Advancements in Gen AI and large language models (LLM) offer enterprises powerful tools to automate customer interactions, enhance content creation, extract meaningful insights from data, and more. By leveraging these technologies in the CMT industries, businesses can improve operational efficiency, deliver personalized experiences and gain a competitive edge.
Here are some tangible examples of how Gen AI already is, or can be, used shortly within CMT industries:
· Improvements in customer support by intelligent chatbots that offer round-the-clock support to customers, providing relevant responses and more personalized customer interactions.
· Improvements in understanding customer sentiment and identifying cross-sell opportunities.
· Network optimization by connected AI/ML models to comprehend network behaviors and improve planning/performance.
· Improvements in network troubleshooting by optimized field service devices which will have enhanced diagnostic and analytical capabilities.
· Reduction of mundane, repetitive tasks in favor of higher creativity in content creation such as text, illustrations and audio.
· Reduction of subscriber churn by more personalized audience experiences with increased accuracy.
· Improvements in advertising and subscription monetization across platforms/devices, where enhanced personalization helps reduce churn.
· LLM allows search engines to provide increasingly more nuanced and synthesized answers to improve accuracy.
· LLM makes unstructured data sources, such as social media and customer reviews, available to help provide personalized service.
· Faster and more precise language translation for both internal and external parties.
· Improvements in IT worker productivity when it comes to generating accurate code, analyzing code bases and identifying security issues.
· Aiding in data management and governance tasks, ensuring compliance with privacy regulations, facilitating data anonymization and improving data quality.
· Improvements in knowledge management; LLM can process a query and map it to relevant documents based on semantic similarity, context and topic modeling to provide a more relevant knowledge retrieval.