Agentic Artificial Intelligence in the Supply Chain and Logistics Market

What is the Agentic Artificial Intelligence in the Supply Chain and Logistics Market forecast to be worth by 2036?

The market is projected to reach USD 76.4 billion by 2036.

• The Agentic Artificial Intelligence in the Supply Chain and Logistics market reached USD 10.7 billion in 2025.
• Demand is forecast to increase from USD 12.8 billion in 2026 to USD 76.4 billion by 2036.
• The market is projected to expand at a CAGR of 19.6% from 2026 to 2036.

What are the defining numbers behind Agentic Artificial Intelligence in the Supply Chain and Logistics Market growth?

An absolute dollar opportunity of USD 63.6 billion is expected between 2026 and 2036.

• Demand Drivers in the Market
  • The primary driver is the gap between event speed and human coordination speed. Ports, carriers, warehouses, suppliers, and customers produce signals continuously, and a delay becomes more expensive when teams must assemble context across separate systems before acting.
  • Agentic AI can monitor the flow, compare response options, and trigger an approved action or request a decision from the correct owner, converting fragmented signals into governed decisions rather than relying on manual reconciliation.
  • Shared event and data standards are enabling cross-enterprise orchestration. GS1 EPCIS Release 2.0 provides a standards base for supply chain event data, and the EU’s eFTI Regulation implementation in January 2025 supports paperless digital freight data exchange.
  • Public programs are building the data foundations. The U.S. Department of Transportation advanced a first-of-its-kind supply chain initiative in March 2024, and the World Bank’s Logistics Performance Index guides where improvements and digital adoption are most needed.
  • Vendors are shipping planning and communication agents. Oracle added Fusion Agentic Applications in June 2026, and Microsoft introduced autonomous Dynamics 365 agents in October 2024 including a Supplier Communications Agent designed to confirm purchase-order delivery and preempt delays.
  • Regulatory frameworks are raising the need for traceability and risk visibility. Germany’s BAFA issued guidance under the Supply Chain Due Diligence Act in December 2024, and NIST’s AI Risk Management Framework gives buyers a governance reference for deploying agents.
• Key Segments Analyzed
  • Logistics Planning Agents anchor the AI Agent Category segment with a 39.0% share in 2026, because they connect demand signals to coordinated execution across multiple downstream processes.
  • Route & Fleet Optimization leads the Logistics Function segment, taking 35.0% of demand in 2026, as it turns continuous route, capacity, and delivery data into measurable efficiency gains.
  • Third-party Logistics Providers is the dominant End-use Industry at 44.0% in 2026, reflecting their reliance on agentic systems to coordinate multi-client operations.
  • Global Logistics Enterprises lead the Organization Type segment with a 46.0% share in 2026, as their scale justifies the integration and control investment agents require.
  • Cloud-native AI Agents anchors the Deployment Framework segment with a 48.0% share in 2026.
• Analyst Opinion at Fact.MR
  • Shambhu Nath Jha, Principal Consultant at Fact.MR, states: “Supply chain operations produce continuous signals that outpace manual coordination, and agents that reconcile planning with execution deliver the clearest value. Logistics Planning Agents already hold 39.0% of the market in 2026 because they connect demand signals to coordinated action across several downstream processes. But fragmented data and unclear authority limits delay production use. Shared event standards, such as GS1 EPCIS and the EU eFTI framework, plus clearly defined execution authority, are the conditions that let agents move from recommendations to governed transactions. Vendors that combine these with domain workflow depth will capture the largest share of spend.”
• Strategic Implications
  • Enterprise application and cloud vendors should make shared event standards and digital freight rules part of their agent design, so agents can coordinate across enterprise boundaries and partners.
  • Domain vendors should lead with planning depth and network visibility, since workflow specificity and operational data context are the entry point for logistics buyer trust.
  • Buyers should define where an agent may recommend, communicate, or execute, because fragmented data and unclear authority limits are the main barrier to production deployment.
  • Channel and service investment should follow the fastest-growing country markets, including the USA, Germany, and Singapore, while adapting to local traceability and port-system strengths.
  • Transformation partners should assemble the data layer and change controls around a chosen platform, because contracts often combine a cloud platform, a domain product, and a systems integrator.

