The DoT Blog

Logo courtesy of ClicData Up until now, the enterprise software industry has spent considerable time making artificial intelligence more accessible. Generative AI assistants can answer questions, summarize documents, generate reports, and increasingly perform analytical tasks that once required specialized technical knowledge, yet beneath this impressive progress lies a persistent problem: AI assistants may be increasingly capable of analyzing information, but that does not necessarily mean they understand the business behind it. For organizations, particularly those in the midmarket, this issue is becoming critical. For example, a company may have sophisticated AI tools at its disposal while its operational data remains scattered across CRM systems, financial applications, spreadsheets, and databases. Connecting these environments is difficult enough. Ensuring that AI systems can access accurate, current, and properly governed information introduces another level of complexity. This i...

Zoho Headquarters in Chennai, India (Courtesy of Zoho Corp.) In its October 28 announcement , Zoho is releasing version 7 of its Analytics Platform , and with it, it is introducing a significant expansion, positioning it as an Agentic Analytics environment designed to connect enterprise data, business context, AI agents, and operational actions. Rather than treating analytics as a destination where users consult information, Zoho envisions it as an intelligence layer supporting decisions across applications, processes, and business functions. The announcement includes a multi-agent architecture for Ask Zia , native semantic and ontology layers, expanded data connectivity, embedded intelligence, and new governance capabilities. Together, these developments suggest something more ambitious than another generation of BI functionality. Zoho is attempting to redefine the role analytics plays within enterprise software. Zoho and a new Era of Analytics For decades, business intelligence (BI)...

  Logo courtesy of Snaplogic It is interesting that, normally when most people think about enterprise AI, they imagine copilots, chatbots, or autonomous agents making business decisions, yet rarely does anyone think about integration platforms. But curiously, this may be precisely where one of the most significant transformations is quietly taking place. In this vein, with the recent introduction of SnapGPTAgentic Assistant , SnapLogic is signaling that integration itself is becoming an AI-native discipline, so, rather than simply connecting applications and moving data from point A to point B, the integration platform is evolving into an intelligent participant capable of understanding intent, generating workflows, and assisting with orchestration. Sure, this may sound like a natural extension of generative AI, but I believe it points to something larger: the convergence of integration, automation, and agentic AI into a single architectural layer. Beyond Low-Code Integration For ...

Image generated with AI So, apparently, Artificial Intelligence is no longer Artificial Intelligence . At least not if you work for the executive branch of the U.S. federal government. On September 29, President Donald Trump signed an executive order titled Inaugurating the Era of Super Intelligence . The order establishes a policy directing executive departments and agencies, to the maximum extent permitted by law, to use “Super Intelligence” and “SI” instead of “Artificial Intelligence” and “AI” in official communications and other non-statutory documents. The accompanying White House fact sheet presents the change as recognition that today's technologies have moved beyond what the term "artificial intelligence"  originally represented. However, there is one particularly interesting detail: For purposes of implementing the order, SI initially means the technologies already encompassed by the existing statutory definition of artificial intelligence. The order then ...

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...