams within game development from day one ensures that every audience experiences the game as authentically as possible, with correct cultural references.
    As well as enhancing user engagement, this early-access approach is helping to reduce the costs of reworking and duplicating content later on.
  • Localizing for Young Audiences: Sham Albdour of Gamester Kids and Marina Ilari of Terra Localizations explored the high-stakes nature of children’s content localization, which has strict compliance demands.
    For young audiences, content needs to be carefully adapted to be age-appropriate, culturally accurate, and ethically responsible. However, there are a number of specific challenges presented when content is multimodal, with localization applied to everything from language and tone, to gesture control and even core game mechanics. AI’s potential for efficiency here is balanced by ethical considerations for impressionable audiences.

Both of these sessions underscored a growing need for ethical AI standards in localization, ensuring inclusivity and cultural appropriateness.

3. Global Expansion: Strategies and Challenges

This year, we were treated to a number of talks that showed how important localization is in growing a business internationally, with sessions addressing monetization, payment systems, and stakeholder engagement.

• Global Monetization Strategies: Kevin O’Donnell from Global10x looked at the importance of supporting multiple currencies and customizing pricing models for local markets.Kevin explained how companies like Netflix and Spotify offer flexible payment options like regional e-wallets, or tiered pricing, to increase customer satisfaction and drive revenue growth in markets with vastly different economic backgrounds.
• Building Localization Competencies for New Markets: Subway’s Carrie Fischer highlighted how companies entering new markets benefit from cross-functional collaboration.By integrating localization early with product, engineering, and operations, they create a seamless and adaptable approach to global expansion.
• Stakeholder Education and Engagement: As localization is now on the radar of more people across the business, Junjun Cao and Karina Drosenos from Salesforce shared some practical strategies for securing stakeholder buy-in.

  By aligning expectations, demonstrating ROI, and fostering collaboration, localization teams can effectively position themselves as strategic assets within global ops.

4. Advancing the Role of Localization in the Age of AI

In a panel moderated by Phrase CEO Georg Ell, lara Altkorn from Braze and Hameed Afssari from Uber shared strategic insights on how to frame localization as a revenue-generating force, deepen customer connections, and maximize AI’s potential. Key takeaways included:

• Making Localization a Revenue Driver: Panelists underscored that localization is more than an expense; it’s a growth driver.

  By aligning localization initiatives with key metrics like customer satisfaction (CSAT) and revenue impact, they demonstrated how localization could fuel market growth. lara Altkorn shared Braze’s approach of using dashboards to track localized versus non-localized performance, highlighting Japan as a case where localized content has outpaced English-language use, showing its effectiveness in engaging regional audiences.

• Building Strong Customer Connections Through Localization: Localization helps global brands forge a deeper connection with diverse audiences, making international markets feel more accessible and personal.

  Hameed illustrated Uber’s use of tailored, region-specific content to boost engagement among riders and drivers. Despite the challenges in measuring localization’s success, the approach fosters brand loyalty in competitive markets. He acknowledged, however, that effective A/B testing in localization requires careful planning due to its complexity.

• Balancing AI with Human Expertise for Quality Localization: While AI is transforming localization, panelists agreed that human input remains crucial to maintain cultural and brand integrity. lara and Hameed both advocated a “human-in-the-loop” approach—using AI to scale and speed processes, but relying on localization experts to oversee quality and alignment with brand values.

  This balance of AI and human expertise allows organizations to efficiently expand localization without sacrificing the essential nuances that resonate with diverse, global audiences.

It’s clear that localization is moving beyond simple translation to serve as a strategic enabler of global growth and customer loyalty, and this is being recognized by more functions across international businesses.

5. Accessibility, Inclusivity, and Linguistic Diversity

A number of this year’s sessions looked at localization’s role in building digital inclusivity and increasing (and championing) accessibility – and the ROI associated with getting this right. 

• AI-Powered Accessibility: Hélène Pielmeier of CSA Research explored how AI can make digital content more accessible to people with disabilities.

  AI tools that automatically generate captions, provide real-time text-to-speech, and create audio descriptions are transforming accessibility.

