As spending shifts to e-commerce, AI is reshaping the landscape

Once a fringe business model, e-commerce has become a staple of purchasing in the U.S. and around the world.

Estimates place e-commerce's share of the world's retail at over 23% in 2025, with this figure expected to reach one-quarter by 2030. Even business-to-business (B2B) purchases are increasingly done online.

At the same time, artificial intelligence (AI) is playing a bigger role in the online shopping experience. From frontline agentic support to chat-native shopping, AI has become an interface for discovering, evaluating, and purchasing products.

As more of the economy moves online and becomes integrated with AI, Passport weighed in on how this tech convergence will impact global commerce.

The Global Economy's Ongoing Migration to E-commerce

Over the past decade, e-commerce has evolved from a fast-growing channel into a foundational mode of shopping. Adoption is widespread in the U.S. and around the world.

The COVID-19 pandemic accelerated this trend, but it also marked a hard reset on customer expectations and set a new bar for digital enablement.

The rise of e-commerce opened corridors for international sales, allowing buyers to access more sellers (and vice versa). This has two important implications for businesses:

1. E-commerce can't be an afterthought; it's an ongoing commitment and a prerequisite for scaling in foreign markets.
2. Customers are embracing cross-border commerce and unconventional platforms, creating opportunities for brands that can navigate them.

These shifts have driven investments in physical and digital infrastructure that have made global demand more accessible than ever.

Though we don't often think of it as such, e-commerce is an ongoing technological revolution that shows no signs of slowing.

How AI is Changing E-commerce

Despite its recent boom in attention, AI has been in e-commerce for decades. Traditional AI and machine learning have powered logistics, forecasts, marketing, payments, and even public-facing recommendations since the industry's early years.

However, generative AI (GenAI) brought these capabilities to new levels and fundamentally changed how people interface with the technology.

Since AI's applications are broad, we'll focus on the three most relevant to e-commerce: customer experience, business operations, and decision-making.

AI as a Customer Experience (CX) Engine

The most visible change that GenAI brought to e-commerce is chat-based interfacing.

While online shoppers had already been interacting with AI in the way of product recommendations and other personalized content, chatbots now represent a viable platform for consumers to discover, evaluate, and purchase products.

These "conversations" can take the form of on-page assistants, but shoppers are more likely to use their preferred platform or voice assistant. This avenue alone has already spawned disciplines dedicated to optimizing content for large language models (LLMs).

Though overall trust and approval for AI is split, there's evidence that public sentiment is warming to AI and chat-native shopping.

GenAI has also enabled businesses to offer more in their digital presentation, such as:

• Storefront translation and localization for foreign markets.
• Automation of support tasks that AI can resolve faster than staff.
• Visual search, virtual try on, and augmented reality (AR) shopping.
• Hyperpersonalization at every touchpoint based on user data and behavior.

Rather than operating behind a curtain, AI can now act as a personal shopping consultant that considers past interactions. When done well, the experience drives conversions, upsells, and repeat purchases.

AI as an E-commerce Operations Engine

Most of the items in this category were present before the rise of GenAI but have since seen significant improvements.

When integrated thoughtfully, AI solutions can improve speed, performance, and scalability.

• Repetitive tasks can be automated with minimal oversight.
• Analytics and forecasts become more robust and predictive.
• Operating at scale is more adaptive, accessible, and affordable.

AI excels at identifying trends and discrepancies, which is often applied in logistics, compliance, cybersecurity, order fulfillment, and inventory management.

By streamlining these procedures, AI solutions enable e-commerce brands to scale with minimal additional overhead. Meanwhile, teams are freed to focus on higher-order tasks.

However, firms that use AI as a quick fix or catch-all solution are unlikely to see meaningful returns. Adding a new technology to your stack can complicate or simplify a procedure, depending on how it's used.

AI as a Decision-Making Engine

The previous section shows how AI-powered analytics can help e-commerce brands make better-informed decisions in less time.

A second option that GenAI offers is giving AI the resources to make a limited range of decisions autonomously; this model is commonly called agentic AI.

Depending on the model, AI agents might have access to live datasets, program interfaces, and even other agents.

Traditionally, optimizing any given procedure has been reactive by nature. Teams analyze data, identify opportunities, and manually tune the engine.

At its best, agentic AI makes this process automatic and continuous. Agents can analyze performance in real time and make meaningful adjustments without additional human input.

