Generative Artificial Intelligence (AI) in Logistics Market Forecast 2025-2034: Analysing Major Trends, Opportunities, and Growth Drivers

What combination of drivers is leading to accelerated growth in the generative artificial intelligence (ai) in logistics market?
The growth of the generative artificial intelligence (AI) in logistics market is likely to be spurred by an increase in e-commerce sales. The convenience of e-commerce, along with a broader selection of products and the growing reliance on digital technology, are boosting its appeal. Generative AI enhances e-commerce logistics through improved inventory management, efficient route planning, and demand forecasting, leading to improved efficiency and cost reductions. For example, a report by the Census Bureau of the Department of Commerce, a US government agency, asserted that e-commerce sales had climbed to an estimated $1,118.7 billion in 2023. By the first quarter of 2024, total retail sales had reached around $1,820.0 billion. E-commerce sales in this quarter witnessed an 8.5% increase (±1.1%) compared to the same quarter the previous year, while total retail sales rose by 2.8% (±0.5%). As such, the spike in e-commerce sales is fueling the growth of the generative AI in logistic market.

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What is the projected compound annual growth rate (CAGR) of the generative artificial intelligence (ai) in logistics market from 2025 to 2034, and what factors influence it?
The market size for generative artificial intelligence (AI) in logistics has witnessed a significant surge in recent times. The market expansion from $0.6 billion in 2024 to $0.8 billion in 2025, with a compound annual growth rate (CAGR) of 32.7%, is expected. The exponential growth in the past can be credited to a growing need for real-time data analysis, a boom in e-commerce, the broadening of global supply chains, enhanced industrial infrastructure, and the increasing prevalence of automation in warehouse management.

The market size of generative artificial intelligence (AI) in logistics is anticipated to witness substantial expansion in the next few years. It is projected to reach $2.46 billion by 2029, with a Compound Annual Growth Rate (CAGR) of 32.4%. The surge in growth during the prediction period can be associated with the escalating demand for instantaneous data, increased usage of IoT machines, growing investments in AI technology, the increasing necessity for supply chain transparency and the ever-growing demand to improve customer experience. The primary trends during the prediction period are likely to be technological progress, self-driving vehicles, predictive analytics, robotic process automation and the advent of digital twins.

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How are the latest trends influencing the growth of the generative artificial intelligence (ai) in logistics market?
Leading corporations in the generative artificial intelligence (AI) for logistics market are increasingly turning to modern technologies, such as natural language interfaces, to boost their operational effectiveness and augment the precision in managing supply chains. The term natural language interface describes a system that empowers users to engage with supply chain management programs or tools using everyday language. This aids in simplifying data inquiry, report generation, and operation management, eliminating the need for specific technical prowess. For example, in September 2023, FourKites, Inc., a USA-founded company specializing in supply chain visibility and logistics technology, unveiled FinAI. FinAI is a generative AI tool crafted to fortify supply chain management initiatives. Fin AI employs a natural language interface to surface insights, automate routine tasks, and streamline operations by scrutinizing vast data volumes, such as 3 million daily shipments, 18 million projected arrival times, and 62 billion annual travel miles.

What are the major segments of the generative artificial intelligence (ai) in logistics market and their role in driving growth?
The generative artificial intelligence (AI) in logistics market covered in this report is segmented -

1) By Type: Variational Autoencoder (VAE), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Other Types
2) By Component: Software, Hardware, Solution
3) By Deployment Mode: On-Premises, Cloud-Based
4) By Application: Warehouse Management, Route Optimization, Inventory Management, Supply Chain Analytics, Last-Mile Delivery Optimization, Customer Service Operations, Other Applications
5) By End-User: Retail, Healthcare, Banking And Finance, Aerospace, Telecommunication, Technology, Other End-Users

Subsegments:
1) By Variational Autoencoder (VAE): Demand Forecasting Models, Anomaly Detection In Logistics Operations, Predictive Maintenance For Fleet Management, Data Imputation For Incomplete Records, Supply Chain Optimization Solutions
2) By Generative Adversarial Networks (GANs): Synthetic Data Generation For Training Models, Route Optimization And Simulation, Image Generation For Inventory And Asset Management, Fraud Detection In Shipment And Delivery, Product Demand Forecasting Through Scenario Simulation
3) By Recurrent Neural Networks (RNNs): Time Series Analysis For Demand Prediction, Shipment Tracking And Forecasting, Customer Behavior Prediction For Delivery Services, Inventory Management Forecasting, Delivery Time Estimation Models
4) By Long Short-Term Memory (LSTM) Networks: Advanced Time Series Forecasting, Predictive Analytics For Supply Chain Performance, Transportation Optimization Models, Order Fulfillment Prediction, Capacity Planning And Resource Allocation
5) By Other Types: Reinforcement Learning For Route Optimization, Hybrid Models Combining Multiple AI Approaches, Flow-Based Models For Real-Time Data Analysis, Self-Supervised Learning Techniques, Edge AI For On-Site Decision Making.

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Which regions are key players in the growth of the generative artificial intelligence (ai) in logistics market?
North America was the largest region in the generative artificial intelligence (AI) in logistics market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the generative artificial intelligence (AI) in logistics market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

Who are the key firms paving the way for growth in the generative artificial intelligence (ai) in logistics market?
Major companies operating in the generative artificial intelligence (AI) in logistics market are Microsoft Corporation, Amazon Web Services Inc., Intel Corporation, accenture* plc, International Business Machines Corporation, Oracle Corporation, Honeywell International Inc., SAP SE, NVIDIA Corporation, Cognizant Technology Solutions Corporation, Epicor Software Corporation, Blue Yonder Group Inc., Coupa Software Incorporated, Kinaxis Inc., ShipBob Inc., Project44 Inc., Vorto Inc., Logility Inc., FourKites Inc., Shippeo SAS, Freightos Ltd., Slync.io Inc., Locus.sh, ClearMetal Inc.

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What Is Covered In The Generative Artificial Intelligence (AI) in Logistics Global Market Report?

•Market Size Forecast: Examine the generative artificial intelligence (ai) in logistics market size across key regions, countries, product categories, and applications.
•Segmentation Insights: Identify and classify subsegments within the generative artificial intelligence (ai) in logistics market for a structured understanding.
•Key Players Overview: Analyze major players in the generative artificial intelligence (ai) in logistics market, including their market value, share, and competitive positioning.
•Growth Trends Exploration: Assess individual growth patterns and future opportunities in the generative artificial intelligence (ai) in logistics market.
•Segment Contributions: Evaluate how different segments drive overall growth in the generative artificial intelligence (ai) in logistics market.
•Growth Factors: Highlight key drivers and opportunities influencing the expansion of the generative artificial intelligence (ai) in logistics market.
•Industry Challenges: Identify potential risks and obstacles affecting the generative artificial intelligence (ai) in logistics market.
•Competitive Landscape: Review strategic developments in the generative artificial intelligence (ai) in logistics market, including expansions, agreements, and new product launches.

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