Artificial Intelligence in Supply Chain Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Offering (Hardware, Software, Services), By Application (Fleet Management, Supply Chain Planning), By End-User (Automotive, Retail, Others), By Region, By Competition 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 18.55 Billion
CAGR (2026-2031)	33.56%
Fastest Growing Segment	Services
Largest Market	North America
Market Size (2031)	USD 105.29 Billion

Market Overview

The Global Artificial Intelligence in Supply Chain Market will grow from USD 18.55 Billion in 2025 to USD 105.29 Billion by 2031 at a 33.56% CAGR. Artificial intelligence in the supply chain refers to the application of machine learning and predictive analytics to automate logistics and enhance strategic decision-making. The market is primarily supported by the critical demand for real-time visibility, which enables organizations to proactively mitigate global network disruptions. Additionally, the relentless drive for operational efficiency compels companies to deploy intelligent systems that optimize inventory levels and streamline distribution processes.

One significant challenge that could impede market expansion is the difficulty of integrating advanced algorithms with fragmented legacy infrastructure, often leading to data quality issues. Incompatible systems prevent organizations from accessing the unified information necessary for effective automation. Despite these hurdles, industry commitment to modernization remains strong. According to MHI, in 2025, the forecasted adoption of artificial intelligence is expected to reach 82% within the next five years.

Key Market Drivers

The rising demand for operational efficiency and cost reduction acts as a primary catalyst for the integration of artificial intelligence within global logistics networks. Organizations are increasingly leveraging machine learning algorithms to automate labor-intensive processes, optimize inventory positioning, and refine predictive maintenance schedules, thereby minimizing downtime and operational waste. This drive toward streamlined workflows is substantiated by industry data; according to Honeywell, July 2024, in the 'Industrial AI Insights' study, 64% of AI leaders cited efficiency and productivity gains among the most promising benefits of deploying artificial intelligence solutions. As profit margins tighten, the ability of AI to identify cost-saving opportunities through granular data analysis becomes indispensable for maintaining a competitive market position.

Simultaneously, the growing necessity for supply chain resilience and risk mitigation is accelerating the deployment of intelligent monitoring systems. In an environment marked by geopolitical volatility and fluctuating trade policies, companies require predictive analytics to anticipate bottlenecks and dynamically reroute shipments. According to Descartes Systems Group, December 2024, in the '2024 Supply Chain Intelligence Report', 45% of global logistics leaders identified supply chain disruptions as a top concern, necessitating robust technological interventions. This urgency to fortify infrastructure against instability is reflected in substantial capital allocations; according to MHI, in 2024, 42% of supply chain leaders reported plans to invest over $10 million in technology and innovation to secure their operations.

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Key Market Challenges

The integration of advanced artificial intelligence algorithms with fragmented legacy infrastructure presents a formidable barrier to market growth. Many supply chain organizations operate on outdated, siloed systems that cannot effectively communicate with modern machine learning platforms. This incompatibility leads to severe data quality issues, as disparate sources generate unstructured or inconsistent information that AI models cannot process accurately. Consequently, companies struggle to derive the actionable insights necessary for predictive analytics, rendering their investment in intelligent automation less effective and slowing the overall pace of digital transformation.

These technical impediments directly hinder the ability to optimize critical functions such as demand planning and network agility. According to the '2025 MHI Annual Industry Report', 44% of supply chain leaders identified forecasting as a top operational challenge. When legacy systems fail to provide a unified data stream, organizations cannot leverage the real-time visibility required for precise forecasting or agile decision-making. This technological disconnect forces companies to rely on manual interventions to bridge data gaps, thereby perpetuating inefficiencies and significantly capping the potential return on investment for AI technologies in the global supply chain sector.

Key Market Trends

The integration of Generative AI represents a transformative shift in supply chain management, moving beyond traditional predictive analytics to enable advanced risk simulation and autonomous decision-making. Unlike legacy systems that rely on structured datasets, these tools can process vast amounts of unstructured information, such as contracts and market reports, to generate actionable scenarios for complex problem-solving. This capability allows organizations to simulate potential disruptions and automatically generate mitigation strategies, significantly reducing the reliance on manual contingency planning. The momentum behind this technology is evident in recent strategic priorities; according to EY, June 2024, in the 'Will GenAI accelerate autonomous supply chains?' report, 73% of supply chain and operations executives are planning to deploy Generative AI technologies to enhance their operational agility.

