AI in the Chain

In the age of artificial intelligence, most conversations about supply chain transformation tend to focus on automation, robotics, and digital twins. Yet, a growing school of thought emphasizes that the most overlooked lever for breakthrough performance is not more algorithms—but more empathy.

Lora Cecere’s latest article, “Reinventing Supply Chains: Focus on Human Factors,” highlights a critical reality: while many technologies deliver marginal improvements, meaningful transformation requires a deep rethinking of how supply chains serve human needs. That means redesigning processes, information flows, and feedback loops with people at the center.

This article builds on that idea and integrates fresh research from McKinsey’s 2025 report “Seizing the Agentic AI Advantage”, which explores how combining human agency with AI assistance creates the next frontier of productivity and innovation.

Why Human-Centered Design Matters Now

Traditional supply chains are engineered for efficiency, not flexibility. They often assume that people are either bottlenecks or fixed resources, rather than adaptive agents with context and creativity.

But today’s challenges—geopolitical risk, climate shocks, and volatile demand—require supply chains that can learn, evolve, and respond in human-like ways.

Human-centered supply chains:

• Prioritize decision augmentation over replacement.
• Create continuous feedback loops between operators, planners, and machines.
• Design for empowerment, not just execution.

Cecere argues that the core issues are not just system failures but design failures—processes that ignore how humans actually work. McKinsey echoes this in their research, stating: “Technology alone is not enough. Value emerges when humans take ownership, guide, and extend AI.”

Three Shifts Needed to Reinvent Supply Chains for Humans

1. Rethinking Roles and Functions

Old model: People serve systems.
New model: Systems serve people.

McKinsey calls this transition “agentic collaboration”—a model where people use AI not just for speed or automation but to extend judgment, creativity, and domain expertise.

Redesign example:

• A demand planner doesn’t just approve AI-generated forecasts—they interact with AI to challenge assumptions, integrate local intelligence, and simulate trade-offs.
• A buyer is supported with AI that flags not just price shifts but ethical or environmental violations in Tier 2/Tier 3 suppliers.

2. Reinventing Information Flows

Supply chains are flooded with data, but information flow is still largely one-way and delayed.

Lora emphasizes that companies need to redesign how information is shared, making it dynamic and bidirectional. That means:

• Real-time visibility across functions (not just downstream).
• Feedback loops between customer experience and upstream planning.
• Interfaces that show “what changed, why it matters, and what to do.”

McKinsey reinforces this with a call to co-pilot systems, where humans can interrogate AI recommendations, simulate alternatives, and provide domain-specific nuance.

3. Enabling Feedback at Every Node

Feedback loops are often limited to monthly KPIs or exception reports. That’s not good enough anymore.

Agentic AI means embedding micro-feedback in everyday workflows:

• Operators report anomalies directly into predictive maintenance models.
• Delivery drivers flag friction in handoffs, triggering workflow redesign.
• Customer service captures emotional signals from interactions, feeding back into supply planning.

The goal? A living supply chain where human experience becomes training data and the system evolves in tandem with the workforce.

Putting It into Practice: Use Cases Across the Supply Chain

Redesign Principles for a Human-Centered Supply Chain

To make this vision real, organizations should:

1. Co-design with users: Involve planners, operators, and suppliers in redesign.
2. Embed transparency: Let users see how AI works and why it recommends what it does.
3. Build for explanation, not just prediction: Prioritize AI tools that can articulate their logic.
4. Train for sensemaking: Enable teams to interpret data, not just view dashboards.
5. Reward feedback: Create incentives for reporting friction, gaps, or surprises.

Future Outlook: From Human-Centered to Human-Augmented

The McKinsey report highlights how agentic AI shifts the narrative: it’s not about humans vs machines, but humans with machines.

Companies that thrive in this future will:

• Cultivate human-AI teams that continuously adapt.
• Build AI-native organizations where feedback is fluid.
• Redesign supply chains for flexibility, context, and care.

Lora Cecere sums it up powerfully: “When people are engaged and empowered, supply chains improve dramatically. That means we must redesign—not automate—first.”

Key Prompts for Redesign

Here are some prompts to start transforming your supply chain:

• “What supply chain processes today feel misaligned with how people actually work?”
• “Where are decisions made too late because of broken feedback loops?”
• “How can we reframe AI from ‘automating tasks’ to ‘augmenting expertise’?”

Conclusion

Supply chains are at a crossroads: either double down on outdated automation—or evolve toward a human-centered, AI-augmented future.

The most transformative supply chains of 2025 will not be the most robotic—they will be the most empathetic, adaptive, and agentic.

And the starting point is not technology. It’s asking better questions, with people in the loop.

References

1. Cecere, Lora (2025). Reinventing Supply Chains: Focus on Human Factors. Supply Chain Shaman. https://www.supplychainshaman.com/reinventing-supply-chains-focus-on-human-factors/
2. McKinsey & Company (2025). Seizing the Agentic AI Advantage. GenAI paradox: exploring AI use cases | McKinsey