LLMs are being applied to an ever-expanding number of business use cases. Many companies now use chatbots as part of their customer service strategies, for example. But thanks to the versatility of these models, creative enterprise software developers are applying the underlying technology to tackle a wide range of tasks that go beyond simply generating linguistic responses.
1. Customer Support Automation
Customer support is the most evident application of LLMs in enterprise settings—especially to customers. Conversational user interfaces, or chatbots, powered by language models can field a nearly unlimited number of inquiries at all hours. This can help dramatically reduce response times stemming from overburdened call center staff, a major source of customer frustration.
Integration of chatbots with other LLM-powered applications can automate follow-up actions after a support call, such as sending a replacement machine part, document, or survey. LLMs can also directly assist human agents, providing them with timely information, sentiment analysis, translation, and summaries of interactions.
A funds manager operating in more than 50 countries and 80 languages has taken advantage of these capabilities to make it easier for its customers to discover and choose the financial vehicles that best fit their needs. The retirement account management specialist modernized its customer support with a custom chatbot that delivered a 150% increase in service levels and 30% reduction in operational costs. Customers now can visit the company’s webpage and ask the chatbot questions about their accounts at any time of day and in many languages.
2. Content Generation and Summarization
LLMs can create original content or summarize existing content. Both use cases are extremely useful to companies large and small, which are putting generative AI to work writing reports, emails, blogs, marketing materials, and social media posts while taking advantage of LLMs’ ability to tailor that generated content to specific groups or individual customers.
Summarization condenses large amounts of information, with sensitivity to the domain, into a format easier for humans to quickly review and absorb. LLMs do this by either assessing the importance of various ideas within a text and then extracting key sections or by generating concise overviews of what they deem the most relevant and critical information from the original text.
LLMs are sometimes critiqued as “summarizing to erage,” meaning their summaries are overly generic and miss key details or important points of emphasis of the original material. It’s also tricky to gauge the reliability of summaries and rank the performance of various models accordingly. Nonetheless, companies are enthusiastically adopting this capability.
One leading cloud communications company deployed LLMs to automatically summarize transcripts of hundreds of support tickets and transcripts of chats taking place daily in almost two dozen languages. Those summaries now help support engineers resolve customer challenges faster and elevate the overall experience.
3. Language Translation
Google’s initial intent in developing transformers was to make machines better at translating between languages; only later did the model impress developers with its broader capabilities. Those developers’ first implementations of this architecture achieved that goal, delivering unrivaled performance in English-to-German translation with a model that took significantly less time and computing resources to train than its predecessors.
Modern LLMs he gone well beyond this limited use case. Although most LLMs aren’t specifically trained as translators, they still excel at interpreting text in one language and clearly restating it in another when they’re extensively trained on data sets in both languages. This breakthrough in breaking down language barriers is extremely valuable to enterprises that operate across borders. Multinational companies use advanced language services to, for example, develop multilingual support for their products and services; translate guides, tutorials, and marketing assets; and use existing educational assets to train workers when expanding into new countries.