Artificial Intelligence Supply Chain Market Size and Share

Artificial Intelligence Supply Chain Market Analysis by Mordor Intelligence
The artificial intelligence supply chain market expanded to USD 7.67 billion in 2025 and is projected to reach USD 35.28 billion by 2030, reflecting a 35.67% CAGR, which positions it among the fastest growing enterprise-technology domains. Accelerated adoption stems from enterprises seeking autonomous and disruption-resistant supply chains, a trend amplified by the current shortage of AI accelerator GPUs that lengthens deployment lead times and compels optimization of scarce compute resources. Generative-AI copilots, proven to cut millions in planning costs at large consumer-goods firms, are scaling rapidly, while context-aware computing integrates IoT sensor data for real-time decision making in complex networks. North America leads in early commercialization and venture funding, yet Asia-Pacific is closing the gap through massive AI infrastructure programs backed by regional governments. Competitive intensity remains high as cloud hyperscalers, niche ISVs, and emerging edge-computing specialists jostle to deliver platform-centric solutions that reduce total cost of ownership.
Key Report Takeaways
- By offering, software solutions captured 47.6% of artificial intelligence supply chain market share in 2024; services are forecast to expand at a 19.34% CAGR through 2030.
- By technology, machine learning led with 37.89% share in 2024, while context-aware computing registers the highest projected CAGR at 23.2% to 2030.
- By application, warehouse and inventory management held 32.8% of artificial intelligence supply chain market size in 2024; risk and disruption management is advancing at a 20.6% CAGR to 2030.
- By end-user industry, retail and e-commerce dominated with 27.7% revenue share in 2024, whereas healthcare and life sciences is projected to grow at 17.63% CAGR through 2030.
- By geography, North America accounted for 41.8% share of artificial intelligence supply chain market size in 2024, with Asia-Pacific poised to expand at an 18.56% CAGR to 2030.
Global Artificial Intelligence Supply Chain Market Trends and Insights
Drivers Impact Analysis
Driver | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
---|---|---|---|
Lower operating costs and error reduction | +8.5% | Global | Medium term (2-4 years) |
Enhanced warehouse throughput via autonomous mobile robots | +6.2% | North America & Europe | Short term (≤ 2 years) |
Surge in Gen-AI copilots for demand forecasting | +7.8% | Global, early North America gains | Short term (≤ 2 years) |
Agentic AI for end-to-end self-orchestration | +5.1% | Asia-Pacific core, spill-over to North America | Long term (≥ 4 years) |
Synthetic data for supply-planning accuracy | +3.4% | Global | Medium term (2-4 years) |
Industry-cloud platforms bundling AI and IoT | +4.7% | Developed markets worldwide | Medium term (2-4 years) |
Source: Mordor Intelligence
Lower operating costs and error reduction
Enterprises deploying AI for predictive maintenance, dynamic routing, and intelligent allocation report 15-20% cost savings and near-perfect order accuracy, gains that free capital for additional AI projects. Automotive manufacturers using computer vision have cut defect rates by 30%, reinforcing a financial case that accelerates platform rollouts across discrete and process industries. Scaling benefits compound over multiple facilities, positioning AI as an essential lever for margin protection during economic volatility.
Enhanced warehouse throughput via autonomous mobile robots
Productivity jumps of 25-50% and incident reductions up to 60% demonstrate robotic systems’ immediate ROI, while emerging humanoid designs promise task-agnostic flexibility without large facility retrofits. Uptake is strongest in high labor-cost regions, with projections that most UK fulfillment centers will add robots by 2030. These gains shorten payback periods and support the growing e-commerce demand surge.
Surge in Gen-AI copilots for demand forecasting
More than half of supply-chain executives are piloting Gen-AI copilots, attracted by the ability to merge social sentiment, weather, and macroeconomic indicators into actionable forecasts. Kraft Heinz’s Lighthouse platform, for example, autonomously adjusts production schedules in real time and improves forecast precision by double-digit percentages[3]Kraft Heinz, “AI Lighthouse expands to global factories,” Kraft Heinz, kraftheinzcompany.com. Natural-language interfaces allow non-technical planners to generate scenarios quickly, democratizing advanced analytics.