  Incorporating these considerations from the outset not only improves compliance with accessibility regulations but also makes information more universally accessible, opening products and experiences up to more potential customers and users.

• Indigenous Language Digitization: Meanwhile, Roy Yokoyama of Motorola explored the technical side of things, looking at the importance of Unicode and CLDR standards in preserving endangered languages.

  For example, Unicode encoding is crucial for ensuring that unique characters, scripts, and diacritical marks are accurately rendered. Without Unicode support, these languages risk distortion, often represented by similar-looking characters from other scripts. This can easily dilute cultural accuracy and the language’s functional utility, compromising meaningful communication and digital inclusion.

  By providing a digital presence for marginalized languages, companies can help revitalize and sustain these languages.

  Craig Cornelius from Google and Tex Texin from XenCraft also discussed the wider implications of this approach, including the complexities involved in designing hardware and software to support languages with large or unique character sets.

As localization bridges linguistic gaps, these considerations are imperative in sustaining the diversity of the digital landscape, ensuring that everyone has a voice online.

6. Women in Localization and Leadership

Inclusivity was also high on the agenda for the entire conference, this year.

Several sessions looked at the role of women in localization, including a major discussion of AI’s impact on the workforce, with insights from Pia Bresnan (Uber) and Allison McDougall (G3).

• Preparing for AI-Driven Roles: In an AI-driven world, adaptability and strategic skills are essential for success.

  Emphasis was placed on mentorship, career advancement, and inclusive hiring practices as pivotal strategies to reduce gender disparities and empower women within localization.

  By fostering these initiatives, organizations can build teams that are resilient, forward-thinking, and representative.

• Encouraging Gender Diversity in AI: Many localization teams are addressing gender bias in AI by ensuring that datasets used to train LLMs mirror real-world diversity.

  This approach is vital for producing AI outputs that authentically reflect varied perspectives.

  Prioritizing inclusive data in AI development not only enhances the quality of insights but also builds AI systems that resonate more accurately across global audiences.

By fostering inclusive work environments and supporting skill development, the localization industry can pave the way for more equitable representation in AI and beyond.

7. Future-Proofing Localization Strategy in the Age of AI

Finally, a panel featuring speakers from Pinterest, Canva, and Adobe underlined the need for a forward-looking AI strategy in localization.

With the technology evolving so rapidly, professionals must be ready to adapt, and advocate for strategic, purposeful integration.

• Clear Vision and Strategic AI Integration: Localization leaders must define clear objectives and choose AI tools that enhance rather than disrupt existing workflows.

  Once again, thoughtful implementation—using AI to automate repetitive tasks while reserving nuanced, strategic work for human experts—was highlighted as the key to success.

• Build Versus Buy: To leverage AI’s full potential, localization teams must decide between building in-house solutions or partnering with specialized providers.

  This choice depends on company priorities, and many speakers encouraged leaders to weigh the benefits of maintenance and support against the risks and challenges of reinventing the wheel.

  These speakers also noted the increasing commodification and consolidation of the AI space, and the cost associations and reliances this brings with it. As with all major technology progressions, there is a balance to be struck between control and the risks of ‘building on someone else’s land’.

• Separating AI Fact from Fiction:  We were also reminded of the importance of a reality-check.

  With all of AI’s hype, critical assessment of AI’s capabilities is more important than ever. Effective stakeholder communication is crucial to maintaining realistic expectations and securing executive buy-in for AI-driven localization initiatives.

  Localization leaders were urged not to see AI as a replacement, or as a magic bullet, but as a tool for enhancement, powered by vital human inputs and skills.

  This strategic approach ensures that localization remains a valuable asset in a company’s global toolkit, enabling long-term success.

 

Conclusion

This year’s conference highlighted the localization industry’s evolution into a critical driver of global business success. From AI integration to ethical standards in accessibility, localization is proving to be more than language—it’s a comprehensive approach to building meaningful global connections.

Audio Version

Prefer to listen to this blog post? Listen to our AI-powered podcast version of this blog, exploring the best AI platforms for global business expansion, and how AI-powered localization, translation models, and hyperautomation can help your business expand efficiently into international markets.

