AI is transforming how we shop online. Here's what you need to know:
- Shift in Search Behavior: 47% of Gen Z users now use generative AI weekly, and 46% start searches on social media instead of search engines. Marketplaces are becoming the primary choice for product discovery, with 47% of shoppers starting their journey there.
- Generative AI's Impact: AI tools are boosting personalization, improving product recommendations, and automating tasks like tagging and content creation. These advancements have led to a 30% increase in conversion rates and higher average order values.
- Multimodal Search: Combining text, voice, and image search is making shopping faster and more intuitive. Voice search already accounts for 40% of e-commerce queries.
- Revenue Growth: AI-powered personalization now drives 24% of orders and 26% of revenue. Platforms like Amazon report up to 40% improvements in listing quality with AI tools.
- Challenges: Privacy concerns, algorithmic bias, and technical limitations remain hurdles. Businesses must focus on ethical AI use, data security, and compliance with evolving regulations.
Takeaway: AI is no longer optional for marketplaces - it’s essential. From personalized recommendations to multimodal search, adopting AI-driven tools is key to staying competitive in the evolving e-commerce landscape.
🔍🤖 What AI-Powered Search Could Change for Ecommerce
How AI-Powered Search Has Developed in Marketplaces
Search functionality in marketplaces has come a long way. What started as simple keyword matching has transformed into advanced systems that grasp context and even predict what users are looking for. This shift highlights how businesses are now leaning into conversational and AI-driven technologies to elevate the search experience.
Market Growth and Key Drivers
The numbers tell a compelling story. The global AI search engines market is projected to hit $108.88 billion by 2032, growing at a steady 14% compound annual growth rate (CAGR) from its $43.63 billion valuation in 2025 [7]. Within this market, web search holds a 61.7% share in 2025, while generative AI leads with 54.2% of the market [7].
Consumer behavior is a major driver of this growth. By 2025, 95% of businesses will have implemented or plan to implement AI, and 74% of marketers are already using at least one AI-powered tool to enhance their strategies [2]. North America takes the lead, accounting for 41.4% of the market share in 2025, thanks to early adoption and significant infrastructure investments [7].
It's not just AI search that's growing. The machine learning market is expanding at a staggering 50.8% CAGR, climbing from $18.4 million in 2019 to an anticipated $216.6 billion by 2025 [5]. This surge in machine learning capabilities is laying the groundwork for the sophisticated search tools that modern marketplaces depend on.
These trends are not just numbers - they’re shaping how AI search is applied across different industries.
Adoption Trends Across Sectors
Different marketplace sectors are embracing AI search at varying speeds, tailoring the technology to meet their unique needs.
E-commerce platforms are leading the charge. They’re using AI to fine-tune search results and offer personalized recommendations. Take Amazon, for example: 35% of its sales come from machine-learned recommendation models [6]. In 2019, Amazon launched Amazon Personalize, enabling merchants to use customer demographics and activity data to create tailored product libraries. This innovation has sped up buying decisions and improved customer satisfaction.
Similarly, The Home Depot has integrated natural language processing into its sentiment analysis models. This approach helps the company better understand customer feedback, boosting both engagement and conversion rates when paired with personalization [6].
B2B marketplaces are also seeing a transformation. Platforms like Global Trade Plaza are leveraging AI to enhance procurement processes. They use real-time analytics and automation for tasks like vendor screening and contract negotiations, making supply chains more efficient [3]. Alibaba B2B employs machine learning to streamline supplier matching and automate negotiations, while Amazon Business offers tools for automatic reordering and supplier recommendations, simplifying purchasing for businesses [3].
In the realm of niche marketplaces, AI is being used to combine e-commerce with community engagement. For instance, Xiaohongshu has integrated AI-driven content curation into its lifestyle platform. This has attracted a financially independent user base, enabling brands like L'Oreal and Coach to set up stores and collaborate with influencers for livestream shopping events [4].
Even traditional retail is benefiting. Zara has introduced AI-powered mobile robots to speed up in-store collections, cutting down customer wait times and enhancing their "buy online, pick up in-store" model [5]. This blend of online and offline experiences highlights how AI search tools are reshaping retail operations.
