US Artificial Intelligence (AI) in Retail Market - Strategic Insights and Forecasts (2025-2030)

Description

US Artificial Intelligence (AI) in Retail Market Size:

US Artificial Intelligence (AI) in Retail Market is anticipated to expand at a high CAGR over the forecast period.

AI emerges as a pivotal force, enabling precise demand prediction and tailored experiences that sustain loyalty amid these headwinds. The booming technological adoption has made US executives to recognize AI's role in not just efficiency but competitive differentiation which is transforming raw data into actionable insights that align inventory with real-time behaviors.

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US Artificial Intelligence (AI) in Retail Market Analysis:

• Growth Drivers

The growing e-commerce penetration has provided a major boost to the technological adoption. Retailers leverage Artificial Intelligence (AI) for real-time personalization, customer analytics, and inventory management which is heightening the demand for natural language processing (NLP) and machine learning (ML) tools that tailor recommendations and lift conversion rates by analyzing purchase histories

The efforts to minimize supply chain disruption exacerbated by global events is fueling adoption of machine learning algorithms in retail sector as these systems cut forecasting errors thereby stabilize inventory management and reduce overstock costs. Likewise, the labor shortages in the retail sector has accelerate demand for AI automation in customer service, freeing associates for high-value interactions and curbing turnover

Challenges and Opportunities

The primary challenge constraining market growth is the data privacy concern which is limiting AI deployment in customer-facing applications thereby dampening demand for unvetted tools and forcing retailers to prioritize compliant solutions, which slows short-term adoption but elevates long-term trust. The algorithmic biases, risk discriminatory pricing or recommendations, eroding consumer confidence and constraining demand for opaque models while creating openings for transparent AI that fosters inclusive experiences.

A secondary challenge dampening the market growth is the high requirement for upskilling technical skills to run the software integrated platforms. Moreover, the ethical integration hurdles, highlighted by National Retail Federation discussions, challenge scalability.

 Simultaneously, a market opportunity exists in the growing emphasis on providing tailored recommendations by analyzing vast consumer data. Likewise, the AI algorithm further assist in demand forecasting which enables in supply chain optimization thereby reducing stockouts.

• Supply Chain Analysis

The US AI supply chains hinge on domestic software development hubs in Silicon Valley and cloud infrastructure from providers like AWS, intertwined with hardware sourcing from Asian manufacturers for GPUs essential to training models. National Institute of Standards and Technology emphasizes data security in this chain, where vulnerabilities in third-party datasets disrupt retail AI reliability, compelling firms to adopt federated learning to mitigate risks.

Logistical complexities arise from bandwidth dependencies for real-time processing, trade dependencies on imported semiconductors expose chains to tariffs thereby increasing costs for computer vision tools in store analytics. Hence, retailers navigate these by prioritizing hybrid models, blending on-premise hardware with cloud services to ensure uninterrupted demand prediction, though geopolitical tensions amplify the need for diversified sourcing to sustain AI-driven efficiencies.

Moreover, the recent reciprocal tariffs can also create obstacle for AI deployment, as it will increase the price of imported components like GPUs, TPUs, servers, cooling systems, and networking gear, all imported from major countries namely China and Taiwan. And as AI deployment in retail relies on affordable, scalable hardware for edge computing, cloud-based analytics, and real-time processing, hence these areas will be hit by tariff-induced price hikes.

US Artificial Intelligence (AI) in Retail Market Government Regulations:

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US Artificial Intelligence (AI) in Retail Market Segment Analysis:

• By Deployment: Cloud

Cloud deployment dominates AI adoption in US retail due to its scalability, enabling retailers to process vast datasets without upfront hardware investments. Retail firms are using cloud-based AI as this model facilitates seamless integration with existing ERP systems, directly amplifying demand for elastic resources amid fluctuating e-commerce traffic. Likewise, cloud architectures support real-time analytics, reducing latency in inventory adjustments through distributed computing, which counters peak-season overloads and drives retailers to prioritize vendors offering pay-as-you-go models. Logistical advantages emerge in multi-site operations; for instance, cloud enables centralized model training on consumer behavior data, propelling demand as retailers seek to unify omnichannel experiences.

