Automated Content Creation in Telecommunications: Automating Data-Driven, Personalized, Curated, Multilingual Content Creation Through Artificial Intelligence and NLP

Praveen Hegde

doi:10.53697/jkomitek.v4i2.2458

Authors

Praveen Hegde Principal Engineer, Verizon

DOI:

https://doi.org/10.53697/jkomitek.v4i2.2458

Keywords:

Artificial Intelligence (AI), Natural Language Processing (NLP), Content Automation in Telecommunications

Abstract

This research explores the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) in content creation within the telecommunications industry. The primary objective is to examine how AI-driven tools can automate personalized, data-driven, and multilingual content, improving operational efficiency and customer engagement. The study also aims to assess the ethical implications of AI in content generation, including issues of data privacy, algorithmic transparency, and bias.A mixed-methods approach was used, incorporating a systematic literature review, case studies, and expert interviews. The literature review focused on AI and NLP applications in telecom, while case studies from companies like Ericsson, Huawei, and Vodafone illustrated real-world AI implementation for content creation and customer engagement. Expert interviews provided further insights into the challenges and ethical concerns surrounding AI adoption in telecom.The results reveal that AI and NLP significantly enhance content personalization and scalability. Telecom companies using AI tools reported increased content output, higher customer satisfaction, and reduced operational costs. However, challenges related to data quality, privacy concerns, and AI bias were identified. Ethical concerns, such as algorithmic transparency and fairness, were also highlighted as critical factors for successful AI implementation.In conclusion, AI and NLP are transforming content creation in telecommunications by enabling personalized, efficient, and multilingual communication. Despite the advantages, ethical and practical challenges must be addressed, including data privacy, model biases, and the need for ongoing AI model training. The research underscores the importance of ethical AI practices to ensure responsible content generation.

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