Challenges in implementing ChatGPT and generative artificial intelligence in various business sectors

Abstract

From customer service to content development, ChatGPT and generative AI can alter corporate processes across industries. Implementing these technologies presents substantial hurdles that firms must overcome to maximize their potential. Generative AI systems need a lot of data to work, which raises privacy and security concerns and makes GDPR compliance difficult. Model biases can perpetuate prejudice or produce inconsistent consumer experiences, affecting brand reputation and trust. These technologies' scalability and adaptation across industries is another issue. Some sectors demand very precise contextual information, yet generative AI models are generally trained on broad, generic datasets, which may limit their efficacy. The significant computational costs and infrastructural requirements of installing these models strain resources, especially for smaller organizations. Companies typically lack the in-house skills to maintain and fine-tune complex AI systems. This chapter analyses these challenges and provides actionable insights to assist firms strategically negotiate the intricacies of using generative AI for sustainable and scalable impact.

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