The integration of generative artificial intelligence (AI) into the advertising industry represents a foundational transformation that is reshaping not only the operational dimensions of advertising practice but also the conceptual frameworks that underpin advertising research and pedagogy. This shift is not confined to the development of new tools or automation of existing workflows; rather, it signals a paradigmatic evolution in how creative and strategic processes are initiated, managed, and assessed across the marketing and communications landscape.

At the heart of this transformation lies the practice of prompting, the art and science of crafting structured inputs to elicit desired outputs from generative AI models such as large language models (LLMs) and diffusion-based image generators. Prompting, in this context, has emerged as a central communicative act that bridges human intention with machine interpretation, thereby redefining the boundaries between authorship, creativity, and computation. As generative AI systems become increasingly embedded in the ideation, production, and distribution of advertising content, the act of prompting is assuming critical importance not only as a technical input mechanism but as a strategic competency that influences message framing, narrative construction, and consumer engagement outcomes.

This technological and communicative shift has significant implications for the advertising industry, where leading firms are rapidly adopting AI to enhance campaign efficiency, scalability, and personalization. From automated ad copy generation and dynamic content optimization to immersive brand experiences powered by AI-generated video and audio, the operational model of advertising is being reimagined. For instance, companies like IBM and Mastercard have deployed generative AI as a core component of internal workflows, while creative agencies such as McCann and FCB New York have utilized AI tools to augment ideation and production, producing award-winning campaigns that blend algorithmic insight with human creativity.

Concurrently, this shift exposes a widening gap between industry practice and academic preparation. While major corporations and agencies are investing in AI fluency and internal prompt engineering capabilities, many business schools have yet to systematically integrate generative AI into their curricula¹. In some cases, the use of tools like ChatGPT or DALL·E remains controversial or poorly understood within academic settings, often dismissed as academic dishonesty rather than recognized as a critical skill for the digital workplace². This disjunction reflects a broader lag in higher education’s responsiveness to technological change, echoing earlier delays in the adoption of data analytics into business programs, a gap that was only addressed after industry had long normalized the practice.

The implications of this divergence are profound. If prompting is now a foundational communication skill in marketing, as essential as writing, presenting, and analyzing data, then academic programs must urgently rethink how they define learning outcomes, assessment standards, and professional competencies¹. Prompting is not merely a shortcut to content generation; it is a meta-skill that requires rhetorical precision, domain knowledge, strategic framing, and critical evaluation. The ability to instruct AI systems effectively and ethically, to evaluate outputs critically, and to refine prompts iteratively is fast becoming a marker of professional readiness in marketing, advertising, and related fields.

This paper aims to provide a comprehensive exploration of the evolving relationship between generative AI, particularly the growing importance of ‘prompting’, and its impact on advertising. It examines current industry practices, insights from marketing executives, analyzes theoretical foundations in advertising research, identifies pressing research gaps, and considers the role of business education in preparing graduates for a prompt-centric marketing ecosystem. In doing so, it positions the act of prompting as both a transformative practice and a necessary area of scholarly inquiry, one that will shape the future of advertising in profound and lasting ways.

Corporate Adoption of Generative AI in Advertising: Drivers, Strategic Expectations, and Economic Implications

The proliferation of generative artificial intelligence (AI) technologies within the advertising sector represents a profound reconfiguration of how firms conceptualize, operationalize, and evaluate marketing activities. What was once considered an emerging trend has now become a central pillar of marketing strategy and organizational infrastructure. In fact, 92% of Fortune 500 companies have embraced this technology and implemented generative AI in their operations³. However, this shift has not been driven solely by deterministic models of financial return or linear technological diffusion. Rather, it is shaped by a confluence of psychological, competitive, and institutional forces, chief among them, the widespread phenomenon of AI-induced strategic urgency, often characterized as “FOMO,” or the fear of missing out.

