• Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • Core Principles of Responsible AI
    • Accountability
    • Fairness
    • Privacy
    • Safety and Security
    • Sustainability
    • Transparency
  • Special Topics
    • AI in Industry
    • Ethical Implications
    • Human-Centered Design
    • Regulatory Landscape
    • Technical Methods
  • Living Dictionary
  • State of AI Ethics
  • AI Ethics Brief
  • 🇫🇷
Montreal AI Ethics Institute

Montreal AI Ethics Institute

Democratizing AI ethics literacy

Generative AI-Driven Storytelling: A New Era for Marketing

January 14, 2024

🔬 Research Summary by Marko Vidrih, an experienced researcher and QA engineer specializing in applying Generative AI and deep learning frameworks in tech companies.

[Original paper by Marko Vidrih and Shiva Mayahi]


Overview: This paper provides a comprehensive analysis of the transformative capabilities of Generative AI in shaping storytelling within the marketing domain. By focusing on real-world applications from industry giants such as Google, Netflix, and StitchFix, the research sheds light on the potential of AI-driven narratives in enhancing consumer engagement and personalization while simultaneously emphasizing the ethical considerations inherent to its deployment.


Introduction

Imagine a world where advertisements and brand stories are not generic but crafted uniquely for each individual, resonating on a deeply personal level. This is no longer the realm of science fiction but a burgeoning reality, thanks to the rise of Generative AI-driven storytelling in marketing. At its core, this research delves into the transformative capability of Generative AI, which, unlike traditional machine learning, crafts narratives that deeply connect with consumers. The study, enriched with real-world examples from giants like Google and Netflix, sets out to understand how this technology is reshaping marketing strategies, offering personalized consumer experiences, and navigating inherent challenges.

Key Insights

The Rise of Generative AI in Marketing

Historically, marketing relied heavily on raw data, emphasizing numbers and statistics. However, the modern marketing landscape has witnessed a paradigm shift. Over the recent years, venture capital investments in Generative AI technologies have soared significantly, indicating a burgeoning interest in this space. Unlike traditional machine learning models, which focus on data analysis, Generative AI is more about creating. It produces deeply personal narratives that resonate with individual consumers, making the content more engaging and relevant.

Evolution of Generative AI Techniques

Generative AI has witnessed a remarkable evolution from its inception. Initially, the industry used rule-based systems, often termed Traditional AI. These systems were adept at sifting through vast volumes of data to extract patterns. However, with the advent of deep-learning models, Generative AI has metamorphosed. It can now generate human-like content, such as stories or narratives. Models like ChatGPT have been at the forefront of this revolution, paving the way for more advanced applications in marketing.

Impact on Customer Engagement

One of the most profound impacts of Generative AI-driven storytelling is on customer engagement. Tailored narratives, personalized to individual tastes and preferences, have a unique charm. They captivate the audience, ensuring that the message is heard and felt. Brands like Coca-Cola have already begun leveraging this technology, creating marketing campaigns with advertisements and stories that consumers connect with. This heightened level of engagement translates to stronger brand loyalty and advocacy.

Ethical Implications

With great power comes great responsibility. The ability of Generative AI to craft compelling narratives brings forth a slew of ethical concerns. There’s potential for creating and disseminating misleading or false narratives that can manipulate consumer perceptions. Organizations and bodies, including the World Economic Forum, have voiced these concerns. They emphasize the need for stringent measures to ensure that the content generated is not just compelling but also truthful and ethical.

Future Prospects and Applications

The potential applications of Generative AI-driven storytelling are vast and not just limited to the present. As technology evolves, the authors foresee its application in real-time storytelling. Here, narratives could adapt dynamically based on real-time consumer interactions. Additionally, integrating Generative AI with augmented and virtual reality could redefine immersive brand experiences. Social media platforms, with their emphasis on content, could also see a revolution with AI-driven narrative generation.

Between the Lines

The transformative potential of Generative AI-driven storytelling in marketing is undeniable. It offers brands an unparalleled tool to connect with their audience on a deeply personal level. However, the ethical implications cannot be ignored. The potential for misinformation or manipulation is real, and the industry must tread cautiously. As we embrace this technology, we must also establish guidelines and best practices to ensure its ethical use. The future of marketing with Generative AI looks promising, but navigating this journey with responsibility and foresight is important.

Want quick summaries of the latest research & reporting in AI ethics delivered to your inbox? Subscribe to the AI Ethics Brief. We publish bi-weekly.

Primary Sidebar

🔍 SEARCH

Spotlight

AI Policy Corner: The Kenya National AI Strategy

AI Policy Corner: New York City Local Law 144

Canada’s Minister of AI and Digital Innovation is a Historic First. Here’s What We Recommend.

Am I Literate? Redefining Literacy in the Age of Artificial Intelligence

AI Policy Corner: The Texas Responsible AI Governance Act

related posts

  • The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

    The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

  • Going public: the role of public participation approaches in commercial AI labs

    Going public: the role of public participation approaches in commercial AI labs

  • The Robot Made Me Do It: Human–Robot Interaction and Risk-Taking Behavior (Research Summary)

    The Robot Made Me Do It: Human–Robot Interaction and Risk-Taking Behavior (Research Summary)

  • Research summary: AI Mediated Exchange Theory by Xiao Ma and Taylor W. Brown

    Research summary: AI Mediated Exchange Theory by Xiao Ma and Taylor W. Brown

  • International Human Rights, Artificial Intelligence, and the Challenge for the Pondering State: Time...

    International Human Rights, Artificial Intelligence, and the Challenge for the Pondering State: Time...

  • Self-Improving Diffusion Models with Synthetic Data

    Self-Improving Diffusion Models with Synthetic Data

  • Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies

    Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies

  • Moral Dilemmas for Moral Machines

    Moral Dilemmas for Moral Machines

  • Research summary: Machine Learning Fairness - Lessons Learned

    Research summary: Machine Learning Fairness - Lessons Learned

  • Should you make your decisions on a WhIM? Data-driven decision-making using a What-If Machine for Ev...

    Should you make your decisions on a WhIM? Data-driven decision-making using a What-If Machine for Ev...

Partners

  •  
    U.S. Artificial Intelligence Safety Institute Consortium (AISIC) at NIST

  • Partnership on AI

  • The LF AI & Data Foundation

  • The AI Alliance

Footer

Categories


• Blog
• Research Summaries
• Columns
• Core Principles of Responsible AI
• Special Topics

Signature Content


• The State Of AI Ethics

• The Living Dictionary

• The AI Ethics Brief

Learn More


• About

• Open Access Policy

• Contributions Policy

• Editorial Stance on AI Tools

• Press

• Donate

• Contact

The AI Ethics Brief (bi-weekly newsletter)

About Us


Founded in 2018, the Montreal AI Ethics Institute (MAIEI) is an international non-profit organization equipping citizens concerned about artificial intelligence and its impact on society to take action.


Archive

  • © MONTREAL AI ETHICS INSTITUTE. All rights reserved 2024.
  • This work is licensed under a Creative Commons Attribution 4.0 International License.
  • Learn more about our open access policy here.
  • Creative Commons License

    Save hours of work and stay on top of Responsible AI research and reporting with our bi-weekly email newsletter.