Generative AI In Logistics Market To Reach USD 13.62 Billion By 2032, Driven By Demand For Real-Time Optimization, Forecasting, And Automation. SNS Insider

(MENAFN- GlobeNewsWire - Nasdaq) Generative AI in Logistics is rapidly growing, driven by AI-led optimization, automation, and real-time supply chain intelligence.

Pune, July 03, 2025 (GLOBE NEWSWIRE) -- Generative AI in Logistics Market Size Analysis:

“ The Generative AI in Logistics Market is projected to grow significantly, expanding from USD 816.15 million in 2024 to USD 13,621.30 million by 2032, registering a robust CAGR of 36.93% between 2025 and 2032. ”

Driving this impressive growth is the increasing use of generative AI for route optimization, demand forecasting, real-time decisions, and warehouse automation. Firms are utilizing these functionalities to increase supply chain transparency, reduce transactional cost, and achieve greater last mile delivery efficiency to have entire logistics operation as competitive and resilient as possible.

The U.S. Generative AI in Logistics Market is poised for exponential growth, increasing from USD 349.23 million in 2024 to USD 4,273.06 million by 2032, at a CAGR of 36.76%. Growth is fueled by rising adoption of AI-driven automation, predictive analytics, and real-time logistics optimization.

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Major Players Analysis Listed in this Report are:

Deutsche Post AG, UPS (United Parcel Services), Schneider Electric, C.H. Robinson, XPO Logistics, FedEx Corp, A.P. Moller - Maersk AS, Blue Yonder, Google Cloud, International Business Machines (IBM), Microsoft, PackageX, Salesforce.

Generative AI in Logistics Market Report Scope:

Report Attributes	Details
Market Size in 2023	USD 816.15 Million
Market Size by 2032	USD 13621.30 Million
CAGR	CAGR of 36.93% From 2025 to 2032
Base Year	2023
Forecast Period	2024-2032
Historical Data	2020-2022
Key Segments	. By Type (Variational Autoencoder (VAE), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Others) . By Deployment Mode (Cloud Based, On-premise) . By End User (Road Transportation, Railway Transportation, Aviation, Shipping and Ports)
Key Growth Drivers	Increasing Adoption of AI-Driven Solutions Across Industries Fuels Growth

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By Type, Variational Autoencoders (VAEs) Lead the Generative AI in Logistics Market, Generative Adversarial Networks (GANs) to Record Fastest CAGR

In 2024, Variational Autoencoders (VAEs) accounted for the largest revenue share around 30% in the Generative AI in Logistics Market. Due to their capacity of processing high-dimensional logistics data with stability, they were suitable for demand forecasting, route optimization, anomaly detections, etc. Moreover, their flexibility to connect with legacy systems made them popular among Logistics as they wanted enhanced AI but didn't want to change their existing infrastructure.

Generative Adversarial Networks (GANs) are projected to grow at the fastest CAGR of 39.62% from 2025 to 2032. Their better capability to produce authentic data aids training of autonomous systems and the logistics decision-making in eyesight. With even greater prediction-enforcement needs in modern supply chain operations, GANs provide more robust environments for simulation, warehouse layouts, and a more rapid build cycle of targeted scenario modeling that will ultimately make them essential components of next-generation supply chain innovations.

By End User, Road Transportation Dominated the Generative AI in Logistics Market, Shipping and Ports Segment to Grow at Fastest CAGR

In 2024, the road transportation segment captured the largest market share around 34% owing to the large operating area and complexity level. Generative AI is a turning point that was quickly adopted for optimization of routes, fuel economy, and predictive maintenance. Road transport was the industry that would most benefit from AI, with direct consumer connectivity and a very large logistics footprint, allowing for improved performance in delivery, cost control, and real-time decisions as used by other industries.

The shipping and ports segment is projected to witness the highest CAGR of approximately 39.94% during 2025–2032. Growing reliance on real-time vessel tracking, congestion management, and predictive maintenance is accelerating generative AI adoption in maritime logistics. With global trade recovery and tightening environmental regulations, port authorities and shipping firms are investing heavily in AI-driven solutions to boost efficiency, ensure compliance, and enable smart, sustainable port operations.

By Deployment Mode, Cloud Segment Captured 68% of Generative AI in Logistics Market

In 2024, the cloud segment led the Generative AI in Logistics Market with a dominant 68% revenue share. The cloud makes logistics firms to start using AI by providing scalability, cheaper, faster, but easy-to-use resources, removing the need to invest heavily in infrastructure before starting with AI. Cloud solutions promote the critical and necessary continuous updating, anticipation-driven analytics, and real-time automation that is indispensable in managing the agility biggest and most dispersed supply chain networks

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North America Dominated the Generative AI in Logistics Market, Asia Pacific to Record Fastest CAGR

In 2024, North America held the largest revenue share approximately 44% in the Generative AI in Logistics Market. The reasons are early AI technology adoption, the head office locations of powerful tech companies, and high-level logistics capabilities. Moreover, the rapid adoption of generative AI across regional supply chain functions is attributed to higher R&D expenditure and a technology-advanced end user create.

Asia Pacific is projected to expand at the fastest CAGR of about 39.18% between 2025 and 2032. The region's rapid digital transformation, booming e-commerce landscape, and rising AI investments particularly in China and India are key growth drivers. As demand for efficient, tech-driven logistics surges, governments and businesses alike are adopting generative AI to enhance route optimization, automate supply chains, and improve forecasting accuracy.

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