How does the Agentic Artificial Intelligence in the Supply Chain and Logistics Market break down by segment?

The market is structured across five analytical dimensions plus region. Logistics Planning Agents leads the AI Agent Category segment. Route & Fleet Optimization leads the Logistics Function segment. Third-party Logistics Providers leads the End-use Industry segment. Global Logistics Enterprises leads the Organization Type segment. Cloud-native AI Agents leads the Deployment Framework segment. Regionally, USA, Germany, Singapore, Japan, Canada, UK, Australia anchor the demand base.

Why do Logistics Planning Agents lead AI Agent Category?

Logistics Planning Agents is projected to account for a 39.0% share in 2026.

Logistics Planning Agents sit upstream of daily execution. They compare demand and capacity before assigning an action, account for inventory and service constraints, and let one agent influence several downstream processes without replacing every operating system. The category benefits from frequent exception cycles, since delays and stock risks create repeated decisions that are costly to coordinate manually. Planning agents lead when they can reconcile TMS, WMS, ERP, and partner feeds, and they lose value when master data is inconsistent or business rules remain undocumented. Oracle announced in June 2026 that its Inventory Planning Command Center and related Fusion Agentic Applications can identify inventory risks and progress work inside established application guardrails.

Why does Route & Fleet Optimization lead Logistics Function?

Route & Fleet Optimization is projected to account for a 35.0% share in 2026.

Route and fleet optimization turns continuous data about demand, capacity, traffic, and delivery windows into directly measurable efficiency, fuel, and on-time gains. Agents can rebalance routes, flag asset constraints, and propose dispatch changes under cost and service policies, then escalate the highest-risk decisions to a planner. This function has clear performance metrics, which makes validation easier and gives buyers a faster business case than broader planning use cases. It is also a frequent, high-volume workflow, so improvements repeat daily and produce the recurring return that supports continued agent spend.

Why do Third-party Logistics Providers lead End-use Industry?

Third-party Logistics Providers is projected to account for a 44.0% share in 2026.

Third-party logistics providers lead the End-use Industry because they coordinate multiple shippers, networks, and service expectations within one operation, which creates the highest volume of cross-system work that agents can reconcile. They run many clients’ freight on shared assets and systems, so a single orchestration layer can improve utilization across accounts. They also bear acute margin and service pressure, giving them a direct incentive to adopt planning and route optimization agents that cut exception handling and improve asset use. Their multi-client scale justifies the integration cost and provides the recurring workloads that support agent deployment.

Why do Global Logistics Enterprises lead Organization Type?

Global Logistics Enterprises is projected to account for a 46.0% share in 2026.

Global logistics enterprises have the transaction volume and systems coverage needed to justify agent deployment. Their networks span regions and business units, creating a strong case for agents that can apply common policy while adapting to local constraints. Large enterprises also have more internal data than smaller operators, supporting richer context and more reliable evaluation. The barrier is governance complexity: a global buyer must define where an agent may recommend, communicate, or execute, and manage identity and data residency across jurisdictions. Microsoft introduced autonomous Dynamics 365 agents in October 2024, with a Supplier Communications Agent designed to confirm purchase-order delivery and preempt delays for procurement teams.

What is accelerating Agentic Artificial Intelligence in the Supply Chain and Logistics Market adoption, and what is holding it back?

Drivers Impact Analysis

Restraints Impact Analysis

Which countries are scaling the Agentic Artificial Intelligence in the Supply Chain and Logistics Market fastest?

