As AI investments surge, organizations face mounting challenges with data quality, integration, and infrastructure needed to scale GenAI and agentic AI. While 89% have revised their data strategies, only 26% have GenAI in production. Success hinges on improving data architectures, embedding analytics into workflows, and aligning leadership priorities around automation, governance, and data productization. Businesses are pivoting from the AI scramble toward structured, disciplined data strategies that support innovation, accountability, and responsible AI. Investments in agentic AI and analytics integration are rising, but confidence remains low, highlighting the need for improved data fluency, talent development, and ethical frameworks.

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