Almost every engineering team today can show the same number: code generated faster than ever thanks to AI assistants. What very few teams can show, when asked honestly, is how much of that code is still reliable six months later. That's the uncomfortable question behind the massive adoption of AI in software development: is the speed we're gaining today real, or is it technical debt whose bill hasn't arrived yet? The numbers that are actually being measured During the early

There's a question almost every company has asked itself in recent months: "Are we already an AI company?" And the answer is almost always measured the wrong way. Companies count how many Copilot licenses they bought, how many chatbots are active, how many people on the team already use ChatGPT in their daily work. With those numbers in hand, many organizations conclude that yes, they've already made it. That conclusion is exactly the mistake that's costing results today. Bei

Almost no company today has a data shortage problem. It has the opposite problem: too much information, scattered across too many places, with no clear way to turn it into an answer when someone actually needs one. A director asks something as simple as "how is this business line's profitability doing this quarter compared to last quarter?" and the answer doesn't come back in minutes. It comes back in days, after someone on the analytics team cross-references data from the ER

Pasar de contribuidor individual a líder implica mucho más que una promoción. Significa desarrollar habilidades como comunicación, delegación, gestión de personas y visión estratégica. El éxito ya no depende solo de tus resultados, sino de tu capacidad para ayudar a otros a alcanzar los suyos. Liderar es cambiar el enfoque del "yo hago" al "nosotros logramos", impulsando el crecimiento del equipo y generando un impacto más amplio.