Amit Mahajan

At Gartner Supply Chain Symposium/Xpo 2026 in Orlando, some of the sharpest minds in supply chain gathered to tackle one central question: what does the next operating model for supply chain actually look like? In a special livestream conversation, Scott Luton sat down with Mike Griswold, VP Analyst at Gartner, alongside fellow Supply Chain Now hosts Karin Bursa and Jake Barr, to unpack the biggest themes emerging from this year’s symposium. The consensus? Supply chain leaders are moving beyond AI fascination and toward something much bigger: redesigning how decisions, workflows, and organizations operate.   AI Is No Longer the Story. Outcomes Are One of the strongest themes from the event was a more mature, pragmatic approach to AI adoption. According to Mike Griswold, many organizations are finally moving past the “shock and awe” phase that dominated conversations a year ago. “People need to figure out exactly what problem or problems AI is going to solve for them,” Griswold explains. That may sound simple, but it represents a significant shift. Instead of experimenting with AI for AI’s sake, companies are becoming more disciplined about identifying operational value and measurable business outcomes. Griswold also warns against a familiar trap: creating “highly efficient…

Special Guest Blog Post written by Chris Cunnane with InterSystems   Demand sensing and demand forecasting are both crucial aspects of optimizing supply chains, but they do have slightly different functions in their approach and focus. Demand sensing uses real-time data and analytics to identify and respond to immediate demand fluctuations, while demand forecasting uses historical data to predict future demand over a longer period (months or years). Different methods, such as statistical modeling and machine learning, are used to enhance the accuracy and adaptability of these processes. Both areas are crucial for companies when it comes to projecting sales, managing inventory, and coordinating replenishment. In the end, the goal is to accurately predict customer demand by using predictive models to forecast future demand. InterSystems surveyed 450 senior supply chain practitioners and stakeholders to examine key supply chain technology challenges, trends, and decision-making strategies across five key use cases: fulfillment optimization; demand sensing and forecasting; supply chain orchestration; production planning optimization; and environmental, social, and governance (ESG). This blog focuses on demand sensing and forecasting.   Current State of Demand Sensing and Forecasting According to the survey results, when asked how they currently forecast demand, 36% of respondents indicated that…