Adoption of Generative AI in Retail & CPG: The Time Is Now

Use Cases & Projects, Scaling AI, Featured Carla Winston

The arrival of Generative AI (GenAI) onto the retail scene ushered in an unprecedented wave of AI hype and a wealth of real disruptive opportunities for decision makers. While some larger enterprises in the industry have been leveraging AI capabilities for some time, GenAI has emerged as a formidable presence that is changing the landscape. 

GenAI enables users to extract data from unstructured sources and natural language inputs with a high-level of accuracy. And it is because of these factors that GenAI has staying power — a staying power that brings with it the means to create personalized experiences for the consumer in real-time and structure data into meaningful insights without the cumbersome task of manually sifting through endless data. The benefits of GenAI are real and tangible now. So it is no surprise that business leaders are taking steps to make GenAI a reality and not just a passing hot topic in their organizations. This article highlights key areas of GenAI implementation in the retail and CPG industry.

Marketing Has Emerged as the Starting Point for GenAI Integration

GenAI is making steady but real headway in the industry, with reports showing that 40% of global brands and retailers are experimenting with GenAI and over 20% are in the implementation stage of their GenAI investment. Many businesses are planning to adopt GenAI as an enhancement to the customer experience. Leading the charge is content generation — the strongest adoption in application. After all, content generation applications allow a timely and cost-effective way to interact with customers, as well as deliver a streamlined avenue for providing personalized campaigns and recommendations. 

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As a result, marketing and customer experience teams are first in line to benefit from the early adoption of GenAI. In fact, of the $4.4 trillion estimated to come from GenAI productivity gains, marketing organizations are expected to be the primary beneficiaries. These teams are able to extract practical value from applications focused on the customer and consumer. For content generation, there are many areas of opportunity for marketing teams. Some of these include:

1. Personalized Content

Personalization has been all the rage for several years and continues to be an area of focus for businesses. Consumers expect and demand a personalized experience. And it’s because of this that marketing teams spend weeks and months crafting well-designed plans and campaigns all in an effort to meet consumer demand. Enter one of the benefits of GenAI and personalized content generation.

Large Language Models (LLMs) can enable marketers to tailor content to consumers based on the individual consumer’s preferences. Instead of marketers assuming the tedious chore of combing through consumer characteristics and behavior, LLMs can do the heavy lifting of discovering what resonates with the consumer. And, in turn, marketers gain the improved ability to target and enhance the consumer experience. 

2. Automated Content:

On top of enabling targeted content, GenAI lends significant support through time-saving automation. AI models can auto-draft content from email messaging, to website content, to social media posts. This simple feat of automating different aspects of content creation opens the door for marketers to not only save time but also focus their efforts on content refinement and adding a personal touch to the messaging.

3. Ideation and Content Variety:

At the crux of marketing is identifying “what’s next” and being on the leading edge of delivering that “next” new thing (e.g., product, service, creative messaging and content) is a consistent aim for marketers. Again enters handy GenAI models that can be used as springboards to realizing “what’s next.” These models enable elevated brainstorming that unlocks the door to innovation and greater content diversity by generating text and images that better connect with the audience: the consumer. Marketers are taking advantage of this capability now and have established expectations for GenAI serving as a “catalyst for unlocking new creative possibilities.”

GenAI models have demonstrated their value-add across numerous areas within marketing and continue to be a source of enablement and enhancement especially when it comes to content generation. With consumer expectations undeniably high for brands and retailers alike (71% of consumers expect companies to deliver personalized experiences), now is the time to test the waters and harness the value of GenAI applications. And what better place to draw initial value than marketing and the priority of bringing personalized experiences, messaging and content to consumers. Afterall, nearly 60% of organizations have forged the path to exploration and implementation of GenAI across the marketing function. 

While many marketing teams are either exploring or have already implemented GenAI use cases, it is not the only domain found along the value chain that is well positioned to benefit from GenAI applications now. In fact, with its ability to transform and structure data, GenAI models have been deployed transversely across supply chain, research & development, market research, public relations, and human resources, to name a few. A host of teams are inundated with unstructured data impossible to make sense of in a reasonable amount of time. But one of the strengths of GenAI is its ability to structure and summarize data insights from various sources. Capitalizing on this means the transformation of data for immediate use for teams.

Transform, Structure, and Answer

Before delving into the transformation of data, organizations should ready their teams for the collaborative effort needed to achieve data quality and alignment, especially for undigitized physical assets (e.g., analog assets, paper, film, microfiche). A recent study revealed that 93% of data leaders believe that a solid data strategy is needed to get the most value out of GenAI and 46% have identified data quality as a value detractor. With cross-alignment checked off the list and the right data inputs pinpointed and prepared, teams are better positioned to extract value with confidence in the results.

GenAI models are adept at uncovering the potential of unstructured data and therefore helping propel teams to greater business success. Success is aided by improving what once may have been manual and document-heavy and transforming it into knowledge management agents, financial reports, training assistance, no-touch decision making or even a repository of truth. And that’s not the half of it. By tuning the LLM to perform a role like the rapid classification of data to give structure to insights and answers to in-house questions, along with enabling validation processes supported by knowledge experts, organizations have a simple formula to enable common value chain tasks and processes supported by knowledge experts. 

There are many avenues for brands and retailers to explore the possibilities of GenAI and what it could mean for their organizations and consumers. While the low-hanging fruit of LLM-enhanced applications like content generation, automation, summarization, and answer retrieval are indeed very valuable to businesses, it is important to note that GenAI is an enabler and not a replacement for creativity, intuition, or experience. The GenAI craze is here to stay, however, and it has proved to enhance efficiency and time savings for internal processes and for reaching the consumer in a personalized way.

As the adoption of GenAI continues, it will be imperative for businesses to extend the value equation of GenAI beyond the common marketing and transversal use cases, but for now, retailers and brands should start by gathering vetted data sources, experimenting and developing relevant use cases, and establishing a framework to govern AI applications. GenAI offers incredible potential for positively impacting the industry and the consumer, and now is the time to set the pace for its productive use. 

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