The Fusion of Creativity and Technology: Generative Artificial Intelligence Tools for Marketing
Year: 2023
Type: dissertation
Abstract: The purpose of this thesis was to investigate the efficacy of Generative Artificial Intelligence (GAI) tools for marketing and sales functions in cooperation with Bazar Helsinki. One objective was to study the extent to which GAI tools promote or hinder authenticity and creativity in busi-ness communication. Another objective was to provide instruction and methodologies to augment ChatGPT- a Large Language Model (LLM), released by OpenAI in late 2022, to generate more creative and valuable outputs. \n \nThe theoretical framework was based on literature about how the field of business communication and technology interact, citing leading authors and researchers in the fields of corporate communications and NLP output augmentation, including Jason Wei & al (2023), Bang Yejin & al. (2023), Cardon Peter & al. (2023) and Kanbach Dominik & al (2023). \n \nThis study was conducted in collaboration with Bazar Helsinki, using interviews conducted with the Artistic Director and CEO to identify staff thoughts and opinions on the adoption of GAI in marketing and sales functions. \n \nThe findings indicated that ChatGPT and other LLMs can be useful for creativity augmentation and offering productivity improvements for text generation and analysis. ChatGPT as a tool, can foster creativity by offering diversity, inspiration, and efficiency to the generation of marketing content. Yet, developing AI literacy is necessary to ensure that these technologies are managed in a way that produces authentic and valuable results. \n \nIn conclusion, ChatGPT and other LMM tools have the potential to revolutionize aspects of content generation in marketing, but their integration requires employee training and investment. The most useful method to augment output quality is to introduce a combination of prompt engineer-ing and retrieval augmented generation (RAG) techniques, as these have been shown to improve generation quality.
Source: Theseus (Ammattikorkeakoulujen)
Author: Adelakun, Bayonle
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6991192950