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Generative AI in e-commerce: The way to an AI-enhanced marketplace

Generative AI in e-commerce
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The advent of generative AI has catalyzed seismic shifts across a plethora of sectors, with e-commerce being one of the most significant beneficiaries. By leveraging the power of generative AI technology, businesses can generate unique content such as product descriptions, images, and even entirely new merchandize. This has the potential to significantly change the way e-commerce entities operate. To illustrate this, consider these forecasts: By 2030, the value of the generative AI sector is expected to grow to USD 110.8 billion. Further, generative AI is predicted to be responsible for 10% of all data generation by 2025, a stark increase from under 1% in 2021, as per Gartner’s insights.

In the ever-evolving realm of e-commerce, specific terminologies often rise to prominence, stimulating discourse in online retail circles and motivating corporations to adapt their operations accordingly. Currently, the term capturing widespread attention is generative AI. While not a nascent technology, it has gradually moved into the spotlight following the surge in popularity of OpenAI’s ChatGPT. This has triggered a discourse among online merchants about the potential advantages of generative AI for e-commerce and its optimal utilization strategies.

In this article, we discuss the transformative potential of generative AI within the e-commerce landscape, highlighting its most beneficial applications across customer experiences. We also explore how e-retailers can leverage this technology to augment their sales. As the e-commerce sector experiences exponential growth, propelled by innovative technologies and evolving consumer preferences, one crucial development is incorporating generative artificial intelligence into diverse facets of customer engagement. Empowered by advanced generative AI models such as ChatGPT, generative AI is changing how online enterprises interact with customers, provide personalized recommendations, and improve overall customer satisfaction.

What is generative AI?

AI underwent multiple stages of advancement to attain a hyper-personalized level in real time. Machine learning, a subset of AI, allows software to learn from human-provided datasets and adapt in real-time, while Natural Language Processing (NLP) focuses on tasks like language understanding and text generation. The merger of these elements led to the birth of generative AI – an intelligent technology that significantly changes our understanding of human creativity and decision-making. The core foundation of these technologies is authentic human creativity, which cultivates intelligence over time.

Generative AI signifies a technological evolution capable of producing a plethora of content formats, encompassing text, images, audio, simulations, videos, and synthetic data. These dynamic algorithms are rooted in machine learning models governed by AI, designed to predict forthcoming sequences of words, images, or videos based on the prior input. This ability to predict in real-time, coupled with intuitive and user-friendly interfaces, has enabled businesses to produce virtually realistic images, videos, and content. The near-absence of human involvement in generative AI opens a new realm of possibilities, particularly in industries such as retail and e-commerce, where customized experiences are imperative.

However, the understanding of generative AI among businesses is still emerging. It’s important to note that generative AI’s capabilities hinge heavily on its fed dataset. This can occasionally result in incorrect, flawed, and unethical outputs, potentially damaging a brand’s reputation and leading to legal complications. Hence, the application of generative AI in online retail should be methodically calculated and continually monitored. Additionally, it’s essential to understand that generative AI functions based on probability and logical reasoning, providing responses derived from existing datasets and matching them with the user-specified context. Despite continuous enhancements, generative AI is still evolving and is not yet ready to independently operate within marketing departments. In e-commerce, generative AI can be employed to craft product narratives, produce visual representations of products, and even devise new product concepts.

Generative AI utilizes various techniques such as neural networks, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) to analyze patterns within existing data and use those patterns to create entirely new content. These AI methods are capable of learning from the data they are provided and using that knowledge to produce novel and often creative outputs, whether it’s generating images, text, or other types of content. Notable examples of generative AI models include Google Bard, Dall-E, and ChatGPT.

A 2023 survey among professionals in the United States involved in marketing and advertising, including e-commerce, revealed that 37% had leveraged generative AI to facilitate their work-related tasks.

Generative AI in e-commerce: The benefits of LLMs and the significance of LangChain

Benefits of LLMs and the significance of LangChain

Large Language Models (LLMs), a significant part of generative AI, are transforming the e-commerce landscape by facilitating the creation of user-friendly chatbots. LLMs are trained on vast amounts of text data and can generate human-like text based on their input. This attribute makes them ideal for developing intelligent chatbots that can interact with customers more naturally and engagingly.

