Platform Providers Target the Language Industry

Did you know that companies have access to subtitling in real-time or that riders can access in-app translation to enable their ride-hailing-service drivers to find them on the left corner of the intersection, rather than on the right? If not, check out what Amazon Web Services presented at the most recent LocWorld in Lisbon, Portugal. Platform providers such as Alibaba, Amazon, Baidu, Facebook, Google, and Microsoft continue pouring money and people-hours into harnessing artificial intelligence (AI) to drive machine learning capabilities throughout their products and services. Here’s why buyers and providers of language-related services and technology need to pay attention and track what these platform firms are building and creating in areas related to natural language processing (NLP) – a capability that all of these companies claim is absolutely critical to their overall success in the AI arena:
For buyers:
What CSA Research has been predicting around translation – and now interpreting – as a ubiquitous service since our earliest days is becoming reality with current advances in AI and machine learning. Localization teams and centers of excellence for globalization have an essential role to play in preparing their organizations to take advantage of enhanced multilingual capabilities for written, spoken, gestured, and digitized signals.

• Educate product and service designers – and your colleagues. Just because AI and the machine learning it enables has more application to real life every day doesn’t mean that it arrives on the scene multilingual and ready to do business locally. Being aware of what platform providers make available may change design plans or enhance local market coverage much more easily than your company originally imagined. 
   
• Go for the gold. Lower costs enabled by the application of all of these new capabilities also present opportunities to increase the amount of multilingual deliverables – whether content, code, services, or programs – through improved ROI. Zero-shot translation, augmented translation, and automated content enrichment are just three examples of the gains being made that will impact content ROI by enabling you to process large quantities of user- and machine-generated content that includes text as well as video.
   
• Benefit from the democratization of artificial intelligence. Don’t worry about becoming an AI expert. Machine learning will come to your software stack from multiple products and integration-ready cloud services. AI is now appearing as wizards that configure machine learning. So, clean and prepare that data that you’ve been storing away for a rainy day.

For LSPs:
Whether or not your prospects and customers are aware of how AI-driven capabilities will affect their business, they soon will be. Don’t wait for them to bring up the issue or to be caught off-guard during a quarterly review when they do. Prepare now to position how your team will be there as they try to figure out how to apply these capabilities with global customers and multicultural audiences within their home market.

• Explore opportunities beyond the narrow scope of classic language services. Instead of fixating on whether you will be Uberize’d out of business, focus instead on how to navigate a world in which neural networks may erode parts of your value proposition. Remember that most clients – regardless of size, vertical, or current challenges – will continue to seek help for putting all of their pieces together to execute complex global content and code strategies. Many of your prospects and customers have already invested a fair amount of time and resources into digital transformation around the huge amounts of data and content that drive their operations. Start finding out now what those pieces are outside of your standard service offerings so that you can craft new ones as quickly as possible.
   
• Identify data that can inform powerful machine-learning applications. Ask your clients and prospects to describe scenarios in which their firms are planning or already applying some form of AI or machine learning. If your contacts stare back at you blankly, encourage them to look outside their silos. You are doing them a favor by pushing them to find out how to prepare to support the multilingual data flows that will originate from these sources.
   
• Reinvent your company as a data-savvy business process outsourcer. Don’t lose sight of the expertise that you have but others don’t – including competitors from outside the language industry. Ready or not, more and more of your clients will turn to you for business process outsourcing services related to content creation, data cleaning, and business intelligence tasks. Big consulting firms such as Accenture and KPMG are often called in to launch enterprise-wide initiatives such as digital transformation. However, they’re rarely good at executing operationally – especially when it comes to local markets. If your team is skilled at building ontologies and taxonomies to give clients a head start on taking advantage of machine learning, for example, make sure they know and don’t forget it.
   
• Analyze the impact of conversational content in multiple languages. What will dialogs with Alexa and Siri mean for your business at the strategic level? The platform providers above are determined to continue to improve speech processing that all of us will use more and more as personal and business consumers – think hand-held mobile devices, home assistants, TVs, and vehicles. Do you have access to training data in languages that these companies – or perhaps some of your clients – need to address gaps in what they currently deliver? Or maybe you have user design expertise for local markets. Start planning now for clients to be asking you to perform integration and testing – or even how to identify use cases around the world.

For technology developers:
Advances in AI and machine learning are moving so quickly that it’s very unlikely that your prospects or customers even know what they could be asking from you. It’s up to you to educate them as to what you see possible as it relates to your products and services.

• Brainstorm about the possibilities. Explore the customer challenges to be tackled if you weren’t constrained by funding or staffing. Spend a half- or full day with your team to sketch out ideas on a whiteboard. 
   
• Unpack your solution. Investigate leveraging components or services already available from platform providers to get started. As text and audio are streamed on top of content for online gaming, streaming services, and social networks, you may identify opportunities for additional automation or the development of micro-services to support multilingual deliverables.
   
• Raise your voice. If you haven’t yet integrated voice enablement capabilities into your product roadmaps, now is the time. Platform providers now make AI and NLP building blocks available for third-party developers, so you shouldn’t have to start from scratch. The current tagline for AWS says it all: “Put machine learning in the hands of every developer.” Track content-centric companies such as Adobe to find out how they’re uncovering and meeting customer requirements with AI-based solutions from a global perspective.

Some platform providers were a bit late to the localization party. Some still struggle to offer enjoyable and seamless experiences for their own customers (yes, Amazon still directs its Swedish patrons to its German site where they depend on machine translation to make their purchases). But their earlier and current shortcomings don’t mean that they’re not heavily investing in NLP at this point. For better or for worse, these are the companies driving innovation for the language industry for now. So, assign someone to stay up-to-date on what they’re doing and prepare for the ride!