Structure, classify, govern, then AI: The biopharma sequencing of trust

Biopharma companies effectively utilizing AI prioritize structuring, classifying, and governing their data before deploying AI capabilities, which prevents costly disorganization and compliance issues. Despite AI's growing use in the industry for data analytics and clinical decision-making, a significant portion of valuable data remains unstructured and inaccessible to traditional AI systems. Companies like a particular Box life sciences customer highlight the importance of establishing a solid content foundation before applying AI, which involves understanding data, classifying it as a trust mechanism, and distributing ownership of content governance among relevant teams. This approach allows organizations to automate metadata extraction, streamline document review processes, and maintain compliance, ultimately enhancing speed, insights, and operational efficiency. By ensuring that AI operates within a governed and auditable framework, biopharma companies can achieve reliable and actionable AI outputs, underscoring the necessity of addressing the content foundation prior to AI model deployment.

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