AI in the Chain

AI is infiltrating every layer of the supply chain. From predictive forecasting and risk detection to intelligent inventory planning and supplier insights, it’s hard to find a process untouched by the promise of artificial intelligence. But beneath the surface of this technological momentum lies a quiet and dangerous truth: most supply chain organizations are not progressing—they’re stagnating.

According to McKinsey’s 2025 global AI report, 68% of companies say they use AI in supply chain operations, yet only 16% have scaled it successfully. That’s not a technology failure. It’s a structural and strategic disconnect. As Lora Cecere, founder of Supply Chain Insights, writes: “AI doesn’t fix broken supply chains. It amplifies both the strengths and the weaknesses of your existing systems.”

This article surfaces the silent crisis unfolding behind the scenes. It builds on Cecere’s latest insights and fresh research from McKinsey, Deloitte, and ASCM to explore the missteps, mindsets, and redesign opportunities that will define which supply chains lead—and which lag—in the AI-powered decade ahead.

Five Silent Signs You’re Falling Behind

1. You’re Automating, Not Transforming
   Many supply chains have automated their reporting and exception handling workflows. But automation is not intelligence. Dashboards that track past performance aren’t decision systems. Cecere emphasizes that automation is often mistaken for progress, when in reality, it reinforces old assumptions at scale.
2. You’re Applying AI to Legacy Taxonomies
   Planning taxonomies—the structural rules around product families, demand types, lead times—were often built for ERP systems in the 1990s. Applying machine learning on top of these static rules creates the illusion of optimization while baking in outdated logic. True transformation requires rethinking the planning model itself.
3. You’ve Bought Tools, But Not Built Capabilities
   The fastest way to stall an AI initiative is to treat it as a software upgrade. Without internal capabilities—data literacy, model interpretation, process redesign—the organization remains dependent on external vendors, unable to test, learn, or adapt AI outputs to operational reality.
4. You’re Stuck in Pilot Purgatory
   McKinsey reports that nearly 70% of supply chain AI pilots never scale. They remain trapped in isolated experiments with no cross-functional ownership. The pattern is familiar: a promising proof-of-concept, some internal buzz, but no rollout, no KPIs, and no traction.
5. You’re Prioritizing Hype Over Usefulness
   Generative AI has captured headlines, but its supply chain applications remain narrow and untested. Meanwhile, mature applications of narrow AI—such as pattern detection, demand sensing, and predictive alerts—are overlooked. Deloitte’s research shows that hype-chasing leads to poor adoption and weak business alignment.

Two Supply Chains, Two Outcomes

To illustrate what this crisis looks like in practice, consider two anonymized global manufacturers—both with similar scale and technology budgets.

🚀 Company Alpha: Redefining Work with AI

• Redefined their supply chain excellence framework around decision quality
• Trained planners on causal forecasting and data literacy
• Applied narrow AI to reduce exception noise and enhance demand sensing
• Embedded test-and-learn loops across channel planning
• Result: 14% improvement in forecast accuracy, 8% fewer stockouts, 6% faster S&OP cycles

🐢 Company Beta: Overlaying AI on Legacy

• Applied machine learning on outdated planning assumptions
• Purchased AI tools but used default settings with minimal internal oversight
• Relied entirely on vendors to define KPIs and direction
• Focused on dashboarding without real process change
• Result: stalled pilots, team misalignment, and no measurable return on investment

The difference was not technology access—it was mindset. Alpha began by defining what “better” looked like. Beta assumed AI would do the thinking for them.

Rethink Before You Retool: Lora Cecere’s Redesign Framework

Cecere warns against the temptation to “AI your way out” of legacy problems. Her latest work outlines the most common mistakes and the smarter alternative path.

🚧 What Not to Do

✅ What to Do Instead

A Practical Checklist: Are You AI-Ready?

Use this simple diagnostic to assess your organization’s readiness for intelligent transformation:

Scoring

• 5 Yes: You’re leading with intention
• 3–4 Yes: You’re progressing but must sharpen your strategy
• 0–2 Yes: You’re likely falling behind—redesign is urgent

From Caution to Capability: Moving Forward in 2025

Here’s how to take the next step in your AI maturity journey:

Each of these cases begins with clarity—not code. Define what success looks like before selecting your model. Then redesign workflows, build team fluency, and iterate. AI doesn’t replace the need for transformation—it accelerates it, but only when the foundation is right.

Action Prompts for Supply Chain Leaders

Before your next AI initiative or vendor meeting, pause and ask:

• What broken process are we trying to fix?
• Are we building long-term capability—or just short-term excitement?
• Have we questioned our planning assumptions before adding algorithms?
• Where can narrow AI deliver visible results in 3–6 months?
• Are our teams trained to interpret, adjust, and challenge AI outputs?

Conclusion: The Real AI Crisis Is Misalignment, Not Technology

The companies that will win in 2025 are not the ones with the flashiest AI stack—they are the ones that redesigned their operating models with clarity, flexibility, and intent. AI won’t fix misaligned, outdated, or opaque supply chains. In fact, it will only magnify the cracks.

But with the right mindset—one that prioritizes rethinking over replacing—AI can evolve from an automation engine into a strategic co-pilot. As Cecere writes, “We don’t need more automation. We need more design thinking. AI only works when we’re clear about what good looks like.”

The question now isn’t whether your supply chain will adopt AI. It’s whether you’ll do it with purpose—or be left catching up to those who already are.

References

Cecere, Lora (2025). Reinventing Supply Chains: Focus on Human Factors. Supply Chain Shaman. https://www.supplychainshaman.com/reinventing-supply-chains-focus-on-human-factors/
Cecere, Lora (2025). Commentary on AI Transformation Blind Spots. LinkedIn. https://www.linkedin.com/posts/loracecere_reinventing-supply-chains-focus-on-human-activity-7338244830147321857-0M9P
McKinsey & Company (2025). The State of AI: How Organizations Are Rewiring to Capture Value. The State of AI: Global survey | McKinsey
Deloitte (2025). Scaling Generative AI Strategy in the Enterprise. https://www.deloitte.com/us/en/services/consulting/articles/scaling-generative-ai-strategy-in-the-enterprise.html
ASCM (2024). 6 Things Supply Chain Professionals Need to Know About AI. https://www.ascm.org/ascm-insights/6-things-supply-chain-professionals-need-to-know-about-ai/
McKinsey & Company (2024). Beyond Automation: How Gen AI Is Reshaping Supply Chains. https://www.mckinsey.com/capabilities/operations/our-insights/beyond-automation-how-gen-ai-is-reshaping-supply-chains
ASCM (2024). Optimize Your Supply Chain with AI and ML. https://www.ascm.org/ascm-insights/optimize-your-supply-chain-with-ai-and-ml/