Connect Your Data in Your AI ‐first Organization

This chapter analyzes the architectural transformation necessary for AI-first organizations to effectively connect data, focusing on cognitive load and trust implications across traditional search, AI inquiry, and grounded AI approaches. It introduces three key topics: AI-first search to synthesis, connecting to people, and connecting to applications, illustrated by a mobile banking platform case study. The chapter explores data selection and combination strategies, including context stuffing, filtering, keyword and semantic searches, and hybrid approaches, concluding with a hybrid search AI legal assistant case study. Advanced search strategies and human-AI collaboration in data pipelines are examined, with parallel keyword search and reranking techniques highlighted for case selection refinement. The role of people in AI systems is categorized into data sources, colleagues, and customers, with frameworks for efficient data management presented. A case study demonstrates the reduction of query volume on senior staff by half through AI-connected knowledge capture systems. The chapter concludes with the Architected Intelligence Principle, stressing the importance of minimizing friction in knowledge bases for accessibility and maintainability. Key findings underscore the necessity of balancing speed, cost, and intelligence in search strategies and the critical role of intuitive documentation in AI systems.