GOODQUESTION Readers Weigh In
What Are Major Pitfalls to Using Arti cial Intelligence in Supply Chains?
TOO MUCH NOISE. In global, multi-tier supply chains,
Avoid the Overreliance Trap
critical supplier information is often unavailable or inconsistent, leading to misleading outputs or excessive “noise” from irrelevant risk flags. Focus on strengthening data foundations, embedding human-in-the-loop validation, and prioritizing practical integration and usability across internal teams and supplier networks. –Marissa Licursi Director, Grant Thornton Stax DATA DISTRUST. If you don’t own and trust your data, AI just scales bad inputs into bad decisions—no matter how polished the dashboard looks. Stakeholders need strong data ownership, governance, and validation before layering on AI. –Carson Joyner Digital Strategy Lead, Gnosis Freight ACCOUNTABILITY DRIFT. When something goes wrong, blaming “what the AI said” doesn’t resolve disruptions. AI works best as an assistive tool—not an authority—when its recommendations are validated, actions are supervised, and humans remain accountable for outcomes. –Doug DeLuca Product Marketing Manager, SAP Business Network AMBIGUITY. Applying AI to processes that aren’t well defined or repeatable introduces ambiguity and hallucinations. Another risk is hype outpacing reality, creating confusion about what AI can do. Leaders should define the type of AI being used and ensure data is curated and structured. –Jack McCrum Director, Optimization and Analytics, Reveel
Overreliance on AI is a big risk. Investing in automation without fixing underlying processes creates new failure points. Chains break down when AI ignores real-world conditions and frontline decisions. The goal isn’t to automate everything, it’s to enhance how decisions are made. Companies getting this right modernize workflows first, then embed AI to improve service, transparency, and trust. –Zach Jecklin Chief Information Ocer, Echo Global Logistics Relying too much on AI can become perilous because every decision directly aects sourcing, pricing, and tax outcomes. Without validation, errors can quickly scale into material, financial, and compliance issues. As tax authorities raise expectations around auditability, transparency and traceability are mandatory to ensure every AI-powered decision can be explained and defended. –Chris Hall Senior Tax Ocer, Global Tax & Compliance, Vertex Inc.
UNCLEAR OWNERSHIP. Many AI pilots succeed locally but stall during scale up. The barrier is rarely the technology. More often, it’s unclear ownership, undefined decision authority, or processes that were not designed to absorb AI-generated outputs. Automation and AI will deliver sustained value when they are scaled enterprise- wide and aligned to execution roles, decision rights, and performance accountability. –Matt Derganc Senior Director, SSA & Co. OVERESTIMATION. A common pitfall is assuming AI understands how logistics really works. Software can optimize routes and forecasts, but it struggles with real-world
issues like customs rules, packaging compliance, or unexpected inspections. Use AI as a support tool while experienced operators keep control of final decisions. –George Wicks-Farr Head of Operations, Pallet2Ship APPLICABILITY. Its use in inventory management with Customs remains limited. Trust is a key concern—stakeholders can’t yet rely on IT to fully run FTZ operations or interface with U.S. CBP systems like ACE. Today, AI is best suited for administrative tasks. As the technology evolves, stakeholders should prioritize validation, oversight, and phased integration. –Jerey Tafel President, National Association of Foreign-Trade Zones (NAFTZ)
6 Inbound Logistics • May 2026
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