How AI is changing the future of the translation management
Programs using machine learning can be faster, more accurate and less expensive when compared to the time and effort required by humans to do the same work. Machines and software systems that can “learn” and optimize efficiency are attractive tools for businesses everywhere, and the translation and localization industry is reaping the benefits of AI.
Translation technology companies are now using AI-based rules engines to change the future of translation management by increasing the automation of workflows, vendor management, and quality evaluation. There are a number of localization features that are possible with current and emerging AI technology.
Making translation workflows more agile
Translation technology can use AI in a few different ways to overcome the many challenges to translation workflows. Projects that take too long to get started, have too many manual processes and use multiple vendors all add complexity to the situation.
In theory, a workflow prediction engine can predict the appropriate workflow for newly-imported content. AI-enabled workflows can determine which combination of machine and human resources is best to optimize quality and accuracy. These intelligent workflows include phase automation, dynamic scheduling, and active monitoring. Specifically, it is possible to do the following:
Phase automation: Phase automation determines which combination of machine and human resources is best to optimize quality and accuracy. It automatically selects and configures workflow phases based on document metadata (content type, campaign), due dates and other client-specific business intelligence.
Phase selection: After uploading a document, the dynamic workflow management engine automates the selection and configuration of each workflow step. Project task duration is calculated by subtracting the requested due date from the job submission date.
The system then intelligently builds the appropriate workflow to meet the project due date without sacrificing quality. The system learns which workflow steps are mandatory versus which are nice to have. Client-specific business logic determines which phases will require more or less time to translate. In addition, metadata informs which phases can be excluded in order to meet the project deadline.
Phase configuration: Metadata not only drives phase selection but also phase configuration, including automatic configuration of translation memory (TM) leverage (sequencing /prioritization or penalization), due dates, phase rules, and assignments and more.
Dynamic scheduling: Dynamic scheduling calculates due dates for each phase of the workflow based on the time available to complete each step in the workflow. The algorithm gathers metadata such as due date, department, word count, content type and author, and then makes a prediction using its machine learning to automatically create the optimal workflow.
It will take into consideration which language service providers (LSPs) are available, their content value, the translation levels — standard machine translation (MT), MT + post-editing, or something more curated — based on content type. It automatically assigns the appropriate teams or individuals based on content type and language. It also auto-calculates phase due dates and makes auto-assignments based on defined business logic.
Automatic assignment of staffing and resourcing: Phase automation includes using AI-driven processing so that staffing and resources are automatically assigned to vendors based on document metadata, target languages and job type. Vendor assignments can be individual or team-based, allowing any team-based assignments to be checked out by vendors so that the project manager doesn’t have to manage vendor availability
Active monitoring and alerts for projects that are at risk, late or past due: AI-based active monitoring recognizes how much content needs to be translated and can make on-the-fly adjustments to meet necessary deadlines. It reduces the need for routine, automated tasks, so project managers can perform tasks that are more valuable to the organization, like problem-solving, responding to urgent issues or focusing on exception management.
Active monitoring identifies which steps can be added, skipped or canceled to meet the desired deadlines. As a part of dynamic workflow management, the software can autocancel a nonmandatory phase that is past due.
If a workflow phase is in danger of missing its due date, its status could be set to “at-risk” so project managers and assignees can see the status and take action. If an incomplete workflow phase misses its due date, its status is set to “past due.” Upon completion, a phase with a status of “past due” will be set to a “late” status.
AI is bringing more flexibility and scalability to translation workflows by reducing the number of manual processes and reducing project turnaround times. It also allows project managers to increase translation speed without sacrificing quality.