Agentic AI for end-to-end self-orchestration
Logistics leaders such as UPS demonstrate agentic AI that evaluates thousands of variables to re-route shipments and allocate capacity within minutes. C3.ai orchestration agents automate sourcing and fulfillment tasks, learning from each cycle to improve resilience. As algorithms mature, procurement software is expected to shift 15% of routine decisions to autonomous agents by 2028[1]C3.ai, “C3 AI Supply Chain Suite introduces multi-hop orchestration agents,” C3.ai, c3.ai.
Restraint Impact Analysis
Restraint | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
---|---|---|---|
Shortage and concentration of AI accelerator GPUs | -12.3% | Global, acute in North America and Europe | Short term (≤ 2 years) |
Fragmented, poor-quality legacy data silos | -8.7% | Global, higher in traditional industries | Medium term (2-4 years) |
Expanding AI-specific cyber and model-poisoning threats | -4.2% | Global, connected ecosystems | Long term (≥ 4 years) |
Emerging trustworthy-AI regulations | -6.8% | Europe and North America, expanding worldwide | Medium term (2-4 years) |
Source: Mordor Intelligence
Shortage and concentration of AI accelerator GPUs
Lead times for top-tier GPUs have reached double digits in weeks, with list prices nearing USD 40,000, prompting enterprises to ration compute and adopt more efficient architectures. Supply risk is magnified by geographic clustering of substrate manufacturing, compelling firms to pre-order capacity years ahead and rethink AI workload placement strategies.
Expanding AI-specific cyber and model-poisoning threats at the edge
Distributed AI deployments introduce new attack surfaces. Research shows data-poisoning assaults can degrade anomaly-detection accuracy by double digits, forcing additional spend on model governance and zero-trust architectures. Heightened cyber exposure can delay pilot-to-production transitions, especially in highly regulated sectors.
Segment Analysis
By Offering: Services surge despite software dominance
Software platforms held 47.6% artificial intelligence supply chain market share in 2024, reflecting enterprises’ preference for integrated suites that span planning, execution, and analytics. Yet services revenue is increasing at 19.34% CAGR as organizations outsource implementation, model training, and continuous optimization to specialized partners. Implementation and managed-service providers benefit from skills shortages and the complexity of multi-vendor ecosystems.
Hardware remains the smallest slice but exerts outsized influence due to the ongoing GPU bottleneck. Scarcity has sparked interest in alternative accelerators such as TPUs and FPGAs, which in turn drives demand for code-porting and model-compression services. Firms that can integrate heterogeneous compute stacks without sacrificing performance are capturing share across the artificial intelligence supply chain market.

Note: Segment Share of all individual segments available upon report purchase
By Technology: Context-aware computing disrupts ML leadership
Machine learning retained 37.89% share in 2024, cementing its status as the default analytic engine for demand prediction and replenishment. Natural language processing accelerates procurement automation by translating contract text into structured insights, while computer vision expands from quality inspection to robotic navigation.
Context-aware computing, however, is scaling fastest at 23.2% CAGR as IoT telemetry feeds real-time optimization engines. These systems adjust decisions based on ambient temperature, equipment health, and traffic patterns, delivering near-instant course corrections. The artificial intelligence supply chain market size linked to context-aware solutions is projected to climb sharply as sensor prices decline and edge-AI frameworks mature.
By Application: Risk management overtakes traditional operations
Warehouse and inventory management dominated with 32.8% share of artificial intelligence supply chain market size in 2024. Automated storage systems and vision-guided picking deliver fast paybacks and measurable throughput gains.
Risk and disruption management grows at 20.6% CAGR, reflecting board-level urgency to address geopolitical shocks and climate-related events. AI-enabled control towers simulate thousands of contingencies, prioritize mitigation actions, and autonomously trigger supplier or logistics adjustments. These capabilities appeal to CFOs seeking quantified resilience metrics for insurance, lending, and ESG disclosures.
Note: Segment shares of all individual segments available upon report purchase
By End-User Industry: Healthcare accelerates past retail leadership
Retail and e-commerce leveraged early AI trials to streamline same-day fulfillment, accounting for 27.7% of revenue in 2024. Investments range from conversational chatbots that handle order inquiries to digital twins that allocate safety stock across networks.
Healthcare and life sciences, advancing at 17.63% CAGR, adopt AI to safeguard cold-chain integrity and meet stringent traceability rules. Pharmaceutical firms integrate context-aware sensors to monitor temperature deviations and push autonomous re-routing instructions when excursions threaten product viability. The artificial intelligence supply chain industry sees life-science companies pioneering AI-driven lot genealogy, setting templates that other regulated sectors will follow.