The results of these implementations are striking. Ivanti used 6Sense's AI tools to centralize its data, leading to a 71% increase in opportunities created, a $263.2 million pipeline, and a 94% increase in opportunities won - adding $18.4 million in revenue [2].
"AI takes the traditional weeks- or months-long research processes and condenses them into algorithms that deliver valuable consumer insight in just minutes. This is the efficiency and the scale that both brands and e-retailers need to convert consumers at the speed of digital commerce." – Jehan Hamedi, CEO & Founder of Vizit [5]
Another example is Bookshop.org, which implemented Meilisearch's AI-powered search. The result? Faster search performance and a 43% jump in sales-based purchases [8]. Even small improvements in search technology can lead to significant gains.
These examples underscore AI's wide-reaching impact across industries. For marketplace operators eager to explore similar solutions, the Top SaaS & AI Tools Directory offers a wealth of resources for tools that can enhance lead generation, sales, and marketing through smarter search and discovery systems.
How Generative AI Improves Product Discovery
Generative AI is reshaping how we discover products in online marketplaces. Research shows that it has become a key driver of success in e-commerce. Brands that embrace AI-powered content strategies are not only dominating search rankings but also refreshing hundreds of SKUs effortlessly and building stronger relationships with retailers [9].
This evolution builds on earlier AI advancements in search, offering richer, real-time insights into products. With e-commerce sales projected to reach an astounding $6 trillion, advanced AI-driven search tools are boosting conversion rates by 30% and increasing average order values by 15% [11]. These advancements are also redefining automated tagging and content optimization, making product discovery smarter and faster.
Automated Product Tagging and Search Optimization
Generative AI is changing the game for tagging, categorizing, and optimizing products for search. Instead of relying on tedious manual processes, AI systems now automatically generate detailed metadata, including alt text, to make products easier to find. Leveraging computer vision and natural language processing (NLP), these systems significantly enhance product discoverability, which can lead to a 94% higher conversion rate [10] [12].
Beyond tagging, generative AI automates the creation and optimization of product content - like titles, descriptions, and FAQs - based on live keyword trends, retailer updates, and brand guidelines [9]. By analyzing customer search behavior and trending keywords, AI fine-tunes listings to stand out. This includes tweaking phrasing, highlighting key features, prioritizing images, and ensuring compliance with marketplace-specific requirements.
Visual search capabilities powered by generative AI are also making shopping more intuitive. Shoppers can now use images instead of text to search for products, creating a more personalized and seamless experience [11].
Take LAAM, for example - a South Asian fashion marketplace with nearly 100,000 products. In December 2024, LAAM adopted AI technologies like computer vision, NLP, and generative AI to personalize user feeds and improve product tagging. The result? A better shopping experience and streamlined internal operations [10].
Real-World Implementations
Many marketplaces are already reaping the benefits of these advancements. One standout example is Amazon's Enhance My Listing (EML) tool, launched in May 2025. This tool uses generative AI to help sellers optimize their product listings. Over 900,000 Amazon sellers now use it, and sellers accept AI-generated content with minimal edits about 90% of the time. Those using these tools report a 40% improvement in listing quality [13].
"I now upload photos and receive content within 15 minutes", says Jason Hunt from Campcraft Outdoors [13].
This efficiency allows sellers to focus on other areas of their business while keeping their listings polished and effective.
Amazon's A+ Content tool takes things further by enabling brands to create engaging narratives and visually appealing product pages. This has led to sales increases of up to 20% for sellers [13]. Etsy, on the other hand, uses "algotorial" curation, blending AI and human insights to expand curated collections from 50 to nearly 1,000 listings, all while maintaining aesthetic quality [14]. eBay employs generative AI to refine search results by analyzing product data, user queries, and historical trends, delivering faster and more accurate results [11]. Shopify also taps into generative AI to personalize search experiences, offering tailored recommendations that boost user engagement and conversions [11].
"We're absolutely blown away by Amazon's Gen AI listings tools... The AI is wonderful, and it's getting better with time. Now it creates detailed bullet points and descriptions and makes our 800+ listings more uniform. So far, we've created about 300 listings with the tool, and they're more discoverable and shoppable than ever", share Michael and Cynthia Gore, founders of C&M Personal Gifts [13].
These examples highlight how generative AI is not just improving product discovery but also transforming the way marketplaces operate - making them more efficient for sellers and more enjoyable for shoppers.