• The United States leads the pace of expansion as large shippers and logistics providers purchase systems that combine port, carrier, warehouse, and purchase-order signals, supported by a first-of-its-kind supply chain initiative from the U.S. Department of Transportation.
• Germany is scaling where industrial buyers place strong emphasis on supplier traceability and documented risk processes, guided by BAFA’s December 2024 standards and audit guidance under the Supply Chain Due Diligence Act.
• Singapore is advancing as port and maritime operators adopt coordinated systems that reduce waiting and improve resource scheduling, with the Maritime and Port Authority highlighting digitalPORT.SG and continued maritime digitalization investment.
• Japan is growing as logistics buyers focus on labor constraints and safe automation, with METI publishing a roadmap in February 2025 for higher-capacity autonomous delivery robots to address logistics labor shortages.
• Canada is expanding across long multimodal corridors where visibility during weather and infrastructure disruptions matters most, with Transport Canada reporting in June 2025 that the National Supply Chain Office was advancing digital tools and data sharing.
• The United Kingdom is building demand as freight operators test technology through partnerships that require measurable field results, supported by £1.8 million of Department for Transport Freight Innovation Fund support opened in May 2024.
• Australia is adopting as buyers manage long distances and uneven network density across road and rail, shaped by the updated National Freight and Supply Chain Strategy released in August 2025.

Country-wise CAGR Forecast (2026-2036)

What is driving the Agentic Artificial Intelligence in the Supply Chain and Logistics Market in the USA?

USA is projected to register a 21.5% CAGR through 2036, supported by integrated port, carrier, and warehouse signals and federal supply chain programs.

Large American shippers and logistics providers purchase systems that combine port and carrier signals with warehouse and purchase-order data. The U.S. Department of Transportation advanced a first-of-its-kind supply chain initiative in March 2024, giving shippers a federal push toward shared data and network-level use cases.

What is driving the Agentic Artificial Intelligence in the Supply Chain and Logistics Market in Germany?

Germany is projected to register a 20.8% CAGR through 2036, supported by supplier traceability and the Supply Chain Due Diligence Act.

Industrial buyers in Germany place strong emphasis on supplier traceability and documented risk processes. In December 2024, BAFA issued guidance on standards and audits under the Supply Chain Due Diligence Act, which drives demand for agentic systems that can monitor supplier risk and evidence compliance.

What is driving the Agentic Artificial Intelligence in the Supply Chain and Logistics Market in Singapore?

Singapore is projected to register a 20.1% CAGR through 2036, supported by coordinated port systems and maritime digitalization.

Port and maritime operators in Singapore favor coordinated systems that reduce waiting and improve resource scheduling. In March 2025, the Maritime and Port Authority highlighted digitalPORT.SG as a one-stop platform and continued investment in maritime digitalization, supporting the data foundation agents need.

What is driving the Agentic Artificial Intelligence in the Supply Chain and Logistics Market in Japan?

Japan is projected to register a 19.3% CAGR through 2036, supported by labor automation and autonomous delivery programs.

Japanese logistics buyers focus on labor constraints and safe automation in distribution and delivery. In February 2025, METI published a roadmap for higher-capacity autonomous delivery robots to address logistics labor shortages, supporting agent frameworks that coordinate delivery automation.

What is driving the Agentic Artificial Intelligence in the Supply Chain and Logistics Market in Canada?

Canada is projected to register an 18.6% CAGR through 2036, supported by multimodal visibility and resilience tools.

Canadian buyers operate across long multimodal corridors and prioritize visibility during weather or infrastructure disruptions. Transport Canada reported in June 2025 that the National Supply Chain Office was advancing digital tools and data sharing after disruptions such as the Jasper wildfires, supporting agentic visibility systems.

What is driving the Agentic Artificial Intelligence in the Supply Chain and Logistics Market in the UK?

The UK is projected to register a 17.9% CAGR through 2036, supported by funded freight technology trials.

Freight operators in the UK test technology through partnerships that require measurable field results. In May 2024, the Department for Transport opened £1.8 million of Freight Innovation Fund support for AI and automation trials, funding the pilots that later scale into agent deployment.

