Once a fringe business model, ecommerce has become a staple of purchasing in the U.S. and around the world.
Estimates place ecommerce’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 artificial intelligence, our experts weigh in on how this tech convergence will impact global commerce.
The Global Economy’s Ongoing Migration to Ecommerce
Over the past decade, ecommerce 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 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 ecommerce opened corridors for international sales, allowing buyers to access more sellers (and vice versa). This has two important implications for businesses:
- Ecommerce can’t be an afterthought; it’s an ongoing commitment and a prerequisite for scaling in foreign markets.
- 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, ecommerce is an ongoing technological revolution that shows no signs of slowing.
How AI is Changing Ecommerce
Despite its recent boom in attention, artificial intelligence has been in ecommerce 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 ecommerce: customer experience, business operations, and decision-making.
AI as a Customer Experience (CX) Engine
The most visible change that GenAI brought to ecommerce 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
- Hyper-personalization 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 Ecommerce 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.
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Repetitive tasks can be automated with minimal oversight.
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Analytics and forecasts become more robust and predictive.
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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 ecommerce 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 ecommerce brands make better-informed decisions in less time.
A second option that GenAI offers is giving artificial intelligence 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: Artificial Intelligence in Ecommerce
As consumer and business spending continues its trend toward online shopping, more of the ecommerce experience is being produced, optimized, and delivered by AI.
The convergence of ecommerce 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 lack of human assistance
For ecommerce 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 Ecommerce
Generative and agentic artificial intelligence 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 Ecommerce
- 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 Ecommerce
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.
Artificial intelligence typically works best as a complement to human decision-making rather than a replacement. The most effective implementations combine automation with expertise.
FAQ: Artificial Intelligence in Ecommerce
New tech raises new questions. Here are answers from Passport’s AI-forward experts.
Will artificial intelligence replace ecommerce?
AI is unlikely to replace ecommerce as a whole, but it’s fundamentally reshaping how customers interact with the process and how businesses scale their operations.
How is artificial intelligence used in ecommerce today?
AI is currently used for a wide variety of tasks, not limited to:
- Personalization & product recommendations
- Supply chain logistics & order fulfillment
- Customer support & pricing optimization
- Compliance checks & fraud detection
Agentic AI solutions, in particular, are enabling ecommerce businesses to automate more complex procedures.
How has artificial intelligence changed ecommerce?
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 artificial intelligence actually improve ecommerce 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 ecommerce store?
At its best, AI can help ecommerce 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 artificial intelligence in ecommerce?
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 ecommerce 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.
Authored by Tony Chen
CTO | Passport
Tony Chen, a seasoned entrepreneur and tech luminary, navigates the realms of software, hardware, and data. Formerly an Engagement Manager at McKinsey, his strategic counsel shaped Fortune 100 companies. As co-founder of Pantry, he revolutionized fresh food retail. Tony’s engineering prowess was honed at Ericsson, Qualcomm, and Intel. Armed with a BS in EECS from UC Berkeley, he’s a trailblazer in every venture, fusing business acumen with technological finesse.