User-Focused Search: Multimodal and Personalized Experiences
Marketplace search is evolving to meet users where they are. By 2025, this means combining image, voice, and text search with personalization that adapts to individual preferences. The numbers speak volumes: the multimodal AI market is forecasted to grow from $2.51 billion in 2025 to a staggering $42.38 billion by 2034, with an annual growth rate of 36.92% [18].
Instead of sticking to rigid search boxes, marketplaces are shifting toward systems that mimic natural human behavior. The result? A more intuitive, efficient, and satisfying shopping experience.
Multimodal Search Interfaces
Multimodal search interfaces tap into how people naturally look for things. They let users search through images, voice, or text - whatever feels easiest. This approach blends searching, browsing, and chatting into a single, seamless process, making product discovery faster and more straightforward [15].
Take Google Lens, for example. It handles nearly 20 billion visual searches every month, with 20% of those tied to shopping [17]. Unsurprisingly, younger users aged 18–24 are among the most engaged with this tool [17].
"Lens is a key part of the future of Google Search, precisely because of its connection to Google's AI initiatives. Google is obsessed with finding new ways for you to search, both in order to make search easier and also to give you new reasons to Google stuff", explains David Pierce from The Verge [17].
Real-world applications of multimodal AI are already making waves. In January 2025, an e-commerce jewelry brand used r2decide's AI to fix a common search problem. Previously, searching for "green" only worked if the product description included that exact word. By encoding images into a shared data space and factoring in user history, the AI enabled shoppers to find green jewelry - even when the word "green" was absent from the text [15].
Voice search is also gaining traction as smartphone users increasingly prefer conversational queries. Instead of typing keywords, people now ask full questions. This shift requires content that feels natural and conversational [17].
"With voice-activated devices becoming more popular, voice search optimization is only getting more important. People are searching with full phrases and questions, so it's key to have content that sounds natural and conversational", notes Rachael Powell, Senior Technical SEO at Lumar [17].
Google's April 2025 Cloud Next event showcased just how advanced these systems have become. Using Gemini's multimodal capabilities, they tackled a 1970s kitchen renovation by analyzing text descriptions, floor plans, and images simultaneously. The AI suggested everything from cabinets to color palettes, estimated costs, and even checked local building codes through Google Search [16].
Behavioral Prediction Models for Personalization
While multimodal interfaces make searching easier, personalization ensures users find what they need. AI systems analyze browsing habits, past purchases, social media activity, and real-time interactions to deliver tailored recommendations.
The impact is undeniable. Research shows that 80% of customers are more likely to buy from brands offering personalized experiences [20]. Even better, 98% of online retailers see higher average order values when personalization is in play [21]. With modern customer journeys involving anywhere from 20 to 500 touchpoints [20], AI helps businesses connect the dots by identifying patterns and predicting user needs.
A standout example comes from Banner Health. In 2025, they used Invoca's HIPAA-compliant platform to implement data-driven personalization, slashing patient acquisition costs by 74%. Orthopedic campaigns saw a jaw-dropping 597% decrease in costs, while neurology campaigns achieved a 13% reduction in cost per acquisition [20].
AI-driven personalization doesn’t stop at recommendations. It powers dynamic marketing campaigns that adjust in real time and creates shopping journeys tailored to individual users [19]. When paired with robust privacy measures, this level of personalization feels helpful rather than invasive.
Another noteworthy trend is the rise of autonomous retail operations. AI systems are increasingly handling tasks independently, relying on learned patterns and real-time data to deliver instant personalization. This ensures shopping experiences keep evolving to match user preferences - without requiring human intervention [19].
Challenges and Ethical Issues in AI Search
AI-powered search has undoubtedly transformed how we access information, but it’s not without its hurdles. Privacy concerns, algorithmic bias, and technical obstacles pose real challenges that demand careful attention from marketplace operators.
Data Privacy and Ethical Challenges
Trust in AI remains a fragile thing. A striking 57% of consumers believe AI poses a serious threat to their privacy, while only 12% disagree with this sentiment[22]. The reason? AI relies on massive datasets for training, which raises pressing questions about how this data is collected, used, and stored[25]. Even more concerning is how AI can connect seemingly unrelated data points to uncover private details, leading to risks like financial harm, security breaches, and even discriminatory practices like targeted phishing or pricing schemes[25].
