Tim Bridges, Global Sector Lead, Consumer Products and Retail, Capgemini.
These days, it’s impossible to avoid the buzz about generative AI (GenAI) in the consumer products and retail space, and most of it relates to general consumer attitudes and consumer adoption—in other words, what people think of it and if they’re using it.
Those are interesting questions, but it’s not just whether consumers are using GenAI; it’s about how—because it’s the “how” that will shape the future of the industry.
Operational efficiency takes center stage.
But the picture is broader than this. GenAI is being used in various ways in the consumer packaged goods (CPG) and retail industries, and not all of them are directly customer-facing. For example, we’re seeing the use of GenAI in supply chain networks.
This is one area in which efficiency gains can make the biggest difference to a CPG business. GenAI is being used in conjunction with predictive AI in areas such as supply and demand planning. The insights it provides have enabled some organizations to increase levels of automation. It means that businesses need to manually handle exceptions in a few cases only, which allows them to focus on more strategic aspects.
The same combination of predictive AI and GenAI is being used to make forecasting more granular. Instead of traditional A/B testing—trying these two approaches in parallel and gauging the results—sales representatives can use artificial intelligence to gauge the likely flows of inventory more easily and can assess brand and product impact in individual stores. This will help CPG organizations and retailers work together to adjust their offer—what they sell and where—and enhance their formulations for maximum advantage.
Transformation uncovers opportunities for sustainability.
GenAI is also being used to address issues of sustainability. Organizations can use the technology to make their product/offer more sustainable, and at the same time make the production process more cost-efficient. In doing so, they can enhance the value of products or services for key demographics for whom these factors are especially important. It’s early days for developments like these, but they’re happening.
CPG companies and retailers continue to collaborate.
Time and again, we see how important the relationship between CPG businesses and retailers is becoming and how great the potential benefits of their cooperation are. In AI, this means the aggregation and collation of data sets can enable both parties to explore not just what people buy but also why people buy.
The larger the body of data, the more robust it will be, and it can help retailers and CPG organizations alike to identify factors that lead to purchases. They can develop processes that accurately target product development and fulfillment specific to geographies, with less R&D and a faster time to market.
Are GenAI avatars the new influencers?
I already mentioned the consumer buzz around GenAI. CPG companies and retailers can use GenAI to further personalize reach with varying demographic behaviors. For instance, online influencers are trusted by some demographics, Gen-Z in particular, and so we’re seeing smaller brands using AI to develop avatars or virtual influencers to extend reach across new social media channels.
Similarly, the use of GenAI in marketing materials such as copywriting and photo generation is going to be joined by moving images. Text-to-video conversion platforms, under test at the moment, could change the way commercials are made and accelerate their use.
However, concerns around the use of GenAI remain. People aren’t naïve. According to the Capgemini Research Institute (CRI), almost two-thirds of consumers (62%) harbor concerns about GenAI producing false or misleading testimonials or reviews (pg. 20). A similar proportion (61%) of AI-aware consumers were concerned about the possibility of bias in GenAI models leading to unrepresentative results.
Organizations using or planning to use GenAI in CPG and retail environments need not merely bear these concerns in mind but need to address them directly. Confidence and trust in a solution depends on putting effort into making it work reliably, in people’s best interests and in line with their expectations. For example, this means that GenAI models may hallucinate as a result of inherent bias, limitations in the training data and lack of real-world understanding.
In general, though, the stats are upbeat. According to Capgemini’s research report, over half (52%) of users have replaced traditional search engines with GenAI tools for product recommendations (pg. 14), and two-thirds of them (66%) welcome such recommendations (pg. 16).
Look to the road ahead.
Stand back and take stock, and you’ll find a common theme here in all of this: GenAI, while exciting and transformative, is a technology that serves rather than drives business. The focus, as ever with new tech, should be not on its dazzling potential but on what organizations want to practically achieve with it. Improved efficiency, greater sustainability, higher customer satisfaction? That’s a pretty good start.
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