Simultaneously, the deployment of AI tools for Scope 3 emissions tracking is becoming critical as companies face intensifying regulatory pressure and consumer demand for transparent environmental stewardship. Artificial intelligence provides the necessary granularity to map indirect emissions across multi-tier supplier networks, addressing the data fragmentation that often hampers sustainability reporting. By automating the collection and analysis of carbon footprint data, organizations can ensure compliance with evolving global standards while identifying specific opportunities for decarbonization within their value chains. This focus on green technology is driving substantial market activity; according to IBM, November 2024, in the 'State of Sustainability Readiness Report 2024', 88% of business leaders surveyed are planning to increase their investment in IT for sustainability over the coming year to meet these ambitious environmental goals.

Segmental Insights

The Services segment is positioned as the fastest-growing category in the Global Artificial Intelligence in Supply Chain Market due to the significant complexities associated with system implementation. Many organizations lack the necessary in-house expertise to configure, integrate, and maintain artificial intelligence solutions within their existing logistical frameworks. This skills gap drives a strong reliance on external providers for consulting, installation, and continuous technical support. Consequently, businesses prioritize professional services to ensure seamless operation and risk mitigation, resulting in a sustained demand for third-party management and optimization.

Regional Insights

North America maintains a dominant position in the global artificial intelligence in supply chain market due to the extensive presence of major technology providers and substantial investment in research and development. The region benefits from high adoption rates of automation technologies across logistics and manufacturing sectors to enhance operational efficiency. Furthermore, support from federal entities such as the National Institute of Standards and Technology facilitates the creation of standards that encourage secure industrial integration. This established digital infrastructure enables enterprises to effectively implement predictive analytics and inventory management solutions.

Recent Developments

• In January 2025, Oracle announced the integration of new role-based artificial intelligence agents into its Fusion Cloud Supply Chain & Manufacturing suite. These AI-driven tools were designed to automate routine workflows and enhance productivity across procurement, manufacturing, and inventory management. The new agents assist professionals by analyzing data to provide personalized insights, generating content, and recommending specific actions to mitigate risks. This development aims to reduce the time spent on administrative tasks such as data analysis and order processing, allowing supply chain leaders to focus on strategic decision-making and improving operational resilience.
• In August 2024, Blue Yonder completed its acquisition of One Network Enterprises for an enterprise value of approximately $839 million. This strategic move integrated a multi-enterprise network with Blue Yonder’s existing digital supply chain platform, creating a unified ecosystem for real-time data sharing and collaboration. The acquisition enables customers to monitor inventory levels and material movements across all trading partners instantly. By incorporating artificial intelligence assistants and solvers from the acquired entity, the company enhanced its ability to detect disruptions and orchestrate resources, thereby facilitating a more agile and interconnected global supply chain.
• In June 2024, Kinaxis launched Maestro, an AI-infused supply chain orchestration platform designed to improve transparency and agility in global logistics. The platform combines proprietary computational techniques with machine learning to manage complex supply chain networks from strategic planning to last-mile delivery. Maestro features a supply chain data fabric that connects internal and external data sources, an intelligence engine for real-time insights, and a generative AI-powered interface. This launch represents a significant evolution from the company’s previous offerings, aiming to help businesses navigate volatility and align strategic objectives with operational execution through advanced modeling and automation.
• In May 2024, Manhattan Associates unveiled Manhattan Active Supply Chain Planning, a unified business planning platform that integrates supply chain planning with execution systems. This solution utilizes generative artificial intelligence to synthesize external data sources with internal patterns, resulting in more accurate demand forecasts and actionable insights. By eliminating silos between inventory, labor, and transportation planning, the platform allows for real-time bi-directional collaboration and optimization across the enterprise. The launch also introduced specialized AI agents for customer service, enhancing the ability of organizations to coordinate resources efficiently and respond dynamically to changing market conditions.

Key Market Players

• IBM Corporation
• SAP SE
• Oracle Corporation
• Microsoft Corporation
• Amazon Web Services, Inc.
• Google LLC
• Cisco Systems, Inc
• Intel Corporation
• Accenture plc
• Kinaxis Inc

By Offering	By Application	By End-User	By Region
Hardware Software Services	Fleet Management Supply Chain Planning	Automotive Retail Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Artificial Intelligence in Supply Chain Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Artificial Intelligence in Supply Chain Market, By Offering:

• Hardware
• Software
• Services

• Artificial Intelligence in Supply Chain Market, By Application:

• Fleet Management
• Supply Chain Planning

• Artificial Intelligence in Supply Chain Market, By End-User:

• Automotive
• Retail
• Others

• Artificial Intelligence in Supply Chain Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE