Dive into success stories and discover how AI can solve shopper pain points like information overload and excessive returns. Understand challenges like data chaos and fear of AI can derail your journey. Learn how to overcome these hurdles and unlock the true potential of AI for your retail business.
Imagine a world where customers find the perfect outfit instantly, receive hyper-personalized recommendations while in complete control of their data, and experience frictionless shopping.
This might be a future reality powered by artificial intelligence (AI) in retail. While chatbots are the most commonly utilized AI tool in retail today, the impact of AI can reach far beyond FAQs and allowing customer service to be available 24/7.
In this article, we’ll demystify the role of AI in retail by providing an overview of some of its uses, and explore success stories through case studies. We’ll also discuss some of the challenges that occur when retailers approach the use of AI.
While traditional AI held promise, its limitations often hindered its practical application in retail. Complex models lacked transparency and made it difficult to trust the AI's output, limiting its application in situations requiring explainability. It also relied heavily on specific data, couldn't adapt to different tasks, and required expensive hardware, making them inaccessible to the masses.
Enter Generative AI (GenAI), exemplified by ChatGPT. When OpenAI launched ChatGPT in November 2022, its user-friendly interfaces and pre-trained models eliminated the need for coding expertise. Cloud-based solutions and lower resource requirements further unlock its potential for widespread use.
By overcoming the hurdles of traditional AI, GenAI opens exciting doors for innovation in the retail landscape and has had a significant impact on the retail industry. Studies show that 40% of retailers and brands are actively exploring GenAI's potential, experimenting with use cases and identifying how it can transform their operations. Moreover, 21% are already taking the leap and investing in its implementation.
But what exactly are retailers doing with AI today? Next, we will delve into the exciting ways this technology is reshaping the retail landscape.
AI-powered chatbots have become a relatively universal feature of digital, retail customer service. They are now used to handle returns, offer basic support, and have even automated supplier procurement negotiations. But the use of AI in retail goes beyond chatbots and is impacting all aspects of a retailer’s business.
I spoke with Lauren Vriens, AI Product Strategist for Fortune 500 companies at Accenture, who advises them on how, when, and why to use AI. We talked about the challenges that retailers are looking to solve, and what challenges arise when implementing solutions with AI.Product Discovery and Matching Preferences
When it comes to the overall customer experience, and what challenge the retailer is looking to solve, Lauren says that “what’s at a retailer’s core motivation is essentially a matching game. They're trying to match their products with someone's preferences, and also trying to be a discovery engine. So they're hoping that they're helping you discover things that you maybe don't know that you want.”
Using the example of browsing for clothes, Lauren says that the shopper “has to think about what goes with this shirt, what shoes, what jewelry, and that's a lot of cognitive load. Especially when the internet is just filled with choice”. Traditional approaches to solve the matching problem include personalized recommendations based on past purchases, browsing behavior etc., and search filters and categorization, such as refined searches based on brand, price, and color.
However, in their current state, these approaches often fall short of meeting customer needs. For example, with personalization, 68% of European shoppers think there should be a limit to the amount of personalization in online stores. Similarly, 63% say that they fear that companies misuse the personal information they collect for personalization.
According to Lauren, “solving the challenge of matching is right in the wheelhouse of AI, because it's vast amounts of data and matching them together. This Is something that AI is much better at than traditional humans or traditional coding.” Furthermore, AI can improve the product discovery process because it can “use signals from a person about what they might like”. For example, AI could be able to understand that a shopper hates the color green and avoid showing this person green clothes. “If the customer keeps getting surfaced green items, that's not going to be a good experience”.
One way retailers are currently approaching an AI solution to this problem is with AI-powered virtual assistants as a tool to enhance the customer experience. These assistants can understand preferences, offer intelligent, personalized product recommendations and shopping journeys, tailor-made promotions and more.
Sephora, for example, uses augmented reality to create a Virtual Artist. With their latest app update, they use facial recognition software to allow users to test lip products and create a seamless shopping experience by allowing them to purchase directly from the app.
