As AI, generative AI and predictive AI continue to grab headlines, fashion retailers and brands are eyeing the technology to give them a competitive edge.
But what do retail executives and brands need to consider before making an investment in these technologies — especially the new kid on the block, generative AI? In the first of a series of stories on the topic, WWD looks to answer that question.
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The Power of AI
Jill Standish, Accenture’s global retail lead, said what sets generative AI apart is that it is the first digital technology to be truly human by design. “It has the ability to provide very relatable and relevant experiences, gives retailers new opportunities to get closer to consumers through ‘conversational commerce,’ a technique reflecting the fact that people are hardwired for more natural, conversational, human-to-human interactions, especially when it comes to inspiration and product discovery,” Standish said, adding that a recent survey from her firm showed that 76 percent of executives “see generative AI as more of an opportunity than a threat and more beneficial to revenue growth than costs reduction.”
Shoppers are also on board. “Consumers are waking up to the possibilities, too,” Standish said. “A recent Accenture survey found that two-thirds of consumers are already using, or would consider using, an AI shopping assistant that recommends other products that pair well with their purchase. A similar number say they are already using or would consider using an AI shopping assistant that recommends products within their budget.”
Alyx Kaczuwka, director of AI product development at XY Retail, a unified commerce platform that works with global luxury brands such as Armani, Fender, Damiani, Isaia and Slowear, said AI has the potential “to revolutionize the retail industry, including store layouts and customer experience.”
Kaczuwka said that by providing insights, speed and flexibility, “AI can help retailers create more intuitive, efficient, and customer-centric stores, using intricate consumer insights, rapid prototyping, and fast modifications.”
Sounds like a dream come true. However, executives need to do a top-to-bottom assessment before writing a check. Shannon Wu-Lebron, corporate vice president of retail industry strategy at Blue Yonder, has some pointers.
“Over the past year, there has been a push for accelerated AI adoption in retail, almost to a fault,” Wu-Lebron told WWD. “For instance, a recent Blue Yonder survey of retail executives found that many leaders claim to be very knowledgeable about AI, yet lack understanding about the different types of AI, or ways to utilize it. This suggests retail leaders are overconfident and may be missing crucial opportunities or deploying AI without a clear set of business objectives.”
Wu-Lebron said retail executives must consider generative AI solutions “based on the maturity and specific needs of their business units, whether it be for customer experiences, assortment planning, inventory placement, or pricing, etc. In addition, retailers should assess their organizational data management readiness. After all, AI won’t yield good results without good data.”
And lastly, Wu-Lebron said not to underestimate the importance of change management with existing employees and business processes when introduced. “In summary, retailers who can identify specific use cases linked to clear business benefits, implementing AI with good data management foundation and successfully navigating the change process will come out ahead in AI-enabled retail transformation.”
What about the size of the business? Justin Honaman, head of worldwide retail and CPG at Amazon Web Services, told WWD that generative AI (GenAI) “will transform the retail experience, and the cloud is going to unlock it by providing tools for retailers of every size.”
“From enhanced productivity and smoother in-store operations to unprecedented levels of personalization and revitalized shopping experiences, GenAI can offer unparalleled benefits for retailers and consumers alike,” Honaman said.
When determining how to use generative AI, Honaman said retailers need to consider two main things: “What are the unique business objectives and customer experiences that GenAI can help them deliver, and the risk factors involved in emerging technologies, like tarnishing the trust they’ve built with their customers by not deploying GenAI in a secure and responsible manner?”
Honaman said retailers and brands need solutions that are relevant to their business objectives. “We’ve all seen the chatbots and early experiments with GenAI, but retailers should be careful not to adopt technology that doesn’t make sense for their business or reflect what their customers truly want.” He said a direct-to-consumer e-commerce brand will likely have different objectives and uses of GenAI than physical stores, “as would a boutique retailer compared to big-box retailers. One company may struggle with efficiency while another may need assistance with design or customer service.
“For example, Adidas uses Amazon Bedrock to enable developers to get quick answers on everything from ‘getting started’ info to deeper technical questions, while The Very Group uses it to deliver interactive and personalized digital shopping experiences to millions of customers,” Honaman said, adding that there are many more use cases of generative AI in retail, “and we’re just beginning to scratch the surface so retailers must consider what’s going to bring them the most value before diving in head first.”
Orders From Above?
Aside from pressures in the market, retail leaders face internal pressure to jump on the AI bandwagon. Ahmed Naiem, president and chief revenue officer at Lily AI, said that many companies “have seen AI become a board mandate, something that is now required in a company’s tech stack.”
“However, business leaders know that they need to substantiate the ROI with legitimate, quantifiable proof points,” Naiem said. “It’s important that retail companies evaluate how quickly these investments can make an impact to their bottom line and how easily they can implement the AI technology into their current business model. The investment needs to be valuable for both their company and the end-user.”
Perhaps the primary issue when considering deploying generative AI is how it can enhance the customer experience (CX). Jadah Hawkins, senior vice president and global market leader for retail and e-commerce at Alorica, said when investing in and implanting generative AI, “retailers must take a multifaceted approach that is centered around the customer experience.”
Hawkins said it is imperative to prioritize CX. “Improving the customer experience needs to be the top priority when implementing generative AI into any business plan,” Hawkins said. “A bad CX strategy can lead to frustration, loss of customers and a lackluster brand reputation. In retail, focusing on CX is especially important to remain relevant as consumer expectations evolve.”
Hawkins also suggests identifying customer pain points and preferences before introducing the technology. “Simply using AI to automate repetitive tasks will not be truly valuable for retailers in the long run,” Hawkins said. “Instead, retailers should use AI to improve pre-existing CX issues and to develop a better understanding of customer needs based on historical data and trends. This will enable brands to make data-driven decisions and personalize the customer journey.”
