When it comes to the different types of artificial intelligence (AI), 1+1+1 can equal more than three. Composite AI combines causal, predictive, and generative AI to supercharge AIOps, reducing mean time to resolution (MTTR) from hours or days to minutes. Composite AI provides explanations of root causes in plain language and often automatically fixes issues before they grow into giant headaches for IT.
First, a brief description of these three types of AI:
- Causal AI analyzes data to infer the root causes of events. For example, if a company’s e-commerce website is taking too long to process customer transactions, a causal AI model determines the root cause (or causes) of the delay, such as a misconfigured load balancer.
- Predictive AI can identify potential issues before they become problems, such as identifying a load balancer misconfiguration that, while it may not affect transaction processing time during normal loads, will result in long delays during a big event such as Black Friday.
- Generative AI (genAI) is based on large language models (LLMs) that enable the technology to understand text and generate explanations for IT in plain language. GenAI can even create working code.
The result of this combination is that instead of working with myriad disconnected tools that only provide insight into their own specific silos — applications, networking, endpoints, and so on — composite AI enables AIOps to provide holistic visibility across the entire environment. So, when an application goes down, instead of having to compare different reports from different tools and then parse out the root cause on your own, AIOps analyzes data across these different functions to provide IT with a succinct explanation in natural language that any IT admin can readily understand.
Plus, composite AI can do more than just point to the root cause; it can also provide actions and even the exact code that will address the issue. Even better, AIOps can learn from past events to predict problems before they occur and, in many cases, automatically fix them without any human intervention at all.
Increase uptime and reduce MTTR
The BMC Helix platform uses composite AI to provide exactly these benefits to IT and does so in a safe and secure way. BMC uses pre-trained LLMs to customize training for IT operations and service management (ServiceOps) domains. These models draw on BMC’s extensive expertise and are trained on organizational IT data and are far less susceptible to “hallucinations” in AI terms. The results are composite AI solutions that can identify, address, and explain root causes and emerging future problems accurately.
AI trained on biased data may produce unreliable results. Building on this key understanding, BMC Helix continually learns from customer data — topology, events, metrics, incidents, tickets, and more — to provide an ever more insightful and predictive view of the environment. This customer data, however, remains on customer systems.
“AIOps brings in a neutral, well-informed, well-educated expert that can learn from your data and past experiences to enable IT to fix issues faster — often before users ever notice a problem,” said Margaret Lee, senior vice president and general manager of Digital Service and Operations Management, BMC Software.
In the end, composite AI enables AIOps to provide even more insightful predictions, more accurate root cause analysis, and broader, higher resolution observability of the environment.
Ultimately, what matters most is composite AI enables IT to provide exponentially better performance and greater uptime to customers.
Address your IT service delivery challenges with the most effective composite AI solution. Visit here for more information or contact BMC.
