Harvard Business Review LogoMay 27, 2026Xinhua News Agency/Getty ImagesMost companies deploying AI in their supply chains are making a common mistake: They are starting with the technology before understanding their data. They launch pilots, experiment with forecasting tools, and deploy isolated optimization engines—then wonder why almost none of it scales. The problem isn’t the AI. It’s that they skipped the step that makes AI trustworthy. Build intelligence on a broken data foundation and you get broken intelligence, every single time.
How Lenovo Built an AI-Powered Supply Chain
Most companies pursuing supply chain AI are making a critical mistake: They start with technology before fixing the data foundation that makes AI reliable. Lenovo took the opposite approach, spending years integrating operational data before building an enterprise-wide AI architecture that coordinates decisions across procurement, logistics, manufacturing, and fulfillment. Its experience offers four lessons for executives: prioritize data quality, build integrated systems, align AI with business priorities, and own proprietary operational intelligence.
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