Box reveals the key to unlocking AI success through data readiness. Jeanette Gessler, VP of Box Consulting, leads a conversation with Mike Carlino from Deloitte, Shivani Majewski from Deloitte Digital, and Nick Read from Box to explore how data preparation drives successful AI outcomes. They zoom into the challenges of governing unstructured data, highlighting the importance of data quality, security, and lifecycle management. The panel underscores the evolving role of AI in transforming business processes, from risk mitigation to enabling faster deal closures, and how enterprises are moving beyond pilot phases to achieve production-ready AI outcomes. Key Moments: Data readiness as the foundation for AI success: The importance of preparing both structured and unstructured data for scalable AI solutions. Tackling ROT (redundant, obsolete, trivial) data: Addressing knowledge ROT to clean and organize valuable data, critical for AI applications. Governance and data security: Ensuring that only authorized individuals access the correct data, especially when it involves sensitive or personal information. Integrating data across platforms: Leveraging data integration tools like Salesforce to empower teams with real-time, accurate information. Risk mitigation in AI adoption: The need to address risk and use a phased approach in AI projects to avoid pitfalls during the transition from pilot to production. Transforming business processes through data-driven insights: Real-world examples, such as in banking and oil & gas, where combining structured and unstructured data results in significant cost savings and process improvements. Jump into the conversation: (00:00) Introduction of panelists: Mike Carlino, Shivani Majewski, and Nick Read (01:08) Data readiness and the importance of foundation for AI (02:03) Exploring the challenges of data quality and ROT (redundant, obsolete, trivial) (03:10) Governance issues around document versioning and data consistency (04:03) The need for proper access management and governance around unstructured data (05:06) The importance of a knowledge graph for structured and unstructured data (05:24) The complexity of the data lifecycle and how it impacts AI outcomes (05:58) Leveraging unstructured data and its governance (07:26) The role of clean and structured data in improving client satisfaction (07:53) Box's internal integration of unstructured and structured data (08:39) Adoption and the importance of change management in integrations (09:51) The value of practical integration rather than demoing for the boardroom (10:00) Mike Carlino stresses risk mitigation when scaling from pilot to production (10:19) The challenge of moving from POC to production in AI projects (11:54) Importance of reliable, clean data in AI use cases (12:09) The strategy behind AI projects and aligning with ROI (12:35) Insights into practical applications for business improvement (13:12) Successful AI use cases in regulated industries like insurance and banking (15:07) Use cases in oil and gas industries and the importance of data integration (16:51) The buzz around AI agents and moving toward practical AI use cases (17:19) Integrating data with platforms like Salesforce and ensuring data quality (17:57) “Chopped data” and its integration into the knowledge graph (18:13) An example of transforming trapped data into valuable insights (19:00) How clients react when data is unlocked and actionable (19:36) The emotional aspect of seeing real results from AI adoption (20:32) Nick Read shares a story about a bank's struggle to unlock data efficiently (21:20) Transforming business processes and meeting clients where they are (22:12) Tailoring solutions to clients based on maturity and needs

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