The hidden risks behind AI-generated product descriptions

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Table of Contents

• Introduction
• Evolution and initial appeal of generative AI
• Disadvantages of using generative AI for product descriptions
  • Rigidity in adapting to current trends or changes
  • Absence of personal touch and creativity
  • Contextual understanding constraints
  • Inconsistent brand messaging
• How to overcome these challenges?
  • Use a collaborative approach of AI and humans
  • Customize AI as per your brand tone
  • Conduct deep research before feeding any information to the AI
• Conclusion

The usage of generative AI for product description writing has become increasingly prevalent. It is estimated that by 2030, the generative AI market will reach $76.8 billion, indicating the enduring significance of this technology. Amazon also announced a new “Generate Listing Content” feature in late 2023, so sellers could automatically generate product descriptions. 

This AI-driven approach to product description generation offers efficiency and scalability, enabling sellers to create listing content quickly and easily. But despite its benefits, using generative AI for product description writing can backfire on eCommerce businesses in multiple ways. 

Evolution and initial appeal of generative AI

Generative AI has rapidly evolved from a niche technology to a powerful one with broad applications. Initially, it intrigued users with its ability to automate tasks and create human-like content, such as text and images. Advancements in deep learning, like generative adversarial networks (GANs) and recurrent neural networks (RNNs), propelled its growth. Businesses embraced generative AI to streamline operations, reduce costs, and enhance customer engagement in marketing and eCommerce.

In creative fields, from art to literature, generative AI sparked innovation by collaborating with artists to generate new ideas and compositions. Initially, the appeal of AI lay in its efficiency, scalability, and creative potential, promising to revolutionize various industries. However, as its usage increased on a variety of products, the users started witnessing a few red flags. Of those, we will be discussing the negatives of using generative AI for product description writing. 

Disadvantages of Using Generative AI for Product Descriptions

Rigidity in adapting to current trends or changes

Generative AI struggle

s to adapt swiftly to evolving trends or market dynamics. This limitation can result in product descriptions that quickly become outdated, impacting sales and customer engagement. Unlike humans, AI models may lack the ability to interpret subtle shifts in consumer preferences, hindering businesses from effectively showcasing their products in a timely and relevant manner. Guidelines and trends on eCommerce websites keep on changing, but the lack of adoption of AI in the current state can create a difference. For example, ChatGPT was last updated in January 2022, which means all the content generated using it will not be up to the current guidelines of eCommerce websites like Amazon. 

Absence of personal touch and creativity

One significant drawback of using generative AI for product descriptions is the absence of personal touch and creativity. AI-generated product descriptions often lack the human touch required to resonate emotionally with customers or effectively communicate the unique features and benefits of a product. AI may fail to cater to the information that can help in establishing a connection between potential customers and the brand, which can further foster brand loyalty. Along with that, generative AI faces creativity constraints as its response is determined by its training and the prompt or context provided during content generation. 

For instance, when prompted with “give a product description of a pen,” AI may provide generic features. In contrast, an experienced writer can add a personal touch, explaining why customers should consider the product, why it is different from similar ones present in the market, or establish an emotional connection, a feature often lacking in AI-generated content. This grants human writers a competitive advantage.

Contextual understanding constraints

Generative AI may encounter challenges in understanding the nuanced context surrounding products, leading to inaccuracies or misinterpretations in product descriptions. AI models may struggle to comprehend factors such as cultural references, colloquial language, or product-specific terminology, resulting in descriptions that are irrelevant or misleading to consumers. This limitation can undermine the effectiveness of product descriptions in conveying the value of a product and may erode consumer trust in the brand.

Inconsistent Brand Messaging

Maintaining consistent brand messaging across product descriptions can be challenging when relying solely on generative AI. These AI models may not fully grasp the brand’s tone, voice, or messaging guidelines, leading to inconsistencies in how products are presented to consumers. This inconsistency can dilute the brand identity and weaken brand perception, making it difficult for businesses to establish a cohesive and memorable brand presence in the marketplace.

How to overcome these challenges?

Now that you have information about the possible hurdles of using generative AI in product description writing, here are some solutions to improve and overcome them:

• Use a collaborative or human-in-the-loop approach

Combining the strengths of AI with experienced product description writers is key to overcoming the challenges associated with generative AI. By involving human editors in the content creation process, businesses can add a personal touch and ensure that descriptions align with what the customers are looking for or showcase the product as a solution to their problem. This collaborative approach allows for the refinement of AI-generated content, resulting in more engaging and effective product descriptions. 

• Customize AI as per your brand tone

Customizing AI models to reflect your brand’s tone, voice, and messaging guidelines is essential for maintaining consistency in product descriptions. By fine-tuning AI algorithms to match your brand’s unique identity, businesses can ensure that generated content resonates with their target audience and reinforces brand perception. This customization process may involve training AI models on specific datasets or adjusting parameters to prioritize certain stylistic elements. 

• Conduct deep research before feeding any information to the AI model

Before feeding any information to AI for content generation, it is crucial to conduct thorough research to understand your target audience, product offerings, and brand positioning. By gaining insights into consumer preferences, market trends, and competitive analysis, businesses can provide AI models with relevant and accurate data to generate compelling product descriptions. You can also provide the tool with recent trends or feedback from customers to avoid generic content generation. This proactive approach helps to mitigate contextual understanding constraints and ensures that AI-generated content aligns with business objectives and customer expectations.

Summing Up

Many companies are increasing their reliance on AI in the content generation process, but there are always some limitations attached to the technologies we use. It is better to be aware of these drawbacks and strategize accordingly. To overcome the challenges, you will need help from professionals. Analyze your requirements and look for the resources that fit your budget. If hiring an eCommerce product description writer for your internal team seems to be out of budget, then you can reach out to eCommerce product description writing services providers that are within your budget. 

In the end, it’s all about how you use generative AI with supervision to engage your target audience by providing the information they want, thus guiding them toward a purchase.