• By Application: Demand Forecasting

The demand forecasting applications anchor AI's value in retail by leveraging historical sales data to predict trends, directly addressing overstock issues that plague majority of US inventories. Likewise, AI-based networks outperform traditional methods in accuracy, which is spurring retailers to acquire these tools to minimize losses from unsold goods amid volatile consumer shifts. This segment thrives on integration with IoT sensors for real-time inputs, thereby enabling proactive replenishment that minimizes stockouts which is a critical draw for cash-strapped chains. Thus, this application's tangible ROI—bolstered by scalable algorithms—positions it as a demand magnet, compelling retailers to embed it deeply for competitive edge in uncertain markets.

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US Artificial Intelligence (AI) in Retail Market Competitive Environment and Analysis:

The US AI in retail landscape features intense rivalry among tech incumbents vying for integration in core operations like personalization and logistics. Major players, including IBM, NVIDIA, and Microsoft, command shares through specialized offerings.

• IBM positions as a hybrid cloud pioneer, emphasizing Watsonx platform for retail AI that orchestrates generative models across supply chains. Official documentation highlights its Sterling Supply Chain Suite, which integrates AI for predictive visibility, reducing disruptions by analyzing multimodal data.
• Microsoft advances through its Cloud for Retail focusing on conversational commerce via Copilot integrations that enhance CRM. Its Fabric platform unifies data lakes for forecasting, that offers high inventory accuracy improvements which is appealing to retailers scaling personalized campaigns. The company has shown active participation in its product development, for instance, in January 2024, Microsoft announced new generative AI and data solutions capabilities for its Cloud for Retail, that enables retailers to offer personalized shopping experience to shoppers and also support store operations.

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US Artificial Intelligence (AI) in Retail Market Developments:

• September 2025: Microsoft introduced Ask Ralph, an AI-powered styling companion for Ralph Lauren, leveraging generative AI to boost conversational commerce in retail apps which assist in meeting multiple personalized requirements of costumers across the inventory available for both, men and women.
• July 2025: NVIDIA announced the AI Blueprint for Retail Shopping Assistants, a generative AI workflow to transform in-store experiences with personalized guidance and reduced returns. The blueprint built on “ NVIDIA AI Enterprise” and “NVIDIA Omniverse™ Platform” can deliver a company’s best sales associate.

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US Artificial Intelligence (AI) in Retail Market Scope:

US Artificial Intelligence (AI) in Retail Market Segmentation:

• By Component
  • Hardware
  • Software
  • Services
• By Deployment
  • Cloud
  • On-Premise
• By Technology
  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Computer Vision
  • Internet of Things (IoT)
  • Others
• By Application
• Demand Forecasting
• Customer Relationship Management
• Supply Chain Management
• Fraud Detection & Loss Prevention
• Others

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Table Of Contents

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities

3.4. Porter's Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations

3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. US ARTIFICIAL INTELLIGENCE (AI) IN RETAIL MARKET BY COMPONET

5.1. Introduction

5.2. Hardware

5.3. Software

5.4. Services

6. US ARTIFICIAL INTELLIGENCE (AI) IN RETAIL MARKET BY DEPLOYMENT

6.1. Introduction

6.2. Cloud

6.3. On-Premise

7. US ARTIFICIAL INTELLIGENCE (AI) IN RETAIL MARKET BY TECHNOLOGY

7.1. Introduction

7.2. Machine Learning (ML)

7.3. Natural Language Processing (NLP)

7.4. Computer Vision

7.5. Internet of Things (IoT)

7.6. Others

8. US ARTIFICIAL INTELLIGENCE (AI) IN RETAIL MARKET BY APPLICATION

8.1. Introduction

8.2. Demand Forecasting

8.3. Customer Relationship Management

8.4. Supply Chain Management

8.5. Fraud Detection & Loss Prevention

8.6. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. Hitachi Solutions

10.2. IBM

10.3. Oracle Corporation

10.4. Intel Corporation

10.5. Accenture Plc

10.6. NVIDIA Corporation

10.7. Kustomer

10.8. Hewlett Packard Enterprise

10.9. Addepto sp. z o.o.

10.10. H2O.ai

10.11. Matellio Inc

10.12. Microsoft Corporation

10.13. Google

11. APPENDIX

11.1. Currency

11.2. Assumptions

11.3. Base and Forecast Years Timeline

11.4. Key benefits for the stakeholders

11.5. Research Methodology

11.6. Abbreviations

LIST OF FIGURES

LIST OF TABLES

Companies Profiled

Hitachi Solutions

IBM

Oracle Corporation

Intel Corporation

Accenture Plc

NVIDIA Corporation

Kustomer

Hewlett Packard Enterprise

Addepto sp. z o.o. 

H2O.ai

Matellio Inc

Microsoft Corporation

Google

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