CIO OPINION
Generative AI can be used to analyze customer data to generate personalized product recommendations , service adjustments or promotional offers .
Customer call center , in store , and online and e-commerce interactions can all be transformed with personalized customer insights , interventions and content .
Generative AI models can also be used to drive customer service automation by improving AI-driven chatbots and virtual assistants to handle routine inquiries and tasks much better .
More automated responses to customer enquiries delivers time and financial savings . It can then help with the delivery of proactive service inquiry content , alerting customers with personalized messages about their service or billing needs , for example .
One of the most high-value uses of Generative AI in telecommunications however is in network optimization .
Complex networks that require continuous monitoring and optimization to ensure maximum uptime and quality of service can be enhanced by training AI models on historical network data to generate predictive models for network traffic , identify potential bottlenecks and suggest optimal network configurations .
Generative AI models to understand network needs for capacity and performance .
Leveraging network performance predictions from supervised ML models , Generative AI can also help to figure out network maintenance plans .
Crucial to successful implementation of Generative AI are :
Required Technologies
Large Language Models ( LLMs ) and Generative Adversarial Networks ( GANs ) are examples of Generative AI models used for creating new data by forming a complex and nuanced understanding of the training data .
GANs can be used to generate new customer journey scenarios or create ‘ synthetic ’ customers for testing service and product scenarios .
LLMs can derive insights from unstructured customer data , call center transcriptions , chatbot conversations and more – all with the goal of creating personalized customer service and engagement content . LLMs also can learn ‘ in context ’ through prompt engineering where a user provides relevant context in unstructured or structured form to the model as part of their query .
Orchestration of multiple complex AI / ML models used for network planning and operations will combine with
This ability to learn on the fly and even be further trained or fine-tuned to obey instructions makes LLMs
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