Interactions: Studies in Communication & Culture - The Human and the Machine: AI in Creative Industries, Jul 2022

Much has been made of the role artificial intelligence (AI) can have in the creative processes of advertising agencies. Not only can it be used to automate tasks and assess the success of advertising on audiences, but it can also alter the relationship between creatives and the work they produce, with computational creativity opening new opportunities to engage in creative and innovative practice. In fact, recent research has emphasized that creative people believe AI technologies will revolutionize the development, execution and dissemination of advertising. Yet, the potential of AI is not without its detractors because of the threat of job losses and ethical conundrums, leading advertising creatives to feel cautious and concerned about the place of this new technology in the industry. Of particular concern is how AI will influence the relationships between clients and agencies. Using data gained from five focus groups held in Aotearoa, New Zealand, with advertising creatives, this research article illuminates the perspectives of current practitioners on the role and potential influence of AI on creative production processes. It finds that most of those attending the focus groups believed that the speed of AI in creating content would increase the pressure placed on agencies to meet the needs of clients, that clients may opt to engage in their own computational creativity costing agencies money and reputation and that aesthetic considerations of agencies and clients may come into conflict.

The advent of advanced artificial intelligence (AI) and machine learning technologies has opened new avenues for qualitative research, particularly in visual data analysis. This pilot study introduced computer-assisted qualitative visual analysis (CQVA), leveraging GPT-4 Turbo and Google Cloud Vision to automate the thematic analysis of visual datasets. Traditional methods, relying on manual coding, are time-consuming and labour-intensive. CQVA addresses these challenges by providing an efficient, scalable and cost-effective alternative. This study had two objectives: developing the CQVA method and applying it to analyse the top 1000 advertisements from the ‘adPorn’ subreddit, offering insights into Reddit users’ advertising preferences. A clear preference was identified for ads utilizing visual metaphors, as these were the most common. Additionally, the importance of engaging visual communication was underscored, with themes employing visually striking and easily comprehensible imagery being favoured by Reddit users. Despite its promise, CQVA required human intervention to guide AI outputs and validate clusters and themes. However, the findings demonstrated CQVA’s potential to revolutionize qualitative visual analysis by significantly reducing time and cost, while maintaining the richness of insights typically achieved through manual methods, thus enabling more efficient and comprehensive analysis of large visual datasets, highlighting the method’s scalability and practicality for future research.

This article explores the transformative potential of large language models (LLMs) in understanding and engaging with the typical customer, referred to as µ^C (mean by customer). Utilizing OpenAI’s ChatGPT, specifically the GPT-3.5 Turbo model, this research demonstrates how LLM completions can capture and reflect linguistic trends, reflected by utilization of established distant reading practices, providing valuable insights into consumer behaviour. Through linguistic vectorization and detailed analysis, the study validates the LLM’s capacity to encapsulate central tendencies in customer data embodied within the corresponding training data set of the LLM. The derived µ^C profile reveals a socially conscious, self-aware millennial audience prioritizing individuality and ethically sourced products. The article concludes that integrating LLMs into business strategies enhances marketing effectiveness, product development and customer engagement, offering a comprehensive understanding of consumer trends and driving improved business outcomes.

Corporate messaging around generative artificial intelligence (AI) in the communications and culture domain focuses upon the reduced labour and democratization of creativity that supposedly comes with the adoption of these systems. From this emerges a dominant narrative that glorifies the productivity benefits of AI adoption, such as how copywriters can facilitate the creation of more copy by querying ChatGPT and proofing its output rather than writing everything from scratch. This article focuses on an underreported element of this narrative, which is how AI companies are changing the underlying structures of these types of creative tasks and remoulding them in the image of modern bureaucracy. This argument follows the work of David Graeber on meaningless labour, where increasingly the system of the world is founded upon the propagation of busy work. Similarly, I draw upon the work of Ivan Illich on ‘radical monopoly’, where he proposes that the most dangerous monopoly is not the economic one that companies such as OpenAI overtly seek but instead identifies the monopoly over how things are done as the more significant hazard. Together, these conceptual frames plot out a unique institutional critique of AI as it is currently sold. This article demonstrates how this is already affecting practice in communications professions and culture industries while also theorizing upon possible ways forward using Graeber and Illich’s counterstrategies to modern institutional monopoly.