"Privacy and proper data governance are foundational to Responsible AI", says Dev Stahlkopf, Cisco Chief Legal Officer[23].
In workplace environments, the risks are equally alarming. Around 64% of respondents express concerns about unintentionally sharing sensitive information when using generative AI tools, and nearly half admit to having accidentally entered sensitive employee data into these systems[23]. Despite these fears, there’s widespread support for privacy safeguards - 86% back privacy legislation, and 96% believe investments in privacy protections yield returns exceeding their costs[23]. To address these concerns, operators can take steps like limiting data collection to only what’s absolutely necessary, employing strong encryption, and ensuring transparency so users can access, modify, or delete their data as needed[24].
Bias in AI systems is another pressing issue. Take Amazon’s 2018 recruiting algorithm, which was scrapped after it was found to penalize resumes that included the word "women's" and downgrade candidates from women’s colleges[27]. Similarly, studies reveal facial recognition systems perform with near-perfect accuracy for white men but show error rates of over 20%, and sometimes as high as 34%, for darker-skinned women[27]. Tackling this issue requires diverse training datasets, bias-aware algorithms, and mechanisms for user feedback to flag and correct discriminatory outcomes[26].
Technical Limitations and Market Saturation
Beyond ethics, technical barriers also limit the capabilities of AI search. Over half of respondents in a Statista survey report little or no trust in AI’s ability to make unbiased decisions[8]. The quality of the data fed into these systems is critical - poorly structured or biased inputs can lead to flawed results[8]. Moreover, integrating AI into older, legacy systems often proves challenging, as many lack the APIs or infrastructure needed to support advanced AI technologies[28].
A lack of skilled professionals in technical and compliance roles further complicates implementation. Meanwhile, evolving regulations around AI accountability and explainability create additional uncertainty for businesses[28]. Misconfigured models or unsecured data pipelines only add to the risks, exposing systems to cyberattacks and breaches[28].
"We're seeing AI agents evolve from content generators to autonomous problem-solvers. These systems must be rigorously stress-tested in sandbox environments to avoid cascading failures. Designing mechanisms for rollback actions and ensuring audit logs are integral to making these agents viable in high-stakes industries", explains Vyoma Gajjar, AI Technical Solutions Architect[29].
Some experts question whether current advancements represent genuine progress or merely a rebranding of older methods. To overcome these issues, companies should establish strong governance frameworks to monitor performance, focus on solving real business problems, and organize private data so AI systems can access relevant information efficiently[29]. These technical challenges, coupled with ethical concerns, highlight the ongoing need for human oversight in AI search.
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Practical Advice for Marketplace Operators
For marketplace operators, having a clear plan that balances forward-thinking strategies with responsible practices is essential. Success starts with defining goals and setting practical steps to achieve them [31].
Balancing Standard and AI-Driven Search Methods
The most effective marketplaces don’t entirely replace traditional search methods - they combine them with AI to create a hybrid approach. This ensures the strengths of both systems are utilized. Keeping traditional search as a backup option alongside AI-driven tools can provide stability and flexibility [37].
Start small by focusing on high-intent queries, then expand gradually. This minimizes risks and helps achieve better outcomes [8]. Instead of trying to do everything at once, prioritize features that meet the most immediate user needs [8].
A content audit is crucial to identify and fix outdated or missing information that might confuse AI systems [38]. Developing an AI-focused content calendar can help keep data accurate and up-to-date, as stale information negatively affects AI performance [38].
Organize content using schema markup, FAQs, and structured data [38]. Write in a clear and conversational tone that’s easy for AI to interpret but still engaging for users. For instance, Alibaba’s machine learning algorithms identified over 500 consumer micro-segments, allowing for highly targeted marketing efforts that boosted conversion rates by 38% [37].
Consistently run A/B tests to refine your search methods and determine what works best for different customer scenarios [38]. Track conversions at every stage of the buyer’s journey to see which search method delivers the best results [38].
This hybrid strategy lays the groundwork for building a broader AI system that improves efficiency and user satisfaction.
Building an AI Ecosystem
Developing an AI-driven search ecosystem requires careful integration with existing platforms and a focus on data quality. Make sure your system works seamlessly with platforms like Shopify, Magento, and WooCommerce [30]. For custom-built platforms, ensure API compatibility to allow real-time indexing and synchronization [30].