Key Considerations for Selecting AI Tools

When selecting an AI-enabled platform to support your global expansion, there are several essential factors to consider:

AI-Powered AutomatedTranslation Engine Selection

Not all translation engines are equal. Some are better suited to certain tasks than others.

You don’t want to tie yourself to a single engine or leave the choice to humans who may not fully understand the pros and cons of each engine.

Instead, look for a platform that integrates with multiple well-established translation engines and uses AI to auto-select the most suitable one for each task.

 

Ease of Adaptation of Translation Models

Your AI tools should allow for easy adaptation of translation models to each customer’s specific language preferences to improve accuracy and align translations with your brand’s specific tone and style.

Look for platforms that let you easily turn past translations into unique language models, tailor-made for your content.

Data Ownership and Confidentiality

Ensure that the platform you choose respects data ownership and confidentiality.

Some of the information you feed into your AI-enabled localization platform will be confidential.

You don’t want this data being used outside of your business, so make sure your use of AI and large language models (LLMs) is robustly ring-fenced.

Automated Quality-Scoring of Translated Content

Look for AI tools that can automatically score the quality of translated content.

This feature allows you to maintain high standards across all markets without requiring constant human oversight.

The platform should ideally use the industry-standard Multidimensional Quality Metrics (MQM) framework for these assessments.

Generative AI-Enabled Recommendations

Generative AI capabilities can provide suggestions for improving translations or adapting content to better fit local markets, saving your team time and effort.

Look for platforms that include predefined, localization-focused prompts to accelerate the refinement process.

Consistency with Brand Voice and Style

Consistency is key when expanding globally.

Your AI tools should help ensure that your brand’s voice and style are maintained across all languages and regions.

Check if the platform offers glossaries of your key terminology to ensure consistent translations, regardless of which engine you use.

Access to the Latest AI Advancements

AI is evolving rapidly, so it’s important to choose a platform that stays up-to-date with the latest advancements.

This will ensure that your business remains competitive in a fast-changing global market.

Integration with Existing Tools and Workflows

Finally, consider how well the AI tools will integrate with your existing systems and workflows.

Localization can touch everything from application code to marketing and customer services and support content.

Does your platform offer APIs to tie in with your other systems?

The Future: AI-Enabled Hyperautomation

As businesses continue to seek ways to improve efficiency and scale operations, AI-enabled hyperautomation is emerging as the next big trend in localization.

Hyperautomation combines a series of automations and AI techniques into seamless, end-to-end workflows, enabling businesses to dynamically present customers with bespoke content based on their interactions with the brand.

This level of personalization can significantly enhance customer experiences and unlock new monetization opportunities.

However, achieving this level of personalization is challenging, especially on a global scale. This is where AI-enabled hyperautomation becomes invaluable.

By accelerating localization work through AI-driven quality assurance and automation, your teams can focus more on strategic tasks that drive faster international expansion, improve customer experiences, boost revenues, and increase your impact.

Take Your Next Steps

AI tools are essential for any business looking to expand internationally.

By choosing the right platform, you can streamline your localization efforts, reduce costs, and enhance customer experiences across the globe.

Ready to explore how AI can help you break into new markets and elevate your global presence? Take a tour of Phrase’s AI capabilities or get in touch for a bespoke demo.

How machine translation software has evolved over the years

Machine translation development has come a long way, evolving through different phases, each with its own ups and downs. Let’s explore the various types of MT technology—including rule-based, statistical, and neural machine translation—in this overview.

Rule-based machine translation tools

The earliest form of machine translation developed in the 1960s, rule-based MT (RBMT), relied on a large, predefined set of linguistic rules that helped the software transfer the meaning of a text between languages. It had overall low quality, and it required adding language rules manually, requiring a significant amount of machine translation post-editing.

Statistical machine translation engines

Statistical MT (SMT), pioneered in the 1990s, builds a statistical model of the relationships between words, phrases, and sentences in a given text. It applies the model to a second language to convert those elements to the new language. Thereby, it improves on rule-based MT but shares many of the same issues.

Neural machine translation systems

Ever since Google brought about the “MT neural revolution” in 2016, machine translation output has significantly improved in quality, consistency, and productivity gains. Thanks to neural machine translation (NMT), algorithms are now able to generate such accurate translations that they are beyond just “good enough,” in many cases reaching human parity in translation.

