Consumer goods takes a look ...
By Liz Dominguez, Managing Editor
The top potential use cases for the consumer goods space, says Mukherjee, are “generating novel, diverse, and personalized copy content, audio, video, and images at speed and scale; and saving time and cost for audiences in different languages spread across other geographical boundaries.”
Read also: Coca-Cola Signs As Early Partner for OpenAI’s ChatGPT, DALL-E Generative AI
Not only can this lead to improved product packaging, he says, but can result in faster A/B testing and improved marketing performance, and can even influence some of the growing e-commerce customer support technologies like chatbots, virtual assistants, and avatars.
“They can provide real-time multilingual customer support and product demos, and any queries can be responded to based on customer sentiment in different regions resulting in improved customer satisfaction and reduced support costs.”
Across product innovation, generative AI can also provide more accurate recommendations, says Mukherjee. “It can scout for trends, social media data, tweets, sentiments, and features based on what customers are currently looking for.”
Harmon believes artificial intelligence could significantly transform three primary areas of business in the consumer goods space:
Merchandising: Generative AI can quickly generate product descriptions and stories
Content Creation: Meta, for example, has demonstrated the ability of AI to generate images and landscapes from voice commands
Consumer Engagement: AI/ML can be used to find the language that generates the greatest response from consumers
Within these areas, CGT sees enormous potential, such as using image conversion tech within the beauty industry to streamline clunky AR applications, creating personalized product regiments by leveraging consumers’ past histories, and further tapping into consumer engagement efforts with generative AI-created quizzes and product education.
It’s easy to see this translating to several other categories, and expanding into even more use cases, but the key, according to experts, is to take it slow.
Keeping a Healthy Dose of Skepticism
Generative AI risks
Why the caution sign? With a technology that is reliant on data inputs, there’s always risk. And in the case of generative AI, these risks include copyright infringement, insensitive content creation, misuse of content, and brand reputation repercussions.
Take the virtual chat example as noted above: According to Mukherjee, chat agent conversations based on generative AI can lead to “surreal hallucinations or uncomfortable conversations, resulting in customers repelling the brand forever.”
While many companies have specific guidelines and parameters to fine-tune responses and hopefully avoid these situations, Mukherjee notes that these parameters will evolve and train as time goes on. Early on, however, content can result in biased and insensitive content, and it’s especially important that consumer goods companies keep an eye for tech providers without transparent data sources and usage policies.
There are several limitations, says Gartner, which notes that ChatGPT, for example, is only trained on data through 2021, cannot cite its sources, does not provide data privacy assurances, and does not currently have a supported API available.
Much of the risks are due to the vastness of generative AI, which generates text and graphics based on the huge amount of data it has processed, says Harmon. This could include personal information and copyrighted info, with the potential to generate incorrect information since there is no human involved in the process. ... '
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