In e-commerce, LLMs play a significant role by enhancing various aspects of the customer experience, streamlining operations, and driving sales. Here’s how LLMs benefit the e-commerce sector:

  • Customer support and chatbots: LLMs can power chatbots and virtual assistants on e-commerce websites. These chatbots can provide instant responses to customer inquiries, helping customers find products, answer questions about order status, and assist with common issues, offering 24/7 support.
  • Personalization: LLMs can analyze user behavior, purchase history, and preferences to provide personalized product recommendations. This improves the shopping experience, increases customer engagement, and boosts sales.
  • Content generation: LLMs can generate product descriptions, reviews, and marketing copy. They can help automate content creation, making it easier for e-commerce platforms to maintain fresh and relevant content for their products.
  • Search and discovery: LLMs can improve site search functionality by understanding natural language queries and returning more accurate results. This enhances the overall user experience by helping customers find what they’re looking for quickly.
  • Product descriptions and reviews: LLMs can automatically generate detailed product descriptions and even synthetic user reviews. This can be particularly useful for newly launched products or those with limited customer feedback.
  • Inventory management: LLMs can analyze data to predict demand for products, helping e-commerce businesses optimize inventory levels and reduce overstocking or understocking issues.
  • Marketing and advertising: LLMs can assist in creating ad copy, email marketing content, and social media posts. They can also help analyze marketing campaign performance data to refine strategies.
  • Language translation: If operating in global markets, e-commerce platforms can use LLMs for real-time language translation to reach a broader audience.
  • Voice shopping: With the rise of voice assistants, LLMs enable voice-driven shopping experiences, allowing customers to place orders, check prices, and get product recommendations using their voice.
  • Data analysis and insights: LLMs can process vast amounts of data, helping e-commerce businesses gain insights into customer behavior, market trends, and product performance. This data-driven approach can inform strategic decisions.
  • Content moderation: LLMs can be used to automatically detect and moderate user-generated content to ensure it complies with community guidelines and maintains a safe online environment.
  • Customer feedback analysis: LLMs can help e-commerce companies process and analyze customer feedback from reviews, surveys, and social media, providing valuable insights for improvement.
  • Market research: LLMs can assist in analyzing market trends, competitive intelligence, and customer sentiment from online discussions and reviews.
  • Dynamic pricing: LLMs can analyze market conditions, competitor pricing, and customer demand to adjust product prices dynamically, optimizing revenue.

One of the most important use cases of LLMs is a chatbot. And a noteworthy aspect of AI-powered chatbots is their ability to learn and adapt over time. They can refine their responses and improve their problem-solving capabilities by analyzing user interactions and feedback. This ability to learn and evolve makes them an invaluable tool for e-commerce businesses looking to provide personalized and efficient customer service.

In this context, LangChain, a software development platform, plays a crucial role. It allows developers to leverage the power of LLMs and build sophisticated chatbot applications for e-commerce platforms. LangChain integrates with various LLM providers like OpenAI, Hugging Face, etc., providing an interface for easy interaction and integration.

LangChain proves beneficial in various contexts, including document analysis, summarization, AI chatbots, and code analysis. Therefore, this technology can be instrumental in an e-commerce setting.

Let’s take a closer look at how LangChain can aid in the creation of e-commerce chatbots.

LangChain offers several modules that can be utilized to create language model applications. These modules can be used individually for simpler applications or combined for more intricate ones. Here are some key components of LangChain:

  • Large Language Models (LLMs): LangChain integrates with different LLM providers like OpenAI, Hugging Face, etc., offering an interface for interaction and integration.
  • Prompt template: This feature is responsible for formatting the input text entered into the LLM model, making constructing and working with prompts easy.
  • Chains: They are crucial for generating contextually relevant text, enabling the language model to make sense of the given text.
  • Agents and tools: Agents are chains with instructions from an LLM and access to a set of tools to interact with other resources.
  • Memory: LangChain provides memory components to manage past chat messages and maintain context, which is essential for chatbot and user conversations.
  • Document loaders: LangChain allows you to input your custom datasets into your LLM, resulting in a unique AI chatbot trained on your specific data.
  • Indexes: LangChain provides utility functions for document structuring, facilitating interaction with documents and indexes.

