Can AI Agents Make Decisions Without Real-Time Enterprise Data?

Envision a fraud detection model embedded in a bank's AI system, continuously monitoring transactions for unusual activity. The warehouse behind it updates itself every 15 minutes; however, that seems like a short interval. A fraudulent transaction took place 13 minutes ago, and the model still hasn't picked it up. The thing is, the model is not faulty. The data being fed to it had already overlooked the critical moment. But how? In this episode of the **Don't Panic! It's Just Data podcast**, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sat down with guest Sundar Nathan, VP of Product Marketing, Enablement and Academy at Striim. They discuss the gap that exists between the time an event takes place in a production system and the time an AI agent becomes aware of it. As the frontier models from companies such as OpenAI and Anthropic become more commonplace, the model ceases to be a source of competitive advantage for any enterprise. What remains then is the data beneath the model and the speed at which that data actually moves. “I'd go even a step further and state that in 18 months the model you select will be less important to you than the data which is feeding that model,” Nathan said. ## 'Everybody Is Renting The Same Brain' Alluding to his personal experience, Nathan tells Dua that his "superpower was memory,” particularly, the ability to remember circuit diagrams and organic chemistry structures as clearly as in a photograph when taking an exam. But with the introduction of Google search, that memory ceased to be an advantage since everyone had the same tool at their disposal. Why is that experience relevant? It’s similar to the use of AI in the enterprise today. "Your competitor next door has access to the same frontier models at the same price, roughly on the same day," he says. According to the Striim Marketing VP, what a competitor cannot rent is an enterprise’s operational data, its business insights and "the institutional knowledge accumulated over decades", in particular, how close an agent's view of production data is to the actual data. "The time gap between the agent and where your production data is measured is the new source of competition," he adds. **Also Read Use Case: UPS Leverages Striim and Google BigQuery for AI-Secured Package Delivery** **Read Use Case: The Hyper-Responsive Payments Organisation** ### Takeaways - The model matters less than the data feeding it within 18 months, Viswanathan predicts. - Four Cs of AI-ready data: current, consistent, consumable, contextual. - Only 23% of enterprises scale AI agents past pilot stage (McKinsey). - Change data capture reads transaction logs directly, in real time. - Governance built for non-human users masks PII/PHI before agents see it. ### Chapters 00:00 - Why AI advantage is moving from models to real-time data 02:06 - Sundar’s path from memory-based learning to simplifying complex tech 04:10 - Why “everyone is renting the same brain” 06:52 - Why impressive pilots fail in regulated production workflows 08:30 - The four audit questions that block deployment 10:43 - Why 15-minute refreshes are not real time for fraud 13:25 - Why enterprises need a nervous system, not a warehouse scan 15:52 - How consumable data turns raw events into agent-ready context 18:29 - What Stream’s intelligent pipe does in practice 21:12 - Governance for non-human users and mission-critical guarantees 22:26 - Stop benchmarking models and start benchmarking data staleness 25:45 - Final takeaway: it really is just data, but it must move faster