<ul><li>Many organizations struggle to move Generative AI projects from pilot to production due to fundamental data issues.</li><li>GenAI models heavily rely on quality data to avoid producing inaccurate or biased results.</li><li>Companies often deploy low-effort AI use cases without addressing deep data infrastructure changes.</li><li>Data reliability, completeness, privacy issues, and governance gaps are key barriers to AI deployment.</li><li>Data governance is crucial for scaling GenAI strategically and ensuring data readiness for AI models.</li><li>Building AI literacy and embedding data governance in organizational culture are essential for successful AI adoption.</li><li>Accountability and transparency are increasingly important in AI due to regulatory requirements and consumer demands.</li><li>Compliance with regulations and establishment of trusted data foundations are vital for building trustworthy AI.</li><li>Strong data foundations lead to reduced model drift, faster speed to value, and increased innovation potential.</li><li>Investing in data quality, governance, and culture can help companies turn GenAI from a promising pilot to a successful implementation.</li></ul>

Why GenAI Stalls Without Strong Governance

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