Tag: AI Perspectives

Stop wasting AI budgets. Learn a practical, problem-first framework to turn pilots into real ROI with better data, adoption and business impact.

If you’re sitting on a mountain of legacy code, maybe a Java monolith or an old .NET setup, Alphonse Kazadi discusses the three ways to use AI that have nothing to do with a chat window.

This blogs argues that the success of a retrieval-augmented generation (RAG) system depends more on data quality, metadata and governance than on model tuning or pipeline optimization. Without clear metadata, document ownership, permissions and freshness controls, AI systems can retrieve outdated or incorrect information, leading to hallucinations. Ultimately, trustworthy AI requires well-structured, governed data, not just more advanced models.

This article explores how the “slow down AI” narrative is weakening innovation by replacing risk management with risk avoidance. It shows how over-governance drives experimentation elsewhere and proposes a builder-first framework for responsible progress.

A mechanical engineer with no recent coding experience shares how generative AI helped him go from ideas to building real software products. By using AI as a learning partner and committing to daily practice, he quickly developed the skills needed to ship working tools and applications.