DoT Blog

Business integration has never been particularly glamorous. It is the plumbing underneath ERP systems, supply chains, APIs, partner networks, data platforms, and increasingly AI applications. When integration works, nobody talks about it, but when it fails, suddenly everyone cares, especially in this era of artificial intelligence (AI). This is making that companies in this segment of the software industry scale positions on every priority list of every tech decision maker. Just a few days ago I had the opportunity to meet with Ulf Persson, Sr. Vice President, Strategic Product Management at SEEBURGER . The German integration vendor has spent roughly four decades in a market built around deterministic problems: move this file, transform that message, connect this trading partner, and expose that application through an API. Its Business Integration Suite (BIS) reflects those roots, covering B2B/EDI, managed file transfer, application integration, APIs, and related integration ...

  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 ...

Logo courtesy of Salesforce For decades, enterprise software has revolved around applications; employees log into CRM systems, ERP platforms, HR suites, and countless specialized tools to perform their work. Initially, AI entered this world as another feature, a chatbot here, a copilot there, and an assistant embedded inside the application. But what if the application itself stopped being the center of attention? That is the intriguing question raised by Headless 360 , Salesforce's latest architectural initiative. The announcement appears to be another AI enhancement to the Salesforce ecosystem. However, if we look a little deeper, it becomes something more interesting: an attempt to redefine how enterprise systems expose business capabilities to AI agents . Moreover, if Salesforce succeeds, Headless 360 may represent a much larger shift than the company itself is emphasizing. So, What Exactly Is Headless 360? The easiest way to understand Headless 360 is to forget a...

  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...

  Credit: Zoho Corporation In early March, Zoho announced the launch of its own Model Context Protocol ( Zoho MCP ). It is not often that a technology announcement signals something bigger than the feature itself, but the launch of Zoho MCP feels like one of those moments, not because it introduces yet another AI capability, but because it hints at how work platforms themselves may evolve in an era increasingly shaped by intelligent agents. Zoho describes its MCP as part of a framework designed to support the next generation of AI-driven work. The specifics revolve around enabling systems that can interact with applications, data, and workflows more autonomously. But beneath the terminology seems to lie a broader idea: the shift from application-centric software toward agent-centric systems of work. This shift is subtle today, but over time, it could become foundational.   From Applications to Coordinated Intelligence For decades, enterprise software has revolved around appli...

  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...