AI In Supply Chain Statistics By Market And Strategies (2026)

At the Beginning

AI In Supply Chain Statistics: Artificial intelligence is changing the supply chain industry by making it more efficient, accurate, and cost-effective. This improvement covers the entire process, from buying raw materials to delivering products to customers. Every company or organization within a supply chain network plays an important role. The actions and decisions of these companies, when combined, shape the overall direction, growth, and potential of the global supply chain management market.

AI-based supply chain management tools are expected to be powerful solutions for helping businesses handle these challenges. By using a connected, end-to-end approach, companies can better manage opportunities and challenges across all areas of their business, from purchasing to final sales. Now, let’s see how AI is impacting the supply chain industry.

Best in the Editor’s Eye

1. The global AI supply chain market could grow from USD 9.2 billion in 2025 to USD 157.6 billion by 2033, at a 42.7% CAGR.
2. In 2025, 38% of respondents expected AI to become critical to supply chain operations, up from 11% in 2022.
3. Around 94% of supply chain organizations plan to use AI and generative AI for decision-making within the next two years.
4. About 85% of executives plan to increase AI investment in 2026, while one-fifth expect an increase of at least 20%.
5. Demand forecasting uses AI/ML at 40% of high-performing organizations, compared with 19% of lower-performing organizations.
6. Only 23% of supply chain organizations have a formal, clearly documented AI strategy.
7. More than 60% of recent supply chain planning projects were completed late or exceeded their budgets.
8. Around 71% of supply chain leaders consider AI disruptive, while 24% view its impact as transformational.
9. Auger raised USD 100 million in Series A funding in June 2026 to develop its autonomous supply chain operating system.
10. BackOps AI raised USD 26 million after its platform reduced customer response times by 93%.

Global AI in Supply Chain Market Statistics

(Source: market.us)

• It is expected to reach USD 9.2 billion in 2025 and USD 13.1 billion in 2026.
• The market is forecast to reach USD 77.4 billion in 2031 and USD 110.4 billion in 2032.
• By 2033, its value could reach USD 157.6 billion, supported by a strong 42.7% CAGR.

AI Adoption of Supply Chain Management

• In 2022, 6% of respondents were not using AI, while 16% were testing AI through pilot projects.
• For 2025, the share of organizations not using AI was expected to decline to 4%.
• Pilot use cases were forecast to fall to 8%, while limited adoption was expected to reach 18%.
• Widespread AI adoption was projected at 30% in 2025.

(Reference: statista.com)

AI/ML Use in Supply Chain Decisions

• The 2023 Gartner Future of Supply Chain Survey compared 119 high-performing organizations with 569 lower-performing organizations.
• In demand forecasting, 40% of high performers used supply chain data with AI/ML, compared with 19% of lower performers.
• For order management and fulfillment, the rates were 33% for high performers and 8% for lower performers.
• In supply planning, 31% of high performers used AI/ML to automate or improve decisions, compared with 12% of lower performers.
• For logistics and distribution, adoption reached 27% among high performers, while only 8% of lower performers used these tools.
• In sales and operations planning or integrated business planning, 24% of high performers applied AI/ML, compared with 10% of lower performers.
• Demand forecasting had the highest adoption rate for both groups among the top 5 processes measured.

(Source: gartner.com)

AI Strategies in Supply Chain Organizations

• The 2025 Gartner AI Adoption in Supply Chain Survey reviewed responses from 114 supply chain professionals about their organizations’ AI strategies.
• Only 23% of respondents said their organization had a formal AI strategy that was clearly defined and documented.
• Around 41% of respondents followed an informal AI strategy. These organizations used a flexible or ad hoc approach instead of a fully documented plan.
• Another 6% of respondents said their organization had a supply chain AI strategy, but they were not familiar with its details.
• The remaining 30% of respondents reported that their organization did not have any supply chain AI strategy.
• Overall, 77% of respondents either lacked a formal AI strategy, did not know its details, or worked under an informal approach.
• Informal strategies were the most common, with a rate 18 percentage points higher than formal strategies.
• Organizations without any AI strategy exceeded those with a formal plan by 7 percentage points.

