물류 분야 생성형 AI 시장 : 시장 기회, 성장 촉진요인, 산업 동향 분석, 예측(2025-2034년)

The Global Generative AI in Logistics Market was valued at USD 1.3 billion in 2024 and is estimated to grow at a CAGR of 33.7% to reach USD 23.1 billion by 2034. This technology is fundamentally transforming supply chain operations by delivering both real-time intelligence and long-term strategic forecasting. By simulating thousands of delivery routes and transport scenarios, logistics providers can fine-tune inventory planning, lower freight expenses, and stay prepared for unexpected disruptions. AI-powered demand forecasting also streamlines resource use, while dynamic routing tools improve delivery timelines. As operational efficiency and cost control become more important, the integration of generative AI has emerged as a key force shaping the market's future.

Generative AI enables logistics firms to enhance service personalization by analyzing customer behavior and preferences. These intelligent systems can trigger real-time alerts, recommend ideal delivery windows, and automatically adjust services based on client interactions. This level of customization boosts customer satisfaction and loyalty while allowing businesses to charge premium prices. In a competitive industry, personalized logistics experiences powered by AI continue to drive momentum. Moreover, with growing pressure to reduce fuel costs and emissions, logistics fleets increasingly rely on AI to suggest optimized routes using traffic patterns, weather predictions, and historical data, making cleaner and leaner operations the standard.

In 2024, the software segment held a 66% share and is set to grow at a CAGR of 32% through 2034. Logistics teams have prioritized AI-driven predictive tools that simulate numerous supply chain disruptions like stock shortages, delivery hold-ups, or sudden demand spikes. These tools help firms adjust operations proactively, improving both efficiency and cost outcomes. These modern solutions offer faster results than older models and integrate easily with legacy systems, making them more attractive than time-consuming, custom-built options.

The cloud deployment segment held a 67% share in 2024 and is expected to maintain strong growth at a CAGR of 32% through 2034. As logistics operations become more geographically dispersed, firms are choosing flexible, cloud-based AI solutions that scale instantly based on fluctuating business needs. Unlike traditional server setups, cloud platforms provide real-time computing power and data storage as demand surges, especially during seasonal peaks or unexpected market shifts. This adaptability makes cloud systems critical for global supply chains, reinforcing their dominance in the sector.

North America Generative AI In Logistics Market held 85% share and generated USD 355.2 million in 2024. The country has emerged as a central hub for advanced AI adoption in supply chains, backed by major tech firms like IBM, Microsoft, Amazon, Oracle, Palantir Technologies, SAP, NVIDIA, and Google. These companies offer enterprise-ready AI infrastructure, giving logistics providers immediate access to cutting-edge capabilities that accelerate algorithm development and deployment. This rapid innovation cycle positions the U.S. as a frontrunner in logistics AI worldwide.

Leading firms in the Generative AI in Logistics Market are doubling down on strategic cloud partnerships, scalable AI models, and industry-specific machine learning tools. They're also focusing on modular AI solutions that adapt quickly to regional and sector-specific logistics challenges. Enhancing user accessibility through API integration, building plug-and-play platforms, and enabling real-time data visibility are common goals. These companies invest in agile development environments and provide low-latency computing to meet real-time logistics demands. Customization capabilities, sustainability-focused route optimization, and predictive analytics are being prioritized to improve customer engagement and reduce operational risks, giving brands a competitive edge in a fast-evolving market landscape.