The backbone of any effective AI system is high-quality data. Provide detailed product information, including accurate descriptions, clear categories, and complete specifications [30]. Enhance relevance by incorporating user activity data like search history, click rates, and purchase behavior [30]. Allow your AI to learn and improve through real-time interactions, making it more accurate over time [30].
When choosing an AI search solution, evaluate providers based on precision, adaptability, and the features they offer, such as natural language processing, image recognition, predictive analytics, and intelligent recommendations [30]. Scalability, security, and strong customer support should also be top priorities [30].
Real-time synchronization between your AI search and product inventory is essential to avoid frustrating users with unavailable items [30]. For popular platforms, pre-built plugins can simplify this process, while proprietary systems may require API-based solutions [30]. Customize the user interface to match your site’s design while ensuring the shopping experience remains smooth and intuitive [30].
Voice search is now responsible for 40% of e-commerce searches, making multimodal capabilities more critical than ever [37]. Additionally, advanced chatbots can handle 70% of customer service inquiries, achieving a 92% satisfaction rate [37]. These advancements highlight the importance of creating a system that supports diverse interaction methods.
Compliance and Data Security Frameworks
Once you’ve balanced search methods and built an integrated AI system, securing these systems becomes a top priority. With the AI market projected to exceed $3 trillion by 2034, regulatory scrutiny is increasing [32]. For example, starting in January 2025, four US states will enforce new privacy laws, followed by New Jersey’s regulations on January 15, and the EU’s Digital Operational Resilience Act (DORA) on January 17 [32]. Strong security measures are critical to maintaining consumer trust.
Adopt flexible compliance strategies that can adapt to changing regulations [32]. Embed privacy considerations into every stage of development and deployment using Privacy-by-Design principles [32]. This is especially important as 81% of Americans believe the risks of AI outweigh its benefits, with data privacy being a major concern [34].
Privacy-Enhancing Technologies (PETs) like differential privacy and federated learning can protect user data while keeping AI functional [32]. In 2023, Gartner found that 60% of compliance officers plan to invest in AI-powered regulatory technology by 2025 [34]. These tools can automate compliance tasks, and IDC predicts that by 2024, AI-driven tools will handle 70% of tasks related to classifying Personally Identifiable Information (PII) [34].
From the outset, embed security features into your AI architecture. Prioritize input sanitization and prompt handling to block malicious activity [33]. Monitor and log AI activities continuously to quickly identify and address potential security threats [36].
Prepare for incidents with a robust response plan that includes multi-factor authentication, encryption, firewalls, intrusion detection systems, and regular audits [35]. Service-level agreements (SLAs), redundancy, and disaster recovery plans can ensure uninterrupted service [35].
"AI represents a future for privacy that has been anticipated for a long time; AI starkly highlights the deep-rooted flaws and inadequacies in current privacy laws, bringing these issues to the forefront", says Daniel J. Solove [32].
Implement frameworks like ISMS (ISO 27001) and PIMS (ISO 27701) to establish risk-based governance models [32]. Work with local consultants and legal experts to ensure compliance with regional laws, as requirements can vary widely [32]. Promote ethical data practices by being transparent about how you collect and use data [36].
Bringing in a third-party team to review your e-commerce store’s AI systems can help identify weak points in your compliance strategy and ensure you’re following industry standards from the start [31].
Conclusion: The Future of AI Search in Marketplaces
By 2025, AI-powered search transitioned from being a novel experiment to becoming a core part of how businesses operate. Chris Young from Microsoft captures this shift perfectly:
"AI is already making the impossible feel possible, and over the past year we've seen significant numbers of people and organizations moving from AI experimentation to more meaningful adoption. This is the start of a full-scale transformation of how this technology will change every part of our lives" [1].
A key driver of this transformation is the rise of AI "agents", which function like the apps of this new era. As Charles Lamanna from Microsoft explains:
"Think of agents as the apps of the AI era. Just as we use different apps for various tasks, agents will begin to transform every business process, revolutionizing the way we work and manage our organizations" [1].
These advancements mean AI systems won't just respond to queries - they'll be capable of planning and executing complex shopping tasks, fundamentally changing how consumers interact with marketplaces.
