Systems | Development | Analytics | API | Testing

Paul McDonough-Smith estimates that many business leaders would struggle to define their organization’s problem clearly in fewer than 25 words. With AI, that lack of clarity can quickly turn into fragmented solutions and misplaced expectations. In this episode of The AI Forecast, Paul Muller sits down with Paul McDonough-Smith, a Visiting Senior Lecturer at MIT Sloan School of Management and a Senior Advisor to NASA's Goddard Space Flight Center, to explore how organizations can approach AI with greater clarity and purpose.

AI changed the process, but not what matters. In the latest episode of The Data & AI Chief, three authors explore what it takes to lead through the AI era, from scaling innovation to building AI-ready data foundations to keeping humans at the center. This episode features: Linda Hill, Harvard Business School Professor and author of Genius at Scale, on scaling innovation and leading through uncertainty.

This is Part 1 of a two-part video series exploring how Qlik MCP tools can be used with declarative pipelines directly within VS Code. In this video, I demonstrate how to use the MCP tools to explore and validate your Qlik environment before building anything—identifying available connections, inspecting source tables, reviewing pipeline project information, and verifying the resources you plan to use.

AI Optimization is a workspace for managing how Qlik Answers understands an application. It brings semantic management into one experience, where you can review AI-generated semantic understanding and make corrections before they reach an answer. The result is a visible, correctable layer where there used to be none. AI Optimization is the central place to manage how Qlik Answers interprets your application. Semantic Understanding, inside it, shows and lets you edit this interpretation of each field and master item.

As enterprise AI scales, traditional data architectures are reaching their limit. Workloads are becoming more distributed, data movement is accelerating, and tech leaders face growing pressure to justify AI spend while delivering real business impact. Chief Technology Officer Sergio Gago breaks down key findings from Cloudera’s latest global survey of enterprise architects, data architects, and cloud leaders.