Table of Contents

Chapter 1 Methodology

• 1.1 Market scope and definition
• 1.2 Research design
  • 1.2.1 Research approach
  • 1.2.2 Data collection methods
• 1.3 Data mining sources
  • 1.3.1 Global
  • 1.3.2 Regional/Country
• 1.4 Base estimates and calculations
  • 1.4.1 Base year calculation
  • 1.4.2 Key trends for market estimation
• 1.5 Primary research and validation
  • 1.5.1 Primary sources
• 1.6 Forecast model
• 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Type
  • 2.2.3 Component
  • 2.2.4 Deployment mode
  • 2.2.5 Application
  • 2.2.6 End Use
• 2.3 TAM Analysis, 2025-2034
• 2.4 CXO perspectives: strategic imperatives
  • 2.4.1 Executive decision points
  • 2.4.2 Critical success factors
• 2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factors affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Industry impact forces
  • 3.2.1 Growth drivers
    • 3.2.1.1 Enhanced supply chain optimization
    • 3.2.1.2 Automation of repetitive process
    • 3.2.1.3 Personalized experience of the customers
    • 3.2.1.4 Cost-efficient fleet & route management
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 Data privacy and security risks
    • 3.2.2.2 Integration complexity with legacy systems
  • 3.2.3 Market opportunities
    • 3.2.3.1 AI driven demand forecasting and inventory optimization
    • 3.2.3.2 Digital twin creation for smart warehousing
    • 3.2.3.3 Autonomous route planning and fleet management
• 3.3 Growth potential analysis
• 3.4 Regulatory landscape
  • 3.4.1 North America
  • 3.4.2 Europe
  • 3.4.3 Asia Pacific
  • 3.4.4 Latin America
  • 3.4.5 Middle East & Africa
• 3.5 Porter’s analysis
• 3.6 PESTEL analysis
• 3.7 Technology and innovation landscape
  • 3.7.1 Current technological trends
  • 3.7.2 Emerging technologies
• 3.8 Case studies
• 3.9 Use cases
• 3.10 Cost breakdown analysis
• 3.11 Patent analysis
• 3.12 Sustainability and environmental aspects
  • 3.12.1 Sustainable practices
  • 3.12.2 Waste reduction strategies
  • 3.12.3 Energy efficiency in production
  • 3.12.4 Eco-friendly initiatives
  • 3.12.5 Carbon footprint considerations

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 LATAM
  • 4.2.5 MEA
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Strategic outlook matrix
• 4.6 Key developments
  • 4.6.1 Mergers & acquisitions
  • 4.6.2 Partnerships & collaborations
  • 4.6.3 New product launches
  • 4.6.4 Expansion plans and funding

Chapter 5 Market Estimates & Forecast, By Type, 2021 - 2034 (USD Million)

• 5.1 Key trends
• 5.2 Variational autoencoder
• 5.3 Generative adversarial networks
• 5.4 Recurrent neural networks
• 5.5 Long short-term memory networks
• 5.6 Transformers

Chapter 6 Market Estimates & Forecast, By Component, 2021 - 2034 (USD Million)

• 6.1 Key trends
• 6.2 Software
• 6.3 Services

Chapter 7 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 (USD Million)

• 7.1 Key trends
• 7.2 Cloud
• 7.3 On-premises

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2034 (USD Million)

• 8.1 Key trends
• 8.2 Route optimization
• 8.3 Demand forecasting
• 8.4 Warehouse and inventory management
• 8.5 Supply chain automation
• 8.6 Predictive maintenance
• 8.7 Risk management
• 8.8 Customized logistics solution
• 8.9 Others

Chapter 9 Market Estimates & Forecast, By End Use, 2021- 2034 (USD Million)

• 9.1 Key trends
• 9.2 Third party logistics providers
• 9.3 Freight forwarders
• 9.4 E-commerce companies
• 9.5 Manufacturers

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 (USD Million)

• 10.1 Key trends
• 10.2 North America
  • 10.2.1 U.S.
  • 10.2.2 Canada
• 10.3 Europe
  • 10.3.1 UK
  • 10.3.2 Germany
  • 10.3.3 France
  • 10.3.4 Italy
  • 10.3.5 Spain
  • 10.3.6 Russia
  • 10.3.7 Nordics
• 10.4 Asia Pacific
  • 10.4.1 China
  • 10.4.2 India
  • 10.4.3 Japan
  • 10.4.4 South Korea
  • 10.4.5 ANZ
  • 10.4.6 Southeast Asia
• 10.5 Latin America
  • 10.5.1 Brazil
  • 10.5.2 Mexico
  • 10.5.3 Argentina
• 10.6 MEA
  • 10.6.1 UAE
  • 10.6.2 Saudi Arabia
  • 10.6.3 South Africa

Chapter 11 Company Profiles

• 11.1 Amazon Web Services
• 11.2 DHL Group
• 11.3 FedEx
• 11.4 Flexport
• 11.5 Four Kites
• 11.6 Google
• 11.7 IBM
• 11.8 Locus
• 11.9 Maersk
• 11.10 Microsoft
• 11.11 NVIDIA
• 11.12 Open AI
• 11.13 Optimal Dynamics
• 11.14 Oracle
• 11.15 Palantir Technologies
• 11.16 Project44
• 11.17 Salesforce
• 11.18 SAP
• 11.19 UPS
• 11.20 XPO Logistics