The numbers tell the story of this rapid adoption. Generative AI usage among business leaders surged from 55% to 75% in just a year [1]. In late 2024, U.S. retail websites saw a staggering 1,300% increase in traffic driven by generative AI searches [40]. Visitors arriving through AI-powered search stayed on websites 8% longer, explored 12% more pages, and had a 23% lower bounce rate compared to those using traditional search methods [40].
Another major shift is the rise of multimodal search. Voice search now accounts for 40% of e-commerce searches [37], while platforms like TikTok Shop reported a 113.8% increase in revenue year-over-year [40]. To stay competitive, marketplaces must embrace these diverse interaction methods that align with how users naturally search and shop.
Still, this new era isn't without its challenges. Ece Kamar from Microsoft's AI Frontiers Lab highlights a critical concern:
"In 2025, a lot of conversation will be about drawing the boundaries around what agents are allowed and not allowed to do, and always having human oversight" [1].
As regulatory frameworks tighten, businesses must focus on data privacy and ethical AI use. Balancing innovation with accountability is no longer optional - it’s essential for long-term success.
The stakes are high, but so are the rewards. Social commerce is projected to hit $2 trillion in global sales, making up 27% of all e-commerce [39]. Companies leveraging AI-driven personalization are already seeing 44% higher customer retention rates [40].
To succeed long-term, marketplaces must evolve. Mark Russinovich of Azure underscores the importance of a broader perspective:
"In 2025 and beyond we're going to increasingly have a holistic view of datacenters, energy and resources, so that we can maximize the efficiency of our entire infrastructure" [1].
This holistic mindset applies to every aspect of marketplace operations, from improving search accuracy to optimizing logistics and customer service. The future belongs to marketplaces that combine strong data foundations, secure systems, and strict compliance with the flexibility to embrace emerging technologies like AR, VR, and advanced personalization tools. AI search is paving the way for intelligent, user-focused environments that anticipate needs and deliver seamless shopping experiences.
FAQs
How is generative AI improving product discovery and boosting sales in online marketplaces?
How Generative AI is Shaping Online Marketplaces
Generative AI is changing the game for online marketplaces, making it easier and more enjoyable for customers to discover products. By analyzing user behavior and preferences, AI creates tailored product recommendations and fine-tunes search results to meet individual needs. The result? Shoppers are more engaged, and conversion rates climb.
AI-powered search engines are also getting smarter. They now understand user intent more effectively, helping shoppers find what they need without the usual hassle. This mix of personalized suggestions and intelligent search tools is giving online marketplaces an edge in a crowded space, all while delivering a smooth and satisfying shopping journey.
What are the main ethical and privacy concerns with AI-driven search in online marketplaces?
Ethical and Privacy Concerns in AI-Driven Search
AI-powered search in online marketplaces brings up some serious questions about ethics and privacy. A major concern is data privacy. These systems often depend on gathering huge amounts of personal information. If this data isn’t managed carefully, it can lead to misuse or even security breaches. What’s worse, many users don’t fully understand how their data is being collected or used, leaving them in the dark about what they’re agreeing to.
Another pressing issue is algorithmic bias. Sometimes, AI unintentionally favors or disadvantages certain groups of people. This can lead to unfair search results or recommendations, shaking user confidence and creating an uneven playing field.
To tackle these problems, companies need to take clear steps. This includes being upfront about how data is used, ensuring users give informed consent, and using strategies like collecting only the data that’s absolutely necessary. It’s also critical to stay up-to-date with changing regulations and ethical guidelines to maintain trust and use AI responsibly.
How are multimodal search interfaces changing the way people shop on marketplaces?
Multimodal Search Interfaces: Transforming Marketplace Shopping
Multimodal search interfaces are changing the way we shop online, offering a more intuitive and personalized experience. These systems allow users to search for products not just through traditional text input but also using voice commands or images. This goes beyond standard keyword searches, making the process smoother and more interactive.
This evolution aligns with shifting consumer habits, particularly among younger shoppers who are drawn to AI-powered tools and features that feel similar to social media interactions. With nearly half of all shoppers now beginning their search directly on online marketplaces, these interfaces enhance product discovery. They also make it easier to compare options and explore products, helping shoppers make faster and more confident buying decisions - all within a single platform.