The use of LangChain for creating LLM-based apps for e-commerce can bolster customer experience, increase sales, and optimize operational efficiency. This technology can help improve product recommendations, customer support, inventory management, pricing strategies, content generation, and overall searchability on e-commerce websites. This shows how LangChain, combined with custom datasets, can significantly improve the operational landscape of online businesses.

Use cases of generative AI in e-commerce

Use cases of generative AI in e-commerce

The surge in online shopping isn’t merely attributable to its ease and comfort but also to the personalized experiences curated across various platforms, attuned to individual preferences and interests. Across sectors, marketers scrutinize characteristics that significantly resonate with customers. In online retail and e-commerce, aspects growth and customer loyalty involve personalization across product offerings, recommendations, search results, promotional emails, and delivery options. Armed with this understanding, e-retailers identify procedures where integrating generative AI technology can yield substantial returns on investment. For instance, incorporating generative AI into e-commerce practices can facilitate customized shopping experiences for each customer. Generative AI is proving to be a game-changer in the e-commerce sector, with enterprises employing it to personalize customer experiences, enhance their operations, and boost sales. The following are some areas where e-commerce organizations are capitalizing on the advantages of generative AI.

Product descriptions and content generation

Navigating an online store can sometimes feel like an endless journey through a digital corridor filled with countless products. This is when concise yet comprehensive product descriptions become invaluable. Traditionally, these descriptions were crafted by writers who would research, write appealing drafts, and incorporate necessary SEO keywords. Although effective, this approach sometimes led to inconsistent product descriptions, losing sales and customers.

Before the integration of generative AI in e-commerce, retailers relied on A/B testing of product descriptions to identify the most captivating versions. However, the recent progress in generative AI technology enables them to standardize descriptions across various sellers and retail marketplaces. Content creators can now instruct generative AI tools like ChatGPT to create product descriptions in line with a specific brand tone and language, ensuring grammatical correctness. This utilization of AI can facilitate the generation of a broad array of content for e-commerce businesses, encompassing product descriptions, reviews, and advertisements. In this case, Natural Language Generation (NLG) algorithms assess product data and generate relevant descriptions to enhance the customer’s online shopping experience. For example, a tool could scrutinize a product’s attributes, advantages, and specifications, then generate an engaging product description that improves customer interaction.

A tangible example of a generative AI application for product descriptions can be found in the platform, Phrasee. It can analyze a product’s features (such as headphones) and generate a description like, “Experience crystal-clear audio with these noise-canceling headphones, featuring cutting-edge noise reduction technology that eliminates ambient noise for an immersive sound quality.” Phrasee can also generate email subject lines or push notifications, saving time for e-commerce brands and enhancing customer engagement with automated and personalized content. Domino’s Pizza and eBay exemplify companies leveraging such ‘AI-enhanced content.’

Product images and ads generation

Generative Adversarial Networks (GANs), a subcategory of generative AI, have found substantial application in the e-commerce sector, particularly in creating product images. GANs, once trained on a dataset of existing product images, possess the capability to generate new, convincingly realistic product visuals that can be utilized in e-commerce or promotional activities. This technique could considerably economize the time and resources expended on product photography and image editing.

Tools adept at image generation, such as DALL-E 2, are already finding their way into advertising. For example, Heinz leveraged an image of a ketchup bottle with a strikingly similar label to their own to demonstrate how AI visualizes ketchup, merely a reflection of the model’s training on a large number of Heinz ketchup bottle photographs. Likewise, Nestle used an AI-enhanced rendition of a Vermeer painting to market one of its yogurt brands, while Mattel is employing the technology to produce images for toy design and promotional objectives.

