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Enterprises are sitting on a goldmine of data, but unlocking its full potential for AI requires a strategic overhaul. While AI offers transformative possibilities for improving user experiences and informing strategic decisions, the journey from raw data to AI-ready assets is complex. Key challenges include the sheer volume of unstructured data – with less than 1% currently leveraged by generative AI – and the need for robust governance, privacy, and security measures.

The industry acknowledges that under 1% of the vast enterprise data stores are utilized by GenAI systems. And of that, over 90% is unstructured.

Overcoming these hurdles demands a multi-pronged approach. Automated ingestion at scale is crucial, enabling organizations to efficiently process vast datasets. This must be coupled with comprehensive data curation and robust governance policies, ensuring data accuracy, completeness, and compliance. By applying these principles to both structured and unstructured data, businesses can build trusted, reliable datasets that fuel effective AI models.

Furthermore, a unified strategy is paramount for scaling AI initiatives. This involves combining in-house expertise with advanced software solutions capable of managing complex data pipelines. The goal is to seamlessly transform data into AI-ready assets within existing governance frameworks, empowering organizations to make data-driven decisions based on a richer, more complete picture.

The ultimate payoff? Enhanced decision-making, optimized processes, and the realization of true ROI from AI investments.