DEI
At IT Patagonia, diversity is lived and built as a team.
We analyze why diverse work teams are not built with speeches, but with strategic decisions that impact culture and business.
Insights
IT training
Programming languages in 2026: which ones will enable you to work in technology
Discover which programming languages are worth learning in 2026, which are the most widely used, and how AI is transforming the way we program. A clear guide for those looking to start or advance in the IT industry.
AI
AI-powered data models: architecture and key decisions for generating sustainable value
How to go from data to decisions with AI: minimal architecture, governance, evaluation and steps to implement business-aligned models.
Great Place to Work
5 keys to retaining talent in the IT industry
In this article we analyze the fierce competition for talent facing the IT industry and share 5 keys to retaining technology teams: authentic value proposition, professional development, organizational culture and empathetic leadership, flexibility and well-being at work, and genuine recognition.
cyberattacks
Incident response: how to prepare to respond effectively to cybersecurity incidents
We analyze how a robust incident response plan enables organizations to react to cyberattacks quickly and effectively, reduce costs, protect sensitive data, and preserve the trust of customers and partners.
IA-first
AI-first strategy: how to build an organization-wide management and adoption model
Why is AI-first a viable operating model and a path to business sustainability? We analyze its challenges, potential risks, roles involved, and a roadmap for moving from isolated initiatives to scale.
We talked about how to evaluate suppliers based on their ability to demonstrate fast and sustained value, with a milestone-based plan, a flexible architecture for changing models, and a team that maintains what has been implemented.
In this video we analyze how to advance in stages in the modernization of a core ecosystem, with continuous testing and a rollback plan, with key people involved and with a clear scope.
We analyze how the cost of AI is controlled from the design stage by choosing between token-based or license-based consumption, setting limits per user, reusing repeated responses with caching, and validating that the business case closes with the maximum monthly expenditure.
We'll tell you why governing data in the AI era requires going beyond data quality. It involves governing the data itself, the model, biases and regulations, and the conversations and documents an agent produces. Why, if the model isn't governed end-to-end, it won't reach production.
After deciding to modernize the mainframe, the first step is to assess the current state, without touching any systems. Learn how a reengineering analysis maps batch and online processes, dependencies, and cost points, and is accompanied by a metrics dashboard (MIPS, batch times, availability) that establishes the baseline.
To determine if an AI is still performing well, observability is essential: automated metrics for response quality, resource consumption, biases, and model availability in production. We'll explain how to monitor model degradation over time.