The FOMO effect: AI adoption as Strategic Necessity

The perceived risks of inaction in the face of generative AI adoption are becoming as strategically significant as the pursuit of its potential rewards⁴. While measurable returns on investment may still vary across industries and applications, marketing executives increasingly view hesitation or resistance to AI integration as a threat to long-term competitiveness. This perception is driven by a growing awareness of three core liabilities: the loss of AI-proficient talent to more technologically progressive firms, reputational damage stemming from an outdated or passive innovation posture, and operational rigidity that hampers responsiveness to rapidly evolving market demands. As a result, generative AI is no longer viewed as an optional enhancement but as a critical component of organizational agility and strategic viability in the digital economy, a phenomenon increasingly described as the ‘race to AI’⁵.

Observations gathered during the 2025 Association of National Advertisers (ANA) Visiting Professor Program, which involved close interactions with marketing executives from major corporations in New York City, including IBM, Mastercard, FCB New York, and McCann, reveal that many current AI initiatives are being pursued not because of clearly established statistical return-on-investment (ROI) models, but due to organizational pressure to remain competitive in a rapidly transforming digital ecosystem. Executives commonly expressed a sentiment that to not engage with generative AI tools, whether for content generation, creative ideation, customer personalization, or market analytics, would risk reputational harm, internal obsolescence, or diminished relevance in the marketplace. This strategic anxiety is intensified by the high visibility of AI-driven initiatives among industry peers, as virtually all prominent firms have publicly committed to incorporating AI into some dimension of their operations or product innovation strategies. Consequently, AI adoption is often framed not just as an opportunity, but as a requirement to signal forward-thinking leadership, attract talent, and maintain investor confidence.

This aligns with emerging industry-wide data. According to a Boston Consulting Group (BCG) global survey, 91% of Chief Marketing Officers (CMOs) reported that generative AI had already delivered a positive impact on efficiency in their marketing functions⁶. Moreover, 70% of organizations had already implemented generative AI tools in at least one operational capacity⁶. The primary use cases cited include personalization (67%), insight generation (51%), and content creation (49%), areas where rapid content iteration and scalable relevance are critical to maintaining competitive advantage in saturated media environments⁶.

Strategic Transformation: Beyond Operational Efficiency

While initial implementations of AI may be focused on tactical gains, there is a clear strategic pivot underway. Generative AI is increasingly being understood as a catalyst for business model innovation and long-term transformation. According to the same BCG study, 50% of CMOs indicated that generative AI was facilitating the development of new products, services, or business models⁶. This finding points to an important redefinition of AI not merely as a cost-saving or automation tool, but as an enabler of conceptual expansion, one that can stimulate ideation, support rapid prototyping, and allow firms to engage in market experimentation at significantly reduced marginal costs.

Table 1. Examples of Corporate AI Adoption in Brand Content Creation and Customer Engagement

This enthusiasm for AI among marketing executives, as shown in the BCG survey, aligns with insights gained from our interactions with CMOs during the Association for National Advertisers Visiting Professor Program. Marketing leaders from companies such as Mastercard illustrated how AI is being deployed beyond traditional promotional functions (See Table 1)⁷. Their AI-powered assistance platforms are designed to empower small businesses with automated strategic guidance, indicating a deliberate push toward AI-enabled service innovation that supports long-tail markets and enhances brand value through democratized access to advanced tools.

Similarly, creative agencies like McCann and FCB New York have incorporated AI into their campaign development workflows, not as replacements for human creativity, but as intelligent augmentation mechanisms that accelerate idea generation and enrich storytelling⁸. The Emmy-winning campaign developed by FCB New York, which used AI to translate the movements of NBA basketball players into tactile feedback for a blind audience, exemplifies this fusion of technology and emotional resonance⁹.

