AI won’t save you from a move to cloud computing

Posted on 12-10-2021 , by: admin , in , 0 Comments

A new survey on data center staffing sponsored by the Uptime Institute indicates the skills shortages continue, and survey participants do not expect artificial intelligence (AI) to reduce skills requirements anytime soon. About 50% of the enterprise data center managers and operators surveyed claim to have difficulty finding skilled candidates, which is up from 38% in 2018. 

There could be a light at the end of the tunnel: Three out of four respondents believe AI-based technology will reduce their data center staffing needs at some point. However, they feel this shift is more than five years away. 

Let’s look at what’s happening right now. The pandemic brought the reliability of data centers into sharper focus. Lockdowns, quarantines, and sometimes just employees’ reluctance to enter buildings during the first few months of COVID-19 meant that many data center employees could not get system access to perform even the most basic operations.

Most enterprises accelerated their planned moves to the public cloud, which is largely pandemic-proof. However, while you can certainly speed things up, it will take years for most enterprises to cross the halfway point of having their workloads on public clouds. 

We have fewer skilled employees in the data center, and we can’t get to the cloud much faster. What are our options moving forward?

Some factions promote AI as the savior. New tools that focus on more effective data center operations can lead the way to an emerging data center model of fewer employees and greater reliability. These tools could solve issues before moving to the cloud or provide cheaper options than public clouds in some cases. 

Reality has a habit of undoing hopeful plans. As the report states, most data center operators don’t see AI tools taking much of the operational load off of people for at least five years. I would say it’s more like seven to nine years, based on historical technology adoption patterns of the last 30 years.

What’s the big issue with the five-to-nine-year model? Most of the R&D dollars are flowing to cloud computing right now. If you stick with traditional systems until AI eases the data center load, you could find that the updates from your hardware and software providers are few and far between, compared to even a few years ago. Unfortunately, a forced march to the cloud because of market drivers rather than actual business needs has its own shortcomings. Although your technology will follow the market evolutions and subsequent support, the move may not be optimal for all existing data center–based technology and may turn out to be more hinderance than help.

I wish I could paint a better picture. 

Some proactive enterprises are ahead of the automation game with their existing data centers. Typically, these technology pioneers saw the value of cloud early on and moved to the cloud faster than their peers. 

Most enterprises will need to implement cloud changes one step at a time. That means their data centers must deal with skills shortages as well as the lack of promised and prophesied AI-driven data center automation. For the next few years, many data center staff will have the toughest jobs in the company. You’ll eventually succeed, but the evolution to cloud will be much rougher than we all initially thought. 

Good luck.