AI in Ecommerce: How Artificial Intelligence Is Reshaping Retail Growth, Pricing, and Operations

In 2025, artificial intelligence is no longer an emerging trend in ecommerce. It is a core operating layer for retail brands competing in the US market. 

What started as isolated pilots has evolved into enterprise-wide systems that influence how products are discovered, priced, fulfilled, and supported. AI now impacts revenue growth, operational efficiency, and brand competitiveness at scale. 

Recent industry data shows: 

• Nearly 90% of retail and consumer goods companies are already using or testing AI 

• 78% of retailers apply AI in at least one core business function 

• 97% of retailers plan to increase AI investment in the next fiscal year 

AI is no longer optional. It is becoming the decision layer behind modern ecommerce. 

The Shift From Pilot Projects to AI-First Ecommerce Strategy 

Retailers are moving beyond experimentation. AI is now embedded across critical workflows, including: 

• Product discovery and recommendation engines 

• Dynamic pricing and promotion optimization 

• Inventory forecasting and supply chain planning 

• Customer service and post-purchase support 

According to recent studies, 60% of retailers plan to increase AI infrastructure investment within the next 18 months. Priority use cases include analytics, personalization, pricing optimization, conversational AI, and inventory management. 

This shift reflects a broader change in ecommerce strategy. Brands are no longer optimizing only for search engines or marketplaces. They are optimizing for AI systems that interpret, rank, and recommend products autonomously. 

AI Agents and Conversational Commerce: How Shopping Behavior Is Changing 

One of the most significant changes in ecommerce is the rise of AI agents and conversational commerce. 

Instead of relying on traditional search and filtering, shoppers increasingly interact with AI-powered assistants that can: 

• Understand intent and context 

• Compare products across categories 

• Provide personalized recommendations 

• Execute purchases on behalf of the user 

This transition is often referred to as agentic commerce. 

Real-World Adoption: Walmart and AI-Assisted Shopping 

In 2025, Walmart announced an integration with OpenAI that allows customers to shop directly through ChatGPT. Shoppers can ask questions, receive recommendations, and complete purchases within a conversational interface. 

This marks a structural shift in ecommerce competition: 

• Brands are no longer competing only for consumer attention 

• They are competing for AI interpretation and selection 

Why Conversational AI Drives Higher Conversion 

Data from multiple retail studies shows: 

• Conversion rates increase up to 4x when shoppers interact with AI-powered chat 

• Purchases are completed 47% faster with AI assistance 

• 64% of AI-driven sales come from first-time buyers 

Conversational AI is not only improving efficiency. It is reshaping who converts and how quickly decisions are made. 

AI-Powered Personalization as a Revenue Driver 

Personalization remains one of the clearest revenue opportunities enabled by AI. 

AI systems allow retailers to personalize experiences at scale by dynamically adjusting: 

• Product recommendations 

• Content and messaging 

• Offers and promotions 

Companies that lead in AI-driven personalization generate up to 40% more revenue than those relying on static experiences. 

Consumer expectations reinforce this shift: 

• 78% of shoppers prefer personalized experiences 

• Personalization increases repeat purchases and long-term loyalty 

Without AI, this level of personalization is not operationally scalable. 

Dynamic Pricing and Promotion Optimization 

AI has fundamentally changed how pricing works in ecommerce. 

Instead of fixed pricing strategies, AI enables real-time adjustments based on: 

• Demand fluctuations 

• Competitor pricing 

• Inventory levels 

• Shopper behavior and intent 

Amazon as the Reference Model 

Amazon has long used AI-driven dynamic pricing and continues to lead in this area. The company is estimated to make millions of price changes per day across its catalog. 

As a result: 

• Up to 35% of Amazon’s revenue is influenced by AI-powered pricing and recommendation systems 

• Different users may see different prices or offers based on context, timing, and intent 

Dynamic pricing is no longer a competitive advantage. For large-scale ecommerce, it is becoming a baseline capability. 

Retail Automation and Operational Efficiency 

While customer-facing AI often receives the most attention, some of the largest gains occur behind the scenes. 

AI-driven automation improves: 

• Inventory planning and demand forecasting 

• Supply chain coordination and logistics 

• Quality control and fraud detection 

• Workforce productivity 

According to McKinsey, AI-powered supply chains can: 

• Reduce inventory levels by 20–30% 

• Lower logistics costs by 5–20% 

• Decrease procurement costs by 5–15% 

Walmart’s Operational AI Stack 

Walmart applies AI across its physical and digital operations, including: 

• Computer vision systems to detect damaged products and packaging 

• AI-based routing to reduce transportation inefficiencies 

• Digital twins that predict equipment failures before they occur 

These systems reduce waste, improve availability, and support faster fulfillment. 

The Role of Hybrid Models: AI and Human Teams 

Despite increased automation, fully autonomous retail operations are not the end goal. 

Consumer research shows: 

• 86% of shoppers value empathy and human judgment over pure speed 

• 89% prefer hybrid models where AI handles routine tasks and humans intervene when needed 

The most effective retailers use AI to: 

• Reduce friction and manual workload 

• Improve decision quality 

• Enable human teams to focus on complex and high-value interactions 

AI works best as an augmentation layer, not a replacement for human expertise. 

What Ecommerce Leaders Need to Focus on Now 

AI adoption in ecommerce is no longer about tools. It is about systems, measurement, and strategy. 

In 2025, AI directly influences: 

• Which products are surfaced or ignored 

• Which brands are recommended by AI systems 

• How pricing, availability, and value are evaluated 

Brands that treat AI as a tactical add-on risk losing visibility. Brands that treat it as core infrastructure gain long-term competitive advantage. 

Final Thought: Measuring Brand Intelligence in an AI-Driven Retail Landscape 

As AI becomes the decision layer of ecommerce, visibility is no longer defined only by rankings or traffic. It is defined by brand intelligence. 

Brand intelligence includes: 

• How AI models interpret brand authority and trust 

• How consistently product data is structured across channels 

• How AI evaluates value, relevance, and reliability 

The challenge for most brands today is not awareness of AI’s impact. It is the lack of clear measurement. 

At Hatch, we are preparing the launch of an internal AI Visibility platform designed to help brands: 

• Measure how AI systems interpret their brand and products 

• Identify gaps in authority, consistency, and data structure 

• Understand brand intelligence signals across AI-driven discovery environments 

This platform is built to answer a critical question ecommerce leaders are now facing:
Is AI confident enough in your brand to recommend it? 

We will be sharing more details soon.
For brands operating in AI-driven retail environments, now is the time to start measuring what AI sees — before those decisions happen without you.