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The last few years have laid the groundwork for US CPG brands to leverage data at a scale and speed that was previously unimaginable. 2024 was the year many supply chains introduced data harmonization, with streamlined and structured data democratizing access to insights across the organization. With the onset of agentic AI, 2025 focused on building contextual layers on top of that data foundation, enriching them with a semantic layer, so systems can grasp business context and surface meaningful recommendations.
The AI-ready data foundation is enabling real progress in automated supply chain optimization. From here, defining guardrails to govern autonomous actions and prioritizing the highest-ROI use cases that justify continued investment will be key for deployment at scale.
The US retail industry is ripe for more integrated solutions to enhance demand forecasting, identify anomalies, automate replenishment, and keep teams informed end-to-end in real-time. The framework is there, and much of the model development is complete. What is left is maturing the development cycles and closing operational gaps to enable a rapidly self-improving feedback loop that will drive real momentum in 2026.
Grade-A data fuel
For decades, retail supply chains have struggled with fragmented data sources, unlabeled records, and data fields that teams have lacked the time or tools to manage effectively. Automatic data harmonization technology has led to significant improvements; however, with millions of rows of new data generated daily, there are gaps – which AI is poised to address in 2026. Agent-led data enrichment and quality assurance can take datasets that are’ 80 percent clean’ and push them closer to true completeness.
The systems can also identify outliers, revive stale fields, and provide insights into pricing, shelf life, and other complex, highly variable data attributes. For example, clustering becomes a breeze when agent-led classification can quickly label 10,000 stores as‘ ski town locations’, with integration directly into analytics and forecasting models. These data sets are then validated for accuracy, enabling the models to continuously improve on clean-up and data management. Data engineering teams are relieved of manual troubleshooting and maintenance, resulting in cleaner, near-100 percent quality input for more accurate and consistent business decisions.
Better results, exponential improvements
With quality foundations improving, supply chains can achieve AI-powered, closed-loop operations that the US retail industry has been chasing. ML demand forecasting models, anomaly detection, and inventory optimization are strengthened
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