• In August 2024, logistics giant Maersk announced a partnership with IBM to enhance its AI-based supply chain management solutions. This collaboration aims to increase transparency and improve the efficiency of shipping logistics using AI-powered tools

• In April 2024, saw Walmart adopting AI-powered predictive analytics tools to improve its supply chain operations, reducing stockouts and improving inventory management at a time of high consumer demand

In this report, the Artificial Intelligence (AI) In Supply Chain Market has been segmented by Offering, Technology, Application, Vertical and Geography.

Artificial Intelligence (AI) In Supply Chain Market, Segmentation by Offering

The Artificial Intelligence (AI) In Supply Chain Market has been segmented by Offering into Hardware, Software, and Services.

Hardware
The hardware segment in the AI in Supply Chain Market comprises components like processors, sensors, and storage devices that support AI model deployment. The increasing integration of AI-enabled chips and IoT devices is driving this segment’s growth. Approximately 30% of the market revenue is currently attributed to hardware investments, with demand rising for edge AI hardware to support real-time analytics at the source.

Software
The software segment dominates the AI in Supply Chain Market, accounting for over 45% of the total share. This includes AI platforms, analytics engines, and machine learning algorithms that optimize planning, demand forecasting, and inventory management. The growth is largely fueled by rising adoption of predictive analytics and real-time supply chain visibility solutions.

Services
The services segment includes consulting, integration, and support services that enable enterprises to implement and manage AI-based supply chain systems. With companies seeking faster AI adoption and smoother system integration, this segment is growing steadily, contributing around 25% to the overall market. The rise in cloud-based service models and managed AI offerings is further accelerating its expansion.

Artificial Intelligence (AI) In Supply Chain Market, Segmentation by Technology

The Artificial Intelligence (AI) In Supply Chain Market has been segmented by Technology into Machine Learning, Computer Vision, Natural Language Processing, and Others.

Machine Learning
The machine learning segment is a key driver in the AI in Supply Chain Market, contributing to over 40% of the total technology share. It enables improved demand forecasting, route optimization, and inventory management through advanced data pattern recognition. With its ability to adapt and improve over time, machine learning models are being widely adopted across supply chain processes to enhance operational efficiency.

Computer Vision
Computer vision technology is increasingly utilized for automated quality inspections, real-time cargo monitoring, and warehouse automation. Accounting for nearly 20% of the technology market, it helps reduce human error and improve accuracy in visual data processing. The demand for visual AI solutions is rising as companies focus on enhancing traceability and compliance in logistics operations.

Natural Language Processing
Natural Language Processing (NLP) is used in chatbots, virtual assistants, and AI-driven supplier communications, enabling better interaction and response mechanisms. It represents around 15% of the technology adoption in supply chains. NLP enhances supplier relationship management and facilitates automated document processing like invoices and shipment records.

Others
This segment includes emerging technologies such as reinforcement learning, predictive analytics, and speech recognition, which are gradually gaining traction. Although currently contributing a smaller share, under 10%, these technologies hold potential for next-generation AI applications in supply chain ecosystems, especially in areas like autonomous delivery systems and cognitive automation.

Artificial Intelligence (AI) In Supply Chain Market, Segmentation by Application

The Artificial Intelligence (AI) In Supply Chain Market has been segmented by Application into Supply Chain Planning, Warehouse Management, Virtual Assistant, Fleet Management, Risk Management, and Others.

Supply Chain Planning
Supply chain planning is the largest application segment, accounting for over 35% of the AI in Supply Chain Market. AI enhances demand forecasting, production scheduling, and inventory optimization, enabling businesses to respond more efficiently to market fluctuations. Advanced predictive analytics and machine learning algorithms are central to achieving agility and reducing costs.

Warehouse Management
AI-powered warehouse management systems use automation, robotics, and computer vision to improve inventory accuracy and order fulfillment. This segment contributes approximately 20% of the total market. Enhanced space utilization and real-time inventory tracking are among the key benefits driving adoption in large-scale distribution centers.

Virtual Assistant
Virtual assistants powered by AI are being implemented to handle customer queries, shipment updates, and supplier coordination. With a growing focus on automated communication, this segment makes up about 10% of the application market. Virtual agents improve response time and reduce the need for human intervention in repetitive tasks.

Fleet Management
Fleet management applications use AI for route optimization, fuel efficiency analysis, and predictive vehicle maintenance. This segment holds nearly 15% of the market share. AI helps reduce logistics costs, improve delivery accuracy, and enhance driver safety across transportation networks.

Risk Management
Risk management systems utilize AI to detect and mitigate supply chain disruptions, fraudulent activities, and compliance risks. Representing around 12% of the market, this segment supports resilient supply chain strategies through scenario modeling and real-time risk assessments.

Others
This category includes applications such as procurement automation, reverse logistics, and sustainability tracking. While currently a smaller portion—less than 8%—these use cases are expanding with growing demand for AI-driven operational insights and environmental compliance.

Artificial Intelligence (AI) In Supply Chain Market, Segmentation by Vertical

The Artificial Intelligence (AI) In Supply Chain Market has been segmented by Vertical into Automotive, Manufacturing, Healthcare, Retail, Food & Beverages, and Others.

Automotive
The automotive sector accounts for nearly 25% of the AI in Supply Chain Market, driven by increasing demand for predictive maintenance, automated inventory control, and real-time tracking. AI enhances production scheduling and logistics optimization, helping manufacturers streamline their global supply chains and reduce operational costs.

Manufacturing
In the manufacturing vertical, which holds approximately 20% of the market, AI is used for forecasting demand, optimizing procurement, and enabling smart factory automation. AI-driven insights help improve material flow and supplier management, leading to better agility and production efficiency.

