By leveraging AI for real-time event processing, businesses can connect the dots between disparate events to detect and respond to new trends, threats and opportunities. In 2023, the IBM® Institute for Business Value (IBV) surveyed 2,500 global executives and found that best-in-class companies are reaping a 13% ROI from their AI projects—more than twice the average ROI of 5.9%.
As all businesses strive to adopt a best-in-class approach for AI tools, let’s discuss best practices for how your company can leverage AI to enhance your real-time event processing use cases. Check out the webcast, “Leveraging AI for Real-Time Event Processing (link resides outside ibm.com),” by Stephane Mery, IBM Distinguished Engineer and CTO of Event Integration, to learn more about these concepts.
An event-driven architecture is essential for accelerating the speed of business. With it, organizations can help business and IT teams acquire the ability to access, interpret and act on real-time information about unique situations arising across the entire organization. Complex event processing (CEP) enables teams to transform their raw business events into relevant and actionable insights, to gain a persistent, up-to-date view of their critical data and to quickly move data to where it is needed, in the structure it’s needed in.
Artificial intelligence is also key for businesses, helping provide capabilities for both streamlining business processes and improving strategic decisions. In fact, in a survey of 6,700 C-level executives, the IBV found that more than 85% of advanced adopters were able to reduce their operating costs with AI. Non-symbolic AI can be useful for transforming unstructured data into organized, meaningful information. This helps to simplify data analysis and enable informed decision-making. Furthermore, AI algorithms’ capacity for recognizing patterns—by learning from your company’s unique historical data—can empower businesses to predict new trends and spot anomalies sooner and with low latency. Furthermore, symbolic AI can be designed to reason and infer about facts and structured data, making it useful for navigating through complex business scenarios. Additionally, developments in both closed and open source large language models (LLM) are enhancing AI’s ability for understanding plain, natural language. We’ve seen examples of this in the latest evolution of chatbots.This canhelp businesses optimize their customer experiences, allowing them to quickly extract insights from interactions in their customers’ journey.
By bridging artificial intelligence and real-time event processing, companies could enhance their efforts on both fronts and help ensure their investments are making an impact on business goals. Real-time event processing can help fuel faster, more precise AI; and AI can help make your company’s event processing efforts more intelligent and responsive to your customers.
By combining event processing and AI, businesses are helping to drive a new era of highly precise, data-driven decision making. Here are some ways that event processing could play a pivotal role in fueling AI capabilities.
By bridging the gap between event processing and AI, companies can help provide real-time data for training AI models, take advantage of data processing in-motion to compute live aggregates that help improve predictions, and help ensure that AI can be applied effectively within an up-to-date business context.
Artificial intelligence can make event stream processing more intelligent and responsive in dynamic and complex data landscapes. Here are some ways that AI could enhance your event-driven initiatives:
Connect with the IBM experts and request a custom demo of IBM Event Automation to see how it can help you and your team in putting business events to work, powering real-time data analytics and activating intelligent automation.
IBM Event Automation is a fully composable solution, built on open technologies, with capabilities for:
Learn more about how you can build or enhance your own complete, composable enterprise-wide event-driven architecture.
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