Captains of the Translation Industry Talk About the Single Biggest Thing They've Learnt
Rory Cowan, founder of Lionbridge, is stepping down this year after 21 years as a highly successful CEO and moving into the role of Chairman. To kick off a new series of interviews with major figures in the translation/language services industry, TAUS wanted to find out the one big thing that Rory has learnt as head of the world’s largest language service provider. He responded with a veritable master class on how industries evolve, and how market participants can anticipate the timing, magnitude and impact of major industry changes.
TAUS: You worked in more than one industry before you started Lionbridge in 1996. What is your major insight about the way innovation develops?
Rory Cowan: The single most important lesson I have learnt is that people always overestimate the time it takes for innovations to happen, but usually underestimate the overall impact innovations make. They imagine things move much faster than they in fact do, yet fail to see the deep implications of what is happening.
The best way to appreciate this lesson is to observe patterns across other industries.
Take banking. In the past, you obtained money from your account via a bank teller, a person who almost literally held the keys to your fortune. Then around the 1970s, computers were introduced into the home offices of banks, operated by a special class of experts who could communicate with the machines quite separately from the branch manager and the bank teller. This was the first step of disintermediation.
Then in the late 1980s and early 1990s, the first automated telling machines (ATMs) came in, and to encourage uptake of this new feature, banks attempted to charge people when they used human tellers inside the bank. As a result, banks lost all their in-bank customers, and began using ATMs on a massive scale as a whole new interface. But remember that it took 20 years for the ATM network to reach economic scale. There is an absorption rate for technologies, both commercial and human. Consumers becoming users of the computer system was the ultimate step in the migration of value to the end-user and the final disintermediation all of the work the tellers had been doing for so long – issuing cash to customers.
Is this a meaningful pattern or a one-off phenomenon?
There are two things worth noting here. One is that computers, which seemed naturally suited to automating financial operations of all kinds, did not transform banking in one fell swoop: it took time. The second is that the “withdrawing money” service operation evolved fairly quickly into a feature on another platform - an IT platform which offered many other activities.
The same goes for another, very different industry - timekeeping. Once upon a time, monarchs “owned” the virtual time machine; later they leased it out to town criers as a service; centuries later, this service was transformed into a tool (a large clock-face on a tower such as Big Ben), and then fairly recently—in the 1800’s as US train conductors needed to keep constant time across the rapidly growing network--into a utility in the form of pocket- and later wristwatches.
Soon the tool was digitized and evolved into a simple feature on a new platform – the smartphone. This scaling up of time telling into a simple feature effectively killed off timekeeping as a special service. The “time-piece” industry responded by developing high-end watches for a small, but profitable market. “Time” became a feature in a mobile platform.
The message is clear: value migrates as technology infrastructure evolves. What varies across each industry is the pace of this migration up the scale ladder from service to tool to feature. This pattern of shifts in scale has naturally been powerfully impacted by the emergence of digital connectivity. Some technologies took centuries, others took decades, or just years.
How has translation scaled towards a feature on a platform?
As we have seen, value in the modern translation industry has shifted from the low-scalability of the proprietary translation department in a large company (1980s), up to the arrival of a service industry (around the 1990s) and then on to the development of tools such as translation memories that embody processes (around 2000). Although this might look like rapid progress, customers have kept paying for tool licenses due to their inevitable bias towards maintaining stability, and not betting on dark horses. We’ve seen that TM, GMS, and now NMT technologies, have all had fits and starts as they work their way through the S-Curve in different applications and at different paces.
Machine translation has been the classic dark horse, of course, waiting for its hour of glory. By the 2010s, some types of localization became a feature on a platform (as we saw for time telling and banking). One good example would be Adobe installing their own localization workspace in their eLearning Platform. The process of translation management is now a feature in the development platform. Structured data, open API’s, intelligent workflows and NMT are accelerating this trend. A final state of play might be the rumors that Amazon will integrate NMT into their AWS service.
What drives pace in this scaling model?
Generally, two things determine the pace of scalability and movement along the S-curve – cost of failure and set-up cost. Translation automation was first adopted in what used to be the very lucrative market of translating basic instructional information in manuals, online FAQs etc. where there was a low cost of failure. The second factor in MT adoption was the fixed expense, or “set-up” cost, that had to be amortized over many words, but those words were bounded by language pairs and direction and domain and the aforementioned cost of failure.
Text with a low level of semantic nuance (call it “concrete” content) will be the first to be machine-translated, whereas more nuanced content (call it “conceptual” content) as found in life sciences, legal, marketing and financial domains, will require other solutions to avoid the potentially high cost of failure if poorly translated. So the pace of MT adoption will be conditioned by the nature of the content, the cost of failure and the fixed expenses of the application in question.
As an example, we’ve solved this equation in our Geofluent service which offers instantaneous chat translation, from a single user interface for customer care, using neural MT. By conditioning chat streams as they enter the NMT engine, limiting the domain, and cloud-enabling the service, we’ve had 20-minute sessions of real-time chat with no comprehension errors.
Due to the combination of low fixed costs for MT development and the ever-tighter time constraints of the market need, the economics are stunning: one English-speaking agent can support multiple languages from a single location, eliminating local, in-country technical support organizations. But even Geofluent took us many years to get right, though our scale enables us to amortize the cost over many deployments.
From what I’ve seen in other industries, the translation industry is behaving on schedule. Of course, people will get overexcited about a new technology and exaggerate the adoption rate at which services will transform into features. At the same time, digital connectedness is speeding up featurization. This is a multi-billion dollar global industry. The featurization process will evolve by application, and, like banking, the first application will be “back office” productivity before being widely embraced directly by the end-user.
What kind of industry, especially for translators, do you see emerging from this service-to-feature shift?
My major worry is that the language industry doesn’t think strategically because it’s highly fragmented. At Lionbridge, we’ve built many businesses that have language at their core, but which are not simply “translation” businesses. Translation is part of a bigger offering. Our experience suggests that you need a fabric of different offerings and markets, because technologies are implemented at different paces in each intersection, as we have seen.
If this industry follows the norm, it will split into a mix of smaller-scale Owner Operators focused on a specialist domain or service mix, and bigger-scale Tech-enabled Providers able to deliver value in multiple markets, as a feature in their clients’ business processes.
At the same time, this S-curve will redefine what it means to be a translator. We shall see linguists and other practitioners evolve from separate job descriptions to a new category, capable of handling domain translation, MT post-editing, multilingual SEO, and quality assurance as a single activity.
Successful translators will grow their skills in adjacent areas and enhance their earnings power and productivity, or focus on high-cost-of-failure end markets. Less successful translators will remain trapped in their current price-per-word, translation-only, outlook. This is, in fact, yet another example of the shift from service to feature: translators and linguists will be expected to demonstrate their value at several stages in a workflow, rather than stick to a single task.
From my experience, people who grow up “inside the industry” focus on applying their skills in higher and higher quality applications. Meanwhile business focuses on more and more efficient means of production, often trading immediacy for quality.
Innovation will almost always surprise you by coming from outside your industry… and it’s generally a simple application of a sophisticated technology. The art of survival is to understand the pace of change as computing architectures and business models shift value up that S-curve. In some translation markets traditionalists have decades to evolve. In other translation applications, it’s already too late for traditionalists.
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