Supply Chain AI ROI depends on an end-to-end value blueprint, not isolated use cases

Al Mendoza of EY argues that many organizations are obtaining incremental value from AI without achieving the transformational returns originally expected. The gap is often caused by fragmented use cases, weak data foundations, inconsistent processes, outdated management systems and insufficient investment in the operating model around the technology.

The article proposes a value blueprint: organizations should begin with an end-to-end business problem, such as forecast-to-fulfillment or order-to-cash, and define how individual AI applications contribute to that wider transformation.

This changes how Supply Chain AI portfolios should be designed and funded. A forecasting model, procurement assistant or inventory agent may perform well locally while the wider process remains slow, contradictory or financially misaligned. Each use case therefore needs a defined role in the broader decision flow, shared operational and financial measures, and accountable owners.

Dataleo perspective: the correct unit of AI transformation is the decision flow, not the use case. The condition for value is an explicit connection between each AI capability and the end-to-end operating model it is meant to improve. The main limitation is that “fix the foundations first” can become an excuse for indefinite data and platform programs. Organizations should use bounded use cases to reveal which data, workflow and ownership problems materially constrain a specific decision while still showing how the work contributes to the wider blueprint.