As businesses increasingly adopt digital transformation and migrate workloads to cloud platforms, their systems naturally become hybrid in nature. While hybrid cloud architecture offers flexibility and scalability, it also presents challenges in maintaining highly resilient systems. System outages can have a significant impact on business operations and revenue. Studies have shown that the frequency of major outages has either remained constant or slightly increased in recent years, and the financial impact of these outages has risen significantly.

There are several factors contributing to the increased impact of outages. The accelerated rate of changes in systems, which is necessary for business agility, often leads to outages. In addition, the lack of skills in managing hybrid cloud systems and the challenges with network and infrastructure components can further exacerbate the impact of outages.

Mitigating Outages with Generative AI

In order to address these challenges and improve the resiliency of hybrid cloud systems, organizations can leverage generative AI along with traditional AI and automation techniques. Generative AI can help in various ways:

Release Management

Generative AI can assist in tracking system changes, summarizing them, and connecting them to specific work items or user stories. This can help in identifying the impact of changes and facilitating rollbacks if necessary.

Toil Elimination

Generative AI, combined with automation, can streamline the process of deploying fixes and reduce the time required for phase gate decision-making, such as reviews and approvals.

Virtual Agent Assist

Virtual agent assist powered by generative AI can provide IT personnel with answers to common incidents and assist in issue resolution by summarizing knowledge management systems. This can significantly speed up problem resolution.

AIOps

Generative AI infused AIOps can create executable runbooks for faster issue resolution based on historical incidents and current system health. This can help predict and proactively address potential issues, improving mean time to resolution (MTTR).

Challenges and Next Steps

Implementing generative AI for improving system resiliency also comes with challenges. These include selecting the most appropriate Large Language Model (LLM), ensuring data lineage transparency, and providing training to IT professionals on prompt engineering and generative AI concepts.

However, generative AI, when combined with traditional AI and automation, has the potential to significantly enhance productivity in IT operations and make hybrid cloud systems more resilient. By mitigating the impact of outages, businesses can ensure uninterrupted operations and maintain customer satisfaction.