Machine translation (MT) powered by artificial intelligence (AI) is a tempting solution for any global organization with an eye on scaling the amount of localized content across languages. It’s impossible to ignore the potential rewards: a faster translation process and a more budget-optimized approach to going global. However, not every organization is ready to deploy MT. Implementation of MT is a strategic move that requires careful thought and planning to achieve the results your business might want. Since every strong strategy starts with asking strong questions, here are 18 probes to utilize before you implement AI or machine translation. The answers will help you determine your readiness to implement this technology and increase your odds of success. Before you can successfully implement MT, find your why. What are you really trying to achieve by implementing AI translation? Depending on your organization’s size, type, and industry, these objectives could include goals including: Maximizing the impact of your localization budget to translate more languages or increase volume Shaving precious days or even weeks off your time-to-market Reaching new markets faster than you could with human translation alone Providing better support for diverse consumers Improving workflows and communication within your multinational organization These goals will guide every decision you make during the MT implementation process. They'll influence the technology you choose, the workflows you adapt, and even the way you evaluate success. AI is only as good as the data it’s trained on. You can make sure that MT will produce results that align with your business goals by feeding it high-quality training data. That means data that is accurate, well-structured, and most importantly, relevant to your business context. Most MT engines start with a generic data set, but adding custom data from your translation memory helps fine-tune the system for better results. But before you upload your Translation Memory (TM), you’ll likely want to audit and update it to ensure it is free from inconsistencies, duplicate entries, and irrelevant or outdated content. Also, verify that the translations in your database are accurate and free from bias. Any inaccuracies could be perpetuated and magnified by the MT system, and there are numerous instances of AI producing biased output as well. A glossary tells translators (machine or human) how you’d like different terms, words, or phrases to be translated. An up-to-date glossary ensures consistency and preserves your brand voice across different languages. For the purposes of machine translation, not all language pairs are created equal. Some languages, such as French or Spanish, can achieve higher-quality results thanks to large datasets. Less common languages will present more quality challenges due to a lack of training data. So will language pairs that contain two wildly different languages, such as Japanese and English. Weigh linguistic complexity against the market demand for each language. Will a less commonly spoken or complex language offer enough ROI to justify the potential challenges in translating it and post-editing the copy? Your target languages may also influence which MT tool is the best fit for your organization, as some engines perform better in different languages than others. Knowing what content is best for MT and AI translation is half the battle. For example, MT excels at handling large volumes of user-generated content, where speed and scalability matter more than pinpoint accuracy. It also excels at translating product descriptions and technical documents, where the language tends to be more standardized. The more creative the content is, the less suitable it is for MT. Translation of nuanced, emotional, and/or highly branded content is about more than words. Culture matters, too. High-visibility content that’s made to engage or entertain your audience typically requires a human touch. There are various types and models that you can choose for MT, and they each excel in different areas. Neural machine translation (NMT) has been in use since the 2010’s. NMT engines are trained on examples of parallel translated text. They use AI to learn languages via neural networks that are modeled after the human brain. The most well-known of these models are the free, publicly available engines like Google Translate or Bing Translate. However, these are just the tip of the iceberg (and not suitable for most business uses because they are not secure.) Google, Microsoft, Amazon, and others also offer generic cloud MT engines aimed at businesses. These are more secure but lack the pinpoint accuracy you can get from a customized MT engine. Customizable MT engines, like DeepL, Google’s AutoML, and Microsoft Custom Translator, can be trained with your own data or data from your industry. This results in better, more relevant translations. The new kids on the block are the Large Language Models (LLMs), such as OpenAI’s ChatGPT or Anthropic’s Claude. LLMs are trained to generate text by predicting the next word in a sentence. NMT systems are focused exclusively on translation, so they often outperform LLMs for this task. This is especially true if they’ve been customized with business-specific and industry-specific data. However, LLMs can perform as well as NMTs for some language pairs. They are also more creative. So, they can craft translations without the need to train on parallel texts. This flexibility gives them a clear edge in some situations, especially when parallel data for a particular language combination or field is sparse. Choosing the right engine is all about zeroing in on your business goals for MT and using the right tools to get what you want. An LSP can be an invaluable resource to help you