Artificial Intelligence in B2B and B2C Ecommerce: A Practical Guide for 2026

Artificial intelligence applied to ecommerce makes it possible to personalize the shopping experience, automate customer service, optimize the product catalog, and detect fraud in real time. In 2026, 62% of retailers already using AI report a direct increase in conversion rate (Salesforce State of Commerce, 2024).

In this article, we share all the key data you need to know to begin to understand how artificial intelligence in ecommerce can be the new technological leap that has already begun.

This article was first published on April 6, 2023, and has been updated in May 2026.

What Is Artificial Intelligence and Why Does It Matter to Ecommerce?

Artificial intelligence (AI) is the ability of a computer system to perform tasks that previously required human intelligence: learning from data, recognizing patterns, processing natural language, and making autonomous decisions. It is not a new concept, but its widespread adoption in ecommerce accelerated irreversibly following the launch of ChatGPT in November 2022, which normalized the use of conversational AI among consumers and businesses alike (OpenAI, 2022).

At Orienteed we have spent years helping B2B and B2C commerce companies evolve technologically. What we have observed over the past two years is a clear shift in mindset: AI has moved beyond being a pilot project for tech companies and has become an operational lever that directly affects business metrics such as average order value, support costs, and cart abandonment rate.

"The question we hear more and more from clients is not 'should we use AI?' but 'where do we start and what ROI can we expect in the first six months?'""
— Francesco Schettini, CEO of Orienteed

What Types of AI Are Relevant for an Ecommerce Business?

Not all artificial intelligence is the same, nor does it carry the same level of maturity for immediate application in an online store. These are the categories that truly matter in a commercial context:

AI type	What it does	Application in ecommerce	Current maturity
Machine Learning (ML)	Learn patterns from historical data	Product recommendation, demand forecasting, fraud detection	High — production
Natural Language Processing (NLP)	It interprets and generates human language	Chatbots, semantic search, review analysis	High — production
Computer Vision	Analyze and interpret images	Visual search, product image generation, quality control	Medium-High
Generative AI (GenAI)	Create new content (text, image, code)	Product sheets, campaign creatives, customized descriptions	High and expanding
General AI (AGI)	Reason in multiple domains like a human	No mature commercial application yet	Investigation

For most ecommerce projects in 2026, the focus should be on ML, NLP, and Generative AI: these are the layers with the most demonstrable ROI and immediate availability on platforms such as Adobe Commerce, Shopify Plus, or headless platforms.

Which Companies Are Using AI in Ecommerce, and What Results Are They Seeing?

AI use cases in ecommerce no longer belong exclusively to large platforms. Amazon, Alibaba, Zara, Walmart, and Adidas were early adopters, but the technology they developed internally five years ago is now available as a service to any online store.

• Amazon AI for personalized recommendations that generate 35% of its total revenue (McKinsey, 2023).
• Zara (Inditex) applies ML in its supply chain to reduce excess inventory, achieving a markdown rate 40% lower than the industry average (Harvard Business Review, 2022).
• Alibaba Alibaba processes more than 100 million daily queries with conversational AI on its Taobao platform (Alibaba Group Annual Report, 2023).

What is relevant for mid-size companies is not replicating Amazon's scale, but applying the same principles — personalization, automation, friction reduction — with AI tools now accessible through APIs and platform modules.

How Is Artificial Intelligence Used in an Ecommerce Store in 2026?

The AI applications in ecommerce with the most demonstrable impact in 2026 are concentrated in five operational areas:

1. Automated Customer Service with Conversational AI

AI chatbots based on LLMs (Large Language Models) resolve between 60% and 80% of support queries without human intervention, according to Gartner (2024). Unlike the decision-tree chatbots of previous generations, current models understand context, tone, and complex requests in natural language.

The cost impact is direct: Zendesk (2024) estimates that each query resolved by AI costs between $0.10 and $0.50, compared to an average cost of $5–$12 for a human interaction. For high-volume stores, this difference represents one of the fastest ROI opportunities in AI.

In B2B projects on Adobe Commerce that we have managed at Orienteed, integrating conversational assistants for recurring order support reduced manual ticket volume by 45% in the first 90 days..

