Where AI Can Actually Work in Supply Chain: A Readiness-Based Look Across SCOR Tasks

Artificial intelligence is advancing quickly. But in the real world of supply chain operations, not every process is equally prepared to benefit. Much of the hype overlooks a fundamental truth: AI doesn’t thrive in a vacuum. It requires structured data, repeatable decision cycles, measurable outcomes, and an environment that supports learning and adaptation.

Understanding where AI can be deployed effectively, and where it must be gated, is now mission critical.

Using the SCOR (Supply Chain Operations Reference) model as our framework, we conducted a comprehensive evaluation of AI readiness and risk across core supply chain tasks. The result is a research-backed scoring matrix that reveals which domains are most prepared for AI adoption and why. The goal isn’t just to use AI. It’s to implement it responsibly and with measurable value.

Why Agentic AI Demands Readiness

Much of the current discourse focuses on what AI can do. But in high-stakes systems like supply chains, a better question is: should we let it?

Agentic AI systems that not only predict, but propose and act requires more than technical capability. It needs environments where learning is safe, feedback is available, and decisions can be audited. Otherwise, we risk introducing opacity, brittleness, or unintended behavior.

To assess this properly, we created a 14-dimension readiness and risk framework that examines not just technical maturity but also organizational and ethical considerations. We divided these into two groups:

Readiness Factors

• Decision frequency

• Reward measurability

• Data availability and latency

• Interface standardization and integration

• Organizational change required

• Evidence of ROI

• Management support

• Workforce skill and cultural openness

• Standards and best practices

Risk Factors

• Legacy IT and integration debt

• Privacy and compliance exposure

• User trust deficits

• Collusion or emergent behavior potential

Each SCOR task was scored from 1 to 5 along these dimensions, using literature from peer-reviewed studies, O*NET, ASCM, FTC and other reputable sources. Every score was documented with rationale.

What Is SCOR and Why It Matters

The SCOR model provides a standardized way to describe supply chain activities. It breaks down processes into six categories: Plan, Source, Make, Deliver, Return, and Enable. It also maps cross-functional tasks like customer service and compliance into supply chain governance.

Using SCOR allowed us to evaluate AI readiness at a process level. This ensured that our scoring aligns with how real supply chains are structured and operated. Each task from forecasting to procurement to reverse logistics was reviewed in context.

Key Findings by SCOR Process Area

Here’s what stood out across the matrix:

• Plan (e.g. Forecasting, S&OP)

  Forecasting scored highest. It’s driven by high-frequency decisions, measurable KPIs, and ample historical data. S&OP, on the other hand, scored low due to low decision frequency, poor integration, and trust gaps.

• Source (e.g. Procurement, Supplier Evaluation)

  Procurement automation (P2P, spend analytics) is mature and AI-ready. Strategic sourcing, however, is slower-moving and harder to measure. Trust and explainability remain barriers for AI-driven vendor recommendations.

• Make (e.g. Scheduling, Maintenance)

  Manufacturing tasks are generally high in frequency and measurability. Predictive maintenance and scheduling are well-supported by IoT and MES systems. Compliance and safety requirements reduce agent autonomy but don’t block AI augmentation.

• Deliver (e.g. Routing, Warehouse Ops)

  Routing and dispatch are among the most AI-ready tasks. They operate on real-time data and generate tangible results. Warehouse tasks (picking, slotting) are also well-suited for reinforcement-based learning.

• Return (e.g. Reverse Logistics)

  Reverse logistics suffers from inconsistent data and low standardization. AI is being piloted for return sorting and fraud detection, but maturity is low. Measurability and governance gaps persist.

• Enable (e.g. Compliance, Risk Monitoring)

  Support functions lag significantly. Enable tasks scored lowest across measurability, change readiness, and user trust. These are areas where AI should be used cautiously, if at all, without full auditability and human oversight.

• Customer (e.g. Agent Assist, Chatbots)

  Customer-facing roles show growing maturity, especially for chatbot deployment and intelligent routing. Trust and privacy are still considerations, but success is increasing due to clear ROI and explainable recommendations.

What Patterns Emerged

The top-performing domains shared five characteristics:

1. High decision frequency

2. Quantifiable results

3. Streamlined systems with modern APIs

4. Evidence of prior success (AI ROI)

5. Low exposure to personal data or regulation

Tasks like forecasting, inventory rebalancing, routing, and spend analytics consistently scored 4 or 5 across these dimensions. Conversely, domains like compliance automation and strategic planning faced gaps in cultural trust, data integration, and reward clarity.

Where to Start and Where to Wait

Start With

• Forecasting

• Routing and dispatch

• Inventory rebalancing

• P2P automation

• Agent-assist tools

Be Cautious With

• Supplier evaluation

• Strategic planning

• Returns processing

• Compliance policy enforcement

  
  

What This Means for Leaders

Choosing where to deploy AI isn't just a technical question. It’s a governance question. Our framework shows that success depends not only on algorithms but also on how well the task environment supports them. Agentic AI is powerful, but it must be matched with readiness, transparency, and control.

By scoring readiness and risk across SCOR domains, we hope supply chain leaders can direct their AI investments where they’ll be most effective. Starting in high-readiness zones reduces risk and accelerates learning. Waiting to automate sensitive or immature tasks avoids harm and builds trust.

If your organization is ready to move beyond hype, this model can help you do so strategically. Because in the end, AI isn’t a magic switch. It’s a capability that must be earned.