Product images play a pivotal role in Product Display Pages (PDP), with each image necessitating a comprehensive team of models, photographers, designers, editors, and creative personnel for the photoshoot. This is an area where generative AI could equip online merchants to generate personalized product pages using textual inputs and historical image data. Adobe Firefly, the latest in a series of creative, generative AI models from Adobe, enables creators to bring their ideas to life with greater efficiency and without limitations. Users can generate lifelike images based on specific subjects, styles, locations, or colors. These creations could then be utilized for commercial purposes, rendering the technology useful in fields such as media, design, advertising, marketing, and education.

With the introduction of generative AI for e-commerce images, brands will have the power to create images in real-time. For instance, a clothing brand could generate images of people from different age groups wearing their garments to appeal to a similar demographic of shoppers. As customers provide more personal data to generative AI models, these models will refine their search results to align with the customers’ search context.

Product recommendations

Today’s shoppers relish personalized shopping experiences, from custom-made product recommendations to individualized content, discounts, and deals. Generative AI opens up an array of opportunities for online retailers to recommend products and services to shoppers based on factors like purchase history, historical data, most searched products, browsing behavior, and wishlist items. Adobe Sensei and Salesforce Einstein AI, potent personalization tools from Adobe and Salesforce, aid brands in curating exceptionally personalized shopping experiences, thus driving sales and customer retention. This strategy assists companies in fostering customer loyalty and bolstering sales.

Stitch Fix, a clothing brand and online personal styling platform based in San Francisco, has redefined the fashion retail landscape. Through a blend of personal stylist expertise and artificial intelligence efficiency, Stitch Fix offers regular custom-made clothing recommendations delivered directly to its customers. The company’s AI scrutinizes data on fashion trends, body measurements, customer feedback, and preferences, furnishing stylists with a carefully curated assortment of recommendations suitable for their customers’ lifestyles and budgets.

Likewise, generative AI can sift through enormous volumes of customer data to discern patterns and trends, enabling businesses to craft highly targeted marketing strategies and personalized product suggestions. Amazon is an example of a company that employs generative AI algorithms to deliver exquisitely personalized product recommendations, a strategy that has significantly contributed to its success. As reported by Forbes in 2021, product recommendations accounted for 35% of consumer purchases on Amazon.

But how does this happen?

The power of LLMs lies in their ability to dive deep into the database of an online store and analyze customers’ shopping behaviors and preferences. This in-depth understanding forms the cornerstone of personalizing the e-commerce experience.

AI-enabled e-commerce chatbots can harness this understanding and transform it into highly personalized interactions. By studying individual browsing history and purchase patterns, these chatbots can curate tailored product suggestions specifically aligned with the customers’ interests.

Such personalization goes a long way in enhancing user engagement and satisfaction. When customers receive recommendations that resonate with their preferences, it leads to a more satisfying shopping experience. This, in turn, stimulates customer loyalty and catalyzes sales growth. In essence, the amalgamation of LLMs’ analytical prowess and LangChain’s facilitative framework can amplify e-commerce personalization, driving customer satisfaction and revenue growth.

Chatbots for customer support

Chatbots, powered by generative AI models such as ChatGPT, are rapidly gaining traction in e-commerce due to their ability to offer round-the-clock customer support and assistance. These chatbots can manage customer inquiries, provide product details, and address prevalent issues. Their understanding of natural language and context facilitates seamless interactions with customers. By automating routine tasks and delivering timely responses, chatbots augment the customer experience while diminishing operational expenses for businesses.

Generative AI has also been instrumental in the advent of virtual shopping assistants accompanying customers on their online shopping expeditions. These assistants leverage machine learning algorithms to understand customer inclinations, suggest suitable products, and aid in the decision-making process. Virtual shopping assistants enable customers to explore new products by proffering tailored recommendations, resulting in elevated engagement and sales.

