Custom AI Models Tailored to Business-Specific Content Needs

Ankur Tiwari

doi:10.53697/jkomitek.v4i2.2457

Authors

Ankur Tiwari AI Powered Content Management System Architect

DOI:

https://doi.org/10.53697/jkomitek.v4i2.2457

Keywords:

Custom AI Models, Content Management Systems (CMS), Content Personalization and Automation

Abstract

This research explores the integration of custom Artificial Intelligence (AI) models in Content Management Systems (CMS) for content creation, curation, and management. The primary objective is to examine how AI-driven solutions, tailored to specific organizational needs, can optimize content workflows, improve productivity, and personalize content at scale. The study also investigates the ethical considerations, challenges, and potential benefits associated with the use of AI in CMS.The research adopts a mixed-methods approach, combining both quantitative and qualitative data. Quantitative data was gathered through surveys distributed to content creators and managers who have experience with AI tools, measuring productivity improvements, time savings, and user satisfaction. Qualitative data was collected through semi-structured interviews, offering deeper insights into the integration process, human oversight, and ethical issues related to AI-generated content.Results show that custom AI models significantly enhance content production efficiency, with respondents reporting increased content output and substantial time savings. The integration of AI also led to higher user satisfaction, particularly due to the personalized and relevant content generated by AI tools. However, challenges such as data quality, model bias, and the need for continuous training were identified. Ethical concerns regarding AI-generated content, including potential biases and intellectual property issues, were also highlighted.The study concludes that AI models tailored to organizational needs provide substantial benefits in terms of scalability, personalization, and efficiency. However, businesses must address the ethical implications and ensure proper human oversight to mitigate biases and ensure content quality and responsibility. Future research should focus on refining AI model transparency and inclusivity

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