Florent Tronquit proposes three questions to test the real value of AI in Supply Chain
Decision capacity, data reliability and explainability as practical filters for AI investment
Three questions before scaling Supply Chain AI
Florent Tronquit argues that asking whether an organization should deploy Supply Chain AI often produces vague answers. A more useful discussion starts with three operational questions.
Does AI genuinely free decision capacity?
The most valuable projects are not necessarily those that replace teams. They remove repetitive trade-offs, low-value replanning and time spent operating fragmented processes.
The expected benefit is more capacity for decisions affecting margin, cash and service—not simply faster production of reports or scenarios.
Are the underlying data reliable?
Deploying generative AI over a fragmented Supply Chain does not resolve inconsistencies. It can accelerate them.
Reliable data, clear definitions and accountable ownership must come before large-scale prediction and automation. Otherwise, the organization risks industrializing incorrect KPIs and poorly challenged decisions.
Do the teams still understand the recommendation?
The third question concerns Explainable AI. When planners no longer understand why a model recommends a stock level, supplier or scenario, they eventually override or bypass the system.
The challenge is therefore cultural and financial as much as technical. Trust depends on whether users can inspect the reasoning, assumptions and operational consequences behind the recommendation.
What percentage of processes is fully automated by AI agents?
Following the publication, Florent Tronquit suggested adding a fourth question: what percentage of the organization’s processes is now fully automated through AI Agents?
This question helps distinguish between three maturity levels: AI used as occasional assistance, AI embedded in a workflow with human validation, and end-to-end process automation performed by agents.
The automation percentage should not be treated as an objective in isolation. For each automated process, the organization should define the boundaries of autonomy, human controls, escalation rules and accountability when the output is wrong.
The original contribution is available in Florent Tronquit’s LinkedIn post.