Importance of Real-Time Data for AI

As enterprises accelerate their deployment of GenAI agents and applications, data leaders must ensure their data pipelines are ready to meet the demands of real-time AI. When your chatbot needs to provide personalized responses or your recommendation engine needs to adapt to current user behavior, traditional batch processing simply isn't enough. We’re seeing three critical requirements emerge for AI-ready data infrastructure. We call them the 3 Rs: 1️⃣ Real-time: The era of batch processing is ending. When a customer interacts with your AI agent, it needs immediate access to their current context. Knowing what products they browsed six hours ago isn't good enough. AI applications need to understand and respond to customer behavior as it happens. 2️⃣ Reliable: Pipeline reliability has taken on new urgency. While a delayed BI dashboard update might have been inconvenient, AI application downtime directly impacts revenue and customer experience. When your website chatbot can't access customer data, it's not just an engineering problem. It's a business crisis. 3️⃣ Regulatory compliance: AI applications have raised the stakes for data compliance. Your chatbot might be capable of delivering highly personalized recommendations, but what if the customer has opted out of tracking? Privacy regulations aren't just about data collection anymore—they're about how AI systems use that data in real-time. Leading companies are already adapting their data infrastructure to meet these requirements. They're moving beyond traditional ETL to streaming architectures, implementing robust monitoring and failover systems, and building compliance checks directly into their data pipelines. The question for data leaders isn't whether to make these changes, but how quickly they can implement them. As AI becomes central to customer experience, the competitive advantage will go to companies with AI-ready data infrastructure. What challenges are you facing in preparing your data pipelines for AI? Share your experiences in the comments 👇 #DataEngineering #ArtificialIntelligence #DataInfrastructure #Innovation #Tech #RudderStack

“𝘝𝘪𝘤𝘵𝘰𝘳𝘺 𝘴𝘮𝘪𝘭𝘦𝘴 𝘶𝘱𝘰𝘯 𝘵𝘩𝘰𝘴𝘦 𝘸𝘩𝘰 𝘢𝘯𝘵𝘪𝘤𝘪𝘱𝘢𝘵𝘦 𝘵𝘩𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘩𝘢𝘳𝘢𝘤𝘵𝘦𝘳 𝘰𝘧 𝘸𝘢𝘳, 𝘯𝘰𝘵 𝘶𝘱𝘰𝘯 𝘵𝘩𝘰𝘴𝘦 𝘸𝘩𝘰 𝘸𝘢𝘪𝘵 𝘵𝘰 𝘢𝘥𝘢𝘱𝘵 𝘵𝘩𝘦𝘮𝘴𝘦𝘭𝘷𝘦𝘴 𝘢𝘧𝘵𝘦𝘳 𝘵𝘩𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘰𝘤𝘤𝘶𝘳.” – 𝘑𝘰𝘩𝘯 𝘉𝘰𝘺𝘥 Boyd’s OODA loop (𝗢𝗯𝘀𝗲𝗿𝘃𝗲 → 𝗢𝗿𝗶𝗲𝗻𝘁 → 𝗗𝗲𝗰𝗶𝗱𝗲 → 𝗔𝗰𝘁) revolutionized decision-making in fast-moving environments like aviation and combat. The same principles apply to AI-driven decision loops—except now, AI agents accelerate the cycle, allowing us to adapt in real-time rather than reacting after the fact. I like to visualize this concept with an infinity loop ♾️. Why? Because decision-making shouldn’t be linear or one-and-done—it should be a continuous cycle of data → insight → action → feedback, constantly learning and evolving. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗶𝘁𝗵 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 Too often, we rely on static monthly or quarterly reports. We analyze trends after the fact, manually interpret the data, and then—maybe—take action. By the time we adjust, the situation has often already changed. 𝗧𝗵𝗲 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗜𝗻𝗳𝗶𝗻𝗶𝘁𝘆 𝗟𝗼𝗼𝗽 With AI, this loop becomes continuous and dynamic: 🔢 Data: Signals are ingested in real time—no more waiting for static reports. 💡 Insight: The system identifies anomalies and emerging cost drivers as they happen. 💨 Action: AI suggests proactive steps before issues escalate—or opportunities vanish. 📣 Feedback: Every action generates new data, refining future recommendations. Instead of a report saying, “𝘊𝘰𝘴𝘵𝘴 𝘸𝘦𝘯𝘵 𝘶𝘱 𝘭𝘢𝘴𝘵 𝘲𝘶𝘢𝘳𝘵𝘦𝘳,” AI delivers real-time intelligence: “𝘛𝘩𝘪𝘴 𝘤𝘰𝘴𝘵 𝘥𝘳𝘪𝘷𝘦𝘳 𝘪𝘴 𝘦𝘮𝘦𝘳𝘨𝘪𝘯𝘨 𝘳𝘪𝘨𝘩𝘵 𝘯𝘰𝘸. 𝘏𝘦𝘳𝘦’𝘴 𝘩𝘰𝘸 𝘵𝘰 𝘢𝘥𝘥𝘳𝘦𝘴𝘴 𝘪𝘵.” 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗣𝗲𝗼𝗽𝗹𝗲, 𝗡𝗼𝘁 𝗥𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗧𝗵𝗲𝗺 This isn’t about automating people out of the process—it’s about amplifying what HR teams, CFOs, and operations leaders can accomplish. The infinity loop represents a system that learns alongside the humans using it, transforming reactive problem-solving into proactive, strategic decision-making. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 (𝗘𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝗶𝗻 𝗛𝗥 𝗮𝗻𝗱 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀) Operations that are data-heavy—like HR benefits—stand to gain the most from this approach. When you close the loop continuously, you turn complex, thorny challenges into real-time, manageable decisions. AI agents provide a whole new way of automating to finally free people to do high impact work. That, in my mind, is where AI’s real power lies. Thoughts? Would love to hear how others are thinking about AI-driven decision loops in their domains.

Learn how JetBlue uses AI for chatbots, recommendations, marketing promotions and operational digital twins using Rockset as a vector database alongside OpenAI and Databricks. JetBlue evaluated Rockset based on the following requirements: * Millisecond-latency queries: Internal teams want instant experiences so that they can respond quickly to changing conditions in the air and on the ground. That’s why chat experiences like “how long is my flight delayed by” need to generate responses in under a second. * High concurrency: The database supports high-concurrency applications leveraged by over 10,000 employees on a daily basis. * Real-time data: JetBlue operates in the most congested airspaces and delays around the world can impact operations. All operational AI & ML products should support millisecond data latency so that teams can take immediate action on the most up-to-date data. * Scalable architecture: JetBlue requires a scalable cloud architecture that separates compute from storage as there are a number of applications that need to access the same features and datasets. With a cloud architecture, each application has its own isolated compute cluster to eliminate resource contention across applications and save on storage costs. “Iteration and speed of new ML products was the most important to us,” says Sai Ravuru, Senior Manager of Data Science and Analytics at JetBlue. “We saw the immense power of real-time analytics and AI to transform JetBlue’s real-time decision augmentation & automation since stitching together 3-4 database solutions would have slowed down application development. With Rockset, we found a database that could keep up with the fast pace of innovation at JetBlue.” Link to detailed case study in comments #openai #ai #ml #chatbotdevelopment #chatbot #databricks