Companies are currently investing heavily in AI-based planning solutions in the hope of making their supply chain planning more accurate, faster, and more resilient. But those who rely solely on technology will be disappointed. Experience shows that artificial intelligence can make the sales and operations planning (S&OP) process even more effective, but it cannot replace it.
The real added value of AI does not come from pinpoint forecasts, but from linking data-based insights with clear decision-making processes. For example, a forecasting algorithm can identify seasonal patterns or peaks in demand. However, it does not explain whether the company has the capacity or budget to meet that demand. This is exactly where S&OP comes in: it balances demand, capacity, and financial targets. Thus, S&OP is much more than just AI-supported forecasting – it is the tactical steering process for the entire supply chain planning.
In practice, many companies are currently facing a paradox. While data science teams are developing highly complex planning and forecasting models, the S&OP process remains unchanged and is often characterized by siloed thinking and manual alignment. Thus, modern technological excellence collides with organizational dysfunction. Even the best system solution will have little impact without an operating model that specifies process, organizational, and steering structures.
A key success factor for the interaction of efficient planning structures with state-of-the-art planning software is the ability to combine system-generated planning results with human judgment. Algorithms recognize patterns, but they do not understand strategic priorities. Companies that successfully integrate AI into their sales and operations planning processes therefore establish an interplay between automated forecasts, human evaluation, and clear escalation logic. This setup allows humans to remain the decision-makers, while the machine acts as an intelligent "early warning system".
AI is also changing the role of planners. They no longer need to collect data manually. Instead S&OP planners act as "business navigators", interpreting forecasts, evaluating capacity scenarios, and providing decision scenarios for the top management. The focus is shifting from data creation to decision quality. This requires new skills in analytics, communication, leadership, and stakeholder management.
Artificial intelligence optimizes supply chain planning – but only where it meets a mature, disciplined S&OP process. Successful companies see AI not as an end in itself, but as a catalyst for better decisions along the supply chain planning process. Anyone investing in "intelligent" planning today should first answer the question: How intelligent is our operating model?