A Brand-new Game
Tim Christensen, senior vice president of product, digital, innovation and AI at Synchrony, said there are other issues to consider. “AI isn’t the only new development in retail — the entire consumer experience has changed,” he said. “People are shopping in new ways, from the search and discovery phase to the check phase. Effective generative AI tools have the potential to be able to parse these new consumer behaviors and enable retail leaders to respond.”
Christensen said despite enormous potential, “there are many issues with generative AI that must be addressed before businesses go all-in, including data privacy, lack of transparency, data reliability, and more. In many effective AI product integrations, consumers may never know they interacted with AI, pointing to the need to ensure the technology is tested internally before it is scaled to consumer-facing products.”
That’s not all. Christensen said for retailers and brands to realize the benefits of how AI may change shopping flows, “businesses must prepare to deploy it in a way that is ethical, responsible and safe. Preparations should include testing in-house, training employees to manage AI, and establishing a company governance structure to maintain consistent standards for AI use.”
Helena Salazar, business development manager at Remote Team Solutions, agreed and said the ethical use of AI is also a significant concern. “The technology should be used responsibly, respecting customer autonomy. Integration with existing systems is another consideration, ensuring that AI complements rather than disrupts current operations.”
“Employee training is essential, as staff need to understand how to work effectively with AI,” Salazar said. “Additionally, considering the long-term scalability of the AI solution is vital; it should be adaptable to future trends and needs. Finally, a cost versus benefit analysis is necessary to ensure that the investment in AI is economically viable and aligns with the business’ strategic goals.”
Michael Rumiantsau, chief executive officer and cofounder of Narrative BI, the data analytics company that works with brands such as Coca-Cola and Pernod Ricard, has three suggestions when considering investing in AI.
First, Rumiantsau said retailers and brands need to understand the technology’s limits, “especially the concept of ‘AI hallucinations’ — when AI can get things wrong or misinterpret data. It’s important for retail leaders to recognize this and have checks in place to ensure AI’s quirks don’t lead to misguided decisions, especially when these insights drive customer interactions and business decisions.”
Secondly, Rumiantsau told WWD that the true value of AI in retail is its “role as a complement to human expertise, not a replacement.”
“AI can crunch numbers and identify trends, but it lacks the nuanced understanding of human decision-makers,” Rumiantsau said. “Human oversight is essential in retail, where complexity and subtlety are the norms. It ensures that AI-supported decisions are practical, realistic and align with the brand’s values.”
Lastly, Rumiantsau said the rapid pace of data change in retail should not be overstated. “AI systems need continuous updates and training to stay relevant. As consumer behaviors and market trends evolve, so must the AI models that analyze them. This adaptability is key to leveraging AI effectively in the dynamic world of retail.”
Making AI a Part of the Core
Accenture’s Standish said for retailers and brands, “generative AI offers a once-in-a-generation opportunity.” But success hinges on how the technology is integrated.
“Embedded into the company’s digital core — which includes cloud, data and security — it has the ability to optimize tasks, manage data, create faster insights, innovate with new experiences, augment front-line workers, and connect and communicate with customers,” Standish said. “While AI has long been central to retail forecasting and supply chain management, retailers recognize that generative AI offers something new and potentially much more powerful. It has the potential to materially impact and reinvent every facet of a retail organization, enhancing the entire value chain and driving both productivity and growth.”
Standish said companies need to invest in training people to work alongside generative AI. She said generative AI will not take anyone’s job, but those who know how to use generative AI will. “Right now, retailers are most frequently applying generative AI today for content creation, IT and assisted software development, knowledge retrieval, and customer service and contact centers,” Standish said. “However, the successful retailers will be those using generative AI to reinvent more strategic areas of the business.”
Dave Anderson, vice president of product marketing and strategy at Contentsquare, said as retailers and brands make plans to deploy generative AI, “they must understand that the path forward is going to be a lot of trial and error because the immaturity of the models that exist, and the people deploying them.”
“This means brands have to learn to adapt this technology for their own needs,” Anderson told WWD. “While the impact that generative AI will have on the overall customer experience will be seen not just in personalization but also in the speed and scaling of content creation overall, brands should be aware that the rules of engagement, oversight and regulation for AI are still unclear, and we may reach a tipping point where advancing AI at rapid speed can come into question.”
A Case Study
Irene Graham, cofounder of Spylix, the Android and iPhone tracker company, said before investing in generative AI at Spylix, “we carefully considered several factors to ensure the best outcomes.”
“Firstly, we assessed our specific business needs and goals to determine how generative AI could complement our existing operations,” Graham said. “This involved identifying areas where AI could enhance efficiency, such as in product recommendation systems or personalized marketing campaigns. Secondly, we evaluated the capabilities and limitations of available generative AI technologies. This included conducting thorough research on different AI models, algorithms and platforms to find the most suitable solution for our requirements. We also considered factors such as scalability, reliability and ease of integration with our existing systems.”
The company conducted cost-benefit analyses to determine any ROI of implementing generative AI. “This involved forecasting the expected benefits in terms of increased sales, improved customer satisfaction, or reduced operational costs, and comparing them against the upfront investment and ongoing maintenance costs,” Graham said.
Did it work? “After implementing generative AI at Spylix, we have seen significant results,” Graham said. “Our personalized recommendation engine powered by AI has led to a 20 percent increase in average order value, while our AI-driven chatbot has reduced customer service response times by 30 percent.”
“These tangible outcomes demonstrate the value of investing in generative AI and highlight its potential to drive business growth and innovation,” Graham said.
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