Automation has come a long way, leading now to its most advanced and buzzworthy state: AI. AI refers to technologies that learn from training data and experience to perform tasks that would otherwise require human intelligence. When applied to a project manager’s (PM) job, automation enables “lights-out” project management, in which software handles the project from quoting to invoicing without the need for human interaction. Over the last few years, a growing number of language service providers (LSPs) operate with business models that are fully or partially automation-driven.
Three levels of automation
AI exists at the end of a continuum running from simple trigger-based automation, through more robust rule-driven expert systems to big data-driven applications that learn from observing projects.
At the beginning, we find rule-based systems that deliver basic automation for configured workflows. LSPs preload price sheets, workflows and stakeholders in the system and enable it to execute the project plan based on clear rules. For example, when a given client sends a project in Spanish, the software automatically runs the files against that client’s translation memory, produces the quote, and sends the pre-translated file to a preassigned translator for that language pair. However, if the system encounters an undefined situation, such as a new customer or new language for a client, the preconfigured workflow stops and calls for human intervention.
The next level of automation includes expert systems that go a step beyond preloading data for each scenario. Instead, they apply complex rules to quote rates and turnaround times, choose workflows and select vendors based on project specifications. In this touchless environment, human intervention occurs when the system flags a need for it. For example, the software may discover a shortage of vendor options that can handle the work in the assigned turnaround time.
At the end comes AI with machine learning. As systems move beyond rules, they learn on their own by analyzing data in the absence of explicit direction. For example, an AI-driven system can predict timelines based on actual translator performance for specific types of content. It flags the odds of a translation passing a preset quality threshold based on analysis of events such as whether the linguist opened a provided glossary. It can also draw previously unanticipated conclusions from escalations to improve the handling of similar cases in the future.
Advances in project management AI
AI can apply to every aspect of project management from vendor selection to timeline calculations, detecting the need to apply rush fees or to special preferences from a client that require a process adjustment.
Business models now exist that are entirely centered around automation where there are no project managers or the few ones on staff have a modified role. So imagine what AI can do on top of that.
Admittedly, right now, AI for language project management is not one big monolithic approach to projects but rather a series of automations to either replace or amplify the skills of humans one micro-process at a time. Full project automation remains rule-based for now in intelligent systems.
Yet while we’re only seeing the tip of the iceberg in terms of pure AI, we expect a complete explosion in this area as LSPs start investing more in cost-saving technology and as tech vendors beef up the AI capabilities in their translation management systems (TMSs).
The readiness to adopt automation
For LSPs to adopt AI, they first need to be open to rule-based automation. In a survey with 324 heads of project management teams at LSPs, respondents told us about the degree of automation used on projects. Note that we asked for actual and not wishful practices.
A low-tech approach dominates. We found that 31% of respondents have PMs who coordinate everything personally. Another 38% rely on automation only to streamline communication and file transfer — a common feature of TMSs.
Other automation adoption remains low. Numbers may seem negligible with just 7% that strive to push all basic jobs to fully automated processes and 4% that use some level of artificial intelligence. Full automation is still in the early adopter phase. Those LSPs with higher levels of automation — particularly on basic jobs — can process jobs faster, cheaper and more profitably. We also expect the balance in this graphic to shift over time to include much more automation.
The impact of AI on project managers
Even the most sophisticated lights-out systems we’ve observed retain some human-centered elements. For example, if LSPs can handle projects from A to Z with their AI, then vendor managers can focus on the relationship with suppliers, and account managers can invest more effort in developing client strategies.
AI in project management is bound to trigger a complete revolution in the long-held and prevalent beliefs of the language services industry. While executives may be tempted to resist the push for automation out of fear of the unknown or lack of technology expertise, automating PM processes becomes even more urgent for LSPs than adding AI via neural machine translation. CSA Research contends that the benefits it delivers in eliminating unnecessary manual touches will allow companies to redeploy its human assets to more valuable tasks. As the technology improves, all providers should review their operations to learn where they could best take advantage of automation in general and AI in particular.
Of course, AI makes the language industry anxious. Even after LSPs switch to heavily automated business models, there will still be people involved. Some LSPs will take advantage of the changes, others won’t, this is no different than what we’ve been seeing for the last 30 years. There is risk, and work to be done, but the sky is not falling.