Journal of Advertising Research

AI and the Future of Advertising Creativity

AI and the Future of Advertising Creativity

For most of advertising's history, creative work has been relatively slow, scarce, and expensive. A campaign moved from brief to concept to finished asset through weeks of human labor, and the cost of producing each execution limited how many ideas a brand could test and how finely it could tailor them. Generative AI is potentially collapsing those constraints. Tools that draft copy, generate images and video, and produce thousands of message variants in minutes are now becoming embedded in the daily workflow of agencies, brands, and platforms (Cui, Liu, and Yuan, 2025; Demsar et al., 2025; Hartmann, Exner, and Domdey, 2025). The result is not a marginal efficiency gain but a potential reordering of how advertising creative is imagined, made, evaluated, and valued. For an industry whose competitive advantage has long rested on creativity, arguably few developments matter more.

Creativity has always been central to how advertising works. Decades of research establish that creative advertising, generally defined as work that is both divergent and relevant, drives attention, processing, memory, and ultimately sales (Reinartz and Saffert, 2013; Rosengren et al., 2020; Smith and Yang, 2004; Smith, Chen, and Yang, 2008). Yet what counts as creative has never been settled. It is judged differently by creatives, account managers, and clients, and it turns on originality, artistry, and strategic fit in ways that resist easy measurement (Amabile, 1983; Koslow, Sasser, and Riordan, 2003; Runco and Jaeger, 2012). Generative AI forces these questions open again. When a model can produce a polished, on-brief execution in seconds, the premium may shift from craft and execution toward ideas, taste, judgment, and the ability to direct the machine. Early evidence is mixed: AI appears able to lift the measured creativity and effectiveness of individual work, in some field settings to apparently superhuman levels, while tending to push outputs across many users toward sameness (Doshi and Hauser, 2024; Hartmann, Exner, and Domdey, 2025). Whether AI expands the creative frontier or flattens it is now an empirical and managerial question of importance.

These dynamics are touching every stage of the creative process. In ideation and concepting, AI can act as a brainstorming partner that generates and recombines directions faster than most teams, shifting the human role toward editing, curating, and directing (Cui, Liu, and Yuan, 2025; Vakratsas and Wang, 2021). In asset production, text-to-image and text-to-video systems are compressing the cost and time of finished creative, making volume and localization feasible where they were once uneconomic (Hartmann, Exner, and Domdey, 2025). In personalization, the same tools make it possible to generate near-infinite variants tuned to context, audience, and moment, reviving long-standing ambitions for dynamic, one-to-one creative while raising fresh questions about distinctiveness and brand coherence (Bakpayev et al., 2022). Each shift promises scale, but scale at the expense of differentiation may be a poor trade for brands that compete on standing out.

The people and organizations that make advertising are being remade alongside the work. Generative AI is reshaping which skills are scarce, which tasks are automated, and how value is captured across the agency and client relationship (Cui, Liu, and Yuan, 2025; Demsar et al., 2025; Yang et al., 2026). It raises hard questions about the future of creative talent: which roles disappear, which are augmented, and which new ones, such as prompt strategist, AI creative director, or model curator, emerge. It may also unsettle agency business models built on billable hours and production fees as the marginal cost of production approaches zero. How agencies, in-house teams, and platforms reorganize creative labor, and how they preserve the human judgment clients still pay for, is likely to shape the structure of the industry in the coming years (Kietzmann, Paschen, and Treen, 2018; Peres et al., 2023).

Building on a fast-growing body of scholarship (Baek, 2023; Lowe, Laffey, and Luo, 2025; Rodgers, 2021; Vakratsas and Wang, 2021), this special issue invites theoretically rigorous and managerially useful research on how AI is changing advertising creativity. Consistent with the mission of the Journal of Advertising Research and its strong practitioner readership, submissions should make a clear contribution to advertising theory while offering actionable guidance for the creatives, agencies, brands, and platforms living through this transition. We are especially interested in work that moves past the observation that AI is disruptive to specify how, where, why, and for whom it improves or degrades creative outcomes.

We welcome proposals that address emerging, underexplored, or theoretically novel questions related to creativity and generative AI. These include – but are not limited to – the following areas:

Ideation, Concepting, and Human-AI Co-Creation

• How does AI change the way creative ideas are generated, selected, and refined, and where in that process is human judgment most valuable?
• When does AI assistance widen the range of creative directions a team explores, and when does it narrow it?
• How should briefs, brainstorming, and creative workflows be redesigned around generative tools?
• What is the most effective division of labor between human creatives and AI across ideation, drafting, and refinement?