Information overload and excessive returns
According to iPaper’s 2023 European Shopper Survey, we know that only 13% of shoppers do NOT feel overwhelmed while shopping on their preferred channels. In addition, a 2022 study found a connection between information overload and an influence on online returns. This, according to Lauren, presents “an opportunity for AI to solve this matching problem and mitigate excess returns, which is hugely unsustainable for both the environment but for retailers as well”.
Farfetch, a luxury digital marketplace, has attempted to combat this problem by integrating AI into their operations to enhance consumer matching. According to their CEO, Jose Neves, by leveraging AI for personalized recommendations, they have improved customer satisfaction, and increased the number of completed transactions.
These case studies illustrate how AI permeates various aspects of retail. The key takeaway is that AI is no longer just a futuristic concept; it's a practical tool delivering tangible results today. However, we are still a ways away from the world imagined in the introduction. Challenges ranging from unorganized data, to knowing when and where to use AI, are just some of the difficulties retailers face when implementing AI.
Data challenges:
Retail's digital transformation hinges on unlocking the true potential of its data. Lauren says that “there are systems out there that will ingest your data or connect into your data lake, or your separate data sources, and help to build a data catalog or a data glossary”. AI can then act as a data detective, uniting scattered information and translating it into clear insights through natural language.
However, retailers often lack organized and clean data, hindering their ability to leverage AI effectively. Lauren says “these companies have so much data, legacy data, data that doesn't make sense anymore, and none of it is categorized or cataloged. Sorting all their data could take at least a couple of years”.
Fear of AI:
Generally, Lauren says that larger companies are hesitant to dive head first into the widespread use of AI due to security concerns, including“data leaks, data privacy and losing control of their IP.” “Bigger companies”, she continues, “are more restrictive and don’t allow their employees to play with AI.” This causes them to move slowly with the implementation and exploration of AI.
Not understanding the problem:
Before implementing AI, retailers need to clearly understand the problem they are trying to solve. “GenAI took the world by storm” and so, many think that it’s the key to overcoming every obstacle.. “Sometimes GenAI is not the right tool,” Lauren claims, ”sometimes the right tool is just automation, or it's just traditional AI that doesn't require creative generation.”
Cultural and organizational challenges:
Change management and communication are essential for successful AI implementation. Retailers need to bring their employees along on the journey and help them understand how AI can benefit them. Lauren says that “training your employees on ‘What is AI? How can it help you? How can you accelerate what you're doing with it?’ Will be hugely beneficial for the companies that embrace that and invest in it.”
Lauren states “the biggest risk to a digital transformation is that there's no clear communication.” The biggest hurdle for proper widespread adoption of AI isn't the tech itself, it's humans. Neglecting employees' concerns and poor communication to them during automation breeds resistance: they fear losing their jobs and identity. Therefore, it is important to bring them on the journey: upskill them, expand their roles, show them their value in the new landscape. AI is a tool that should augment, rather than replace the human factor, for a truly successful, collaborative digital transformation.
Invest in data governance: Retailers need to invest in data governance to ensure that their data is clean, organized, and accessible.
Educate employees: Educate employees about AI and how it can benefit them. This will help to reduce fear and resistance to AI adoption.
Start small: start with a small AI project to build competency and confidence before taking on more ambitious projects.
Focus on the problem: Choose the right AI tool for the specific problem you are trying to solve.
Communicate effectively: Communicate clearly with employees about how AI will be used and how it will impact their jobs.
Remember, successful AI implementation requires a strategic and thoughtful approach. By addressing these barriers and following these practical steps, retailers can unlock the transformative power of AI and gain a competitive edge in the evolving retail landscape.
AI in retail is no longer a distant dream; it's a reality shaping the future of the industry. When embraced correctly, you can unlock a world of possibilities, from personalized customer experiences to data-driven insights that fuel growth. We should see AI as a journey, rather than a destination. Start small, experiment, and continuously learn to reap the full benefits of this transformative technology.
