Operations | Monitoring | ITSM | DevOps | Cloud

Previously, I discussed the challenges of adopting AI in enterprises, focusing on middle managers’ concerns about its impact on their roles. In case you missed it, you can read it here: AI resistance isn’t where you expect it In this post, I’ll highlight the crucial steps for ensuring successful AI adoption. All business transformations are complex by nature because they change the organizational balance – that is, the equilibrium of power held among different leaders.

In recent years, container platforms – also referred to as container orchestration systems – have grown in popularity and transformed the processes of software development, testing, and deployment.

Enterprise operations monitor various metrics associated with the stability, performance, availability, and other such aspects of business, application, and IT infrastructure. These could be business KPIs such as footfall, checkout time, and sales of the flagship stores. These could be performance metrics such as the response time of business-critical applications. These could be the queue length or enqueue rate of the backbone message queues.

Artificial Intelligence (AI) has lots to offer to IT operations. AI capabilities vary from detecting anomalies to suppressing alert noise to predicting future incidents to even planning for growth and change. However, enterprises struggle in making the best use of AI. In this blog we present our views on how to go about systematic adoption of AI to accelerate and optimize AIOps.

As the world moves towards sustainability, IT operations and digital workspaces are becoming critical in achieving corporate sustainability goals. The digital transformation of businesses—especially in light of hybrid and remote working models—has increased the demand for IT infrastructure, which in turn raises concerns about energy consumption, carbon emissions, and environmental impact.

Workload management is ubiquitous when it comes to automating critical business processes. With time, workload management as a technology is going through a gradual evolution, from ‘just automation’ to an orchestrator of intelligent automation. This necessitates a layer of observability and intelligence to facilitate the move from workload automation to workload management.