There are many AI-driven solutions being touted as capable of solving enterprise-level challenges, but they fail to discuss the infrastructure and data requirements for success. This needs to change. LLMs can only deliver value when supported by robust infrastructure and high-quality, well-structured data. This is particularly true when thinking about leveraging LLMs to drive new levels of accuracy or personalization, such as tailoring content to match a brand’s tone of voice or specialized industry cambodia whatsapp number data terminology. In addition, common enterprise use cases, like translating user-generated reviews, aren’t possible with LLMs on their own. Such tasks require an infrastructure that can handle large volumes of incoming content and turn around translations in near-real-time.
All of this requires an infrastructure where data flows seamlessly. Even more crucially, it demands sophisticated data repositories, such as translation memories, that are up to date and clean to fuel AI applications. Common approaches rely heavily on in-house, domain-specific data. The data feeding these AI systems must not only be of high quality but also properly formatted and organized to enable effective use and outputs.
“End-to-End” Claims
Building on the need for proper infrastructure, let’s talk about the concept of “end-to-end automation.” This is the promise that so many AI-driven solutions promise today, and it’s one where many fall short. suggests that the information input into a system would be able to travel all the way through to a successful output without any intervention. However, most enterprise-grade AI solutions today aren’t able to achieve this promise because they are, in essence, a number of point solutions that have been cobbled together.