Are AI Deployments All They Are Cracked Up to Be?

05Jun

Are AI Deployments All They Are Cracked Up to Be?

Enterprises worldwide challenge their language service providers with a litany of requirements: growing volumes of source content, more target languages, immediate turnaround times, tighter budgets, and a budding demand for ubiquitous translation and interpreting. Meeting these enterprise scalability needs will test the processes and technology of many service providers – many traditional processes will burst at the seams and aging translation software won’t keep pace.

What’s the solution? LSPs have to identify where workflows bog down, determine whether machines would work faster and better than humans at those points, invest in automating them if the answer is yes, and reassign employees to where they add value. Are LSPs doing it? Data from our survey of language service providers shows that automation lags for most providers. We contrasted usage in two areas that benefit from more machine and fewer human touches, machine translation and translation management systems.

Automation of Linguistic Tasks through Machine Translation

In our survey, CSA Research found that 51% of LSPs in our dataset already use some form of MT software, but fully 80% of these companies have tried neural machine translation, a deep-learning-driven form of machine translation. We applied our LSP Metrix™ capability-competency model to the use of machine translation and found that MT adoption more or less follows the natural maturity curve of LSPs.

LSP maturity equates to a high level of MT capability. In our analysis, we discovered that 93% of the most mature LSPs are MT-capable, meaning that they have tried out the technology. Those that haven’t experimented with MT are interpreting-centric companies, where automated interpreting software for spoken language is not as market-ready as it is for written language. At the opposite end of the spectrum, when we apply our Metrix model to the least mature LSPs, we find that only about one-third of companies at Stage 0 have some MT capability in-house.
MT capability may not mean LSPs are using it in production. Deployment at the project level remains minimal for most. We found that 57% of respondents using neural MT use it on less than one-tenth of their projects. Most providers don’t trust it sufficiently, feel it hurts more than it helps on jobs, struggle to find a systematic way of integrating the technology with translation processes, or face clients that don’t want it used on their projects.
Systematic users are few and far between. A meager 2% of LSPs with neural MT process almost all of their translation work through that technology. Such companies remain pioneers with business models fully centered around machine translation capabilities and their customers tend to have scalability needs that trigger full adoption.

Automation in Translation Management Systems

We also examined automation in translation management systems, finding that 80% of our survey respondents declared they’re using a TMS. However, we again found that this high number is no guarantee of deployed automation. Accordingly, we analyzed responses about the level of automation: 1) 54% of respondents say they have “partial” automation capability – that is, the TMS can handle most tasks but requires human project managers for some decisions or approvals; 2) just 28% of our sample claim “full” automation capability, where no humans are involved in this “lights-out” model of project management.

We asked respondents that use some automation what type they used – 86% have built systems based on older rule-based software, another 33% use more advanced but still dated technology, and just 9% claim any artificial intelligence solutions based on machine learning. Thus, most automation on the project management side tends to be old-school, traditional approaches that don’t leverage the data-based knowledge that most LSPs accumulate through their TMS workflows.

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Why Does Enhanced Automation Matter So much?

Neural MT garnered a lot of attention among LSPs. However, only about half of them have the capability to use it and do so only on a negligible percentage of their projects. This low adoption and usage has a business impact. We asked LSPs about their attitude toward MT technology and its impact on their business. When we correlated their actual MT usage against that attitude, we found that LSPs with a conservative approach toward technology have weaker compound annual growth rates than those that have an aggressive position.

We also correlate their attitudes against company size, contrasting LSPs that appear on our Top 100 LSPs™ lists against the unranked survey respondents. The distinction is less clear. Size has less to do with growth than attitude. We consider correlation of growth with an aggressive approach to MT here as a proxy for openness to new technologies in general. MT and AI aren’t factors limiting growth but instead enabling it.

However, despite the increase in mainstream openness to MT, deployments still lag. Likewise, automation in project management systems remains mostly rule-based, showing the anxiety over machines gaining more control over projects and the lack of expertise in moving to next generation automation. Over time, the cost and perceived complexity of the enabling technologies will drop and enable many more medium-sized enterprises to advance their automation use. Adoption and deployment rates for the various forms of AI are bound to keep increasing and to permeate more aspects on language production work.

About the Author

Hélène Pielmeier

Director of LSP Service

Focuses on LSP business management, strategic planning, sales and marketing strategy and execution, project and vendor management, quality process development, and interpreting technologies