Neural machine translation software employs artificial neural networks to process and translate text by learning from extensive multilingual datasets. Most of today’s MT software providers rely on neural MT since it offers more nuanced translations and the flexibility to add new language pairs, enabling them to learn and improve with increased usage.

Why use machine translation software?

 The introduction of modern machine translation software based on neural learning has elevated MT to an essential asset in business translation.

Let’s take a closer look at the 3 core benefits of machine translation software:

• Greater speed and volume
• Enhanced accessibility
• Reduced costs

Greater speed and volume

With ongoing improvements in machine learning algorithms and hardware, machine translation software is becoming even faster and more efficient. Not only is it capable of translating millions of words almost instantaneously, but it’s also continually improving as more content is translated.

For high-volume projects, MT software not only handles volume at speed, but it can also integrate with other software platforms such as content or translation management systems to organize that content. This helps maintain the structure and meaning of content when translating it into multiple languages.

Enhanced accessibility

Most machine translation applications offer support for up to 100 languages—and some of them even more. This means that translations can be simultaneously delivered to multiple target markets, creating a win-win situation for both businesses and customers.

By breaking down language barriers and enhancing the customer experience, MT has made content, products, and services more accessible to potential buyers around the world. This expanded reach also allows businesses to grow their market share and boost their profits.

Reduced costs

The combination of high-speed throughput and the ability to select from existing language pairs covering dozens of combinations means that machine translation software can reduce translation costs, even when human translators may still be post-editing.

Put simply, MT software starts the translation process by providing the initial output—human translators then come in to refine the translations, ensuring they capture the original content’s meaning and adapt it for the target audience in a specific region.

What is the best machine translation software?

Given the innovation potential in neural machine translation, global tech giants have been increasingly disrupting the language industry over the years, prompting an important question: What is the best machine translation software on the market, and what sets it apart from the rest?

As much as the MT software landscape evolves rapidly, there’s no one-size-fits-all answer: The best MT software varies based on your specific translation requirements. Even the widely held belief that NMT systems deliver high-quality translations has its own intricacies and limitations.

Key factors to consider when evaluating machine translation software
Capabilities	Different tools may excel in specific domains, so choose one that aligns with your translation needs (e.g., medical instructions vs marketing collateral).
Ease of use	Ensure the MT software has a user-friendly interface and is easy to integrate into your existing translation workflow.
Cost	Consider the pricing model, whether it’s free, subscription-based, or per-word.
Support	Evaluate the level of customer support offered, which can be crucial when issues arise while integrating or using the software.
Privacy	Examine the provider’s privacy policy and check its implications for your business with your legal department—to safeguard against potential data loss or privacy issues.

Google Translate

There’s hardly anyone who hasn’t heard of it: Google Translate has been integrated into nearly every product in the Google ecosystem and has achieved high-quality results—not only for the most common language pairs but also for less popular ones.

Google Translate was launched in 2006, but it was only a decade later that Google made one of the most significant leaps in recent commercial MT history by transitioning from a statistical, phrase-based system to a neural machine translation system. This marked the birth of Google Neural Machine Translation (GNMT).

In fact, it was a double leap. The first part involved abandoning the statistical paradigm, which was still tied to word-for-word translation and lacked context. Recurrent neural networks or RNNs (a specific form of neural networks used to encode and decode sentences) significantly improved their ability to understand context, resulting in much more fluent translations.

While other machine translation processes were already utilizing artificial neural networks, the real innovation lay in the introduction of the so-called “attention mechanism” or Transformer. This approach enables the system to translate lengthy sentences without sacrificing context and quality. Unlike RNNs, which gather information from each part of a sentence and can be computationally expensive, an attention layer can focus on the most critical aspects of the source text, regardless of sentence length.

At present, the free version of Google Translate allows you to translate text into over 100 languages by either typing or copy-pasting the text into the web interface. You can also translate entire documents by using drag-and-drop functionality and view the history of recently translated sentences, all without requiring a login or account.