What is driving the Agentic Artificial Intelligence in the Supply Chain and Logistics Market in Australia?

Australia is projected to register a 17.2% CAGR through 2036, supported by long-haul visibility and network planning.

Australian buyers manage long distances and uneven network density across road and rail, with port and remote operations adding complexity. The updated National Freight and Supply Chain Strategy released in August 2025 frames the planning context that supports agentic visibility and network optimization.

Who leads the Agentic Artificial Intelligence in the Supply Chain and Logistics Market?

The competitive landscape divides into three strategic positions. Enterprise application and cloud providers place agents close to operational data and transaction systems, with access to identity and integration services. Supply chain domain and physical AI providers compete through planning depth and network visibility, with simulation and warehouse execution providing further differentiation. Transformation firms connect these components to customer processes and data estates, with implementation reach and change management as their advantage.

Enterprise application and cloud platforms: Microsoft Corporation, Amazon Web Services, Inc., Google LLC, Oracle Corporation, IBM Corporation, and SAP SE. Supply chain domain and physical AI platforms: NVIDIA Corporation and Blue Yonder Group, Inc. Transformation and integration services: Accenture plc, Infosys Limited, and Wipro Limited.

Which companies are the key providers?

Key companies profiled in the Agentic Artificial Intelligence in the Supply Chain and Logistics market include Microsoft Corporation, Amazon Web Services, Inc., Google LLC, Oracle Corporation, IBM Corporation, SAP SE, NVIDIA Corporation, Blue Yonder Group, Inc., Accenture plc, Infosys Limited, and Wipro Limited.

• Microsoft Corporation
• Amazon Web Services, Inc.
• Google LLC
• Oracle Corporation
• IBM Corporation
• SAP SE
• NVIDIA Corporation
• Blue Yonder Group, Inc.
• Accenture plc
• Infosys Limited
• Wipro Limited

Bibliography

• World Bank. (2023, April 21). World Bank Releases Logistics Performance Index 2023.
• National Institute of Standards and Technology. (2023, January 26). AI Risk Management Framework.
• GS1. (2022, June 22). GS1 Standards Repository: EPCIS Standard Release 2.0.
• European Commission. (2025, January 9). Towards Paperless Freight Transport: EU Takes a Step Forward with eFTI Regulation Implementation.
• U.S. Department of Transportation. (2024, March 20). Biden-Harris Administration Announces New Milestone in First-of-its-Kind Supply Chain Initiative.
• German Federal Office for Economic Affairs and Export Control. (2024, December 13). Supply Chain Due Diligence Act: BAFA Publishes New Guidance.
• Maritime and Port Authority of Singapore. (2025, March 5). Driving Growth and Innovation to Strengthen Maritime Competitiveness and Resilience.
• Japan Ministry of Economy, Trade and Industry. (2025, February 26). Future Vision for Autonomous Delivery Robots.
• Transport Canada. (2025, June 17). Transportation in Canada 2024: Trends and Outlook.
• UK Department for Transport. (2024, May 1). £1.8 Million Boost for Innovation to Decarbonise Freight.
• Australian Department of Infrastructure, Transport, Regional Development, Communications and the Arts. (2025, August 22). Updated National Freight and Supply Chain Strategy Released.
• Microsoft. (2024, October 21). Transform Work with Autonomous Agents Across Your Business Processes.
• Oracle. (2026, June 29). Oracle Adds New Fusion Agentic Applications to Help Customers Improve Supply Chain Performance.

This Report Answers

• The report provides strategic intelligence on the Agentic Artificial Intelligence in the Supply Chain and Logistics Market across the segment categories that drive industrial and enterprise purchasing behavior.
• Segment analysis identifies Logistics Planning Agents as the leading sub-segment within the AI Agent Category segment, with a 39.0% share in 2026.
• Regional outlook evaluates USA, Germany and Singapore while Japan, Canada complete the country growth comparison.
• Competitive analysis profiles Microsoft Corporation, Amazon Web Services, Inc. and Google LLC alongside Oracle Corporation and IBM Corporation, followed by additional active providers within the category.
• Category assessment covers demand across primary segment groupings within the market, evaluated by value (USD) and share.
• Use-case assessment covers key application areas and end-use sectors identified in the market analysis.

