Artificial intelligence (AI) has emerged as a disruptive force, driven by rapid technological advancements that harness sophisticated computing capabilities, intricate algorithms, and vast datasets. This enables machines to replicate human cognitive and emotional functions, autonomously performing tasks like problem-solving and reasoning [1]. The progression of AI has been a transformative force across various industries, including finance, healthcare, manufacturing, marketing, and more [2]. Through the lens of advertising, its progression was accelerated by dynamic shifts in advertising strategies, fundamentally transforming how brands interact with their target audiences. Today, advertisers leverage AI to craft personalized experiences, target specific demographics, and make quicker decisions [3]. The integration of AI into advertising represents a paradigmatic shift in contemporary marketing strategies.

One prominent aspect of AI in Industry 4.0 are generative models. These models foster real-time communication across equipment, software, and human operators, offering manufacturers immediate insight into key production processes [4]. One example is Generative Artificial Intelligence, commonly known as “Generative AI”. It was introduced decades ago, but it is only in recent years that it has evolved rapidly. Much of the current discourse revolves around large language models and image-generation models, where these tools allow users to create articulate-sounding texts and compelling images using an English-language prompt which helps users generate content. This, however, is just one facet of the broader scope of Generative AI [5]. Today, it holds the potential to revolutionize how brands connect with their audiences, ushering in new advertising avenues. Generative AI utilizes extensive datasets comprising images, text, or other forms of data to generate fresh iterations of text, visuals, or predicted data at the user’s request. The field of computer science propelling Generative AI is experiencing rapid evolution, witnessing monthly breakthroughs [6]. In 2023, reputable sources including CNBC [7], Forbes [8], LinkedIn [9], and Google Blogs [10] attested to the monumental impact of Generative AI on the advertising industry. It was said that artificial intelligence advertising spending across the globe in 2022 was estimated at $370 billion, together with predictions of $1.3 trillion in the next decade [11]. This burgeoning technology has revolutionized advertising endeavors, from the precision of targeted ads to the potential for dynamic content creation and even the nuanced analysis of images and videos for marketing purposes.

AI has transformed the advertising industry through various means. It harnesses the ability of data-driven decision-making and processing [13], and more. One notable technique is machine learning, which is the technique behind enhanced targeting accuracy as it can predict the most relevant advertisements for audiences rooted in pre-existing or contextual user data [14]. For example, the media streaming service Netflix employs a machine learning algorithm to curate customized content recommendations from viewers’ past viewing habits and preferences [15]. Natural language processing (NLP) is a technique that empowers computers to comprehend and process human language. It analyzes and comprehends human language [16]. Within advertising, NLP is used in applications for sentiment analysis; these provide brands with invaluable insights concerning perception and enhanced customer feedback. For example, KLM (airline) programmed a chatbot to deal with customer queries [17]. Image recognition expands the capabilities of artificial intelligence to data visualization; it enables the identification of scenes and objects in images. It can support out-of-home (OOH) advertising agencies in acquiring maximum reach for their businesses by optimizing the panel’s geospatial positioning following the detected traffic in the pedestrian area and optimizing the advertising content flashed on panels, as well as the response of people when they view it [18]. Predictive analysis, another advertising technique, provides better recommendations to users that allow them to maximize the value of the services and products presented to them, which results in a high-value proposition and customer satisfaction [19]. Unilever uses this technique through their app, “Flower”, which uses AI to generate a human-like profile that can interact with users on social media platforms [20]. Following this, recommender systems, a cornerstone of AI-powered advertising known by many today, analyzes audience behavior to offer customized suggestions to enhance user experience and drive engagement. This is most prominent in e-commerce as it greatly benefits from applying informatics through this technique [21]. The culmination of deep learning further elevates the capabilities of artificial intelligence, specifically in image and speech recognition tasks. This technique is utilized across many social network platforms. Today, deep learning covers almost all techniques, from image classification to object detection, which can inform targeted advertising strategies [22]. Examples of this are Deep Mind, Wolfram Alpha, and Aysadi [23].

2.2. Opportunities of Innovation in Advertising: Generative Artificial Intelligence