Data-for-AI: A Golden Opportunity for LSPs?

The data boom has presented Language Service Providers (LSPs) with a significant opportunity to grow their business by offering data for AI services.

With their expertise in language and large, geographically-distributed supply chains, LSPs are well-positioned to provide large volumes of data to companies building AI solutions.

However, according to Olga Blasco from Lion People Global, as with any new venture there are challenges and pitfalls that must be navigated.

One significant challenge is the technology and services required for data for AI. These are very different from traditional translation services and may require significant investment in custom annotation tools.

Olga says that LSPs must carefully consider whether this investment is worth the journey and whether they can compete with established companies in the data for AI market.

Another challenge is revenue recurrence and profitable scale.

Olga reminds LSPs that as demand for data for AI evolves, so too will clients’ needs and disposition to different pricing models.

LSPs must be ready to understand how to position themselves to keep revenue coming in every month, Olga says.

Another potential pitfall is the quality of the deliverables.

The translation of binary allocation annotation, for example, can create mistakes as languages have different contexts for yes and no.

Algorithms used to assess the quality of data must be able to cope with these issues to ensure the delivery of clean, publishable data that isn’t too expensive to produce.

Olga thinks this challenge is reminiscent of the software localization projects of the 1990s. Until a whole cycle of maturity was completed, clean, publishable software was expensive to produce, and there were many reworks and issues to be ironed out before the industry matured.

Despite these challenges, Olga says the potential for LSPs to enter the data-for-AI market is significant.

By providing services only, such as offering a large pool of resources to work on annotation platforms, or developing custom annotation tools, LSPs can compete in the market and provide valuable data to companies building AI solutions.

In conclusion, the data boom has created significant growth opportunities for LSPs to offer data for AI services.

However, LSPs must carefully consider the challenges and pitfalls involved, including technology and services required, revenue recurrence and profitability at scale, and the quality of deliverables.

With careful consideration and investment, LSPs can navigate these challenges and establish themselves as players in the growing data for AI market.

If you want to know more about this area, visit Lion People Global’s website where you can watch back our M&A Talks series of videos and fill out a questionnaire that entitles you to one hour’s free consultation on getting a LSP or language tech business ready for sale.