We have normalized a dangerous lie in the adoption of corporate Artificial Intelligence: believing that if the answer is good, the user won't mind waiting. The operational reality is quite different. In a production environment, friction destroys adoption. If your...
Let's do an exercise in technical honesty. Building an AI assistant that answers questions about internal documentation is, today, trivial. A junior developer can whip up a functional demo in an afternoon using Python, LangChain, and an API key. In the controlled...
There is a dangerous cycle in the corporate adoption of Artificial Intelligence. It starts with the enthusiasm of a successful demo using the most powerful model. It continues with an optimistic rollout across the company. And it ends abruptly with the first cloud...
One of the most feared phrases in a digital strategy meeting is: "We already have the pilot working, can we open it to the whole company tomorrow?" The short answer is no. The long answer involves understanding that Artificial Intelligence, especially Large Language...
The profitability of an Artificial Intelligence project does not depend exclusively on the power of the selected model but rather on the efficiency with which it is deployed within the organization's operational structure. In this analysis, we evaluate from both a...
In the technology decision-making rooms of large corporations, a dangerous axiom persists: Until our data is perfectly clean and centralized, we cannot make the leap to Artificial Intelligence (AI). While this premise stems from logical prudence, it has become the...
Over the last decade, the digitalization of large organizations centred on eliminating repetitive tasks. Under the concept of RPA (Robotic Process Automation), companies optimized costs by delegating mechanical processes to systems. However, in today’s landscape,...
The adoption of Artificial Intelligence in the corporate environment has moved past the experimental phase. The current challenge no longer lies in selecting the most powerful foundational model but in integration engineering. The concept of "Smokeless Applied AI"...
Retrieval-Augmented Generation (RAG) has ceased to be an experimental novelty to become the backbone of the most efficient corporate AI applications. However, in the leap from proof of concept to production, a structural error is frequently observed: attempting to...