Edelman and the NFL have embraced generative AI to enhance both content creation and customer engagement strategies. Edelman, a global communications firm, leverages AI to streamline content generation for its clients by utilizing AI-driven data analysis to produce highly relevant, segment-specific content, thereby improving messaging accuracy and speed¹⁰. Additionally, Edelman employs AI for sentiment analysis, allowing real-time assessments of consumer reactions, enabling agile responses to emerging trends and shifting public opinions. This approach has led to more dynamic and responsive campaigns that resonate with audiences. Similarly, the NFL utilizes AI to boost fan engagement and content personalization, analyzing player performance, fan behavior, and viewing preferences to generate tailored content. AI-driven features like personalized game predictions, highlights, and interactive experiences ensure a more immersive, individualized fan experience¹¹. This strategic adoption of AI strengthens the NFL’s connection with its audience, enhancing brand loyalty and fan satisfaction.

Personalization and Co-Creation as Engagement Strategies

The emergence of generative AI has not only optimized the mechanics of content production but also revolutionized the way brands conceptualize consumer engagement. Central to this shift is the use of AI to facilitate personalized and co-created experiences, wherein consumers interact directly with brand assets, influence content outcomes, and thereby co-author their relationship with the brand. This represents a departure from traditional top-down advertising models and moves toward a participatory paradigm of marketing communication, underpinned by algorithmic intelligence and user-centric design.

Personalization has long been a goal in marketing strategy, but generative AI enables it at a previously unattainable scale. With the ability to analyze vast datasets in real time, including behavioral, demographic, and psychographic information, AI systems can dynamically tailor advertisements to individual users across digital platforms. A McKinsey report found that companies that excel at personalization generate 40% more revenue from those activities compared to their peers¹⁷. In the context of generative AI, this personalization is further refined: natural language models such as GPT-4 can generate unique ad copy for specific customer profiles, while diffusion-based models like DALL·E or Midjourney create customized visuals reflecting individual preferences. This level of granularity enhances relevance, improves click-through rates, and increases customer satisfaction¹².

One of the most notable examples of AI-driven personalization is Nutella’s Unica campaign in Italy, where the brand used an AI algorithm to generate over 7 million unique packaging designs for its product jars¹³. Each jar was visually distinct, reinforcing the message of individual uniqueness and thereby strengthening emotional bonds between the consumer and the brand. The campaign achieved high levels of consumer engagement and media coverage, proving that AI-enabled design at scale could simultaneously drive operational efficiency and deepen brand equity.

Beyond personalization, co-creation has emerged as a powerful mechanism for fostering consumer loyalty and interaction. Rather than merely receiving brand messages, users are now empowered to participate in the creative process itself, often facilitated by brand-sanctioned AI tools. Coca-Cola’s Create Real Magic campaign, launched in collaboration with OpenAI and Bain & Company, provided users with a generative art platform that allowed them to design custom visuals using Coca-Cola brand elements, such as its iconic bottle shape and script logo¹⁴. These consumer-generated creations were then displayed on digital billboards in high-traffic locations like Times Square and Piccadilly Circus, effectively blending crowdsourced creativity with high-visibility media execution. This campaign exemplified a new model of brand-consumer interaction in which AI acts as the mediating agent, enabling scalable, yet deeply personal creative contributions.

Other brands have followed suit. Adidas, for instance, launched an AI-powered sneaker customization tool that enables consumers to design their own footwear with pattern suggestions, color palettes, and slogans generated through machine learning models trained on fashion trends and customer data¹⁵. Similarly, Nike has explored AI-assisted product customization platforms that generate recommendations based on user history, social media activity, and contextual data such as local weather patterns or recent athletic achievements¹⁶. These AI-driven personalization tools are not only enhancing the consumer experience but also providing brands with valuable data to refine future product offerings and marketing strategies. From a business perspective, the return on these AI-driven personalization and co-creation strategies is beginning to manifest in both qualitative and quantitative forms. According to Salesforce’s State of Marketing report, 73% of consumers expect companies to understand their unique needs and preferences, and 62% are more likely to remain loyal to brands that deliver personalized experiences¹⁷.