understand your options and select the right tools for the job. Think of customization as giving your system a little extra coaching. This can range from training the MT system with your company's preferred terms to fine-tuning it to handle the special lingo common in certain industries, like legal or medical. While customization does require an upfront investment in time and resources, it teaches your MT to speak the language of your business. The result? Stronger translations that result in improved outcomes. Those publicly available tools we mentioned before are tempting, especially when they’re free. The tradeoff is privacy and data security. If your business deals with sensitive consumer data or the content to be translated includes your organization’s intellectual property, that tradeoff is not worth the risk. MT is not a plug-and-play solution. Integrating it into your current workflows requires a deep understanding of your existing processes and a clear vision of when and where your new technology should intersect and integrate. Integration may involve updating or even overhauling your existing content management systems (CMS) and translation workflows. These changes could range from introducing new software connectors that enable smooth data flow between your MT system and CMS to modifying content creation practices to better suit MT requirements. Remember, MT isn't just a tool; it's a part of your larger content strategy and needs to be seamlessly integrated into your existing ecosystem for maximum efficacy. Machine translation is rarely perfect right out of the box (this is referred to as “raw MT”). Human review and editing are necessary to elevate machine-generated translations to the level of quality your brand and audience expect. Certain sectors require more post-editing than others: in sectors such as healthcare a mistranslation could change the course of someone’s life, and so post editing by skilled linguists with relevant expertise and training is a must. It's important to plan how, when, and by whom machine-translated content will be reviewed and edited. Will you rely on in-house experts? Or will you outsource to professional language service providers? An effective MT PE process requires experience, training, and expertise in the relevant subject matter. MT engines perform best with clear, clean, and concise source text. Where possible, eliminate idioms, jokes, local references, jargon, and complex sentence structures. These trip up even the most advanced AI translation systems. Keep your source content straightforward, clear, and simple to lay the groundwork for translations that require minimal post-editing. Confidentiality laws and data security requirements can make MT a risky business if not managed carefully. Examine all the legal and regulatory implications before you deploy an MT program, especially if you do business in sensitive or highly regulated industries like healthcare and finance. Technology is just one part of the equation; the human element is equally critical. Adopting MT is a strategic move that will likely introduce both cultural and operational shifts in your organization. Prepare your teams for these changes by addressing any resistance head-on. Training empowers everyone—from translators to content managers—to effectively use MT tools and manage the post-editing process. Offer training programs, seminars, and other forms of support to facilitate a smooth transition. You’ll need to roll out through training and upskilling program not just for those directly involved in translation but also for the rest of your content team. How will you know if AI translation is working for your business? Figure out your key performance indicators (KPIs) that align with your motivations for adopting MT in the first place. This strategic direction will help you understand how well your MT system is performing, along with where it needs to be adjusted. The vendor you choose will play a significant role in how effectively you can meet your translation goals. Some characteristics to consider include: Support services: Will they partner with you to pursue your business goals? Technology: Which MT engines do they deploy for their clients? How secure is their data handling? Customization options: Do they offer the ability to fine-tune the MT engine for your specific industry? Post-editing: do they provide post-editors as part of their service? As your business grows, so will your translation needs. Choose a provider and system that can grow with you as you expand into diverse global markets. Jumping into full-scale implementation without testing the waters is risky. A pilot test helps you make data-driven decisions before you commit to full-scale implementation. It will provide you with insight into how well the MT system integrates with your existing workflow, the quality you can expect, and any unforeseen challenges. When planning for MT, setting a budget is as crucial as selecting the technology. Consider not just the upfront costs of implementation but also the ongoing expenses for maintenance and improvements. Remember to factor in the potential savings—from reduced translation costs to quicker time-to-market—that an effective MT strategy can yield. Machine translation can be a gamechanger for organizations looking to expand their global reach and connect with diverse consumers. It helps you reach more markets, more quickly, without increasing your current budget. But it’s not always simple to implement. Feeling overwhelmed? We're here to help you navigate these complex decisions and put a custom solution in place that meets your specific needs. Contact our language experts today to talk about building a tailored MT/AI translation program that will help you reach your global goals efficiently and effectively.