AI is transforming enterprise operations, but traditional systems not designed for AI pose integration challenges, prompting MongoDB and Capgemini to modernize data infrastructures for AI-driven applications. Challenges include data fragmentation and outdated infrastructures, which hinder AI adoption. MongoDB's flexible document model supports structured and unstructured data, essential for AI applications like semantic search and recommendation engines, while Capgemini aids in restructuring and migration to AI-ready systems. Real-world use cases highlight the successful application of these solutions, such as AI-powered field operations streamlining processes and improving safety in the energy sector, AI-assisted anomaly detection enhancing vehicle reliability in the automotive industry, and AI-driven efficiency in the insurance sector with Capgemini's GenYoda. In investment portfolio management, AI agents supported by MongoDB enhance decision-making by analyzing large datasets and adapting to market changes. MongoDB’s capabilities in data retrieval and vector search facilitate advanced portfolio insights, promoting intelligent asset allocation and risk management. Leadership transitions at MongoDB see Dev Ittycheria retiring as CEO, with Chirantan “CJ” Desai taking over, bringing extensive growth-at-scale experience to guide MongoDB's next phase, especially as AI and data-intensive applications align with MongoDB's strategic strengths.