Beyond the Brush

Introduction

The rapid development of Artificial Intelligence (AI) has imposed changes across various industries, and the creative sector is no exception. The integration of Generative AI (GenAI) tools in creative workflows has opened up new avenues for artistic expression and efficiency. The expanding role of it prompted a research agenda to explore its impact on the creative workforce. Weglarz et al (2025) conducted various initiatives to study the adoption of Generative AI within creative industries. The research has been disseminated across multiple formats and academic forums.

Identifying New Forms of Creative Expression using Artificial Intelligence

The creative industry, encompassing fields such as design, advertising, music, film, and visual arts, is undergoing significant transformation driven by the Artificial Intelligence revolution. Those advancements have unlocked unprecedented levels of efficiency and creativity. Generative AI, in particular, has become a powerful tool for creative professionals, enabling them to push the boundaries of their work. The adoption of Generative AI in the creative industry is growing because professionals use it to streamline tasks and enhance their productivity (Sanchez, 2023).

One of the most notable applications of AI in the creative industry is in the realm of visual arts. Complete works of visual art or design can be generated by AI, highlighting it as a standalone creator. For instance, AI-generated art has been showcased in galleries and exhibitions, highlighting the potential of these technologies to redefine artistic expression. World prominent galleries like The Museum of Modern Art- MoMA, The Whitney Museum of American Art, and the National Gallery in London have showcased such art. The Whitney Museum featured a collection of Harold Cohen’s AARON, one of the first artificial intelligence programs for art (The Whitney Museum of American Art, 2024).

Figure 1. Harold Cohen’s AARON Art. (Whitney Museum of American Art, 2024)

The second category of creative outputs enabled by AI is the AI Co-created content, widely used in the advertising industry. Major corporations like Kraft Heinz, Coca-Cola, McDonald’s, Dove, Milka, and NIKE used AI-generated content as a part of their campaigns. For instance, Dove launched “The Code” a short film showing how AI image generators create stereotypical images of women, while releasing "Real Beauty Prompt Playbook," a guide for achieving more inclusive AI-generated images.

Similarly, Milka created a customizable campaign, where they allowed consumers to create personalized rap songs performed by the popular Dutch rapper Snelle, using three advanced AI tools to achieve it. These advertising campaigns serve as high-visibility examples of how AI can redefine creative output and make it customizable. This level of personalization can lead to more effective marketing campaigns and a better return on investment for advertisers.

Generative Artificial Intelligence is rapidly becoming a creative partner rather than just a tool. From algorithmically composed music to AI-generated visual art exhibited at world-renowned galleries, the boundaries of authorship and originality are being stretched. In this shifting landscape, the creative industries spanning design, advertising, music, film, and visual arts are experimenting with new hybrid workflows that integrate both human intuition and machine intelligence.

Our recent research explores how creative professionals are embracing (and resisting) this technological shift. Drawing on interviews, campaign analyses, and empirical models, our study identifies the drivers and barriers that shape the adoption of Generative AI in creative settings.

While AI has proven to enhance efficiency and productivity, human skills such as critical thinking and emotional intelligence remain essential to ensuring high-quality outputs that resonate with audiences. More examples of AI-enabled artistic expressions and more detailed findings were recently presented at the British Academy of Management (BAM) conference through the poster Cre-AI-tivity: Understanding the adoption of Generative AI in the creative industries. This visual summary outlined the key adoption drivers identified through the study and provided early insights into user expectations and barriers.

The findings reveal an emerging typology of creative collaboration with AI ranging from AI-Generated Content, to AI Co-Created Content and AI-Enhanced Content. These categories represent three distinct approaches to creative production with AI:

● AI-Generated Content: Fully autonomous creations by AI systems.

● AI Co-Created Content: Collaborative outputs where both human and AI contribute meaningfully.

● AI-Enhanced Content: Human-led work enriched through AI tools.

These modes of creation resonate with the OECD’s (2025) AI Literacy Framework, which has been informed by and synthesized from various foundational models (aiEDU, 2004; Allen et al., 2023; Druga et al., 2023; Furze, 2004; Miao et al., 2024; Mills, 2024; Vuorikari et al., 2022 ).

The OECD framework is structured around four core competencies:

● Engaging with AI: Reflected in AI-Enhanced workflows, where professionals interact critically with tools to enrich outcomes.

● Creating with AI: Closely linked to Co-Creation practices that blend human and machine input.

● Managing with AI: Evident in workflows where creatives integrate AI into processes while maintaining oversight.

