In today’s world, there is an endless number of software tools and systems that a company can use to accomplish most day-to-day tasks. Everything from team organization charts and payroll to content management and sales strategy can be found in a software platform out in the world. And while some industries tend to avoid online tools, plenty of existing and emerging industries gravitate towards them.  Localization is no different. For a long time, the workflows to get content from one language to another have been tedious, manual, and time consuming. Since there are so many potential parties involved, it may seem like that tradition is here to stay. But recent advancements and adaptations to technology have allowed the localization workflow to become streamlined and simplified. Now more than ever, companies with localization and translation needs are turning to a more automated process to get to market faster and more affordably.  So what does the modern localization process look like? Here are just a few key factors that can modernize your localization workflow. Continuous Localization Plenty of people see localization and translation and think work. Manual, step-by-step, disorganized work. One thing we hear a lot from localization managers is that they work on a more fragmented basis, waiting to download and upload content from their content management system (CMS) if they had time. A modern workflow, however, should operate with automation in mind. Instead of putting content translation on the backburner or emailing files back and forth on a per-project basis, companies should know that localization is happening when they need - no more ifs. That’s where connectors and APIs come into play, linking CMSs and systems to each other to enable a seamless flow of information. For example, the San Francisco based company WalkMe uses Wordpress as its CMS of choice. WalkMe’s localization workflow now includes hugely important automation, allowing content to flow seamlessly from Wordpress to Lilt via a connector. Now, as the team adds new or updates existing content, it knows that everything will be easily translated and updated. All-In-One Tool One of the more common workflow issues that teams run into is the transition from tool to tool. Many teams use separate translation management systems (TMS) and language service providers (LSP), and combined with a separate CMS, it’s easy for steps to be missed. A fragmented production process like that can be difficult to manage and even harder to scale. Instead, a modern localization workflow opts for an alternative solution - an all-in-one LSP that incorporates a TMS so teams can easily manage the entire process and all requisite vendors in one place.  Carolina Faustino, Localization Lead at Sprinklr, was facing that exact issue prior to making a change. “Managing all our vendors and workflows used to occupy a large amount of my day, and honestly wasn't really what I wanted to spend my time doing," she said. With an updated workflow, however, she now has time back to “focus on things like setting [her company’s] localization strategy, building out end-to-end processes, managing [her team’s] budget, and helping [her] team's business partners better understand the value of localization." A World Without Post-Editing A company’s content and brand are its heart and soul. Without those two valuable pieces, it would be difficult to survive. When it comes to localization, no company would only trust a translation algorithm to do all of the work.  Using a workflow that involves raw machine translation or even MT post-editing, however, is probably a scary thought for many localization experts out there due to potential quality concerns. But there's a new way to use MT that doesn't involve post-editing, and the quality and speed results it produces are substantial. Instead of using MT post-editing, companies like Lilt combine the power of human translators with the speed and agility of an Adaptive Neural Machine Translation Engine.  Instead of having the MT pre-translate the document and a human translator post-edit, adaptive NMT used in a human-in-the-loop model allows for faster, more consistent, and more accurate translations across the board. In this workflow, the translation engine makes suggestions for translators as they’re doing their work. If they accept or reject any of the suggestions, the adaptive system learns in real time for future use and applies those updates to suggestions moving forward. This really gives human translators a new, more powerful tool than before, and something that will only get better with time. Want to learn more about how you can modernize your workflow? Click here to get in touch with us to find out more about how you can add Lilt to your localization tools today.