AI in eCommerce — Future Trends

AI is transforming every industry, but eCommerce stands out as one of the fastest adopters. This is largely due to the culture that defines digital commerce: an environment built on experimentation, rapid iteration, and a constant appetite for testing what works. Unlike traditional sectors, eCommerce businesses are used to moving fast, adjusting strategies on the fly, and embracing change. In recent years, the focus on scaling operations, automating processes, and driving efficiency has only intensified, making AI a natural fit. The technology aligns perfectly with eCommerce’s drive for performance and agility, accelerating adoption across the board.

AI integration is critical in optimizing business functions and improving the overall consumer experience, allowing brands to leverage automation, dynamic pricing, and enhanced customer trust.

One of the most impactful ways AI is reshaping eCommerce isn’t by fixing obvious bottlenecks, but by unlocking massive opportunities for optimization. A prime example is data analysis. For years, brands have focused on collecting first- and zero-party data — through popups, account preferences, and user surveys, yet much of this data remains underutilized. Beyond basic marketing segmentation, there’s often a gap between data collection and actionable insights. In many cases, teams simply lack the time, resources, or specialized skills to meaningfully analyze the volume of data they’ve gathered. Even brands capturing millions of data points, like individual customer preferences, struggle to put it to work. AI bridges that gap, helping brands surface insights that would otherwise be buried. And it’s not just about strategy: for lean eCommerce teams, AI also plays a critical role in streamlining day-to-day operations like product data input and hygiene, unglamorous but essential tasks that, when automated, can remove significant friction.

Together with Liam, we wanted to identify a few key opportunities where AI can drive real impact in eCommerce, not just in theory, but in ways brands are applying it today.

One of the earliest and most accessible applications of AI in eCommerce has been content generation, particularly product descriptions and ad copy. When ChatGPT gained mainstream attention, platforms like Shopify were quick to integrate AI tools that let merchants auto-populate text fields using simple prompts. While this functionality is helpful for early-stage entrepreneurs launching stores, more advanced brands are now pushing the boundaries with customized content pipelines. Instead of generic outputs, they’re using large language models (LLMs) fine-tuned on their brand guidelines and tone of voice. In some cases, AI-generated content is automatically routed through tools like Airtable, where it’s reviewed by a content team or published directly to their eCommerce platforms. Some brands are leveraging tools like EMFAS, which provide end-to-end configuration and integration into their tech stack.

↑ https://emfas.ai/

Beyond text, the next wave of innovation is in generative image and video content. Brands like Hugo Boss, H&M, Mango, and MKI Miyuki Zoku have openly embraced AI to create marketing visuals and product assets at scale. These implementations vary from tailored, brand-specific workflows ensuring consistency in style and presentation, to plug-and-play apps like Greenroom, which integrate directly with Shopify and product catalogs to generate dynamic media quickly. As visual quality improves and workflow integrations mature, generative media is becoming a core part of modern eCommerce content strategy.

↑ HUGO BOSS utilizes AI-powered fashion campaign

↑ AI Campaign launched by MANGO in 2024

Will we reach a point where Product Detail Pages (PDPs) are entirely AI-generated and continuously optimized? The short answer is: likely yes — but it won’t happen overnight, and not every element will shift at once. In many cases, brands are already tailoring PDPs based on where a visitor is coming from or who they are. It’s not unusual to see variations of a product page depending on whether the user arrived via paid search, is a returning customer, or has certain preferences stored in their profile. What holds most teams back from going further is simply the time and resources needed to create, test, and manage these variations. This is where AI steps in with the ability to rapidly detect patterns in user behavior and context, AI can dynamically adjust content, layout, or messaging at scale. As personalization and optimization tools become more sophisticated, the idea of a fluid, self-adjusting PDP tailored to each individual user starts to look less like a future vision and more like an emerging standard.

For brands with large datasets, whether it’s product catalogs, customer behavior data, or order history, AI has already proven to be one of the most impactful tools in daily operations. While there’s still a lot of debate around AI-generated content and how best to apply LLMs for product descriptions or marketing copy, machine learning for data analysis is a much more mature and proven use case.

One of the clearest areas of value we’ve seen is in managing product data for brands with extensive catalogs—think 100,000+ SKUs. Here, AI helps improve data hygiene, catch anomalies in real time, and spot trends across vast amounts of information—faster and more accurately than a human could, ultimately leading to enhanced customer service. It’s also running 24/7, so brands can proactively detect and resolve gaps in product information as they appear.

