Breaking the Silos: Scaling AI in Real Supply Chain Operations
1 May 2026
In This Session
- Neo Tangent joins global brands to discuss AI-driven orchestration in supply chain automation.
- Eric Lee shares operational insights from real-world supply chain environments.
- Panel discusses barriers such as workflow fragmentation and legacy processes.
- Frameworks for AI impact and scaling strategies explored in depth.
Neo Tangent recently participated in a Reuters panel on how AI-driven orchestration can scale supply chain automation.
The discussion focused on a practical question many organizations are facing today: why do promising automation initiatives struggle to move beyond pilots into real operational scale?
Operational Execution, Not Technology, Defines AI Success
A consistent theme from the panel was that scaling AI is less a technology challenge and more an operational one.
Eric reiterated the importance of addressing fundamentals before introducing AI.
“You shouldn't automate something that's not really reimagined or thought of. You have to map out everything.”
Eric Lee
Neo Tangent’s teams consistently begin with process re-examination and redesign to ensure the underlying workflows are fit for automation.
Focusing On Where It Creates Measurable Impact
Eric outlined Neo Tangent’s framework for identifying where AI can deliver the greatest value:
- High manual effort and repetitive tasks
- High variability or exception rates
- Clear and measurable operational outcomes
These conditions commonly occur in document validation, supplier communication, exception triage, and reconciliation across PLM, ERP, logistics, and finance systems.
Neo Tangent’s work with AI-enabled document checking — supported by human-in-the-loop controls — shows measurable improvements in accuracy, speed, and workload reduction.
A Portfolio-Driven Approach to Scaling
Neo Tangent adopts a portfolio approach, running multiple small automation initiatives across workflows. This approach accelerates learning and uncovering scalable solutions, even if not all initiatives succeed.
Driving Adoption Through Transparency and Cross-Functional Alignment
A recurring theme across the panel was the human side of AI adoption.
Eric emphasized that resistance is rarely about the technology itself.
“People are not resisting AI. People are resisting uncertainty.”
Eric Lee
Neo Tangent addresses this by keeping humans in control of decisions, removing low-value work, clarifying ownership, and measuring performance from day one through transparent scorecards.
Conclusion
The webinar underscored a core industry insight: AI in supply chain is no longer a technology problem — it’s an operational execution problem.
Key Takeaways
- AI scaling challenges stem from operational execution, not technology limitations.
- Fragmented workflows and unclear ownership remain major barriers.
- High-impact AI targets include repetitive, manual, and high-exception workloads.
- Portfolio approaches accelerate learning and enable scalable automation.
- Adoption succeeds when transparency and human-in-the-loop controls are prioritized.
For Neo Tangent, this reinforces a practical approach: redesign workflows first, then apply automation in a controlled, measurable way within live operations.