Geography Analysis
North America captured the highest artificial intelligence supply chain market share at 41.8% in 2024, buoyed by robust venture funding and the presence of technology giants that bundle AI, cloud, and edge services into turnkey offerings. Strategic acquisitions such as Blue Yonder’s USD 839 million purchase of One Network Enterprises illustrate a platform-consolidation wave driven by customer demand for end-to-end solutions. Regional enterprises also benefit from early regulatory clarity, supporting faster pilots and scaleouts.
Asia-Pacific is the fastest growing region with an 18.56% CAGR through 2030. National programs in China, Japan, and South Korea subsidize AI infrastructure, while manufacturing powerhouses deploy agentic AI to counter labor shortages. Governments are additionally funding semiconductor self-sufficiency projects to reduce exposure to overseas GPU supply risks, an incentive that accelerates domestic AI adoption.
Europe maintains a steady growth path as sustainability and trustworthy-AI regulations spur demand for transparent, auditable AI workflows. Enterprises invest in AI to track Scope 3 emissions, optimize reverse logistics, and comply with the EU Artificial Intelligence Act. Elsewhere, early-stage deployments in Latin America and Africa focus on basic visibility and demand-planning use cases, often delivered via cloud-based subscription models that lower entry barriers.

Competitive Landscape
The artificial intelligence supply chain market is fragmented, with the top five vendors controlling less than 20% of total revenue. Cloud hyperscalers—Amazon Web Services, Microsoft Azure, and Google Cloud—capitalize on infrastructure scale, offering pre-trained models and integrated data pipelines that shorten time to value. Specialized providers such as Kinaxis, C3.ai, and Blue Yonder differentiate through domain expertise and industry-specific accelerators.
Strategic activity underscores a pivot toward ecosystem orchestration. Blue Yonder’s acquisition of One Network Enterprises aims to unite control-tower visibility with multi-enterprise collaboration, while Oracle embeds autonomous agents directly into its ERP suite to reduce manual workflow handoffs. NVIDIA deploys a USD 1 billion venture fund to seed “core-AI” startups that drive GPU demand, positioning itself as both component supplier and value-chain orchestrator.
Edge-AI and synthetic data segments attract newcomer funding given the immediate need to conserve compute cycles and address privacy constraints. Partnerships such as Kinaxis-Databricks pair deep supply-chain algorithms with cloud-native data platforms, signaling a broader convergence between data engineering and decision intelligence[2]Kinaxis, “Kinaxis and Databricks partner on AI-powered orchestration,” Kinaxis, kinaxis.com. Despite intense M&A, white-space opportunities persist in aerospace-defense, life-science cold chains, and emerging-market logistics where few vendors offer turnkey AI solutions.
Artificial Intelligence Supply Chain Industry Leaders
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Amazon Web Services, Inc.
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IBM Corporation
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Microsoft Corporation
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SAP SE
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NVIDIA Corporation
- *Disclaimer: Major Players sorted in no particular order

Recent Industry Developments
- June 2025: Amazon introduced Wellspring mapping, a next-generation demand-forecasting model, and natural-language robotics upgrades, backed by USD 1.2 billion in workforce skilling investments.
- May 2025: SAP released its enterprise AI playbook at Sapphire 2025 with an emphasis on agentic intelligence for supply-chain differentiation.
- April 2025: Kinaxis and Databricks integrated Kinaxis Maestro with the Databricks Data Intelligence Platform to support predictive and autonomous orchestration at scale.
- March 2025: Kraft Heinz expanded its AI Lighthouse platform across global factories to enhance demand forecasting and production optimization.
Global Artificial Intelligence Supply Chain Market Report Scope
Artificial intelligence (AI) is set to transform supply chain activities, from planning and production to management and optimization. AI enhances decision-making and boosts operational efficiency in supply chains by analyzing vast data sets, forecasting trends, and executing intricate tasks in real-time. With AI-driven systems, companies optimize routes, streamline workflows, enhance procurement, reduce shortages, and automate tasks end-to-end.
The study tracks the revenue accrued through the sale of the artificial intelligence supply chain offerings by various players across the globe. The study also tracks the key market parameters, underlying growth influences, and major vendors operating in the industry, which supports the market estimations and growth rates over the forecast period. The study further analyses the overall impact of COVID-19 aftereffects and other macroeconomic factors on the market. The report’s scope encompasses market sizing and forecasts for the various market segments.