● Designing with AI: Present in advanced uses where creators develop or tailor AI systems for unique artistic outputs.

Together, our typology and the OECD (2025) AI Literacy framework provide a complementary lens to understand the evolving literacy and agency required for responsible, meaningful AI integration in the creative sector. We co-created this image to illustrate the alignment between the OECD’s (2025) AI Literacy Framework and our previous research. This conceptual connection highlights how our work is in line with the European Commission’s broader vision of promoting an inclusive and responsible adoption of technology (OECD, 2025, Weglarz et al., 2025, a).

But adoption is not just about innovation, it’s also about trust, usability, and support. Our research applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model alongside brand equity metrics to understand why some professionals embrace GenAI tools while others hesitate. Surprisingly, brand recognition can actually deter adoption, suggesting a tension between traditional brand identities and emerging technological capabilities.

These insights, presented at international academic forums such as the British Academy of Management and Rii Forum, invite us to rethink what creativity means in an era of algorithms and how educators, designers, and artists can respond with agency and intentionality.

Understanding the UTAUT Model

As the usage of Generative AI in the creative sector increases, the need for understanding its adoption grows, with the UTAUT being a useful model for adoption prediction (Yin et al., 2023, Menon & Shilpa, 2023, Cabrera-Sánchez et al., 2021). The UTAUT model, developed by Venkatesh et al. (2003), is a widely recognized framework for understanding technology adoption. It considers four key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy refers to the perceived benefits and usefulness of the technology, while effort expectancy is the ease of use associated with the technology. Social influence is the degree to which individuals perceive that important others believe they should use the technology, and facilitating conditions refer to the availability of resources and support to use the technology.

Brand Equity Factors

In addition to the UTAUT constructs, the study incorporates consumer-based brand equity factors, specifically brand recognition and brand trust. Brand recognition is the ability of consumers to recall or identify a brand within a product category, while brand trust represents confidence in a brand's ability to deliver on promises and meet expectations. For industries with limited technological knowledge, brand equity can serve as a substitute for direct quality assessment, alleviating risks (Abbasi et al., 2017).

Key Findings

The results revealed several important insights. Performance expectancy was the strongest predictor of behavioural intention to use Generative AI tools. Creative professionals are more likely to adopt AI tools that enhance productivity and streamline workflows. Facilitating conditions, such as the availability of robust support systems and resources, positively impact adoption. Comprehensive training and technical support are crucial for overcoming the learning curve associated with new technologies. Trusted brands alleviate concerns about data reliability, and output quality, fostering sustained use. Interestingly, brand recognition had a negative influence on behavioural intention. This suggests that strong associations with traditional creative tools may create resistance toward adopting new AI solutions. Effort expectancy and social influence did not show significant influence on behavioural intention, which contradicts previous research.

Conclusion

The adoption of Generative AI in the creative industry presents both significant opportunities and strategic challenges. The initiatives undertaken by Weglarz et al. (2025) highlight several key drivers of adoption. Most notably, performance expectancy, facilitating conditions, and brand trust. These findings point to the importance of demonstrating how AI tools can enhance productivity and output quality, which should be central to any marketing or adoption strategy.

Equally important is the provision of robust support systems, including training and technical assistance, to ease the learning curve for creative professionals. Building brand trust through ethical practices, transparent communication, and partnerships can further reduce resistance to adoption. At the same time, the negative effect of brand recognition underscores the need to clearly differentiate AI-powered tools from traditional creative offerings, aligning them with user expectations.

As the creative industries evolve, addressing these factors will be critical to fostering successful AI integration. Doing so will empower professionals to fully harness the creative and operational potential of these technologies — driving innovation, improving efficiency, and expanding the boundaries of artistic expression.

References

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Weglarz, D., Pla-Garcia, C., & Jiménez-Zarco, A. I. (2025, b). Cre-AI-tivity: AI in creative industries must balance automation with human oversight to uphold inclusivity, originality, and cultural sensitivity. http://hdl.handle.net/10609/152457

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Authors

Dominika Weglarz, Phd Candidate at the Universitat Oberta de Catalunya.

Dr. Cintia Pla-García, Senior Lecturer at the Universitat Oberta de Catalunya (UOC) and Assistant Professor at TecnoCampus at Universitat Pompeu Fabra (UPF).

Dr. Ana Jiménez-Zarco, Associate Professor at the Universitat Oberta de Catalunya (UOC), external Professor at Universidad Pontificia de Comillas, and senior researcher at I2TIC-IAlab Research Group.

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