Generative AI Use Case Library for the Retail and Wholesale Industry

Analyst Perspective

Gen AI is revolutionizing the customer experience across the value chain.

The new era of generative artificial intelligence (AI) in the retail and wholesale sector brings unprecedented opportunities for enterprises, both big and small, to revolutionize customer experiences, drive innovation, enhance operations, and unlock new levels of growth via cost and competitiveness. By embracing advancements across the value chain, retailers and wholesalers will be able to stay at the forefront of a fast-evolving industry.

For the retail and wholesale sector, this is a once-in-a-lifetime opportunity. Embedding generative AI into the digital core of an enterprise will change the ability of the enterprise to optimize operations, manage information, generate quicker insights, innovate with new experiences, augment front-line workers, and connect and communicate with customers.

This report provides a generative AI retail and wholesale case library that will help organizations analyze applications based on capabilities and sources of value.

Rahul Jaiswal
Principal Research Director, Retail
Info-Tech Research Group

Executive Summary

Info-Tech Insight

The advent of generative AI in the retail and wholesale industry ushers in a new era, presenting unparalleled opportunities for enterprises of all sizes to revolutionize customer experiences, drive innovation, optimize operations, and unlock new levels of growth while elevating cost efficiency and competitiveness.

Generative AI is an innovation in machine learning

Generative AI (Gen AI)
Gen AI is a form of machine learning. In response to prompts, a Gen AI platform can generate new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and thereby different use case applications:

• Audio – Converts text to sound
• Visual – Enables text to image, video, or web design conversions
• Code – Creates code in various programming languages based on human language prompts
• Text – Creates text-based outputs such as articles, blog posts, emails, and information summaries

Machine Learning (ML)
ML is an approach to implementing AI, whereby the AI system is instructed to search for patterns in a dataset and then make predictions based on that set. In this way, the system "learns" to provide accurate content over time (think of Google's search recommendations).

Artificial Intelligence (AI)
AI is a field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems operate on preset rules, such as customer service chatbots.

Info-Tech Insight

Many vendors have jumped on "Gen AI" as the latest marketing buzzword. When vendors proclaim to offer Gen AI functionality, you need to pin down exactly what is "generative" about it. The solution must be able to induce new outputs from inputted data via self-supervision – not trained to produce certain outputs based on certain inputs.

Gen AI yields added value potential for retailers and associated trades in marketing and support functions

Awareness and Readiness

AI is the fastest-growing category for new spending, according to Info-Tech's CIO Priorities 2023.

• Marketing – Brands are leveraging Gen AI, ML, and recommendation engines to predict customer usage patterns by building on shopper data and devising intelligent, insight-enabled marketing services.
• IT Support – Enterprises are leveraging AI models that cover many areas, from coding network security architecture to testing and auditing. Information security procedures usually focus on confidentiality, integrity, and availability to safeguard sensitive data from potential cyberattacks.

Enterprises are already leveraging artificial intelligence

Awareness and Readiness

AI will move out of technology and become mainstream. Many AI capabilities seem cutting edge now as organizations prioritize AI as a technology investment. In a few years, foundational models that produce images, text, or code will become accessible with a commercial license and an API integration. AI will become embedded in off-the-shelf software and drive many new features that will quickly become commonplace.

You will need AI to compete. Organizations will have to work to adopt AI-enhanced products and services to stay even with the competition and meet customer expectations. Those who want a competitive advantage must build a data pipeline to train their custom AI models based on their unique data sets.

Determining Gen AI use cases is crucial to value delivery

Emerging technologies present concerns in the adoption process

The primary step in developing a Gen AI strategy is building use cases for informing the business case. Collecting generative AI use cases across the organization is a significant endeavor among cross-functional teams.

Demonstrating benefits is a significant hurdle for gaming and hospitality organizations who want to adopt and implement a Gen AI initiative because AI is an emerging technology. Finding use cases that justify the costs and risks while delivering business value can be challenging.

Don't spread yourself too thin. Use case prioritization ensures that your organization can put time and resources into a new initiative with the highest likelihood of seeing value.

Prioritization of use cases can be difficult when there are many competing priorities. Some use cases could be too large or complex to tackle. Some departments could be envious of others that are chosen for prioritization. It is crucial to determine and provide evidence of the "why."

Impact of AI on wholesale & retail industry growth in 2025 by percentage

AI has the potential to increase economic growth rates by a weighted average of 1.7 percentage points by 2035 across 16 industries.

Info-Tech's approach and team can help, irrespective of where you are in your digital journey

Measure the value of this document

Document Objective
Highlight best-in-class use cases to spur the initiative planning and ideation process.

Measuring Your Success Against This Objective
There are multiple qualitative and quantitative direct and indirect metrics you can use to measure the progress of your initiative pipeline's development. Some examples are:

• Increased initiative pipeline value
• Number of capabilities impacted by your initiative pipeline
• Enhanced understanding of the impacts of the initiatives, aligned to your organization's capability map
• Better understanding of which sources of value are being addressed or under-addressed in your organization's initiative pipeline

See Establish Your Transformation Infrastructure in the Digital Transformation Center for more details.