L'intelligence artificielle, la généralisation du numérique, la transformation des métiers et l'évolution rapide des sociétés africaines sont en train de modifier profondément le monde dans lequel grandissent les jeunes Sénégalais.
What if the problem isn't that Senegal lacks technology, but rather that we still too often design technology for a world that isn't our own?
In the Senegalese political arena, one question deserves to be asked honestly: are we confusing influence and leadership?
A deep dive into the architectural collision between agentic-oriented data models and the legacy relational paradigm.
A deep dive into one of the most seductive anti-patterns in agentic AI, and what actually works instead.
Artificial Intelligence (AI) is everywhere—transforming industries, reshaping business models, and dominating headlines. Yet, despite the billions invested, many organizations report disappointing outcomes from their AI initiatives. The algorithms work. The tools are powerful. But the results? Often underwhelming.
Implementing an Enterprise Architecture (EA) modeling language like ArchiMate in a large, complex organization presents a significant strategic decision: should the language be "diluted" for quicker adoption, or should a gradual implementation respecting its core principles be pursued? This choice profoundly impacts the long-term effectiveness, consistency, and value derived from the EA practice.