2. Catalog Personalization and Product Recommendations

ML-based recommendation engines analyze browsing behavior, purchase history, and similarity with other user profiles to surface the products most likely to convert. This is the AI use case with the most documented return in ecommerce.

According to Epsilon (2023), 80% of consumers are more likely to buy when a brand offers them personalized experiences. In B2B environments, personalization has an even more pronounced effect: business buyers who see catalogs tailored to their industry and purchase volume convert at a rate 2.3 times higher than those browsing a generic catalog (Forrester B2B Commerce Report, 2023).

3. AI-Powered Product Content Generation and Optimization

Generative AI enables the creation and optimization of product listings at scale — something especially valuable for catalogs with thousands of SKUs. Tools such as Adobe Sensei (native to Adobe Commerce), the ChatGPT API, or vertical solutions like Jasper Commerce generate descriptions, SEO metadata, and campaign copy in seconds from product attributes.

At Orienteed we have developed ValidoAI, a testing evaluation and automation tool for online stores and websites into which we have integrated AI to identify and resolve user experience errors throughout the purchase process — from the product page through checkout and virtually any other type of web page. Initial internal results show a 30% reduction in audit time compared to the manual process.

The ability to generate content variants tailored to different customer segments — by language, industry, or funnel stage — is one of the most underestimated competitive differentiators of generative AI in ecommerce.

4. Sentiment Analysis and Voice of the Customer

NLP algorithms process reviews, social media comments, and support transcripts to identify satisfaction and dissatisfaction patterns at a speed impossible for human teams. This capability turns unstructured feedback — the most abundant and least leveraged type — into actionable intelligence.

Companies that monitor customer sentiment in real time with AI detect product or experience issues up to 4 times faster than those relying on periodic surveys (Qualtrics XM Institute, 2023). In B2B ecommerce contexts, where account retention is critical, this response time can mean the difference between renewing a contract and losing it.

5. Real-Time Fraud Detection and Prevention

Ecommerce fraud exceeded $48 billion in global losses in 2023 (Juniper Research, 2023). ML-based detection systems analyze hundreds of variables per transaction — purchase velocity, geolocation, device patterns, behavioral history — to identify anomalies in milliseconds, before the payment is processed.

Unlike fixed-rule systems, ML models are continuously updated with new fraud patterns, reducing both false positives (blocking legitimate purchases) and false negatives (undetected fraud). Payment platforms such as Stripe and Adyen already incorporate these layers natively.

What Should a Company Consider Before Implementing AI in Its Ecommerce Operation?

Implementing AI in ecommerce is not simply a matter of activating a feature — it requires a quality data foundation, integration with the existing platform, and clarity about which business problem to solve first.

Precondition	Why it matters	Warning sign
Quality historical data	Machine learning models need clean, representative data to learn.	Catalog of products without standardized attributes
Integrable platform	Adobe Commerce, Shopify Plus, and headless platforms have open APIs for AI	Closed or legacy platforms without API
Prioritized use case	Start with the area with the greatest impact on business (support, recommendations, or fraud)	Implementing AI "for everything" without defined KPIs
Team with adoption capacity	AI requires monitoring, adjustment, and continuous improvement.	Assuming that AI works on its own after deployment

At Orienteed, when a client asks us where to start, our recommendation is always the same: identify the point of greatest friction in your current funnel — cart abandonment, support response time, return rate — and start there. AI is not a strategy in itself; it is a tool in service of a specific business problem.

Conclusion: AI in Ecommerce Is Not a Future Advantage — It Is a Present Competitive Requirement

In 2026, artificial intelligence in ecommerce has moved beyond emerging technology and become competitive infrastructure. Companies that have not yet taken any steps in this direction are not competing on equal footing with those that are already personalizing, automating, and analyzing with AI.

The good news is that the barrier to entry is lower than ever: leading commerce platforms integrate AI natively, LLM APIs are accessible from any technology stack, and the first use cases with a clear ROI — automated support, recommendations, fraud detection — do not require large upfront investments.

Talk to the Orienteed team if you want to define an AI roadmap for your B2B or B2C ecommerce platform.