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Executive Summary Luxembourg published its AI strategy as part of a broader ‘Accelerating Digital Sovereignty 2030’ strategy to position the country as an international reference for data valorization through AI. The strategy identifies 11 flagship projects across public administration, finance, health, and culture sectors. While the strategy has strong political sponsorship with significant infrastructure investments, it suffers from common execution challenges that threaten its success. The strategy has three critical gaps: the absence of well-defined outcomes and success metrics for each sector, no prioritization among 11 flagship initiatives, and lack of accountability mechanisms. This pattern is not unique to Luxembourg’s AI strategy; it undermines technology strategies worldwide by avoiding difficult decisions and spreading resources too thin. ...

Building this site was also an experiment to evaluate AI-augmented coding paradigms. Biased by my long tenure at AWS, I chose Kiro.dev with its spec-driven development approach to run this experiment. The fact that I was an early pre-GA adopter with generous 1000 free task credits also helped. In this article, I share what worked, what didn’t, and how I made Kiro effective for my needs. The Kiro Promise Kiro’s product promise is to help developers do their best work by bringing structure to AI coding with spec-driven development — from prototype to production (see https://kiro.dev/). The Kiro IDE is built on top of Code OSS (the open-source foundation of VS Code). Its differentiating experience is the spec-driven development mode, in which you use the Kiro AI assistant to define requirements1, a design document2, and a list of implementation tasks3. By completing these with Kiro, you get functional code with documentation and tests. ...

Three critical factors determine initiative success: clarity of vision, strong sponsorship, and effective execution. Learn how to avoid common failure patterns.