Artificial intelligence supply chain market is segmented by offering (hardware, software, services), by technology (machine learning, computer vision, natural language processing, context-aware computing, others), by application (supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, planning & logistics), by end-user industry (manufacturing, food and beverages, healthcare, automotive, aerospace, retail, consumer-packaged goods, other end-user industry), by geography (North America, Europe, Asia Pacific, Latin America, Middle East and Africa). The report offers market forecasts and size in value (USD) for all the above segments.
By Offering | Hardware | AI accelerator chips (GPU, TPU, ASIC) | |
Edge devices and sensors | |||
Robotics and AMRs | |||
Software | AI supply-chain platforms | ||
Predictive analytics suites | |||
Services | Implementation and integration | ||
Managed and support services | |||
By Technology | Machine Learning | ||
Computer Vision | |||
Natural Language Processing | |||
Context-Aware Computing | |||
Other AI Techniques (Graph, GANs) | |||
By Application | Supply-chain planning and SandOP | ||
Warehouse and inventory management | |||
Transportation / fleet routing | |||
Risk and disruption management | |||
Virtual assistants and chatbots | |||
Procurement and sourcing optimisation | |||
By End-User Industry | Manufacturing | ||
Automotive | |||
Food and Beverages | |||
Healthcare and Life-Sciences | |||
Retail and E-commerce | |||
Aerospace and Defence | |||
Consumer-Packaged Goods | |||
Other Industries (Energy, Chemicals) | |||
By Geography | North America | United States | |
Canada | |||
Mexico | |||
South America | Brazil | ||
Argentina | |||
Rest of South America | |||
Europe | United Kingdom | ||
Germany | |||
France | |||
Italy | |||
Spain | |||
Nordics | |||
Rest of Europe | |||
Middle East and Africa | GCC | ||
Israel | |||
South Africa | |||
Rest of Middle East and Africa | |||
Asia-Pacific | China | ||
India | |||
Japan | |||
South Korea | |||
ASEAN | |||
Australia | |||
New Zealand | |||
Rest of Asia-Pacific |
Hardware | AI accelerator chips (GPU, TPU, ASIC) |
Edge devices and sensors | |
Robotics and AMRs | |
Software | AI supply-chain platforms |
Predictive analytics suites | |
Services | Implementation and integration |
Managed and support services |
Machine Learning |
Computer Vision |
Natural Language Processing |
Context-Aware Computing |
Other AI Techniques (Graph, GANs) |
Supply-chain planning and SandOP |
Warehouse and inventory management |
Transportation / fleet routing |
Risk and disruption management |
Virtual assistants and chatbots |
Procurement and sourcing optimisation |
Manufacturing |
Automotive |
Food and Beverages |
Healthcare and Life-Sciences |
Retail and E-commerce |
Aerospace and Defence |
Consumer-Packaged Goods |
Other Industries (Energy, Chemicals) |
North America | United States |
Canada | |
Mexico | |
South America | Brazil |
Argentina | |
Rest of South America | |
Europe | United Kingdom |
Germany | |
France | |
Italy | |
Spain | |
Nordics | |
Rest of Europe | |
Middle East and Africa | GCC |
Israel | |
South Africa | |
Rest of Middle East and Africa | |
Asia-Pacific | China |
India | |
Japan | |
South Korea | |
ASEAN | |
Australia | |
New Zealand | |
Rest of Asia-Pacific |
Key Questions Answered in the Report
What is the current size of the artificial intelligence supply chain market?
The artificial intelligence supply chain market size reached USD 7.67 billion in 2025 and is projected to grow rapidly toward USD 35.28 billion by 2030.
Which region leads adoption of AI in supply chains?
North America holds 41.8% of revenue due to strong cloud-infrastructure ecosystems and early enterprise pilots.
What application area is growing fastest?
Risk and disruption management shows the highest CAGR at 20.6% as firms prioritize resilience.
Why are services growing faster than software?
Companies need external expertise to deploy, govern, and refine AI models, pushing services to a 19.34% CAGR through 2030.
How is GPU scarcity affecting adoption?
GPU shortages extend deployment timelines and shift focus toward compute-efficient architectures and edge-AI strategies.
Which industries are accelerating investment beyond retail?
Healthcare and life sciences are advancing at 17.63% CAGR to leverage AI for cold-chain integrity and regulatory compliance.
Page last updated on: June 17, 2025