Sep 25, 2023
4 min to read
Explore the rapid ascent of artificial intelligence in the retail landscape, and the potential risks and rewards associated with implementing this technology. From real-life success stories in data enhancement and personalized marketing to potential pitfalls such as bias and privacy concerns, this article offers valuable insights, as well as a downloadable asset!
Printed books took more than 50 years to become widely available across Europe. The concept of the Internet dates back to the 1960s, yet it wasn’t until the 20th century that it permeated every-day life.
In contrast, ChatGPT reached 100 million users in just two months, with over 13 million unique users every single day.
Artificial intelligence came on the scene at unprecedented speed, and it can be difficult to comprehend how such widespread adoption of such an advanced technology impacts how we live and work, especially as you’re navigating through the AI hype-beasts and doomsday predictors.
With that in mind, let’s dive into the good, the bad, and the ugly when it comes to real-life AI use cases to give you a better understanding of AI applications.
AI algorithms can standardize product data by enforcing consistent formats, categories, and naming conventions. It can be trained to identify errors, duplicates, inconsistencies, or missing information within hundreds of thousands of product listings across any number of channels. This ensures that all product information is structured uniformly, making it easier for customers to navigate and compare items on your eCommerce or retail site.
Machine learning algorithms can ingest vast amounts of customer data to produce data-driven insights into customer preferences and behavior. For example, consumer brands can use AI to analyze customer purchase histories and browsing patterns to offer highly personalized product recommendations and marketing content based on what similar customers have purchased in the past.
While AI is no magic wand, it can act as a key that unlocks doors into global markets and channels by providing the opportunity to create tailored, localized content at scale. AI-generated translations still require human oversight; it’s not just a “copy and paste” situation. But this technology democratizes the ability to translate titles, descriptions, shipping sizes or timelines, and units of measure, all while considering cultural nuances or local regulations.
In the retail industry, chatbots and virtual assistants powered by AI can provide round-the-clock customer service to provide support regardless of time zone or language. While a robot often can’t, and shouldn’t, be used to solve complex problems or troubleshoot intricate issues, this technology can be used to triage common technical questions or at least intelligently route particular questions to the right humans for answer.
Utilizing AI-driven demand forecasting enables retailers to fine-tune their inventory management. This involves training AI algorithms using past purchase data and market trends, accounting for variables such as increased holiday demand or sudden drops in sales. By doing so, retailers can make precise predictions for order quantities, effectively mitigating costly overstock or understock situations.
AI is exciting, but it’s dangerous to view this technology through rose-colored glasses. Let’s take a look at some of the risks that AI can pose to an organization (and no, it’s not robot world domination).
A strong brand identity creates an emotional connection with customers. It goes beyond mere product features and taps into the values and personality of the brand. But as AI continues to grow in popularity, with a third of all desk workers saying they utilize large language models daily at their job, we run the risk of creating a sea of similar, template-based, algorithm-generated content. Maintaining a distinctive brand identity and voice becomes an even more crucial factor as content generation becomes more automated.
Machine learning models learn from large datasets. If these training datasets contain biases or inaccuracies, the model can learn and perpetuate those biases. In the context of global brands and retailers, this can result in inaccurate, discriminatory, or unfair outcomes in various aspects of operations, from product recommendations to legal compliance and more. For example, if the data used for training AI models is based on a customer base that skews heavily urban, you may accidentally draw insights about shopping behaviors in rural or suburban communities that aren’t accurate.
Collecting and analyzing customer data raises privacy and security concerns. Brands must implement robust cybersecurity measures to protect sensitive customer information from data breaches or cyberattacks, and be transparent with customers about the types of data they collect, why it’s collected, and how it will be used.
There’s no denying that AI is a complex technology, and implementing an AI solution into your tech stack is no small feat. You need to ensure that you have the right folks on your team to prepare your internal teams for adoption, and you need to ensure that you have the right technology in place to integrate smoothly with the new solution and seamlessly communicate product information (not to mention, you need an organized single source of truth for product information).
Some customers may perceive AI-driven interactions as impersonal and devoid of the human touch, or they can sometimes feel like they’re losing control over their interactions with others. If customers perceive that a brand prioritizes automation over genuine customer interactions, they may be less likely to remain loyal and more inclined to switch to competitors who offer more personalized and human-centric services. It’s important to acknowledge this hesitation and alway consider the customer’s perspective when implementing new technology, ensuring that your technology is enhancing your team’s work and not overpowering.
Product data serves as the foundation upon which AI technology is built and trained. Just like a house’s stability relies on a strong foundation, the effectiveness of AI systems hinges on the quality of the data they are trained on. Inaccurate, incomplete, or irrelevant data can lead to unreliable AI outcomes.
If you’re interested in learning more about how to harness product information for impactful AI results, check out our AI Center of Excellence for resources, real-life case studies, and more. Or, download our “AI: Risks & Opportunities” one-pager for a summary of what we’ve covered today.
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