It’s also possible to translate up to 500K characters per month using Google’s powerful Cloud API for importing and exporting translations, e.g., from and into computer-assisted translation tools. More characters and features are available in the paid versions, which offer functions like creating user-defined translation models using Google AutoML. In other words, every user can train Google’s engine with their own bilingual data.

DeepL

The wunderkind of machine translation software, DeepL, was launched in 2017 to further develop Linguee, the world’s largest database of human translations. The engineers at the German-based company applied the latest deep-learning technique (hence the company’s name) to get state-of-the-art machine translation software by training the models on Linguee’s data.

The results are very similar (or better, depending on language pair, field, and evaluation system) than the ones from Google Translate—which is still often considered the benchmark for translation quality—DeepL itself claims to be “the world’s most accurate translator.”

DeepL Translator offers a free machine translation software tool for seamless integration into the whole Windows/iOS ecosystem. Users can translate text to and from a total of 33 languages (as of 2024), by clicking CTRL+C twice—or translate entire Word and PowerPoint documents per drag-and-drop.

Also, both in the web interface and the standalone app, you can customize translations by “forcing” terminology for selected language pairs. Already in the free version, users can decide to use specific (corporate) terminology in their translation for certain language pairs by just switching a toggle. This way, DeepL tackles one of the weaknesses of machine translation we mentioned before—accuracy.

A wider, unlimited terminology function is available in the DeepL Pro (paid) version, along with other settings, such as the option to make the target text more formal or informal—something that can have a massive impact on the tone of voice in languages like German or Italian.

Remarkably, DeepL is one of the few top-notch MT providers not currently offering the option to customize the translation model by using proprietary, pre-translated data to train the engine.

On the other hand, DeepL Pro offers subscribers a CAT tool integration as well as end-to-end encryption of their translated data.

SYSTRAN Translate

SYSTRAN is the first company to offer machine translation for commercial purposes. Founded in 1968, it continues introducing interesting innovations—the latest being pure neural machine translation (PNMT).

Its free machine translation tool, SYSTRAN translate, allows users to translate text “on the go” into 140+ language combinations while trusting the power of the open-source community. “Open source” means freedom with the source code, allowing for more creative freedom and enabling technology agility.

Among the big players in the MT field, SYSTRAN is the only one sharing its knowledge—and profiting from cooperating—with OpenNMT, an open-source ecosystem for neural machine translation and neural sequence learning. This same engine, founded by SYSTRAN in December 2016, together with Harvard NLP, has already been successfully used in many research and corporate applications.

Paid subscribers can leverage this technology while also taking advantage of SYSTRAN’s PNMT to customize their MT models with their own bilingual data.

SYSTRAN is also one of the few companies that don’t just speak about NMT for each aspect of the translation cycle. It defines its own model as hybrid translation, combining rule-based and statistical machine translation to guarantee:

• Predictable and consistent translations
• Compliance with corporate terminology
• Out-of-domain usability
• High performance
• Learning from existing mono and multilingual corpora with minimal customization costs, etc.

Microsoft Translator

Much like Google Translate, Microsoft Translator is integrated into Microsoft’s own search engine, Bing. Moreover, many Microsoft products now include the possibility to translate documents (Office), messages (MS Teams, Skype), or posts (LinkedIn) between 90 languages and dialects using their home-brewed MT system—also based on the newest neural network technology with an attention-based model.

Microsoft officially launched Translator in 2009, but the product gained more relevance and was updated only in the last few years. Today, it’s also available as a standalone mobile app for both iPhones and Android devices.

An interesting feature in Microsoft Translator’s free version is the translation of frequently used expressions relevant to daily life. Users can quickly translate commonplace questions and statements for conversations with a single click. The app also supports the translation of street signs through image recognition.

For those seeking to integrate Microsoft’s translation technology into their translation workflows through a paid subscription, the offerings are extensive. Besides the “standard” text translation, with Azure Cognitive Services Translator, it’s possible to translate entire documents, utilize a REST API, and leverage AI Containers. Most importantly, it allows for the machine translation customization.

According to Microsoft itself, with about 10K bilingual translated sentences (input and output), it’s possible to improve the translation models to fit the customer’s needs better.