While the use of AI in chatbot functionalities might seem outdated, integrating generative and conversational AI can significantly enhance customer support and service. Besides the advantages of reducing staffing requirements and maintaining active support around the clock, chatbots have significantly evolved from handling 15-20 decision trees to managing an infinite number of them. With sophisticated generative models, online retailers can experiment with various conversational styles to match the shopper, customize every message for increased engagement, and respond to queries with human-like empathy and emotion, creating an experience akin to human-to-human conversation. Collectively, these factors result in improved chat experiences for shoppers, leading to higher cart completions and increased sales.

New product design

Companies can harness the power of generative AI to utilize Generative Adversarial Networks (GANs) in designing innovative products based on existing ones. This quickens the product development process and fosters efficiency in creating novel and avant-garde products, enabling brands to maintain a competitive edge and cater to customer demand for fresh and improved merchandise.

Generative design has found substantial use in industries where aesthetics and structural performance are both pivotal. For instance, New Balance, the footwear brand, capitalized on generative design to craft unique shoe sole geometries. The brand employed proprietary software developed by Boston-based company Nervous System, which allows the customization of soles tailored to individual users’ foot support requirements and aesthetic tastes. This approach merges structural functionality with personal style preferences, providing each user with a unique and comfortable experience.

Customer engagement

In the past, universal marketing campaigns could effectively draw shoppers to physical stores or websites. However, as customer preferences have evolved, they no longer wish to be subjected to generic marketing tactics for products or services they have no interest in or never sought out. Modern, tech-savvy shoppers are well aware of their value and won’t hesitate to consider your competitors if their desire for personalized experiences isn’t met. This has led to the emergence of targeted marketing campaigns. By segmenting audiences based on behavior and demographic information, marketing campaigns can be tailored to specific interests, ensuring the return on investment of your marketing strategies and guaranteed sales.

Enhanced customer engagement can be achieved by creating virtual assistants and chatbots that provide personalized support and recommendations. For instance, businesses can employ generative AI to devise a chatbot capable of answering common business-related queries like system access requests or annual leave applications.

Generative AI can also personalize the customer experience in other ways, such as generating tailored content or customizing the user interface. For example, a business might leverage generative AI to produce personalized product descriptions or modify its website layout based on each customer’s preferences. This approach enhances the user experience and contributes to customer retention and loyalty.

Improving inventory and supply chain management

Generative AI is a powerful tool for enhancing customer experiences and enabling e-commerce businesses to manage their inventory effectively. Through the analysis of historical sales data, customer behavioral patterns, and market trends, AI tools like ChatGPT can predict demand and optimize inventory levels. A LangChain-integrated system, coupled with an LLM, can delve into your catalog data, offering a detailed analysis of product demand, inventory availability, and emerging trends. This assists businesses in minimizing storage costs, averting stock shortages, and ensuring the availability of popular products. Enhanced inventory management ultimately results in improved customer satisfaction and heightened operational efficiency.

Additionally, the conversation surrounding the transformation of inventory and supply chain management is gaining momentum, especially post-pandemic. Geographic restrictions have plagued supply chains while warehouses struggle with excess, unmovable inventory. Coupled with delivery delays, many brands grapple with the challenge of maintaining a balance between supply and demand. Numerous scenarios exist where integrating generative AI into e-commerce could augment current supply chain tech stacks for better visibility and tracking.

Generative AI, equipped with conversational capabilities, like a chatbot can promptly address pressing queries such as ‘Where is my order?’ Generative AI for e-commerce can swiftly resolve order-related inquiries. Generative AI can also scrutinize current sales data to make precise recommendations for efficient inventory management. By studying historical data, market trends, and user sentiment data, AI assists brands in optimizing their inventories and making informed decisions concerning manufacturing and production.

Generative AI for e-commerce can forecast product demands, empowering businesses to optimize and scale their supply chain networks in preparation for peak traffic periods and quieter days. This predictive capability contributes to a more efficient, responsive supply chain management system.

Fraud detection and prevention

The e-commerce industry faces significant risks from fraudulent activities. However, generative AI models like ChatGPT can assist businesses in identifying and reducing fraud risks. AI algorithms can detect irregularities and flag dubious transactions in real-time by analyzing extensive datasets and learning from historical patterns. This proactive approach allows e-commerce businesses to shield themselves and their customers from potential fraudulent activities, cultivating trust and loyalty.