We’re seeing brands increasingly integrate AI into their PIM (Product Information Management) systems to automatically categorize and tag products, enrich metadata for better site search and discovery, and reduce the manual workload on their eCommerce teams. The result isn’t just efficiency—it’s also a more discoverable and better-structured catalog for the end user.” says Liam.

AI-powered chatbots and virtual assistants are now handling a significant portion of front-line customer support for eCommerce brands. What used to require a team of agents working around the clock can now be managed, at least in part, by AI systems trained on past support conversations, product FAQs, and even order data.

Modern AI chatbots can:

• Instantly answer common questions about shipping, returns, or product availability.
• Pull up order details or shipping status when given minimal input.
• Escalate only more complex or sensitive queries to a human.

The biggest benefit? Reduced ticket volume and faster time to resolution, especially during peak shopping periods. Brands can offer 24/7 support without needing to scale their customer service teams linearly. And with tools like Zendesk AI, Gorgias, or Intercom integrated with Shopify or headless platforms, it’s easier than ever to build support flows that feel human and helpful.

However, it's also noted that AI customer service, which relies on chatbots, might fail to offer the same support and empathy as a human representative. If done poorly, it can cause friction, customer dissatisfaction, and a poor reputation. There will always be a need for real humans to manage AI tools and handle situations that technology can't

Google Cloud’s Patrick Marlo demoed a next-gen virtual agent for “Simple Home and Garden.” Acting as a customer, he showed how the agent could assist with purchases, recommend products using video, handle complex requests like price matching, and escalate to a human when needed. The agent even scheduled a landscaping service—showcasing how AI can deliver seamless, end-to-end customer support.

↑ Live demo of Google Cloud’s Customer Engagement Suite showcasing personalized e-commerce experiences during the 2025 Next keynote. https://youtu.be/A4tsbJD3bbw?si=CIucTFTS7qdeilek&t=25

Many shoppers don’t type precise product names. They write vague queries like “black dress for wedding,” “something warm but stylish,” or “Nike shoes like Air Max but cheaper.” Traditional search engines often break down in these scenarios.

This is where AI-powered search tools shine. Instead of relying on strict keyword matching, they use semantic search, natural language processing, and product metadata to understand intent. Platforms like Klevu, Algolia, and Constructor are now helping Shopify brands surface more relevant products—even when the query is ambiguous.

Here’s how it works in practice:

• Intent recognition: AI can tell the difference between someone searching “hat” for a beach trip versus a formal event, and tailor the products shown accordingly.
• Learning individual preferences: With every click, return, and purchase, AI systems fine-tune recommendations to match personal style and behavior.
• Hyper-targeted experiences: Advanced AI (sometimes called agentic AI) can dynamically re-rank results in real time, reducing bounce rates and surfacing the right product at the right time.

Ultimately, it’s not just about “finding” products—AI-powered discovery helps convert more users by bridging the gap between how people think and how products are structured in a database.

We shared more about Intelligent Site Search in our article about smart merchandising.

Generating and Optimizing Ads

AI tools are directly assisting in writing and optimizing advertising copy. This includes writing product titles and descriptions, which is especially helpful for feed-based advertising. Generative AI, in particular, is used to scale the production of marketing collateral and tailor it to different audiences. For example, a copywriter can use generative AI to customize a marketing email for various customer segments. Marketers can also get feedback from generative AI on brand messaging to ensure it aligns with target personas. Shopify has even integrated AI for creating product descriptions within its dashboard.Product Intelligence: Smarter Decisions From Data

AI is giving brands a sharper edge when it comes to product intelligence—helping them not just understand what to sell, but also how and when to promote it. From monitoring competitor pricing to spotting emerging trends, AI tools analyze vast datasets (like SKU performance, search demand, or social signals) to inform better decisions across merchandising, marketing, and product development.

Platforms like Trendalytics lead this space by analyzing social trends, search behavior, and catalog data to surface high-opportunity products and time-sensitive demand spikes. Retailers can then align ad budgets and inventory planning to what’s likely to move.

AI-driven campaigns have demonstrated significant results. Chronopost saw an 85% increase in sales revenue after using AI-driven campaigns during their 2022 holiday season. Source

Liam says“At Vervaunt, we’re currently running a couple of initiatives in this space, including those utilizing machine learning alorithms. One is focused on large-scale benchmarking: aggregating data across clients to surface high-level trends in consumer behavior. Another internal project is using AI to analyze the visual characteristics of ad creatives—looking at colors, models, scenes, copy density—and mapping those patterns against performance. That way, we’re building a constantly updating library of what’s working and when. For example, nature-driven visuals might trend in spring, while bold, text-heavy creatives convert best during holiday promotions.”

