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How Retailers Can Increase Profits With Generative AI

Our experts share the top retail use cases for generative artificial intelligence across customer experience, supply chain and back-end e-commerce.

In this article:
What is generative AI?
Generative AI is a type of artificial intelligence model that can be trained to create new content based on a sample data set. The most advanced chat-based models, like ChatGPT’s GPT-3, can effectively mimic human conversation.
How can retailers take advantage?
Retailers can integrate generative AI to drive down costs in three main areas: customer experience, supply chain and back-end e-commerce.
What are the drawbacks?
Early-stage adopters need to address the technology’s flaws, risks and limitations through focused experimentation to avoid potential disillusionment and consumer backlash.

Generative artificial intelligence (AI) has the power to completely transform retail experiences, from the automated creation of online storefronts to personalized customer shopping journeys. But is now the time for retailers to embrace the nascent technology?

That’s the question that the retail industry is grappling with after recent advances in the area of generative AI or an artificial intelligence approach that can generate new content such as images, videos, audio, text and code that did not exist before.

However, integrating conversational AI into workflows and customer journeys presents a steep learning curve for associates, customers and brands alike. The flaws and risks of the emerging technology, including inherent biases, lack of consumer trust and factual inaccuracies, will require time and effort from retailers to combat.

“How can we use a technology like this to catapult businesses into the next area of growth and drive out inefficiencies and costs?” says Sudip Mazumder, senior vice president and retail industry lead at Publicis Sapient. “And how can we do this ethically?”

We asked Publicis Sapient retail and AI experts where retailers can integrate the technology right now and what leaders need to know about its capabilities and limitations.


What generative AI is—and what it isn’t

Generative AI is a broad term used to describe artificial intelligence models that can generate new data samples that are similar to training data. ChatGPT, made public by OpenAI in late 2022, can write poetry or informational articles, create trip itineraries, give life advice, take tests and answer any prompt or statement you  throw at it, utilizing the entire internet (pre-2021) as its training data set.

Despite the initial breakthrough of ChatGPT’s large language model, the power of generative AI goes far beyond chatbots. Advanced generative AI models have the power to create infinite, contextualized content of any format. Studies show that humans already can’t tell the difference between text, code and images created by artificial intelligence and content created by people.

Generative AI uses probability, not human-like reasoning
However, generative AI systems like GPT-3, the model used to create the ChatGPT application, don’t generate text based on logical reasoning or human intelligence. They’re simply generating the most likely “correct” responses based on the context defined by the available data set, i.e. the logical next step in the sequence.

For example, if a retailer used AI to create a series of personalized advertisements across different customer demographics, the generative language model would simply use a probability distribution to predict what should come next based on the prompt and generate a plausible-looking advertisement.

 A screenshot of the ChatGPT interface, prompting the model to generate a text-based beverage advertisement for a specific demographic.

While these limitations prevent generative AI from running marketing departments anytime soon, there are still several retail applications that are ripe for the most impressive aspect of the technology: thepower of human-like prompt continuation.

How retailers can take advantage of generative AI

Conversational commerce: generative AI and the customer experience

Generative AI could quickly change the way customers interact with retailers online if brands can integrate the technology into their digital experiences.

“The modality of online shopping interactions, and e-commerce interfaces themselves, may soon change,” says Sara Alloy, head of retail experience at Publicis Sapient. “You’re going to see a much better quality of search with more tailoring, customization and efficiency.”

1. Conversational product search

While most shoppers can use search bars to find the products they’re looking for, conversational commerce (powered by generative AI) would accelerate the search process and could lead to higher conversion rates and average basket size for retailers.

Brands can implement A/B tests of “chatGPT-like” product search bars to assist customers in finding specific products more efficiently, like searching for all the ingredients in one recipe or all the clothes in a full outfit.

2. Chatbot support

Customer service chatbots powered by generative AI can reduce staffing needs and support agents by providing complex and engaging responses. While many chatbots currently have only 15 or 20 decision trees, advanced generative models open up the potential for chatbots with infinite paths of conversation.

Retailers also have the opportunity to play around with conversational styles that match their brand and personalize interactions for customers, changing the negative perception of automated chatbot features.

3. Cross-selling and upselling

Generative AI can provide much more intelligent shopping suggestions based on search history across online stores and even other owned brands. While retailers currently use analytics and tags to monitor and enhance consumer experiences, generative AI could more easily suggest the next logical purchase or step in a customer journey.


Secondary decision-making: generative AI and supply chain optimization

What about use cases outside of consumer interface? There are a variety of human-to-human and human-to-machine interactions that could be enhanced and streamlined through the conversational ability of large language models.

