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Reading Time: 3 minutesAgentic AI is often described in terms of intelligence: reasoning capabilities, planning, learning, and autonomy. Yet intelligence alone is not what ultimately defines an agent. An agent is defined by its ability to act, and action, by its very nature,…
Reading Time: 3 minutesAs I hope I’ve shown through posts 1 and 2 in this series, developing semantic models is not a purely technical exercise, nor is it a purely conceptual one. It is a delicate act of mediation between experience and formal…
Reading Time: 2 minutesThe recent Iran-Israel conflict is beginning to reshape the economics of enterprise technology. A sustained energy shock is raising fuel, transport, and input costs, hardening inflation expectations and making capital more selective, just as investors grow less tolerant of long…
Reading Time: 2 minutesThis is the third post in my four-part series about AI transformation. The second covered the role of the data team, and the first covered trust. AI innovation is happening at an extraordinary pace. New models, frameworks, and tools are…
Reading Time: 3 minutesAcross nearly every industry, organizations are racing to transform how they operate with AI. But despite billions of dollars in investment, real-world results have often been disappointing. Studies consistently show that a significant number of AI initiatives fail to reach…
Reading Time: 3 minutesIn the first part of this series, I provided an introduction to semantic modeling, covering the power of the maieutic approach. In this post, I’ll look into one of the fundamental difficulties of semantic modeling: Its ongoing, iterative nature. “My…
Reading Time: 4 minutesBusiness users want simple answers to apparently simple questions, such as “What counts as a customer, or revenue?” or “Which KPIs can I trust?” and it is up to the data professional, the data architect, or analytics leads, to create…
Reading Time: 5 minutesFor years, enterprise data architecture has relied on collecting, transforming, and replicating data across multiple systems to serve different analytical and operational needs. This approach has delivered value, but it has also introduced unwanted side effects, over time: unnecessarily duplicated…
Reading Time: 3 minutesSemantic models, logical data architectures, and why Denodo is the ideal companion for traditional AI, generative AI, and RAG Artificial intelligence has made extraordinary progress in recent years. From predictive models to generative systems capable of producing text, images, and…
Reading Time: 5 minutesThe insurance industry has spent decades refining its ability to predict risk, building sophisticated actuarial models, investing in analytics, and advancing underwriting science. But 2026 marks a fundamental turning point. Competitive advantage will no longer come from predicting outcomes more…
