ibi Declares Dashboards Dead, Bets on Agentic AI for Autonomous Action

NEW YORK, NY – August 24, 2026 – The business intelligence world has long been defined by the dashboard—a mosaic of charts and KPIs that serves as the command center for corporate decision-making. Today, at its annual customer Summit, ibi, a business unit of Cloud Software Group, took a sledgehammer to that foundation, declaring the era of passive data visualization over and betting its future on a new paradigm: autonomous operational execution driven by agentic AI.

With the launch of its Enterprise Agentic AI Engine, natively embedded within the flagship WebFOCUS Data & Analytics Platform, ibi is making one of the boldest strategic pivots in the recent history of enterprise software. The company isn't just introducing another AI assistant to help users find data; it's unleashing an autonomous engine designed to diagnose, prescribe, and ultimately act on that data with minimal human intervention. It’s a move that seeks to redefine the very value proposition of BI, shifting it from a tool for retrospective analysis to a proactive driver of real-time business operations.

From Passive Viewing to Autonomous Action

“The era of the static dashboard is officially over,” declared Daniel Ortolani, Senior Vice President and General Manager at ibi, during the keynote address. “Enterprise CTOs don't need another chatbot that merely summarizes data—they need an agentic engine that automatically authors rich visual reports and diagnoses root causes in real time.”

Ortolani’s statement cuts to the core of a growing fatigue in the enterprise. For years, the promise of BI was to empower every employee with data. The reality, however, often involved complex, rigid dashboards that answered what happened but left business leaders to manually investigate why. ibi’s new engine aims to close that gap by moving beyond passive chart viewing.

At the heart of the WebFOCUS 9.3.8 release are three core capabilities that define its agentic nature:

1. Automated Visual Content Authoring: Users can make plain-English requests, and the AI Assistant will instantly generate and execute the creation of custom reports, interactive charts, and dashboards. This eliminates the manual, often time-consuming design process that has traditionally been the domain of specialized BI developers.

2. Autonomous Root-Cause Analysis: This is where the engine’s intelligence truly shines. When a metric variance is detected—say, a sudden drop in regional sales—the AI doesn't just flag it. It autonomously executes background queries, slicing and dicing data to move beyond tracking the symptom to pinpointing the precise cause.

3. Prescriptive Action & Governance: After diagnosing the problem, the assistant prescribes immediate operational steps to business leaders. Crucially for enterprise adoption, it also maintains transparency by exposing the underlying query logic, schemas, and data sources to technical teams, ensuring that the AI’s “thinking” is auditable and trustworthy.

This progression from description to diagnosis to prescription represents a significant leap in capability, aligning with a broader industry trend that sees AI not just as an analyst but as an active participant in the workflow.

A Crowded Field of Intelligent Co-Navigators

ibi is not launching its AI into a vacuum. The enterprise software landscape is now teeming with AI-powered “copilots” and assistants. Microsoft’s Power BI Copilot helps users generate reports and DAX formulas through conversation, while Salesforce has deeply integrated its Einstein Copilot across its platform to automate CRM tasks. Tableau AI and Qlik AI, likewise, leverage generative AI to augment human analysis with natural language querying and predictive insights.

This industry-wide stampede toward AI validates ibi’s central premise that the old way of doing BI is insufficient. However, ibi is attempting to position its offering as something more than a “copilot.” The term “agentic” is a deliberate choice, signaling a system designed for a higher degree of autonomy. While many copilots are designed to assist a human user in completing a task, an agent is conceptualized to understand a goal and then independently devise and execute the steps needed to achieve it. ibi's focus on “autonomous operational execution” is a direct appeal to this more advanced vision of AI's role in the enterprise.

The Strategic Advantage of Model Agnosticism

Perhaps the most strategically significant feature of ibi’s new engine is what the company calls “Model-Agnostic Freedom.” Unlike competitors who often tie their AI capabilities to a proprietary or preferred Large Language Model (LLM), the ibi AI Assistant is engineered for architectural flexibility. It connects seamlessly to a range of commercial LLM subscriptions, including those from OpenAI, Gemini, and Anthropic.

For CIOs and enterprise architects, this is a critical differentiator. The LLM landscape is evolving at a breakneck pace, and the best model for a given task today may be superseded tomorrow. An agnostic approach prevents vendor lock-in and empowers organizations to build a flexible, future-proof AI stack. It allows them to choose the most cost-effective model, the one that best meets specific compliance requirements, or the one that excels at a particular function, like code generation or financial analysis.

This flexibility extends to the platform's role as a developer co-navigator. By allowing technical teams to author and debug complex code from plain-English prompts, WebFOCUS becomes a more powerful and productive environment. This dual focus—empowering business users with autonomous insights while accelerating professional developer workflows—creates a comprehensive value proposition.

Putting Autonomous Execution to the Test

Abstract capabilities are one thing; tangible business impact is another. The true test for ibi will be how “autonomous operational execution” performs in the messy reality of corporate data.

Consider a supply chain manager at a global CPG company. Previously, identifying a bottleneck might require days of sifting through logistics dashboards and cross-referencing inventory reports. With an agentic system, the manager could be proactively alerted that a shipment of raw materials is delayed. The AI would have already performed the root-cause analysis, identifying a weather event impacting a specific shipping lane. It could then prescribe an immediate action: re-routing a portion of the shipment through an alternate port and automatically adjusting production schedules at the affected factories to minimize downtime.

This is the future ibi is selling: a system where insights are inseparable from the actions they inspire. By building in robust governance that makes the AI’s logic transparent, ibi is addressing the critical issue of trust, which remains the biggest barrier to the adoption of truly autonomous systems in the enterprise. The journey from static dashboards to autonomous agents is just beginning, but with this launch, ibi has firmly planted its flag in what it believes is the future of business intelligence.