In a world where hackers and spammers continually attempt to infiltrate online stores, there can be incidents of illegitimate purchases or returns that can erode profit margins and result in the loss of customer trust. Generative AI algorithms can be deployed in online retail to detect and prevent such illegal and fraudulent activities. They can identify unauthorized users or those with suspicious histories and block them from accessing online stores. Over time, this practice saves brands substantial amounts of money and safeguards profit margins.

Auto-generating personalized transaction flows

Presently, most web pages adhere to a standard and fixed structure, displaying the same content, images, and banners to all users, regardless of their individual preferences and choices. Generative AI allows e-commerce retailers to offer highly personalized site experiences tailored to customers’ interests. In other words, every shopper would encounter a custom site where products are auto-populated based on the customer data retained in the backend system. This would yield a level of personalization at an unprecedented scale, finely tuned according to a shopper’s behavioral and demographic data.

Generative AI in e-commerce: Real-life applications

Many e-commerce enterprises have effectively incorporated generative AI to enhance their operations. Here are some noteworthy examples:

  • Amazon: As one of the key players in e-commerce, Amazon uses generative AI to curate personalized content for its customers. Instead of deploying chatbots like OpenAI’s ChatGPT and Google’s Bard, Amazon has capitalized on its stronghold in the cloud space through Amazon Web Services (AWS) to introduce two new AI language models on its platform, including Bedrock. Bedrock enables customers to utilize Amazon’s machine learning models without their data being incorporated back into the pool of information used to train these systems, addressing a critical concern for organizations aiming to create their own generative AI products.
  • Shopify: Shopify recently unveiled Shopify Magic to assist sellers in saving time, increasing sales, and reaching a larger shopper audience.
  • eBay: eBay harnesses generative AI to operate its Smart Store, which aids sellers in crafting a personalized shopping experience for their users.
  • Etsy: Etsy utilizes generative AI to pioneer the future of B2B Commerce in emerging markets.
  • Alibaba Group: The Alibaba Group recently introduced its generative AI model, Tongyi Qianwen, or “truth from a thousand questions.” The plan is to incorporate this model into the company’s applications shortly. This model can summarize meeting notes, draft emails, and create business proposals. It will also be integrated into Tmall Genie, Alibaba’s voice assistant. Alibaba Group’s CEO, Daniel Zhang, stated that this technology “will bring about big changes to the way we produce, work and live our lives.” The ultimate goal is to allow clients to construct their own custom large language models, and user registration has already begun.
  • Carrefour’s implementation of AI-powered customer service: Rather than relying on traditional chatbots with a limited array of interactions, many retail businesses are turning towards generative AI applications for a vast range of responses to customers’ inquiries. This advanced technology enhances the overall shopping experience by offering contextual and relevant product information, transforming how customers navigate e-commerce platforms. In particular, Carrefour, a French grocery retail giant, has adopted this innovation by integrating a ChatGPT-based chatbot on its official website. Now, consumers can use natural-language AI to aid their daily grocery shopping journey. The “Intelligent Assistance” feature on the homepage allows shoppers to receive personalized product recommendations based on their budget, dietary restrictions, and even specific meal ideas. Moreover, being connected to Carrefour’s website search engine, the chatbot provides real-time product suggestions, accompanying customers throughout their purchase journey.
  • The collaborations of Anthropologie, Everlane, H&M, and LOFT with Google for a virtual try-on tool: Online product visualization has always been a significant challenge for both customers and retailers, especially for experiential items like clothing or furniture. Generative AI has emerged as a game-changer in this regard, enhancing the process of product image creation and eliminating the need for time-consuming, expensive in-studio shoots. Brands like H&M, LOFT, Everlane, and Anthropologie are partnering with Google to launch a GenAI-powered virtual try-on tool for women’s apparel. Starting with the brand’s product catalogue image, the tool generates real-time AI-rendered visuals showing how the item would fit on a variety of real models in different poses.