What does the Agentic Artificial Intelligence in the Supply Chain and Logistics Market cover?

The Agentic Artificial Intelligence in the Supply Chain and Logistics Market covers the systems, services, and software categories defined by AI Agent Category, Logistics Function, End-use Industry and related market attributes.

The Agentic Artificial Intelligence in the Supply Chain and Logistics Market covers the full range of products and services segmented by AI Agent Category, Logistics Function, End-use Industry, Organization Type, and Deployment Framework within the 2026-2036 forecast horizon. Coverage includes Logistics Planning Agents, the largest sub-segment within the AI Agent Category segment, and extends across all primary categories identified in the report.

The market differs from adjacent consumer or industrial categories because commercial value comes from the function and performance requirements captured within the supplied segmentation framework. Commodity components and general-purpose categories remain outside the boundary unless they are explicitly formulated or configured for the defined market application.

What is included in the scope?

Agentic Artificial Intelligence in the Supply Chain and Logistics Market products and services deployed across the major segment categories and geographies profiled in the report.

The scope includes all product categories segmented by AI Agent Category, Logistics Function, End-use Industry, Organization Type, and Deployment Framework covered through secondary research, supplier validation, and demand modeling. Logistics Planning Agents, the leading sub-segment within the AI Agent Category segment, is included with full value and share analysis alongside the complete set of profiled countries, companies, and end-use applications identified in the report.

What is excluded from the scope?

Commodity inputs, general-purpose goods, and unrelated service lines are outside the scope.

The scope excludes products and services that are not specifically designed, configured, or deployed for the Agentic Artificial Intelligence in the Supply Chain and Logistics Market. General-purpose or horizontal categories that may be used incidentally across multiple markets are excluded unless they are sold within a product or service bundle specific to the market under analysis. Adjacent categories and standalone commodity components remain outside the boundary unless their principal commercial function falls within the supplied segmentation framework.

How was the analysis built?

Fact.MR combines desk research with supplier, buyer and expert validation to define the market boundary and test segment adoption, pricing, deployment and regional demand assumptions. Company portfolios, product and technology documentation, regulatory material, industry publications and interview input are reconciled with activity-level data and country-level conditions before forecasts are updated.

Forecasts are validated through supplier checks, industry interviews, and demand-side input that tests assumptions on adoption, product trends, pricing, and regional dynamics. Portfolio mapping, regional demand assessments, and distributor feedback help confirm market direction. Ongoing monitoring of regulatory developments and competitive product launches supports continuous model and forecast updates.

• Data Validation and Update Cycle:
  • Forecasting uses activity-level data, product adoption rates, attachment ratios, application demand, technology penetration curves, and average pricing benchmarks. Models also incorporate regulatory impact, regional adoption patterns, technology substitution effects, and format or deployment preferences across end-use applications and geographies.
• Market-Sizing and Forecasting:
  • Desk research draws on company reports, investor presentations, product specifications, press releases, patent filings, and third-party market data. Government publications, regulatory filings, standards documentation, and industry body reports are also reviewed. News monitoring and business intelligence sources are systematically evaluated to track product launches, partnership activity, and competitive positioning.
• Desk Research:
  • Primary research includes interviews with product managers, technology suppliers, system integrators, procurement specialists, and end-user organizations. Input from domain experts, application engineers, and sales teams involved in product specification, deployment, and commercialization is evaluated alongside buyer feedback on purchasing criteria, adoption drivers, and competitive evaluation.
• Primary Research:
  • 120+ sources, 40+ company portfolios, 25+ countries, 20+ interviews.