However, autonomous AI is still high-risk, high-reward. Given too much autonomy or too few parameters, an agentic AI can cause far greater disruptions than a faulty chatbot. Agents handling simple and well-defined tasks with human oversight are still the best practice.

Convergence: AI in E-commerce

As consumer and business spending continues its trend toward online shopping, more of the e-commerce experience is being produced, optimized, and delivered by AI.

The convergence of e-commerce and AI adoption has already shifted consumer expectations and continues to introduce new dynamics, challenges, and opportunities to consider.

For example, shoppers are widely split in their experiences with AI enablement. Common reactions include:

• Enjoyment of the functionality and time saved.
• General distrust of AI and data management practices.
• Frustration from unhelpful agents or a lack of human assistance.

For e-commerce brands, the opportunity is significant, but so is the risk. Embracing AI effectively requires more than adopting new technology but integrating it in a purposeful way that solves problems without creating new ones.

Deciding If, When, and How to Use AI for E-commerce

Generative and agentic AI have introduced more nuance in the age-old balance of technology and traditional labor. These new advancements lead to familiar questions, including whether to buy in and, if so, where and how to implement.

There's no one-size-fits-all answer to these questions; there are simply too many AI variants and use cases for an easy yes or no. However, noting the common risks and best practices will help you make an informed decision.

Risks of AI Implementation in E-commerce

• Poorly executed features can feel irrelevant or intrusive.
• More cybersecurity and data management considerations.
• Poor experiences with AI agents can increase cart abandonment.
• Over-automation can dilute the brand or impede the customer experience.
• Fragmented systems can create operational complexity rather than reducing it.

There's a notable tendency for leaders to overestimate what AI can replace and underestimate how much human oversight it needs.

Best Practices for Adopting AI in E-commerce

A practical approach starts with focusing on a high-impact area, such as:

• Inventory management
• Customer personalization
• Dynamic price optimization

These use cases tend to deliver measurable results relatively quickly. Piloting an isolated use case gives you a quick and affordable idea of whether your approach to AI is working.

Ensure that your AI initiative aligns with measurable business objectives. Technology should support clear outcomes, whether that's conversions, costs, or error rates.

AI typically works best as a complement to human decision-making rather than a replacement. The most effective implementations combine automation with expertise.

FAQs: AI in E-commerce

New tech raises new questions.

Will AI replace e-commerce?

AI is unlikely to replace e-commerce as a whole, but it's fundamentally reshaping how customers interact with the process and how businesses scale their operations.

How is AI used in e-commerce today?

AI is currently used for a wide variety of tasks, not limited to:

• Personalization and product recommendations.
• Supply chain logistics and order fulfillment.
• Customer support and pricing optimization.
• Compliance checks and fraud detection.

Agentic AI solutions, in particular, are enabling e-commerce businesses to automate more complex procedures.

How has AI changed e-commerce?

AI has enabled dynamic personalization of the customer experience, making every step of the sales funnel more effective (when implemented well). AI has also improved the accessibility of scale, allowing teams to increase their capabilities without as much added overhead. AI's analytical and automation capabilities are powering complex multinational operations.

Does AI actually improve e-commerce conversion rates?

Yes, when done right. AI-supported shopping can reduce friction, improve relevance, and optimize performance metrics through automated iteration and analysis.

How do customers feel about using AI when shopping?

Sentiment is mixed, but adoption is growing quickly. Many consumers report using AI for product discovery and research.

What are the benefits of integrating AI in an e-commerce store?

At its best, AI can help e-commerce businesses operate more efficiently, improve their customer experience, and scale without increasing headcount. Like any technology, these benefits are limited to how effectively it is leveraged.

What are the biggest risks of using AI in e-commerce?

At its worst, AI can erode data quality, frustrate customers, leak sensitive data, and increase operational complexity. Agentic AI can go a step further by mismanaging any procedures under its control. Teams can minimize these risks with careful implementation and thorough human oversight.

How should e-commerce brands implement AI?

Brands should begin with a low-risk, high-impact use case. Piloting multiple new programs at once muddies the results and risks excessive complication. Look for applications that are both practical and measurable, such as integrating an AI solution built for a task that currently occupies a lot of your time or resources. Whatever starting point you choose, make sure it ties into a key performance indicator or business goal.

This story was produced by Passport and reviewed and distributed by Stacker.

Copyright 2026 Stacker Media, LLC

This story was originally published April 29, 2026 at 5:00 AM.