Healthcare
AI in healthcare supply chains, contributing about 15% of the market, supports inventory forecasting, cold chain monitoring, and drug traceability. It helps ensure timely delivery of critical supplies, reduces stockouts, and improves regulatory compliance in pharmaceutical logistics.

Retail
The retail segment holds a significant share of around 18%, leveraging AI for demand forecasting, personalized inventory planning, and real-time replenishment. AI also enables omnichannel logistics and enhances customer experience through automated supply chain decisions.

Food & Beverages
In the food and beverages industry, which represents nearly 10% of the market, AI helps manage perishable inventory, optimize delivery scheduling, and monitor supply freshness. Enhanced traceability systems and waste reduction capabilities are critical advantages of AI integration.

Others
This segment includes industries such as aerospace, energy, and consumer goods, where AI adoption is still emerging. Although it currently contributes less than 12%, growing interest in digital transformation and AI-driven logistics intelligence is expected to expand adoption across these verticals.

Artificial Intelligence (AI) In Supply Chain Market, Segmentation by Geography

In this report, the Artificial Intelligence (AI) In Supply Chain Market has been segmented by Geography into five regions; North America, Europe, Asia Pacific, Middle East and Africa and Latin America.

Artificial Intelligence (AI) In Supply Chain Market Share (%), by Geographical Region

North America
North America leads the AI in Supply Chain Market with over 35% share, driven by early technology adoption and strong presence of tech giants and logistics providers. The region benefits from mature infrastructure, widespread use of AI-powered analytics, and increasing investments in digital supply chain transformation.

Europe
Europe holds around 25% of the market, with strong demand for AI-based automation in sectors like automotive, retail, and manufacturing. Emphasis on sustainable supply chains and regulatory compliance has fueled the growth of AI solutions focused on traceability and efficiency.

Asia Pacific
Asia Pacific is the fastest-growing region, contributing nearly 22% to the market, driven by increasing e-commerce penetration, large-scale manufacturing hubs, and rising investment in smart logistics. Countries like China, Japan, and India are leading the regional push towards AI-led supply chain optimization.

Middle East and Africa
The Middle East and Africa region accounts for approximately 10% of the market. Growth is supported by the region’s efforts to modernize infrastructure and adopt AI technologies in port management, logistics hubs, and energy sector supply chains.

Latin America
Latin America represents about 8% of the AI in Supply Chain Market. Increasing digital transformation efforts and the rise of AI-based logistics startups are improving supply chain transparency and cost-efficiency across industries such as retail and agriculture.

This report provides an in depth analysis of various factors that impact the dynamics of Global Artificial Intelligence (AI) In Supply Chain Market. These factors include; Market Drivers, Restraints and Opportunities Analysis.

Drivers, Restraints and Opportunity Analysis

Drivers

• Demand for Efficiency and Optimization
• Rising Complexity and Globalization
• Need for Resilience and Agility

• Growing Data Availability and Connectivity : Growing data availability and connectivity is a major driver of the global artificial intelligence (AI) in supply chain market. The proliferation of IoT devices, smart sensors, and cloud-based platforms has enabled organizations to generate and collect vast amounts of real-time data across the entire supply chain. This abundance of structured and unstructured data serves as the foundation for AI algorithms to deliver predictive insights, demand forecasting, route optimization, and risk mitigation.

  Enhanced connectivity allows for seamless integration between suppliers, manufacturers, logistics providers, and retailers, enabling end-to-end visibility and coordination. AI tools can analyze this data to automate decision-making, identify inefficiencies, and adapt to disruptions more effectively. As digital ecosystems expand and data becomes more accessible, AI-driven solutions are becoming essential for boosting operational agility, reducing costs, and improving customer satisfaction throughout the supply chain.

Restraints

• High Implementation Costs
• Data Privacy and Security Concerns

• Shortage of qualified professionals : Shortage of qualified professionals is a significant restraint on the global artificial intelligence (AI) in supply chain market. Implementing AI-driven solutions requires a specialized workforce with expertise in data science, machine learning, supply chain analytics, and AI infrastructure management. However, the demand for such skilled professionals far exceeds supply, especially in emerging markets and traditional logistics sectors where digital transformation is still evolving.

  This talent gap slows down AI adoption, increases implementation costs, and places added pressure on organizations to invest in workforce training or rely heavily on third-party service providers. Without access to qualified personnel, businesses may struggle to fully leverage AI capabilities, leading to inefficient deployments, underutilized tools, and suboptimal return on investment. Addressing this skills shortage is critical to unlocking the full potential of AI in transforming global supply chains.

Opportunties

• Predictive Maintenance
• Supply Chain Optimization
• Autonomous Decision-Making

• Sustainable Supply Chains : The growing emphasis on sustainable supply chains offers a major opportunity for the global artificial intelligence (AI) in supply chain market. Organizations are under increasing pressure to minimize their environmental footprint, reduce waste, and enhance transparency across sourcing, production, and distribution. AI technologies can support these goals by enabling precise demand forecasting, optimized route planning, and real-time monitoring of emissions and energy usage.

  AI-powered tools also facilitate the identification of eco-efficient suppliers, ethical sourcing practices, and circular economy strategies, helping companies align with sustainability regulations and consumer expectations. As sustainability becomes a core business priority, AI will play a critical role in transforming supply chains into more responsible, data-driven ecosystems—unlocking both environmental and competitive advantages for forward-thinking enterprises.

Key players in Global Artificial Intelligence (AI) In Supply Chain Market include:

• Google
• IBM
• Facebook
• Amazon Web Services Inc
• SAP
• Microsoft Corporation
• Oracle Corporation
• Baidu
• Alibaba
• Tencent

In this report, the profile of each market player provides following information:

• Company Overview and
• Market Share Analysis
• Key Developments
• Financial Overview
• Strategies
• Company SWOT Analysis