A multinational consumer goods company operating across hundreds of countries faced significant challenges managing its complex data and technology landscape, which included thousands of systems. The organization struggled with fragmented “metadata pockets” and lacked consistent enterprise-wide standards, making it difficult to connect knowledge across business units and regions. Leadership had limited visibility into potential return on investment, making it hard to justify funding and resulting in semantic initiatives remaining in isolated pilots. AI adoption was also restricted, as solutions were not aligned with end-user workflows, and executives lacked a clear, prioritized roadmap for investments that could deliver measurable business value.

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