The latter means that if MT is still not perfectly fine out of the box, it’s not a serious problem. It's still possible for a linguist to improve the raw output more quickly than translating the same text from scratch. When a linguist, known as a post-editor in this context, corrects raw MT output, productivity gains can run from 20% to 50%. Considering that the results can be virtually indistinguishable from a traditional translation, using MT sounds like a no-brainer.
There are some caveats, of course. The MT engine must be the right engine. The content must be the right content. And most importantly, the engine must be properly trained for the content, language pair, domain and even, at a more granular level, product line.
Complex technology though it may be, this is not what is limiting MT to the early adopters. Rather, it's the lack of human resources. The issue that you hear echoed throughout our industry is that there are simply not enough post-editors willing to work with MT.
If my company has managed to retain a large pool of post-editors, it may be because we understand one basic truism: post-editors really hate poor MT output. They hate it so much that last year, post-editor anger over being asked to mop up bad MT was the most hotly debated topic on LinkedIn’s Automated Language Translation Group. And who can blame them? . . .