Amazon Translate

Last but not least, Amazon Translate, another online machine translation system from a Big Tech company—relatively young, having been launched in 2017, yet very powerful. Given the backing of its parent company, it’s not surprising that Amazon Translate has achieved remarkable results in the short time since its release. Users looking for a free solution might be disappointed, though: To use Amazon’s machine translation engine, you need an AWS account.

With an AWS account, you get two million free characters per month—and the range of functionalities offered by this NMT system is huge:

• Customization (terminology and parallel data)
• Encoding terminology
• Batch translation (Amazon S3), etc.

What about ChatGPT?

We would be remiss not to mention OpenAI’s ChatGPT in the conversation about the best solutions for automated translation, especially given how much attention generative AI has been attracting since its arrival in late 2022.

 

While not necessarily built with translation in mind, GPT-like applications can easily perform language-related tasks and are a viable and attractive alternative to the more established MT solutions mentioned above.

In fact, GPT-4o (and other large language model-based solutions such as Claude 3 Opus, PaLM2 Text Unicorn, or Google’s own Gemini Pro 1.5, among others) perform as well as MT solutions. In some combinations of domain and language pairs, they can possibly even outperform MT. Today, LLM-enabled translation is quickly becoming a part of the language services landscape.

As in the previously mentioned cases, GPT-like applications come with their pros and cons, which businesses need to carefully weigh when considering automated translation solutions:

• LLM-based automated translation is cheaper than traditional MT, 
• Yet it is also significantly slower than traditional MT, making it less suitable for scenarios where real-time translation capabilities are needed
• Same as MT, LLMs do offer potential for customization through the use of techniques such as retrieval-augmented generation (RAG) or prompt engineering

Choosing the right machine translation tool for your needs

This overview shows that even though almost all machine translation developers have moved towards NMT, each provider has gone in its own way. On one hand, there’s the strategy of reaching a broader audience with a completely free, user-friendly tool. On the other hand, some providers offer more customization options geared towards tailoring the tool to your specific needs.

Recognizing that each provider has its strengths and weaknesses, it’s clear that there’s no single best machine translation software—nor a one-size-fits-all solution.

This will help you find the right combination of MT engines to meet your specific translation needs. However, you can unlock the full potential of machine translation only when you’re able to access and use all tools in one place. Thanks to a cloud-based translation management system, this is now well within your grasp.

Make the most of MT software with Phrase TMS

When businesses need to utilize machine translation at scale, they need technology that can provide them with the best of both worlds: efficiency and quality.

Phrase TMS, the enterprise-ready translation management system within the Phrase Localization Platform, makes it effortless for growing companies to leverage machine translation. Organizations achieve an unprecedented ability to enter new markets more quickly and efficiently.

Phrase TMS users can employ a dedicated machine translation management tool, Phrase Language AI, to implement MT into their translation workflow with fast and cost-effective translations that don’t compromise on quality.

Fully embedded into Phrase TMS, the advanced MT management capabilities that come with Phrase Language AI let you:

• Start translating immediately with no developer time or effort using fully managed MT engines from leading providers like Google, Amazon, DeepL, or Microsoft.
• Add any of the 30+ supported generic and custom engines manually if you ever prefer to use a specific MT engine.
• Extend high-quality MT to every employee: The powerful Phrase Language AI API allows you to scale the value of MT with company-wide access for to secure company-approved machine translation.
• Enjoy unlimited machine translation for post-editing workflows so linguists can work more efficiently.
• Work with the best engine—auto-selected, based on your language pair and content type.
• Automatically filter out content that shouldn’t be machine-translated.
• Delegate quality testing, legal and security evaluation, setup, and payment of machine translation engines to dedicated machine translation experts on the Phrase team.
• Leverage your translation memories to increase translation quality by up to 50% with Phrase NextMT—the first TMS-ready MT engine.
• Ensure the MT engines use your preferred terminology with the correct morphological inflection—reducing post-editing effort.
• Preserve formatting and placeholder tags from source to target content automatically.
• Get a score for each machine-translated segment, based on past performance data, to post-edit only where needed.
• Achieve up to 55% cost savings with MTPE compared to human translation.