Unlike previous virtual try-on attempts, such as the AR-powered “Be Your Own Model” by Walmart, generative AI sets a new standard by accurately reflecting how clothing items drape, fold, cling, stretch, and form wrinkles and shadows, all while operating on a large scale. This technological leap not only enhances the customer experience but also improves the way products are displayed in the digital landscape.

Generative AI challenges to consider in e-commerce

Generative AI’s increasing utilization in e-commerce has given rise to unique challenges, particularly when transitioning these models into a production environment. The concerns range from quality assurance (the potential for harmful brand and financial impact if users encounter “hallucinations”) to scalability issues (finding the ideal balance between performance, latency, and costs).

The market is experiencing a hype phase, leading to considerable pressure to “announce something.” We have seen multiple companies declare their integrations with generative AI applications (like OpenAI’s ChatGPT and other generative AI tools and large language models). However, as thrilling as generative AI applications in e-commerce might be, staying grounded is essential. It’s crucial to understand that the challenges and risks associated with these applications are far from trivial.


Mitigating hallucinations in e-commerce: The first concern we will address is quality assurance and the threat of hallucinations. It’s a known fact that large language models (LLMs) are often incorrect while confidently stating these errors. Hence, despite the impressive capabilities of generative AI, it’s currently marred by hallucinations and inaccuracies. In the realm of e-commerce, accuracy is indeed a critical factor. Respecting inventory, store availability, and entitlements is paramount. Here are some types of hallucinations that a generative AI in e-commerce might encounter:

  • Fabricated reviews: Customers may depend on counterfeit or biased reviews when purchasing, leading to misinterpretations of a product’s quality or performance.
  • Fictitious discounts: Retailers might give an illusion of a discount by artificially inflating the original price or offering short-term promotions that do not genuinely offer any savings.
  • Illusory inventory: Retailers might advertise products that are out of stock or unavailable, misleading customers into thinking they can buy an item when it’s impossible.

To mitigate these issues, a combination of generative AI capabilities and the accuracy of knowledge bases can be used. Product discovery vendors are crucial in exercising due diligence to ensure that models are appropriate and effective for specific tasks.

Latency issue

While hallucinations present a significant challenge in applying generative AI in e-commerce, they aren’t the sole issue.

It’s a known fact that a suboptimal user experience can deter online shoppers, with 88% indicating they would hesitate to revisit a site following an unsatisfactory experience. Several factors can contribute to a less-than-ideal shopping experience, with latency being a critical concern in e-commerce.

Studies, such as one by Akamai, highlight the negative impact of latency on conversion rates, showing that a 100-millisecond delay in website load time can decrease conversion rates by 7%. Amazon’s findings echo this, showing a 1% drop in revenue with a 100ms delay and a 20% drop in traffic when page rendering is delayed by .5 seconds due to loading more results.

In an era where time is of the essence, introducing new technologies that could potentially deteriorate your user experience by impacting latency is the last thing any business would want.

Generative AI lags behind traditional search requests, typically executed in under a hundred milliseconds. Waiting for a detailed response from the prompt and then obtaining results might not provide the most seamless experience for shoppers at present.

While we remain optimistic that smaller, more accurate models and continuous technology advancements will likely improve latency, we are still far from the anticipated speed of a typical search-driven shopping experience. Hence, latency considerations should be factored into the design of the user interface.

Future prospects of generative AI in e-commerce

The future of generative AI in e-commerce teems with potential and is on course to reshape the industry’s landscape. As these AI algorithms continue to evolve and become more sophisticated, businesses can anticipate more personalized and immersive customer experiences.

