IN THIS ISSUE

• Why AI-Generated Models Might Be Hurting Your Brand's Image

• Crafting Unique Content in a Saturated Market

• AI Content Creation and Copyright Law: Essential Insights for Marketers

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Why AI-Generated Models Might Be Hurting Your Brand's Image

When brands use artificial techniques, like AI, to create models that represent different types of people, it can make those people feel bad about the brand.

Recent research sheds light on a critical issue: how do underrepresented consumers perceive and respond to brands using AI-generated models to represent them?

Why This Matters

Brands increasingly leverage AI to create digital models that reflect diverse body types, ages, sizes, and skin tones. This approach aims to enhance inclusivity and resonate with a broader audience.

Yet, studies reveal a significant gap in understanding how these efforts are perceived by the very communities they intend to represent.

The research highlights that the use of AI-generated models can inadvertently trigger negative responses among underrepresented groups.

Specifically, consumers within LGBTQIA+ communities and those with disabilities perceive AI-generated models as less authentic compared to human models. This perception leads to feelings of social identity threat and a diminished sense of belonging to the brand.

Key Takeaways

1. Negative Brand Attitudes: Studies consistently show that the intention to use AI-generated models negatively impacts underrepresented consumers' brand attitudes. This reaction stems from the perceived artificiality of AI-generated representations, which fails to align with the authenticity and inclusivity these groups seek.

2. Serial Mediation of Effects: First, consumers experience a heightened social identity threat, and second, they experience a decreased sense of belonging to the brand. These factors contribute to a cascade of negative perceptions and attitudes towards brands employing AI-generated models.

3. Impact of Firm Motivations: Consumer perceptions are significantly influenced by the perceived motivations behind a brand's use of AI-generated models. When brands are perceived as intrinsically motivated—genuinely committed to diversity and inclusion—the negative effects on brand attitude are mitigated. In contrast, extrinsic motivations such as cost-saving measures or superficial diversity initiatives exacerbate negative perceptions.

While AI offers scalability and efficiency in creating diverse visual content, it must be complemented by genuine efforts towards inclusivity and authenticity. Brands that align AI use with intrinsic motivations for diversity representation are better positioned to enhance consumer trust, loyalty, and positive brand associations among underrepresented groups.

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Crafting Unique Content in a Saturated Market

"Generative AI makes it very easy to share fairly well-written, fairly accurate information, on a staggering array of topics. Humans will never beat AI at this game, and frankly, we shouldn’t try."

AI can produce high-volume content at speeds that humans cannot replicate. But, then, most of it is a "sea of sameness".

Why This Matters

This ease of creation comes at a cost: it diminishes the uniqueness and value of such content.

As AI evolves, the competitive edge no longer lies in regurgitating common knowledge but in creating new, insightful information that AI cannot replicate.

Key Takeaways

1. Experimentation: Creating Proprietary Data

By conducting original research, running unique tests, or gathering proprietary data, content creators can produce insights that are exclusive and unavailable elsewhere. This not only adds value but also establishes authority and attracts audiences seeking novel information.

For instance, in-depth industry surveys or detailed product performance analyses offer genuine insights that resonate with readers seeking authoritative content.

2. Experience: Authenticity and Authority

While AI can simulate expertise, it lacks the authenticity and firsthand experience that human creators bring to the table. Readers increasingly value content from sources with real-world experience and practical insights.

Whether it's reviewing products, sharing personal anecdotes, or detailing niche expertise, content enriched with genuine experience builds credibility and trust.

This differentiation becomes crucial as consumers become more discerning about the sources they trust amidst a deluge of automated content.

3. Effort: Going Beyond the Ordinary

There is a flood of AI-generated content. Effort stands out as a differentiator.

Brands and creators willing to invest in high-quality content experiences—such as interactive tools, multimedia presentations, or comprehensive guides will build a deeper connection with their audience.

These efforts not only demonstrate commitment to excellence but also create memorable, engaging content that resonates long after consumption.

By focusing on creating new information, sharing authentic experiences, and investing in substantial content efforts, brands and creators can carve out a niche that AI cannot replicate.

In essence, while AI excels at efficiency and scale, its limitations lie in innovation and creativity—areas where human creators thrive. By embracing these unique strengths, content creators can not only survive but thrive in an era dominated by technological advancements.

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AI Content Creation and Copyright Law: Essential Insights for Marketers

The integration of AI into content creation has revolutionized marketing, enabling the rapid generation of diverse content forms like social media posts, blogs, and videos. However, this technological advancement raises critical legal questions that marketers must address to mitigate risks effectively.

Why This Matters

At the core of generative AI tools like ChatGPT and Midjourney lies Large Language Models (LLMs), empowered by transformer and diffusion models. These AI systems interpret human language and generate responses based on extensive datasets, including publicly available internet data. However, their outputs can range from factual to speculative ("hallucinated"), depending on the input and training data.

Using AI-written content raises 3 key legal questions:

1. Data Usage by AI Platforms

2. Ownership of AI-Generated Content

3. Liability for Inaccuracies or Plagiarism

Marketers, you must be mindful of:

• Copyright Ownership: Marketers must be vigilant in assessing ownership rights when using AI to create content based on existing works or ideas. AI-generated outputs that incorporate copyrighted material from external sources can complicate ownership claims and potentially lead to legal disputes.

• Liability Management: Proactively managing liability involves ensuring AI-generated content complies with copyright laws and is free from inadvertent plagiarism or inaccuracies. Implementing stringent validation processes and utilizing plagiarism detection tools are essential safeguards.

• Compliance with Emerging Laws: As AI laws evolve globally, marketers must stay informed and adapt practices accordingly. Recent legislative developments, such as disclosure mandates in Utah, underscore the need for transparency regarding AI involvement in content creation.

Beyond legal compliance, ethical considerations are crucial. Marketers should ensure AI tools are used responsibly to avoid perpetuating biases or compromising user privacy. Upholding ethical standards strengthens consumer trust and enhances brand credibility.

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