Asset Production and Creative Scale

• How does near-zero marginal cost production change what advertising creative gets made, and how much of it?
• What is gained and lost when finished assets are generated rather than crafted?
• How does AI-produced creative compare with human-produced work on effectiveness, quality, and cost (for example, click-through, attention, and brand lift)?
• How does production at scale change media planning, creative testing, and iteration?

Personalization and Variant Generation

• How does generative AI change dynamic creative optimization and one-to-one message tailoring?
• How can brands produce thousands of variants without eroding distinctiveness and brand consistency?
• When does personalized AI creative outperform a single strong idea, and when does it not?
• How do consumers respond to creative that is visibly machine-tailored to them?

The Future of Agencies and Creative Talent

• How are agencies, in-house teams, and platforms reorganizing creative labor around AI?
• Which creative roles and skills are being automated, augmented, or newly created?
• How does AI reshape the agency and client relationship, the pitch process, and value capture?
• What happens to agency business models when the cost of production approaches zero?

What Counts as Good Creative Work

• Does AI change how creativity is defined, judged, and rewarded in advertising?
• How should originality, distinctiveness, and craft be valued when execution becomes commoditized?
• Does widespread AI use homogenize advertising creative, and how can brands resist sameness?
• How should creative awards, evaluation standards, and quality benchmarks adapt?

Measurement, Methods, and Effectiveness

• What methods (field experiments, computational and multimodal analysis, large-scale A/B testing) best capture the effect of AI on creative outcomes?
• How can creativity itself be measured at scale across large volumes of AI-generated work?
• How can researchers study homogenization, distinctiveness, and the diversity of creative output?
• When does AI-generated creative help or hurt brand building and long-term equity, and what guardrails keep it on-brand and effective?

References

Amabile, T. M. (1983). The social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology, 45(2), 357–376.

Baek, T. H. (2023). Digital advertising in the age of generative AI. Journal of Current Issues & Research in Advertising, 44(3), 249–251.

Bakpayev, M., Baek, T. H., van Esch, P., & Yoon, S. (2022). Programmatic creative: AI can think but it cannot feel. Australasian Marketing Journal, 30(1), 90–95.

Cui, W., Liu, M. J., & Yuan, R. (2025). Exploring the integration of generative AI in advertising agencies: A co-creative process model for human-AI collaboration. Journal of Advertising Research, 65(2), 167–189.

Demsar, V., Ferraro, C., Sands, S., & Kohn, A. (2025). Harmony or discord? The intersection of generative AI and human creativity in advertising. Journal of Advertising Research, 65(2), 150–166.

Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290.

Hartmann, J., Exner, Y., & Domdey, S. (2025). The power of generative marketing: Can generative AI create superhuman visual marketing content? International Journal of Research in Marketing, 42(1), 13–31.

Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263–267.

Koslow, S., Sasser, S. L., & Riordan, E. A. (2003). What is creative to whom and why? Perceptions in advertising agencies. Journal of Advertising Research, 43(1), 96–110.

Lowe, B., Laffey, D., & Luo, Y. (2025). Advertising and generative AI: How can advertisers leverage new AI tools? Introducing a special issue on generative AI in advertising. Journal of Advertising Research, 65(2), 146–149.

Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269–275.

Reinartz, W., & Saffert, P. (2013). Creativity in advertising: When it works and when it doesn't. Harvard Business Review, 91(6), 106–111.

Rodgers, S. (2021). Themed issue introduction: Promises and perils of artificial intelligence and advertising. Journal of Advertising, 50(1), 1–10.

Rosengren, S., Eisend, M., Koslow, S., & Dahlén, M. (2020). A meta-analysis of when and how advertising creativity works. Journal of Marketing, 84(6), 39–56.

Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96.

Smith, R. E., & Yang, X. (2004). Toward a general theory of creativity in advertising: Examining the role of divergence. Marketing Theory, 4(1–2), 31–58.

Smith, R. E., Chen, J., & Yang, X. (2008). The impact of advertising creativity on the hierarchy of effects. Journal of Advertising, 37(4), 47–61.

Vakratsas, D., & Wang, X. (2021). Artificial intelligence in advertising creativity. Journal of Advertising, 50(1), 39–51.

Yang, J., Dong, C., Chu, S.-C., & Rheu, M. (2026). Transforming advertising in the age of generative AI: Exploring advertising professionals’ perceptions of human-AI value co-creation. International Journal of Advertising, 45(1), 139–170.