Hype or Value?

21. August 2025

๐Ÿš€ ๐‡๐ฒ๐ฉ๐ž ๐จ๐ซ ๐•๐š๐ฅ๐ฎ๐ž? โ€” Evaluating AI in Supply Chain

A big thank you to Lynn (D’Silva) Cinelli and all participants for an ๐ž๐ฑ๐œ๐ž๐ฉ๐ญ๐ข๐จ๐ง๐š๐ฅ๐ฅ๐ฒ ๐ข๐ง๐ฌ๐ข๐ ๐ก๐ญ๐Ÿ๐ฎ๐ฅ ๐ฌ๐ž๐ฌ๐ฌ๐ข๐จ๐งย on how to navigate AI hype and focus on solutions that create ๐›๐ฎ๐ฌ๐ข๐ง๐ž๐ฌ๐ฌ ๐ฏ๐š๐ฅ๐ฎ๐ž.ย Lynn contextualized technologies that offer promise: agentic AI, machine learning, generative AI, Robotic Process Automation and deep learning.

Lynn shared a structured evaluation approach, assessing AI initiatives across five key lenses:
โœ… ๐๐ž๐ง๐ž๐Ÿ๐ข๐ญย โ€” Does it unlock real business value?
๐Ÿ’ฐ ๐ˆ๐ง๐ฏ๐ž๐ฌ๐ญ๐ฆ๐ž๐ง๐ญ | ๐…๐ž๐š๐ฌ๐ข๐›๐ข๐ฅ๐ข๐ญ๐ฒ โ€” What resources are needed? Can the organization absorb?
โš ๏ธ ๐‘๐ข๐ฌ๐ค |ย ๐‚๐จ๐ฆ๐ฉ๐ฅ๐ข๐š๐ง๐œ๐žย | ๐„๐ญ๐ก๐ข๐œ๐ฌย โ€” Where are the pitfalls? Does it respect policies?
๐Ÿค ๐‡๐ฎ๐ฆ๐š๐ง-๐€๐ˆ ๐’๐ฒ๐ง๐ž๐ซ๐ ๐ฒย โ€” How do humans and AI collaborate?
๐Ÿ“ˆ ๐’๐œ๐š๐ฅ๐š๐›๐ข๐ฅ๐ข๐ญ๐ฒย โ€” Can the solution scale across the business?

๐Ÿ’ก Core Insights
๐€๐ˆ ๐ข๐ฌ ๐ง๐จ๐ญ ๐š ๐ฌ๐ข๐ง๐ ๐ฅ๐ž ๐ญ๐ž๐œ๐ก๐ง๐จ๐ฅ๐จ๐ ๐ฒ, ๐›๐ฎ๐ญ ๐š ๐ฌ๐ฉ๐ž๐œ๐ญ๐ซ๐ฎ๐ฆ โ€“ including RPA, machine learning, LLMs, and agent-based tools. Evaluating solutions requires understanding their position within this landscape.
๐€๐๐จ๐ฉ๐ญ๐ข๐จ๐ง ๐ฌ๐ญ๐ซ๐š๐ญ๐ž๐ ๐ฒ ๐ฆ๐š๐ญ๐ญ๐ž๐ซ๐ฌ โ€“ Start small, test simple use cases, and scale only where value is proven. This โ€œexperimentโ€“pilotโ€“scaleโ€ approach helps avoid overinvesting in hype.
๐€๐ˆ ๐ข๐ฌ ๐š๐ฅ๐ซ๐ž๐š๐๐ฒ ๐ฉ๐ซ๐ž๐ฌ๐ž๐ง๐ญ โ€“ Most participants are using AI tools both professionally and personally. Some companies are already advancing with targeted, promising initiatives in planning, automation, and data mining.
๐“๐ซ๐ฎ๐ž ๐ฏ๐š๐ฅ๐ฎ๐ž ๐ž๐ฆ๐ž๐ซ๐ ๐ž๐ฌ ๐ฐ๐ก๐ž๐ง ๐ก๐ฎ๐ฆ๐š๐ง๐ฌ ๐š๐ง๐ ๐€๐ˆ ๐œ๐จ๐ฅ๐ฅ๐š๐›๐จ๐ซ๐š๐ญ๐ž โ€“ The most promising cases involved AI augmenting human work, not replacing it: e.g. bots surfacing planner queries, automation reducing manual data collection, or analytics flagging risk.
๐€ ๐Ÿ๐ซ๐š๐ฆ๐ž๐ฐ๐จ๐ซ๐ค ๐›๐ซ๐ข๐ง๐ ๐ฌ ๐œ๐ฅ๐š๐ซ๐ข๐ญ๐ฒ โ€“ Evaluating AI initiatives through 5 lenses (Benefit, Investment, Risk/Compliance/Ethics, Human-AI Synergy, Scalability) helps avoid โ€œshiny object syndromeโ€ and focus on use cases that align with business priorities.

In our breakout groups, we explored six practical use cases โ€” from duty and contract enforcementย to AI chatbots assisting plannersย โ€” applying this framework to see where AI is already delivering results.
โฌ‡๏ธ Below you find the summary table of the examples we discussed:
๐ŸŒ Join our complimentary industry-wide IBP and S&OP work-group sessions to share best practices, expand your network, and learn with peers.
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