(Source: gartner.com)

Impact of AI on Retail Supply Chains

• Demand management receives the largest impact at 27%. AI helps retailers predict product needs, manage inventory, and reduce the risk of shortages or excess stock.
• Identifying and addressing internal problems accounts for 20%. AI can find process delays, operational gaps, and supply chain risks more quickly.
• Real-time updates represent 19%. These updates give retailers better visibility into inventory, shipments, warehouse activity, and delivery progress.
• AI improves last-mile deliveries by 13% through better route planning, faster delivery decisions, and more efficient use of drivers.
• Robot-assisted production accounts for 10%, helping automate repeated tasks and improve operating speed.
• Disruption management represents 8%. AI can help retailers respond to transport delays, supply shortages, and unexpected changes.
• Quality control receives the smallest share at 5%, but AI can still support product inspections and early defect detection.
• Overall, the chart covers 7 areas, ranging from 5% to 27%.

(Source: oodles.io)

Supply Chain AI Transformation Pitfalls

• More than 60% of recent supply chain planning projects were delivered late or exceeded their planned budget.
• The first stage is value creation, strategy, and roadmap development. However, fewer than 1 in 3 companies complete a value diagnostic, leaving potential benefits unclear.
• The second stage focuses on designing the target solution and selecting vendors. Many companies overlook this step and choose tools without properly reviewing available options, which can cause weak decisions and lost value.
• The third stage covers implementation and system integration. Around 25% of supply chain leaders said their objectives were not aligned with system integrators’ incentives, reducing execution quality and impact.
• The final stage involves change management, capability development, and value delivery. Only 13% of global senior executives said their companies were properly prepared to close skills gaps.

(Source: mckinsey.com)

AI Investment in Supply Chain Stats

• 94% of supply chain organizations plan to leverage AI and Gen AI technology for decision-making in the next two years.
• 64% of supply chain professionals believe that AI/Gen AI capabilities are important or extremely important when choosing new technologies.
• 85% of executives intend to invest more in AI in 2026, and one-fifth plans to see a 20% or more increase.

Agentic AI Supply Chain Model

• Agentic automation connects supply chain and business applications such as ERP, CRM, integrated planning, product lifecycle management, legacy systems, and enterprise APIs.
• It also uses partner systems, including transport, warehouse, and inventory management platforms. External inputs may include sensor, weather, security, economic, market, and geopolitical data.
• Its core capabilities include data integration, predictive analytics, impact evaluation, feedback loops, risk analysis, decision support, and human-machine collaboration.
• Integrated planning uses external data and current conditions to predict demand and supply. It can also connect planning systems across regions and business units.
• Procurement optimization supports real-time sourcing, supplier-capacity checks, and supplier-risk reduction.
• Inventory optimization automatically replenishes stock and balances inventory using real-time information.
• Production optimization predicts output, allocates resources, and adjusts raw materials and finished goods.
• Logistics optimization improves routes using traffic, weather, and customer data while supporting robotics and automated vehicles.
• Customer and field-service automation provides 24/7 support and combines customer feedback from several channels.

(Source: ibm.com)

Recent AI in Supply Chain Stats

• On September 17, 2026, Magnetic raised USD 18 million in Series A funding to expand AI agents for manufacturing, procurement, and supply chain workflows.
• On September 15, 2026, SPS Commerce launched new agentic AI tools, including MAX, Visibility Management and Decision Intelligence, supported by a network processing more than 2 million transactions daily.
• On August 25, 2026, logistics orchestration startup Nauta planned to raise USD 20 million to USD 30 million in Series A funding after completing a strategic investment round.
• On August 4, 2026, Blue Yonder reported 27 new customers and partnerships with NVIDIA and Syndigo during Q2 2026 while expanding its AI-powered supply chain platform.
• On June 18, 2026, T-Systems and SupplyOn partnered to connect Europe’s largest industrial supply chain network with the Industrial AI Cloud for automated procurement and logistics.
• On June 3, 2026, AI supply chain software company Auger raised USD 100 million in Series A funding, led by Oak HC/FT, to develop its autonomous operating system.
• On April 19, 2026, SAP announced new AI agents for production data, material reservations and outbound logistics, with general availability planned for Q2 2026.
• On April 18, 2026, logistics AI platform Loop raised USD 95 million in Series C funding, bringing its total capital raised to USD 210 million.
• On April 15, 2026, an MHI and Deloitte survey found that 71% of supply chain leaders viewed AI as disruptive, while 24% considered its impact transformational.
• On March 11, 2026, BackOps AI secured USD 26 million in Series A funding after its supply chain platform reduced customer response times by 93%.

Conclusion

AI is quickly becoming the standard way supply chains are run, not just an extra tool. Companies that use AI well are already doing better than those that don’t, but many are still struggling to actually put AI to work properly or plan for it clearly.

Even so, most companies plan to invest more in AI soon. New funding and AI tools launched throughout 2026 show the industry moving from small tests toward fully automated supply chains.

FAQ