DEVOPSdigest

As the Great Resignation continues and turnover rates climb, organizations across industries are struggling to keep top talent — especially in the developer, delivery, platform and security realms. Ongoing digital transformation efforts have put additional pressure on organizations to keep up with the accelerating pace of innovation; multi-cloud environments are becoming the new norm — bringing with them novel IT complexity concerns; and cybersecurity threats continue to proliferate across the enterprise.

For organizations looking to recruit and retain skilled developers and security practitioners, AI goes a long way in supporting teams and individuals. AI not only reduces the burden on strapped developer and security teams — which saves developer resources and boosts business outcomes — it also allows them to spend more time on what they like most, that has the most impact: innovating.

Multi-Cloud Proliferation = DevOps Burnout

In the past, each new cloud environment came with a static set of monitoring tools for IT teams to incorporate into pre-existing toolkits. That means with every cloud environment added, infrastructure teams had to spend an exorbitant amount of time manually pulling insights from various individual dashboards to identify activity across their given environment. As a result, monitoring infrastructure has historically been a drain on critical developer resources.

Today, 99% of organizations have a multi-cloud environment, with the average spanning five different platforms (AWS, Microsoft Azure, Google Cloud, IBM Red Hat, and more). In fact, 57% of IT leaders agree that using multiple monitoring solutions to manage multi-cloud environments makes it difficult to optimize infrastructure performance and resource consumption.

Adding Open Source to the Mix

What's more, the growing adoption of cloud-native architectures and open-source technologies have made multi-cloud environments even more complex and dynamic — creating further challenges for infrastructure monitoring. While Kubernetes enables organizations to scale infrastructure up or down to match demand, the constant change makes it difficult for teams to monitor and maintain infrastructure performance.

Many IT leaders today believe traditional infrastructure monitoring solutions are no longer fit for purpose in a world of cloud and Kubernetes — and the time has come for them to be replaced by platforms that can provide end-to-end observability across multi-cloud environments (which according to Gartner, is one of the 10 most common challenges faced by CIOs today). Today, with multiple toolkits spread across the business, IT staff must be experts in myriad different platforms to monitor and resolve issues within each one. It makes sense that today IT work strains developer teams.

Consolidation, AI and Automation as Developer Superpowers

Enter AI. Developers today can and should rely on AI-driven solutions to automate as many of their routine, manual tasks as possible. Automatic, continuous discovery and instrumentation, for example, can reduce manual effort while maintaining end-to-end observability across hybrid, multi-cloud environments. But observability alone isn't enough.

According to recent data, nearly half (42%) of IT teams waste time on manual, routine work across environments — work that can easily be automated. This productivity drain all too often generates missed revenue opportunities because of innovation delays. Access to real-time data insights that can help teams optimize their environments effectively and efficiently is essential. Organizations need an intelligent, automated approach to observability to focus on innovation and optimizing user experiences, as opposed to managing and monitoring IT incidents.

We have also seen a big push towards open standards to avoid vendor locking. AI solutions help developers in their day-to-day business through automating manual tasks, but these solutions must also follow the need for more and open standards, for example Open Telemetry.

Today, the success of digital transformation, innovation and a satisfied developer workforce depends on AI and automation. Without these three critical ingredients, IT teams will remain bogged down by manual labor, innovation will be hindered, and in the end, the ongoing Great Resignation among valued IT workers will continue.