Amr Mohamed reframes AI in demand planning as a research assistant, not a decision-maker
Moving planners from manual reporting toward interpretation and strategic influence
AI should change the planner’s role, not only the forecast
Amr Mohamed challenges a common assumption about Supply Chain AI: that its primary role is to automate the demand plan or generate a better baseline forecast.
His argument is that using AI only to refresh forecast numbers amounts to improved reporting. The more meaningful opportunity is to help planners understand why demand is changing and where their attention is required.
From data processing to business interpretation
Demand-planning teams often spend significant time cleaning data, preparing reports and explaining numerical changes. AI can reduce this workload by processing large SKU portfolios, identifying anomalies and highlighting unexpected demand movements.
However, the system does not automatically understand the commercial or operational context behind those signals. It may identify that a promotion underperformed, demand increased unexpectedly or a regional pattern changed, but local teams still need to establish why.
- Was the change caused by a promotion, pricing decision or competitor action?
- Is the signal linked to a supply constraint rather than true demand?
- Does the forecast reflect current local-market knowledge?
- Should the planner accept, modify or reject the recommendation?
Ask why the AI is recommending a change
Mohamed proposes replacing the question “Is the AI right?” with “Why is the AI suggesting this?” This shifts the planner’s role from producing numbers toward interpreting signals, challenging assumptions and influencing business strategy.
The contribution reinforces the role of AI as Decision Support. It can narrow the search space and prioritize exceptions, while planners contribute market knowledge, cross-functional discussion and accountable judgment.
The original contribution is available in Amr Mohamed’s LinkedIn post.