In many organizations, the workforce is shifting away from specialists toward larger generalist populations, which means fewer full-time employees dedicated solely to IT infrastructure. This is exacerbated by the fact that the workload will be increasing or at least remain unchanged for the majority. The expectation for IT professionals is that they will have to do more with less. One means of accomplishing this will be to use products that capitalize on artificial intelligence (AI) and machine learning (ML), so IT staff can predict problems sooner, and in some cases before they occur. Such tools also provide a way to better manage infrastructure through greater automation, allowing encumbered infrastructure professionals to be more hands-off.
The 451 Take
In our Voice of the Enterprise Digital Pulse, Organizational Dynamics 2018 survey
, 14% of organizations said they are shifting from IT specialists to generalists. Highly skilled specialists are becoming increasingly difficult to find, and they are often too expensive for organizations to justify given that most IT budgets are increasing at a modest rate. Additionally, infrastructure must keep pace with the changes happening to applications and workloads – for example, new types of workloads that are often distributed in nature, such as containers. This means organizations not only have to adjust the specialist/generalist ratio of infrastructure professionals but also ensure that the skill sets across the workforce are aligned with the changes – cloud-centric skills, in particular, tend to be a weak point for organizations. The way to do more with less is to have better tools, which should also help counteract the skills gap some organizations are facing. Reducing infrastructure management complexity can go a long way toward making generalists more valuable and effective, and tools that integrate AI and ML make tasks such as provisioning and problem remediation easier – a step closer to the goal of modernized, agile and cost-effective infrastructure.
More Work for Infrastructure Professionals
Our Voice of the Enterprise: Storage, Organizational Dynamics 2018 survey
shows that for 64% of IT infrastructure teams, the amount of work has increased over the past 12 months. (See Figure 1). More than one-quarter (29%) said there was no change in the amount of work, and only 7% experienced a decrease. This is coupled with the fact that enterprises must grapple with continuing data growth, which challenges existing infrastructure deployments and models.
Figure 1: Workloads for IT infrastructure teams over the last 12 months
In the same VotE survey, 12% of organizations said they expect the number of full-time employees (FTEs) dedicated to IT infrastructure to decrease in the next 12 months, and 57% said they expect the number of FTEs to stay the same (See Figure 2). This is in-line with the way that many organizations are pushing for fewer IT specialists in favor of more generalists. The prognosis is not terribly positive for infrastructure teams when it comes to their workloads; however, there is hope in the form of superior tools.
Figure 2: The number of IT infrastructure FTEs over the next 12 months
AI and Machine Learning Lend a Hand
With existing infrastructure in need of modernization, there are technologies that offer the potential to fuel the transformation and increase the efficiency of current systems, as well as the personnel that manage and maintain them. AI and ML are two of the most prominent examples of this. In our VotE: Storage, Budgets and Outlook 2017 survey, 72% of respondents said they expect AI and ML capabilities to simplify IT management (See Figure 3).
Figure 3: Most respondents expect use of ML and AI to simplify IT management
When faced with the reality that they cannot hire more or even sustain the current number of specialists, organizations must turn to improved tools to compensate. With proactive management and problem prediction, AI can help ease the burden on staff and aid in self-optimization. Storage vendors are already leveraging AI to help customers find problems before they become problems through predictive analytics. The most widespread use of AI in storage is in support services, with products such as NetApp's ActiveIQ and HPE's InfoSight (via its Nimble Storage acquisition from last year). These AI-based support services benefit users because they provide a more holistic view of the customer landscape, then help inform users what they should ideally be deploying and why. We will look at some of these AI support offerings in the storage vendor landscape in an upcoming report.