Retailers across the industry are rapidly adopting AI and advanced analytics to drive personalization, optimize supply chains, and improve operational efficiency. However, many organizations struggle with fragmented data platforms, siloed MLOps processes, and inconsistent governance that limit their ability to scale ML and GenAI initiatives. As regulatory scrutiny increases and agentic AI becomes more prevalent, retailers must ensure enterprise-wide data lineage, governance, and interoperability. Without a unified Data and AI foundation, innovation slows, trust erodes, and the ability to turn data into real-time, intelligent decisions is significantly constrained.
Problem Statement
Sobeys Inc., one of Canada’s leading retailers under the Empire umbrella, is pursuing a tech-first strategy to deliver exceptional shopping and food experiences. As its Advanced Analytics team scaled ML across marketing, retail operations, and merchandising, the organization faced growing challenges from siloed MLOps practices and inconsistent data governance. Leadership recognized that future ML, GenAI, and agentic AI initiatives would require unified governance, end-to-end lineage, and seamless cross-team interoperability. Without an enterprise foundation, scaling innovation risked fragmentation, compliance gaps, and slowed time to value. This realization prompted a proactive shift toward building a governed, scalable Data and AI platform.
Impact
- 70% faster dataset onboarding with unified, enterprise-wide governance
- Always audit-ready with end-to-end data lineage and controls
- ML & GenAI use cases activated faster, from idea to production
- Lower storage and compute costs by eliminating redundancy
- Trusted, transparent data across teams and platforms
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