Laurie Pimentel-Johnson

Six industry leaders reveal how to build data agility—and why it’s the key to competitive advantage. Most teams spend 70–90% of their time preparing data—not analyzing it. By the time data is ready for analysis, the market has moved. Opportunities vanish. Competitors act. One company nearly built a $400M facility 550 miles from the optimal location. The cost of that mistake would have been $2 billion over the plant’s lifetime. They caught it with always-on data analytics. Would you? “In uncertainty-driven environments, expanded analytical capacity translates directly to resilience. Organizations that can model more futures make more informed commitments.” Download the white paper to discover: Why data—not talent or technology—is the real bottleneck How leading organizations are building reusable data infrastructure that cuts prep time by 80% What data agility unlocks: faster refreshes that deliver savings now, coverage across every business and region, and the capacity to finally tackle cost-to-serve, risk analysis, and inventory optimization The shift from ad-hoc projects to always-on decision-making capability Featuring insights from veterans of Cargill, Nestlé, McKinsey, and more. DOWNLOAD NOW

Special Guest Blog Post written by Dr. Johannes Hangl with e2open   AI is no longer an experiment in global trade compliance. It’s already being applied in product classification, document-to-declaration workflows, risk targeting, and sanctions screening. At the same time, regulators and customs authorities are adopting AI themselves. This is raising expectations for data quality, transparency, and governance across the entire trade ecosystem. With the EU AI Act set to apply from August 2026, companies that have not yet implemented human-in-the-loop controls, drift monitoring, and defensible audit trails are running out of time to close the gap.   Where AI is already adding real value today: HS and ECN classification   Product classification has become one of the most practical AI use cases. Modern tools can now suggest harmonized system (HS/ HTS) and export control (ECCN) codes, explain the rationale, and attach confidence scores and audit metadata to each decision. This direction mirrors what customs authorities are doing. Administrations such as German Customs have discussed using machine learning to improve targeting and risk detection. It appears both sides of the border are moving toward data-driven decision support. AI does not remove accountability. It changes how accountability is exercised.   Practical…