top of page

Research Preview: Retail-Tech Landscape—Generative AI Applications


Generative AI (artificial intelligence), also known as GenAI, rests on the shoulders of decades of AI research. Software developers and engineers have been busy developing applications to manage and control large language models (LLMs) so that more businesses and individuals can access the power and benefits of GenAI.


This Research Preview offers an early look at our forthcoming Retail-Tech Landscape of selected innovators that provide generative AI (artificial intelligence), also known as GenAI, applications. Understand the need for GenAI applications and uncover the growth potential and key categories of the market.


Look out for the full report to discover the featured companies! We focus solely on startups—privately held tech companies that were founded within the last 10 years.


This Research Preview offers an early look at our forthcoming Retail-Tech Landscape of key innovators that provide generative AI (artificial intelligence), also known as GenAI, applications.


GenAI rests on the shoulders of decades of AI research, driven by ever more powerful and cost-effective computing software. Software developers and engineers have been busy since the launch of ChatGPT 3.5 in November 2022, developing applications to manage and control large language models (LLMs) so that more businesses and individuals can access the power and benefits of GenAI.


Our full Retail-Tech Landscape discusses the need for these applications and presents key solutions providers in this field. The report aims to inform senior-level retail executives, as compared to AI engineers or CIOs, on two fronts: (1) offering information on a robust group of GenAI applications, which frees enterprises from the significant time, investment and technical challenges involved in undertaking the creation and management of AI platforms; and (2) providing examples of existing applications that may already serve an enterprise’s needs.


The Need for GenAI Applications


  • When ChatGPT 3.5 was released, using GenAI technology for business applications required the formulation of long prompts that included detailed instructions and data. Creating and fine-tuning these prompts required a brand new set of skills and capabilities, making the use of GPT models challenging. As such, the technology industry has since been developing applications that control, operate and manage LLMs, freeing users to focus on their core business while accessing the power of GenAI. GenAI applications build on the infrastructure laid down by cloud computing platforms and AI chips.

  • GenAI applications offer a higher level of sophistication than can be obtained from typing sentences into generic chatbots. They are able to create the detailed prompts required to generate more sophisticated and nuanced results, and apps can layer GenAI to achieve even better results.

  • Brands and retailers can now use GenAI-powered apps and copilots to accelerate how they do business. Many apps currently use GenAI to access corporate knowledge bases and procedures, and GenAI will increasingly enable enterprises to access their own corporate data to find relationships, opportunities and the answers to “what if” questions.

Generative AI Application Providers, by Category

The Retail-Tech Landscape present 76 privately held companies globally that have developed GenAI applications that leverage the power of GenAI models, enabling users to create and elevate an array of content types. The featured companies were founded within the last 10 years and offer GenAI applications that are relevant to the retail industry. Startups that have been acquired by retailers are not included in the list.


We categorize the featured GenAI application providers into seven areas:


  • Audio: Using AI to automate and enhance creative processes related to sound and music, including generating royalty-free music, creating audio content and producing other audio elements.

  • Code Generation: Accelerating the creation and translation of software code, including generating code snippets, creating complex algorithms and producing functional scripts.

  • Customer Service: Deploying AI to enhance service through improving support-agent workflows through copilots and chatbots.

  • Imagery and Design: Using AI to automate and optimize creative processes related to visual content, including generating visual artwork, designing graphics and creating other design elements.

  • Language/Text: Harnessing AI to automate and elevate various dimensions of written communication.

  • Marketing: Deploying AI to enhance customer interactions and streamline marketing efforts, including the generation of generating personalized advertisements and optimizing marketing language.

  • Productivity: Utilizing AI to streamline and enhance various work-related tasks, such as generating meeting notes, assisting with task management, providing AI-driven scheduling and time management solutions, and facilitating efficient information search and retrieval.

  • Video: Utilizing AI to automate the creation and editing of videos, transforming text or scripts into visually compelling video content.

The Coresight Research CORE Framework brings clarity and order to the numerous opportunities for GenAI technology in retail, organizing the opportunities into four main categories—broadly, communication, operations, experience and research—as depicted in Figure 1. Most of the applications highlighted in this report correspond to the communication branch of the framework, except for code generation, which can support every category.


Figure 1. The Coresight Research CORE Framework for Generative AI in Retail


Source: Coresight Research


コメント


bottom of page