Discover advanced MT management features with the Phrase Localization Platform and push MT technology to the next level with our enterprise-ready solution.

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The Power of AI to Build Deeper Customer Connections while Saving Time and Costs

Localization is much more than just translation; it’s about forging meaningful connections with your customers.

While translation focuses on converting text from one language to another, localization ensures that every aspect of your business feels native to the target market.

AI can play a transformative role in localization, enhancing how businesses connect with customers on a deeper level. 

By leveraging AI, businesses can automate and optimize various aspects of localization, ensuring that content, products, and services feel genuinely tailored to each market.

For example, AI-enabled localization tools can review machine-translated content and accurately identify potential quality issues, removing the need for humans to check all content in detail.

Instead, they can focus on the material that needs the most attention. Additionally, AI with generative capabilities can refine translations to better align with your brand voice or fit specific web or app components, saving your team from manual rewrites.

 AI-powered tools can analyze local preferences and behaviors, offering insights that help customize everything from product features and marketing strategies to customer support and payment models.

Additionally, AI can enhance real-time translations and cultural adaptations, ensuring that language nuances, tone, and context are spot-on. 

This level of personalization enables businesses to scale localization efforts efficiently, making every interaction feel more authentic, relevant, and aligned with the unique needs of each market, fostering stronger customer relationships.

Success Story:

BlaBlaCar has saved more than 100 hours on localization per year, Heetch saw a 100% saving in developer time, and FUJIFILM reduced the need for human pre- and post-processing of its translations by 50%.

Scalability
By increasing the level of automation and reducing the fraction of content requiring translator post-editing and review, AI enables your team to manage localization efficiently, allowing your business to scale globally more quickly.

By freeing up localization team time, you can reduce the cost of entering new markets while empowering the same team to handle an increased workload.

Success Story: Business Scalability

what3words uses Phrase to manage translations into 60+ languages, which has been a key enabler of its worldwide expansion.

Speed to Market
Time is of the essence when entering new markets or rolling out new features globally.

AI-enabled localization software can compress the duration of your localization work, allowing you to enter new markets faster and accelerate subsequent releases of products, services, or functionality in these geographies.

Success Story: Fast Feature-Rollout

Deliveroo uses Phrase to enable rapid rollouts of new features across the markets it operates in, thereby ensuring its customers around the world can benefit from its latest innovations as quickly as possible.

Enhancing Customer Experience
AI-equipped localization tools improve the overall quality of your offering in each market, making it easier to create the seamless, high-quality user experiences that attract and delight customers, turning them into brand advocates.

Success Story: High-Quality Customer Experience

 

A well-known fashion retailer is achieving 100% matches for its machine-driven translations of short sentences, thereby offering a high-quality experience across all its markets.

Conclusion: AI as a Catalyst for Global Success

AI is not just a tool for overcoming the challenges of international expansion—it’s a catalyst for achieving faster, more efficient, and more successful global growth.

In our next post, we’ll explore how to choose the right AI tools to support your expansion and look at the future of AI in global business.

1. Speed Matters: Optimize Site Performance for Global Users

The backbone of a frictionless experience is speed—especially for global customers who may face unstable networks or device limitations.

Alba Simon, Product Lead at Asics, highlighted the importance of performance in delivering a seamless, localized experience for e-commerce users.

“Driving a seamless experience through site speed is crucial in reducing friction, especially in ecommerce where customers expect fast, effortless transactions,” explained Alba. 

For Asics, optimizing site performance ensures customers in diverse regions experience fast load times, reducing frustration and improving the likelihood of completing purchases.

Localization plays a key role here—ensuring that your site is not just fast, but optimized for the technological capabilities and browsing behaviors of users in different parts of the world.

Alba also shared a powerful insight that businesses can take to heart: “For every second your site loads faster, conversion rates improve by an average of 17%.” This reinforces just how essential speed is in reducing friction and driving conversions.

 

2. Localization Best Practices: Shift Left for a Seamless Experience

One major takeaway from the webinar was the importance of “shifting left”—integrating localization early in the product development process.

Olga Safonova, Senior Product Manager at Workday, stressed that this early integration prevents friction by making the user experience feel more native and intuitive, no matter the language or region.