  • Augmented Reality (AR) and Virtual Reality (VR) shopping experiences: Pairing AI with AR and VR technology could improve the shopping experience, making it more immersive and interactive. For instance, an AR app powered by generative AI could allow customers to visualize how a piece of furniture might look in their home before purchasing.
  • AI-powered voice assistants: With the growth of smart home devices, the use of voice assistants for shopping is on the rise. Generative AI can help develop more advanced voice assistants to understand customer needs better and offer personalized recommendations.
  • Hyper-personalized marketing: Generative AI can analyze vast amounts of customer data to create hyper-personalized marketing strategies. This means tailoring every aspect of the marketing campaign, from the ad creative to the messaging to the individual customer’s preferences and behavior.
  • Enhanced supply chain management: Generative AI could lead to more efficient supply chain management by forecasting demand more accurately, optimizing inventory levels, and identifying potential supply chain disruptions before they occur.
  • Automated customer service: Generative AI can power chatbots to handle various customer service inquiries, providing instant responses at any time. This not only improves the customer experience but also significantly reduces operational costs.
  • New product development: Generative AI could also be used to design new products based on customer preferences and market trends, speeding up product development and reducing product failure risk.
  • Fraud detection: With its ability to identify patterns and anomalies in large datasets, generative AI can enhance the security of e-commerce platforms by detecting and preventing fraudulent activities in real time.

In summary, the integration of generative AI in e-commerce has the potential to redefine the industry, offering enhanced shopping experiences and efficient operations that can drive growth and profitability for businesses.

How LeewayHertz’s generative AI solution, ZBrain, can help e-commerce merchants?

LeewayHertz has developed ZBrain, a generative AI platform, that builds custom LLM-based applications. Harnessing the capabilities of ZBrain, e-commerce operations can experience a transformative boost. ZBrain empowers businesses to craft tailored applications by training them with their proprietary data, enhancing both internal operations and customer-facing interactions. As a result, an improved customer experience can be achieved while simultaneously reducing operational expenses.

ZBrain’s multifaceted applications allow for rapid responses to customer inquiries and invaluable assistance in managing inventory, saving retailers substantial time and effort. Here are some specific applications of ZBrain’s e-commerce optimized LLM-based app:

  • Inventory management: ZBrain apps can assist in enhancing inventory control, devising strategies that optimize inventory levels and improve overall operations.
  • Price optimization: The apps can offer pricing strategies that adapt in real-time, considering variables such as product demand, competitive pricing, and customer behavior data.
  • Customer service: The apps can promptly answer customer queries and resolve issues by providing the necessary information and proposing appropriate solutions.
  • Sales reporting: They are capable of offering comprehensive sales reports and insightful analytics based on product performance, sales trends, and customer behavior data.
  • Staff management: Help retailers manage staff by creating training resources and tracking staff performance metrics.
  • Market trends and competitor analysis: ZBrain apps provide insights into market trends and conducts competitor analyses, helping retailers make more informed decisions and stay ahead of the competition.

ZBrain offers an intuitive platform that simplifies the process of building a custom LLM-powered app for e-commerce. Connect your knowledge base to ZBrain’s platform, customize the app as per your requirements, and deploy it for seamless integration into your existing workflows, all leading to exceptional user experiences.


The introduction of generative AI, including models like ChatGPT, marks a pivotal turning point in the e-commerce sector. By enabling personalized customer interactions, augmenting product suggestions, enhancing inventory management, and strengthening fraud detection, generative AI is opening up a world of possibilities for online businesses. As AI technology rapidly advances, it opens up exciting new opportunities for the e-commerce industry to explore and innovate.

Given the ever-evolving dynamics of the retail sphere, businesses find themselves in a challenging environment amidst the intricacies of shifting consumer expectations, price sensitivities, escalating online competition, and fluctuating market trends. In such a landscape, they are eager to test and deploy any promising technology that assures increased sales with less time and investment.

In this context, generative AI indeed presents enormous potential, especially within the retail and e-commerce sector. However, it’s imperative to undertake a thorough study and continuous monitoring before integrating it into core operations. Every technology comes with unique challenges and uncertainties, and until these aspects are completely understood, it’s prudent not to dive headfirst into its deployment.

Leverage the power of generative AI in e-commerce with LeewayHertz’s generative AI services and solutions. Elevate your business operations and stay ahead of the curve!

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Author’s Bio

Akash Takyar

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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