Knowledge
Analyzing what changes is only the beginning. The important thing is to understand what it implies.
We amplify the analysis of the AI Research Lab and specialists in our ecosystem on issues that are transforming technology, business, and society.
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The five mistakes companies make when implementing AI agents
Read more →: Los cinco errores que cometen las empresas al implantar agentes de IAThe success of AI agents depends much less on the model than we usually think and much more on the enterprise architecture: data, security, governance, oversight, and process design.
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Governing uncertainty: when should AI abstain
Read more →: Gobernar la incertidumbre: cuándo una IA debe abstenerseThere are labyrinths that are not traversed by walking, but by remaining silent. Artificial intelligence has become one of them: a mirror that multiplies possible paths and, at the same time, conceals the point at which the reflection ceases to be legitimate. To govern…
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AI requires purpose
Read more →: La IA requiere propósitoThat a system functions and meets applicable requirements does not answer the fundamental question: why should AI be involved in a particular decision? The analysis posits purpose, oversight, and accountability as prerequisites for automation.
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Evaluating generative AI requires looking beyond the model
Read more →: Evaluar IA generativa exige mirar más allá del modeloEvaluating generative AI requires considering more than just model performance. Language, context, instructions, system usage, and behavior are all part of an evaluation that must necessarily be multidimensional.
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AI will not be reliable if it is not representative
Read more →: La IA no será confiable si no es representativaRepresentativeness cannot be incorporated once a system is finished. It begins by defining what problem will be solved, what data will be used, and who participates in the decisions that will determine its operation.
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Small models, real impact: the AI race at the edge
Read more →: Modelos pequeños, impacto real: la carrera de la IA en el edgeThe evolution of enterprise AI doesn't depend solely on ever-larger models. Privacy, latency, costs, and efficiency are driving the development of smaller, specialized models capable of performing specific tasks closer to where data is generated.
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Medical AI: Data infrastructure is the real barrier to entry
Read more →: IA Médica: La infraestructura de datos es la verdadera barrera de entradaThe competitive advantage in medical AI is shifting from the model itself to the origin, quality, and governance of data. Regulation and traceability are making information infrastructure a strategic condition for developing reliable systems.
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Cybersecurity and AI: The First Line of Defense to Secure the Corporate Future
Read more →: Ciberseguridad e IA: La Primera Línea de Defensa para Blindar el Futuro CorporativoThe combination of increasingly sophisticated attacks and a shortage of specialized talent is changing the role of AI in cybersecurity. This analysis addresses its potential as a defensive capability and the implications for particularly vulnerable sectors.
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Article
Are we already in the era of “vibe comms”?
Read more →: ¿Estamos ya en la era de las “vibe comms”?By Natalia Sánchez, Co-CEO of Weber Shandwick. The term “vibe coding” was coined in February 2025 by Andrej Karpathy, co-founder of OpenAI and former AI lead at Tesla. In his definition, Karpathy described a future where artificial intelligence (AI) tools…
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The Agentic Era 2.0: Why 2026 Will Be the Year of Total Autonomy and the Definitive End of 'Co-pilots'‘
Read more →: La Era Agéntica 2.0: Por qué 2026 será el año de la Autonomía Total y el fin definitivo de los ‘Copilotos’Beyond the Copilot: Their Strategy for the Autonomous Enterprise of 2026. The first wave of generative AI, embodied in "Copilots," has offered a false sense of disruption in the face of the imminent arrival of autonomous agents. While they have optimized…