Generative AI as a communication vehicle could reduce costs and create more seamless experiences for supply chain leaders, specifically through accelerating secondary decision-making.

"We have supply chain control towers for visibility and tracking. We also have proper prediction and forecasting algorithms that use AI,” says Rakesh Ravuri, CTO at Publicis Sapient. “Generative AI could add a layer of decision-making support to existing technology for a variety of unique contexts.”

There are several scenarios where generative AI could add to current supply chain technologies:

1. Where is my package?

The biggest question in supply chain is always: “where is the package?” Currently, stakeholders have to reach out manually or use limited tracking information to access package touchpoints across a variety of different databases.

Generative AI with conversational capabilities could answer this question much faster without room for error or confusion. For example, a customer or associate could ask a chatbot specifically, “when will the package get to me” or “can the package be rerouted,” and generative AI could resolve these simple questions interactively.

2. Packing configurations in transport

Supply chain managers currently optimize packing configurations using AI-powered algorithms, but generative AI could provide a trouble-shooting layer for unique contexts. For example, something may be leaking from the floor, or managers may prefer a configuration with a one meter space for people to walk in. A manager could ask generative AI for a solution to a specific packing scenario using simple language.

3. Generating shipping labels

Generative AI also has the power to quickly determine new configurations for shipping labels. Introducing new elements to this critical real estate is often a complex decision-making process based on unique variables with lots of room for error and inefficiency.

A large language model could generate new label printing combinations without violating any constraints to assist decision-makers in finding the best possible use of space.

Automated content generation: generative AI and back-end ecommerce

Not only can generative AI improve front-end customer experience, it can also automate workflow on the back end. While creative work from generative AI models lacks complexity and nuance, new systems can easily automate simple, consistent content tasks at a human level.

"Generative AI can speed up content creation for commerce,” says Ravuri. “Future iterations of these models will hopefully provide more transparency and fewer errors, but the information still needs to be reviewed and validated.”

There are several scenarios within back-end e-commerce transactions where generative AI can assist with content creation:

1. Consistent product descriptions

Many retailers are already investing in AI to automatically A/B test product descriptions to find the most engaging variation. However, recent advancements in AI’s contextual ability allow retailers to automatically standardize descriptions across a variety of sellers.

Often vendors upload wildly inconsistent product descriptions to retail marketplaces. Rather than manually rewriting product descriptions, content writers can directly prompt generative AI with specific instructions and constraints to create product descriptions that are standardized, grammatically correct and in line with brand tone.

2. Personalized product images

When customers browse e-commerce websites, each product image requires photographers, graphic designers, models and creative staff for the shoot. Generative AI would allow retailers to generate personalized product images for each customer simply based on text descriptions and historical image data.

For example, an athletic apparel retailer could automatically generate an image of a college student wearing a sports jersey for a 19-year-old customer. If customers are willing to provide more personal data or even their own prompts, generative AI could show the customer each product in a variety of different contexts of their choosing.

3. Auto-fill transaction flows

The same use case could also apply more generally to full web pages, allowing retailers to guide their vendors and customers even faster through the e-commerce journey. Right now, most e-commerce website flows are generic and fixed or based on simple inputs like time zone or what channel people are coming from.

Generative AI would allow retailers to provide abbreviated site experiences for each customer and each vendor, automatically filling in product, store or customer information on the back end.

The drawbacks of generative AI for retailers

However, experimenting with this new technology before widescale adoption poses a number of risks and challenges.

The creator of ChatGPT previously said it was a “mistake to be relying on it for anything important right now” due to its propensity to provide strikingly believable yet nonfactual answers without the ability to vet or validate them.

ChatGPT’s creator also noted that “regulation will be critical and will take time to figure out.” Retailers looking to develop their own generative AI models before industry regulation will need to encourage AI literacy and create guardrails for their associates to avoid consumer backlash.

Brands need to be very transparent with people about when they’re communicating with AI and make those choices wisely,” says Alloy. “We’ve already seen public outrage over inappropriate usage of the technology in sensitive situations.”

Getting started with generative AI

The place to begin is conversational commerce. “It’s the first inroad to test out this technology, before expanding it to other areas of your ecosystem,” says Mazumder.

"Retailers should start experimenting now because this technology has the potential for a serious uptick in customer engagement and revenue." - Sudip Mazumder

Sudip Mazumder
Sudip Mazumder
Retail Industry Lead, North America
Rakesh Ravuri
Rakesh Ravuri
CTO, SVP Engineering
Sara Alloy
Sara Alloy
Retail Experience Lead, NA