“Integrating localization earlier in product development ensures fewer obstacles when scaling globally, making the user experience more natural and seamless,”

Olga explained. This approach not only reduces the need for last-minute adjustments but also ensures that the product is culturally and linguistically adapted from the start, which is critical for a positive customer experience.

Olga emphasized the importance of involving localization experts in the early design stages, stating: 

“By involving localization experts early in the design process, we can interpret global insights and prevent misunderstandings that could frustrate users later on.”

This forward-thinking strategy can save time and resources, while improving the overall user experience.

By treating localization as a core element of product design rather than an afterthought, businesses can prevent friction before it happens, leading to smoother interactions for global users.

 

3. Cultural Adaptation: Tailor Experiences to Fit Regional Expectations

Localization goes beyond language—it’s also about understanding the cultural context of your users. 

Carolina Freire from Uber spoke about how cultural adaptation can reduce friction and elevate the user experience by making products feel more relevant and intuitive for different markets.

“Understanding the cultural nuances of your users reduces misunderstandings and allows for a more intuitive product experience,” Carolina noted.

Uber, a globally recognized brand, takes special care to tailor its app’s user experience to align with local cultural norms, preferences, and behaviors.

An excellent example from Carolina’s discussion is how cultural missteps—such as assuming universal meanings for icons—can cause friction. 

For instance, Amazon faced confusion when launching in rural India because users didn’t associate the magnifying glass icon with search but rather thought it was a ping-pong paddle! While it might seem funny in retrospect, this really highlights how important it is to understand local perceptions of design elements.

By aligning your product’s experience with local norms—whether it’s payment methods, design aesthetics, or communication styles—you can create a smoother, more engaging experience for global customers.

4. Broadening Testing: Ensure Consistency Across Regions

Testing is another critical piece of the localization puzzle. Artem Bobrovskiy from Global App Testing highlighted the need to expand testing efforts across different geographies to catch potential issues unique to specific regions before the product is launched.

Expanding testing across geographies is key to identifying friction points that are unique to specific regions,”

For a truly frictionless experience, products must be rigorously tested in real-world conditions across various countries and devices. This ensures that users in different regions encounter a consistent, high-quality experience, without unexpected bugs or usability issues.

Artem also pointed out the importance of testing in production: 

“Testing in production is crucial because it’s the only way to match the actual experience of your customers.” 

This hands-on approach helps companies identify subtle, location-specific friction points that can go unnoticed during development, ensuring a smooth experience across the globe.

5. Discovery and Delivery Adaptation: Scale Your Process for Global Success

To effectively reduce friction across different markets, both the discovery and delivery processes in product development must be adapted. 

Marcs Wilkinson, Director of Product Design at Phrase, explained that this approach ensures friction is minimized for diverse user personas. By refining these processes, companies can ensure that the localized version of their product is not only culturally relevant but also seamlessly functional.

“Adapting the discovery and delivery process not only helps in scaling globally but also ensures that friction is minimized for different user personas,” Marcs explained.

Early localization planning ensures products meet the cultural and technical needs of different markets from day one, eliminating the need for costly post-launch adjustments.

Marcs also highlighted the importance of planning for localization early: 

Localization isn’t a checkbox you tick when entering a new market. It requires planning from day one, with adaptable systems and resources in place.” 

This approach leads to a product that scales smoothly across regions, with minimal adjustments required to deliver a great experience to all users.

Conclusion: Localization as the Key to Reducing Friction in Global Products

Reducing friction in global products is about more than just solving surface-level issues—it’s about designing products that resonate with users across the world. Localization plays a critical role in this process, from early-stage product development to testing and adaptation for different markets. 

By embedding localization at every stage, businesses can reduce friction, create more intuitive experiences, and ultimately improve customer satisfaction.

Whether it’s optimizing site speed for various network conditions, integrating localization early, or tailoring the user experience to cultural expectations, each of these expert tips highlights the power of localization in delivering a frictionless global product. 

As you look to improve your product’s global reach, consider how you can implement these strategies to create smoother, more enjoyable experiences for your users—no matter where they are.