DoT Blog

  As the enterprise AI conversation keeps a fast-evolving pace, a year ago, most organizations were still experimenting with copilots, generative AI interfaces, and isolated proof-of-concept deployments; today, the discussion is shifting toward something far more operational: how to run AI agents reliably, securely, and at production scale. This is the context behind the latest announcement from Dell Technologies , which introduced what it describes as its “production-ready agentic AI” infrastructure spanning everything from deskside workstations to large-scale data center deployments. We are talking of Dell’s new Deskside Agentic AI solution. At first glance, this may sound like another infrastructure vendor trying to ride the AI wave, but underneath the announcement lies a more important signal: the enterprise market is beginning to move from AI experimentation to AI operationalization . And this transition changes everything. The Shift from AI Features to AI Systems One ...

  Image generated with AI “These are dangerous times. Never have so many people had access to so much knowledge, and yet resistant to learn anything.” ― Tom Nichols, The Death of Expertise: The Campaign against  Established Knowledge and Why it Matters, 2nd Edition I’ve spent a significant part of my life devoted to work in the data management space, from programming to designing data solutions, and also a significant part of it managing, analyzing, and influencing (a word from these times) the importance of data and information in the decision-making processes of organizations. Also, for the most part, I’ve been staying in my lane, not too much involved in politics or the politics of the corporate world in a public manner. Yet, there is a growing paradox in today’s leadership landscape, one that is now too big to be ignored, at least by me, combined with an increasing politicization happening in the tech industry, or at least becoming more public. On one hand, we are living t...

  Logo courtesy of Databricks Over the last couple of years, the conversation around AI in enterprise software has moved quite quickly. Not long ago, the focus was on large language models (LLMs) and generative assistants; today, the discussion is shifting toward something more operational: agentic systems , AI agents capable of reasoning, planning, and acting across workflows. With the introduction of Genie Code , this past March, Databricks is making a clear statement about where it believes the future of data engineering and analytics is heading. The announcement positions Genie Code , the company’s tool designed for data teams for AI solution development, as a tool that allows developers and data professionals to build, orchestrate, and operate AI agents that interact directly with data environments. The interesting part is not just the tool itself. It’s, in my view, what it signals about the direction of the modern data platform.   From Data Pipelines to Agenti...

  Logo courtesy of  Amazon.com, Inc. For the past few years, the AI conversation has been dominated by large public models and cloud-scale services, but quietly, another trend has been gaining traction: the push toward private, autonomous AI agents running closer to where data and decisions live. With the recent introduction of OpenClaw on Amazon Lightsail , Amazon Web Services (AWS) is nudging that conversation forward. This new offering essentially allows developers and organizations to run autonomous AI agents in their own AWS-controlled environment using relatively simple cloud infrastructure. On the surface, this might look like just another developer-friendly deployment option, but if we step back for a moment, it hints at a deeper shift into how organizations might design AI systems in the near future.   From AI Assistants to Autonomous Agents So far, most of today’s AI implementations still operate basically as assistants. You prompt them, they respond, even in en...