This kind of intelligence—whether it’s SKU-level or creative-level—lets brands act faster and with more confidence. It’s not just about what’s trending, but why, and how to make that insight actionable across campaigns and product strategy.

While most conversations around AI in eCommerce focus on flashy front-end features—like chatbots or product recommendations—a lot of its real impact happens in the background. AI is increasingly used to automate repetitive backend processes, reduce manual errors, and make supply chains more responsive to real-world signals.

Smarter Inventory Management & Replenishment

AI helps retailers predict demand, track sales velocity, and automate restocking decisions—without needing someone in ops to manually crunch Excel sheets.

Modern systems can:

• Analyze sales patterns and seasonality to forecast future demand.
• Trigger automated replenishment orders based on stock thresholds or promotional calendars.
• Factor in supplier lead times and shipping constraints for better planning.

This kind of predictive inventory management minimizes stockouts, reduces dead stock, and smooths cash flow—especially important for omnichannel brands with inventory spread across warehouses, stores, and 3PLs.

Supply Chain Optimization

AI can also track real-time logistics data, helping brands reroute shipments, avoid delays, or adjust fulfillment strategies on the fly. This is especially useful during peak seasons or when dealing with global supply chain issues.

➡ Example: For instance, Mandaue Foam, a prominent furniture retailer in the Philippines, utilizes Shopify Plus’s automation features to streamline order processing. By implementing Shopify Flow, they automated their order management process, ensuring that orders are fulfilled from the nearest of their 28 physical locations with available inventory. This approach not only expedited delivery times but also eliminated the need for manual order tracking, leading to a 200% increase in order volume and a 151% year-over-year sales growth. Source

Large language models (LLMs) like ChatGPT, Perplexity, and Google’s SGE (Search Generative Experience) are starting to change how generative ai in ecommerce affects people

discover brands online and exemplify the ai use in ecommerce. And that shift is already affecting SEO.

What’s Changing:

• Brand visibility in traditional search results is dropping as LLMs generate direct answers—often without linking out to external websites.
• This shift has a tangible negative impact on organic traffic, especially for content-focused brands that previously ranked well on Google.
• Instead of fighting for page-one rankings, brands now need to consider how and where they appear within LLM-generated outputs.

You can chat directly with AI to get tailored product recommendations—just ask for what you need, and the system will suggest the best options. While the checkout process currently takes place on the eCommerce site, the entire discovery and selection happens right here in the chat.

↑ You can chat directly with AI to get tailored product recommendations—just ask for what you need, and the system will suggest the best options. While the checkout process currently takes place on the eCommerce site, the entire discovery and selection happens right here in the chat.

To address this, forward-thinking companies are using predictive analysis tools like AthenaHQ—a platform designed specifically to monitor brand presence in AI search experiences. AthenaHQ helps brands understand:

• How often they’re being mentioned or recommended in LLM responses.
• Which competitors are gaining visibility in AI-driven results.
• What actions they can take to improve their relevance in generative search.

But it’s not just about visibility—AI search is becoming a conversion channel in itself.

Shopify recently introduced native integrations with ChatGPT and Perplexity, allowing users to:

• Discover products via conversational queries (“show me minimalist black sneakers under $100”)
• View real-time product data from Shopify merchants
• Buy directly from within the chat experience, without visiting a traditional storefront

This is a major shift in how to use AI in ecommerce. It means that brands who want to stay discoverable need to think not just in terms of Google SEO or Meta ads—but also how their products are structured, tagged, and made available to LLMs through APIs like Shopify’s.

AI isn’t a silver bullet—but used intentionally, it can unlock real efficiencies, smarter decisions, and better customer experiences across the eCommerce stack, showcasing the benefits of AI in eCommerce From generating on-brand content at scale to optimizing fulfillment routes or surfacing the right product in search, we’re already seeing the early stages of how AI is becoming embedded in the daily operations of modern commerce teams.

As Liam shared throughout this piece, the brands who benefit most aren’t just adopting AI—they’re weaving it into their strategy, tech stack, and workflows in a way that complements their people and amplifies their output.

The opportunities are here. The question now is: how fast can you adopt?