2026从智能协同助手到企业全域转型_AI_重塑供应链发展新要求洞察报告_26页_1mb

BCG Executive Perspective: AI's New Mandate in Supply Chains

Core Content

In 2026, AI and GenAI are no longer just potential tools for supply chains but are now being executed to drive tangible value. The evolution of AI capabilities—from chatbots to systems that can reason and execute multistep workflows—has transformed the landscape, yet many organizations are still struggling to realize significant impact from their AI investments.

Main Points

• AI Maturity and Value Realization:

  • Less than 25% of companies have achieved AI maturity at scale in supply chain.
  • Only about 30% report measurable AI value in planning use cases.
  • Most remain focused on master data, forecasting, and limited automation.

• Agentic AI as a Game Changer:

  • Agentic AI can eliminate the tradeoff between effectiveness and efficiency, enabling faster and better decisions at scale.
  • It allows for more frequent and granular optimization, unlocking new revenue, cost savings, and working capital improvements.

• Emerging Operating Model:

  • A human-agentic supply chain operating model is emerging, where AI autonomously handles routine tasks, while human teams manage exceptions and continuously improve the system.
  • By 2030, SCM organizations are expected to have a model where AI plans and executes, and humans focus on strategic decisions.

• Five Strategic Moves:

  1. Invest in a robust data foundation: Clean, connected data and modern cloud-native platforms are essential for AI to function effectively.
  2. Start where decision density and value intersect: Focus on areas with high frequency and clear financial impact, such as planning and trade negotiations.
  3. Rebuild workflows around AI-led enterprise optimization: Shift from functional negotiations to end-to-end (E2E) optimization.
  4. Adopt a hybrid build-and-buy approach: Balance strategic value, customization, cost, and innovation pace.
  5. Make AI decisions transparent, auditable, and explainable: Build trust across departments by ensuring AI logic is clear and accountable.

Key Challenges in AI Adoption

• People, organization, and processes (70% of respondents): The biggest barrier to AI adoption, often due to resistance to change, lack of expertise, and siloed operations.
• Technology (20%): Integration with existing systems and tools is a challenge.
• Algorithms (10%): Model accuracy and reliability remain concerns.

What is an Agent?

• An agent is AI that uses tools to accomplish goals.
• It observes, plans, and acts autonomously, leveraging internal and external systems to execute tasks and optimize outcomes.

Benefits of Agentic AI in Supply Chains

• Granularity: Enables daily inventory optimization at the SKU level, reducing out-of-stocks and increasing sales.
• Decision Density: Supports high-frequency, high-impact decisions with a balance of revenue, cost, and service.
• Cross-functional Optimization: Facilitates simultaneous optimization across price, service, cost, and risk, enhancing overall performance.

Real-world Impact Examples

• CPG Company:
  • Redesigned replenishment to be proactive and cross-functional.
  • Achieved a 2-4% increase in in-stock percentage, 4-10% improvement in fill rate, and 40-60% administrative savings.
• Industrial Goods Firm:
  • Integrated GenAI tools to run complex scenarios and root-cause analysis.
  • Achieved a 25% increase in EBITDA and a 3x reduction in process cycle time.
• Specialty Materials Leader:
  • Used AI to optimize spare parts sourcing and inventory.
  • Achieved 2-5% savings in MRO spending and 15-20% inventory reduction with minimal service impact.

Future Outlook

• By 2030, the role of humans in supply chain will shift from routine execution to strategic decision-making and system improvement.
• The journey to an agentic-enabled supply chain involves incremental upgrades, from task-specific enhancements to full automation.
• The goal is to create a predictive, unified view of the supply chain that facilitates enterprise-level decisions and improves operational economics.

Summary of Key Figures

• Inventory reduction: 15-30%
• Service rate improvement: 5-15%
• Administrative savings: 40-60%
• Time reduction:
  • Divide by 10: Time to understand upstream scenarios
  • Divide by 5: Time to make plan and execute
• CO₂ reduction: 20-50%

Conclusion

The shift to agentic AI in supply chains represents a new mandate for leaders, requiring a strategic and structured approach to adoption. By focusing on data foundations, high-impact areas, and a hybrid platform strategy, organizations can unlock significant value and move toward a more efficient, effective, and unified supply chain model.