One of the greatest privileges of teaching is connecting academic research with real business challenges.
As a lecturer in Supply Chain Management at Munich University of Applied Sciences (Hochschule München), I have the opportunity to supervise bachelor’s and master's theses that address highly relevant topics for today's supply chain leaders. One such project explored the emerging role of Agentic AI in Supply Chain Planning.
To ensure the research reflected real industry perspectives rather than purely theoretical assumptions, I invited professionals from my international supply chain network to participate in the survey in spring 2026. The response was overwhelming: 104 supply chain professionals and executives contributed their insights, providing valuable evidence on how organizations currently perceive the opportunities and challenges of Agentic AI.
The findings provide a fascinating snapshot of where the industry stands today and where it is heading.
From Generative AI to Agentic AI
Artificial Intelligence is evolving rapidly. While many organizations are still exploring the capabilities of Generative AI, the next wave has already begun: Agentic AI. Unlike traditional AI systems that generate predictions or recommendations, Agentic AI can autonomously coordinate tasks, evaluate alternatives, orchestrate workflows, and continuously adapt decisions based on changing conditions.
For supply chain planning with its interconnected processes, competing objectives, and constant uncertainty, this represents a significant step forward.
Industry Sees Significant Potential
The first conclusion from the research is unmistakable.
Companies are highly optimistic about the future role of Agentic AI.
- Average potential rating: 4.22 out of 5
- Approximately 90% of respondents see high or very high potential for Agentic AI in supply chain planning.
This demonstrates that Agentic AI is no longer viewed as a futuristic concept. Instead, organizations increasingly recognize it as a key enabler of next-generation planning.
Where Is the Greatest Potential?
Respondents assessed six major planning functions across the end-to-end supply chain.
The highest overall potential was identified in:
- Inventory Planning
- Procurement Planning
- Demand Planning
- Production Planning
- Transportation Planning
- Distribution Planning
Interestingly, when participants were asked to identify the single planning area with the greatest future potential, Demand Planning ranked first.
This indicates that organizations see forecasting, demand sensing, and intelligent demand management as strategic entry points for Agentic AI, while Inventory Planning consistently receives strong ratings across all respondents.
The Biggest Challenge Is Not AI
Perhaps the most important insight from the study is this: The technology itself is not perceived as the primary obstacle.
Instead, respondents identified two fundamental prerequisites:
- High-quality data
- Integrated planning data across systems
Only around 2% of participating organizations report having fully integrated planning data today, while nearly half describe their planning landscape as only partially integrated. This confirms what many supply chain transformation projects have demonstrated over the past decade: AI can only be as good as the data and processes that support it.
Faster Decisions, Better Coordination
Another strong finding relates to organizational agility.
- More than 93% of respondents believe Agentic AI can help organizations respond faster to changing demand, supply disruptions, and capacity constraints.
- Around 87% also expect significant improvements in coordination across planning functions.
This may ultimately become Agentic AI's greatest contribution.
Rather than optimizing isolated planning tasks, Agentic AI has the potential to orchestrate entire planning processes—from demand planning through procurement and production to inventory and distribution.
A Surprising Finding
One particularly interesting result challenges a common assumption: Organizations with a higher level of digital maturity did not automatically perceive greater potential in Agentic AI. The corresponding research hypothesis could not be confirmed. This suggests that enthusiasm for Agentic AI extends well beyond digitally advanced organizations. Companies appear to recognize its strategic value regardless of where they currently stand on their digital transformation journey.
Why This Matters for Supply Chain Education
One of the strengths of applied research at Munich University of Applied Sciences is its close connection with industry. Projects like this demonstrate how academic research and business practice can complement each other. By combining current scientific developments with feedback from experienced practitioners, students gain a much deeper understanding of emerging technologies, while companies benefit from independent research grounded in real-world experience. For me, this collaboration between academia and industry is essential. It allows us not only to discuss future technologies but also to understand how organizations are preparing to implement them.
My Final Thoughts
The study clearly demonstrates that companies do not see Agentic AI as a replacement for ERP systems or Advanced Planning Systems. Instead, they view it as an intelligent orchestration layer capable of connecting planning systems, coordinating decisions across functions, and enabling more adaptive, end-to-end planning. The greatest challenge is therefore not building better AI models, but building better data foundations, integrated processes, and effective governance.
For supply chain leaders, the message is clear: Organizations that invest today in data quality, end-to-end process integration, and supply chain capabilities will be best positioned to unlock the full potential of Agentic AI tomorrow.
About the Author
Andrea Walbert is Managing Director of PMI Production Management Institute GmbH, an ASCM Premier Elite Partner specializing in supply chain education, consulting, and professional certification. She is also a Lecturer in Supply Chain Management at Munich University of Applied Sciences (Hochschule München), where she supervises master's research on emerging supply chain topics and actively connects academic research with industry practice. This article is based on a supervised master's thesis examining the potential of Agentic AI in Supply Chain Planning, supported by insights from more than 100 supply chain professionals.
