Abstract
McKinsey & Company estimates that Generative AI could create between $240 billion to $390 billion in economic benefits for retailers, possibly boosting margins by as much as 1.9 percentage points [1]. This paper aims to find a middle ground between all the hype surrounding Generative AI and its actual potential in retail. We’ll look at practical applications offering genuine advantages while addressing overhyped scenarios and providing tips for effective integration of AI tech. Readers will come away with valuable insights on how to use Generative AI wisely without falling into common traps.
CCS Concepts
- Applied computing → Online shopping
- Computing methodologies → Natural language processing
- Information systems → Recommender systems; Search interfaces
Keywords
Generative AI, Retail, Machine Learning, Recommender Systems, Natural Language Processing
Introduction
We kick things off with an overview showing how generatively driven technologies are transforming retail landscapes—pointing out significant chances for enhancing shopper experiences while optimizing processes [6]. Plus we discuss just how much cash retailers are pouring into these new technologies along with their impact on global markets.
Key Applications of Generative Ai in Retail
Next up is our dive into major ways GenAI can be used within retail settings:
- Smart Assistants: Discover how interactive chatbots powered by GenAI offer customized answers about products [3] while guiding both customers and staff.
- Curated Shopping Experience: See how retailers can use GenAI to craft personal shopping journeys that feel like having your own virtual assistant—with tailored comparisons based on specific criteria.
- Enhanced Search Capabilities: Learn about advancements such as guided navigation improvements alongside better understanding user queries which leads to refined search accuracy [5].
- Recommender Systems: Find out how GenAI fine-tunes suggestions around products creating fresh categories for easier discovery aligned closely with marketing strategies [2].
- Multimodal Product Content: Explore using GenAI for extracting features efficiently—from generating optimized titles automatically through alt text creation aimed at improving accessibility plus SEO efforts.
- Marketing Optimization & User Experiences: Uncover ways that data-driven campaigns get enhanced thanks due diligence toward consumer behavior benefiting overall site experience optimization via AIdriven innovations!
The Hype Trap – Overblown Use Cases
After discussing useful applications we’ll dissect some currently hyped-up uses cases where expectations might’ve gotten ahead ourselves including:
- End-to-End Search Systems: Here’s why thinking you can rely solely upon gen ai technology managing every part independently ignores conventional components necessary—a hybrid approach proves smarter!
- Comprehensive Recommendation Models: We’re diving deep here too; it turns out leaning completely onto generators alone misses business goals impacting traditional algorithm performance negatively instead pairing them together reaps rewards!
- Fully Automated Customer Service? Sure thing but let’s not forget humans still play critical roles navigating complex issues requiring empathy far beyond what bots provide alone.. While generative AI automates many tasks, human oversight remains essential to maintain creativity, ethical standards, and quality control. Retailers must strike a balance between automation and human input.
- Automated Messaging/Writing Needs Creativity Too! Letting machines do this work risks losing coherence across branding voice unless human input stays involved consistently throughout messaging processes[4]!
Challenges and Considerations
1. Data Privacy and Security
The use of generative AI requires access to vast amounts of customer data, raising concerns about data privacy and security. Retailers must ensure compliance with regulations such as GDPR and implement robust data protection measures.
2. Transparency and Trust
AI-generated content, such as product descriptions and images, can sometimes be misleading. Retailers must prioritize transparency and ensure that AI outputs align with brand values and customer expectations {8}
Future Implications
The adoption of generative AI in retail is expected to accelerate in the coming years. By 2025, 50% of fashion executives identify product discovery as the top use case for generative AI [9] . Key trends driving this adoption include:
- Multimodal AI: Combining text, image, and video capabilities to create richer shopping experiences [10].
- Advanced Personalization: Leveraging AI to create hyper-personalized experiences at scale.
References
- McKinsey & Company (2024)
LLM to ROI: How to Scale Gen AI in Retail
A comprehensive industry insight exploring the economic impact and integration strategies of Generative AI in retail.
Read the full article - Yashar Deldjoo et al. (2024)
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Presented at the 30th ACM SIGKDD Conference, this paper reviews how generative models are shaping the future of product recommendation systems.
DOI: 10.1145/3637528.3671474 - Feriel Khennouche et al. (2023)
Revolutionizing Customer Interactions with Generative Chatbots
This arXiv paper discusses the challenges and insights behind deploying AI-driven chatbots for FAQ and customer support systems.
Available at: arXiv:2311.09976 - Katherine Lee, A. Feder Cooper, James Grimmelmann (2024)
Talkin’ ’Bout AI Generation: Copyright and the Generative-AI Supply Chain
From the Symposium on Computer Science and Law, this study explores copyright and legal implications of generative AI content.
DOI: 10.1145/3614407.3643696 - Zheng Liu et al. (2024)
Information Retrieval Meets Large Language Models
This paper, presented at the ACM Web Conference, dives into how language models are transforming traditional search and information retrieval.
DOI: 10.1145/3589335.3641299 - Mari Sako (2024)
How Generative AI Fits into Knowledge Work
Published in Communications of the ACM, this article reflects on GenAI’s role in reshaping how professionals manage and apply knowledge.
DOI: 10.1145/3638567 - Macy Takaffoli, Sijia Li, Ville Mäkelä (2024)
Generative AI in UX Design: Industry Insights
A study from the ACM Designing Interactive Systems Conference on how UX teams and companies use GenAI in practice.
DOI: 10.1145/3643834.3660720 - (Digixplanet, 2025).
- (BoF Insights, 2024)
- (Retail TouchPoints, 2025)
Note: This text was grammar-corrected and structured with the assistance of ChatGPT.
