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Reading Time: 3 minutesAcross Northern Europe, public sector organisations are not short of data; they are short of outcomes. From the UK’s push for integrated digital services, to the Nordics’ advanced data-sharing ecosystems, to the Netherlands’ focus on trusted, citizen-centric governance, a common…
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 minutesThis is the last post in my four-part series about AI transformation. The third covered the role of the AI team, the second covered the role of the data team, and the first covered the core ingredient of trust. For…
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 minutesThis is the second post in my four-part series about AI transformation. The first covered trust, the most important ingredient in this transformation. As enterprises push toward AI-driven operations, data teams are facing a new mandate. In the past, their…
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: 2 minutes“…greater than 80% of IT leaders believe their enterprise data is not ready for agentic AI…” – Gartner, from “Innovation Guide for AI Agents” Agentic AI holds great promise, but as this quote from Gartner makes clear, success has been…
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: 2 minutesAI has fueled a massive hunger for data. Many data science teams operate under the assumption that “more data equals better models,” leading to a culture of collecting and storing everything possible. This pervasive mindset directly clashes with the final…