This convergence of personalization and co-creation, mediated by generative AI, reflects a broader shift in advertising from static messaging to dynamic engagement. Brands are no longer simply crafting narratives to be consumed; they are designing platforms for shared storytelling. In this model, generative AI functions not only as a productivity tool but as a collaborative interface between the brand and its audience. The resulting ecosystem is one that is more adaptive, inclusive, and emotionally resonant, qualities that are increasingly vital in a crowded and fragmented digital media environment.

As this trend continues, it raises critical research questions around authorship, brand control, creative equity, and ethical considerations. For example, how should intellectual property be treated when consumers co-create branded content using proprietary AI tools? What are the implications for brand consistency when user-generated content becomes part of the public brand narrative? These questions highlight the need for new theoretical and empirical frameworks in advertising research to account for the evolving dynamics introduced by generative AI.

Organizational Efficiency and Ecosystemic Shift

AI’s most immediate and measurable impact remains in the domain of operational efficiency. The 2023 Forrester Report on U.S. advertising agencies revealed that 91% of firms are currently using or exploring generative AI technologies, and over 50% anticipate substantial disruptions to their core business processes within the next two years¹⁸. These expectations are based on demonstrable efficiencies gained through AI-driven automation of copywriting, media planning, customer segmentation, A/B testing, and performance analytics¹⁸.

In many agencies, AI has become an integral part of the production pipeline, reducing cycle times, lowering creative development costs, and enabling the simultaneous generation of multiple ad variants. The result is a shift toward real-time advertising ecosystems, in which responsiveness and iteration are valued more than static campaign planning. This transformation is especially critical as brands attempt to engage increasingly fragmented and fast-moving digital audiences.

Beyond individual firms and agencies, the deployment of generative AI is contributing to the emergence of a new trillion-dollar global industry centered on AI infrastructure, services, and applications. Market analysts project that the generative AI economy will surpass $1.3 trillion by the early 2030s, encompassing a range of verticals that include AI software platforms, model training services, data engineering consultancies, and specialized hardware production such as GPUs and neural processing units (NPUs)¹⁹. Companies like NVIDIA, Microsoft, and OpenAI have become foundational actors in this ecosystem, providing cloud-based AI services and computational platforms that are powering a significant portion of enterprise AI activity . Simultaneously, a rapidly growing tier of AI-native service providers, including creative prompt engineers, AI auditing firms, and algorithmic marketing consultants, is reconfiguring the advertising value chain by offering bespoke solutions that integrate technical fluency with branding expertise.

The generative AI boom has also accelerated demand for custom LLMs (Large Language Models) and domain-specific AI agents, particularly in regulated industries or culturally sensitive domains, where generic tools may produce suboptimal or non-compliant outputs. This has stimulated investment in AI safety, explainability, and governance frameworks, which are now viewed as essential components of responsible deployment²⁰.

Thus, Generative AI is advancing at an unprecedented pace, rapidly embedding itself across nearly every facet of business operations, from marketing and customer service to product development and strategic planning. While the financial implications of these transformations are still taking shape, early indicators suggest an optimistic outlook driven by the introduction of AI-powered innovations. Examples such as Mastercard’s AI-driven business assistance platforms for small enterprises and IBM’s internal AI systems for business process optimization exemplify how organizations are beginning to leverage AI not only to enhance efficiency but to unlock entirely new value propositions.

In the advertising sector, AI-powered ad creation tools are enabling brands and agencies to generate personalized, high-quality content at scale, significantly accelerating campaign development cycles and enabling dynamic adaptation to real-time audience insights. These initiatives reflect a broader shift in how businesses define innovation, prioritizing adaptability, intelligence, and automation. Hence, even in the absence of deterministic financial projections, generative AI is widely perceived as a strategic imperative, essential not only for achieving competitive parity but for building resilient, scalable, and future-ready operational infrastructures capable of navigating the next wave of digital transformation.

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