There was a time when "real-time analytics" meant a dashboard that updated every 15 minutes. For most enterprises, that was revolutionary, enabling teams to monitor performance without waiting for end-of-month reports. AI-powered agents can now monitor live data streams, flag anomalies, trigger workflows, and serve up insights tailored to each user's context, all without requiring them to open a single dashboard. The value is no longer in the view, but in the velocity of insight-to-action.
This shift isn't about abandoning dashboards entirely, not at all. But it's about recognizing that modern AI has changed what "real-time" actually means. And that realization means that the simple days of dashboards, BI, and data viz that Power BI and Tableau introduced us to, are quickly coming to an end. Today, business users want analytics that not only visualize what's happening, but interpret the signals, recommend next steps, trigger workflows, and in many cases take action automatically.
Domo is setting the pace in this evolution. Rather than bolting AI onto legacy BI tools, Domo is architecting its platform around agentic intelligence: autonomous, customizable agents that continuously analyze, interpret and respond to data. Built on a foundation of governed, connected data, these agents give organizations the speed, precision and trust they need to compete in an AI-driven world. In short, Domo is showing what business intelligence looks like after the dashboard.
Why this matters now and why Domo's moment is coming
In recent months, Domo has provided clear signals that the opportunity is accelerating. For instance, the company reported its first quarter of fiscal 2026 (ended April 30, 2025) revenue of US $80.1 million and subscription revenue of US $71.4 million. Even more compelling: subscription Remaining Performance Obligations (RPO), a forward-looking metric showing committed revenue not yet recognized, climbed to US $408.2 million, up 24 % year-over-year, and the portion of that RPO expected beyond 12 months hit US $182.3 million, a 61 % increase.
These numbers suggest that Domo is not only booking new deals but generating longer-term recurring commitments, the kind of foundation that supports betting on growth. Beyond the numbers, Domo has deepened its strategic partnerships. In June 2025 the company announced an expanded collaboration with Snowflake Inc. to deliver its full portfolio on the Snowflake Marketplace, including Domo's low-code/no-code "Magic ETL" engine, reverse-ETL, and agentic AI via its Cortex AI framework.
This integration means that organizations already using Snowflake's AI Data Cloud can layer in Domo's intelligence and action-orchestration on top, shortening time-to-value and lowering friction. That's a strategic advantage.
Dashboards will still exist, but they're no longer the point. Generative AI has captivated the enterprise world, sparking visions of always-on agents, self-optimizing processes, the autonomous business, and trillion-dollar productivity gains. But there's a problem.
The Real Bottleneck? AI Readiness
Despite $30 to $40 billion in enterprise GenAI investment, 95% of organizations report zero measurable return, according to MIT's 2025 State of AI in Business report. It's not for lack of ambition or even technology. In many cases, the AI is ready and waiting. But the businesses behind it aren't.
The organizations struggling today aren't failing because their AI models are weak. They're failing because their data foundations are broken, littered with silos, inconsistency, and outdated infrastructure. That's why businesses are turning to AI and data products companies like Domo to both get the data ready for AI and then build the AI agents and apps that allow them to happily claim that they are among the 5% succeeding.
MIT puts it plainly: "The vast majority remain stuck with no measurable P&L impact." Why? Because of unclean data, lack of integration, broken workflows, and immature infrastructure.
Think of it as data debt, the cumulative effect of years of fragmented systems, duplicate records, disconnected workflows, and siloed teams. That debt starves AI of the context it needs to act intelligently. And the longer it goes unpaid, the more expensive it becomes.
The 5% of organizations seeing AI gains aren't necessarily using more advanced tech. They've simply done the hard work of getting their house in order: AI's true power isn't in prediction, it's in action. But action without context is chaos.
Context means knowing that a customer didn't just click an email, they abandoned a cart, called support, and churned last quarter. It means understanding that a sales drop isn't just a red number, it's linked to inventory delays, regional pricing, and a marketing shift.
AI is no longer an experiment, it's an expectation. But it's not magic. The future of enterprise success won't come from buying AI. It will come from building with it. And the platforms that make that possible? Those are the ones to watch.
