Advanced Computing in the Age of AI | Thursday, September 28, 2023

AI Will Eliminate Roles – And We Want It To 

By implementing AI carefully and with purpose, enterprises have the opportunity to automate mundane tasks that typically require human cognitive skills, allowing their employees to focus on more strategic business initiatives. Learning to leverage AI will be a necessity for future businesses to remain competitive.

There is little question that artificial intelligence will have a major impact on the job market. AI technologies such as OpenAI’s ChatGPT are already making some jobs obsolete while at the same time creating new ones.

This is not unusual. In fact, it is a continuation of the same trend that began when the first farmer hitched a plow to a pair of oxen and replaced the labor of 100 field hands. More recent innovations like robotics and process automation have produced all manner of new jobs – and sometimes entirely new industries – more than making up for the jobs they eliminated.

AI takes automation to a new level given its ability to perform cognitive functions on par with humans across all segments of the enterprise, including the office. This is causing some concern that AI automation will eliminate more jobs than created, and possibly redefine the nature of work in society. In essence, when most manual functions are done by machines, what should be the human contribution to productivity and prosperity, and how should they be rewarded? Time will tell if the AI technology transition will be on par with historical transitions or radically redefine society’s character.

Opportunity for Growth

Job elimination is always a difficult process, especially when entire classes of jobs are fading away. But it is also true that in every transition new opportunities are created and, in this case, the rise of AI automation represents an opportunity for growth, evolution and the creation of new jobs in areas such as computer science, networking, data centers, sensors and robotics.

AI is highly adept at doing two main things: ingesting and interpreting vast amounts of data at extremely high speeds and learning from its past to adapt to new situations. This allows AI to perform the many functions required to manage and optimize complex, data-intensive environments. And with networking complexity growing and becoming a national economic necessity on par with electricity and water, AI is becoming a necessity.

The key challenge that most organizations face when it comes to networking is that it is very expensive in both capex and opex. Modern network architectures rely on many layers, formats, protocols and interchanges in order to serve the needs of their users, whether these are customers, partners or employees. Traditionally, this has required a lot of hands-on, manual labor. Functions like monitoring, load balancing and resource management often require teams of highly paid professionals working 24/7, and that’s just when things are working properly.

AI promises to vastly simplify this environment and lower costs dramatically, in large part by transforming problem-solving methodologies and taking over the many mundane tasks that currently occupy most, if not all, of the network engineer’s attention.

A few key examples:

Load Balancing

Modern IT environments are highly chaotic with workloads rising and falling at a rapid pace. This often leads to over- and under-provisioning of network resources, both of which harm productivity and the bottom line. AI has the capacity to monitor this activity in real time and then make the proper adjustments to ensure the relationship between resource consumption and productivity remains optimized.


Threats to modern data infrastructure are on the rise, in large part from AI-driven sources. Using AI to combat these attacks provides the most effective response while still maintaining data and system availability. Not only is AI more adept at handling the volume of monitoring and analysis required in today’s environment, it can also provide deeper insight into data patterns and operational conditions to identify the threats that would otherwise have gone unnoticed.

Design and Development

As AI becomes more knowledgeable about the network environment, it can start recommending ways to optimize and expand both physical and virtual ecosystems. In part, this takes advantage of AI’s ability to project future needs using available data – not just technology developments and emerging architectural profiles, but things like market trends and consumer data so that AI can create networks that are more in-line with business needs.


As ChatGPT and other forms of Generative AI are essentially “virtual assistants,” the potential for AI to be used in a support role in the enterprise becomes clear. And in general virtual assistant and conversational interfaces become the user interface of the future. For example, AI can identify and even predict network problems that previously would require a network engineer to drive to a location to troubleshoot, costing hours and dollars. AI can lead to a ‘self-driving network’ that takes prescriptive actions, which then frees up the engineer’s time to do higher-value work such as planning and executing projects that would have previously taken a back seat to the ‘whack-a-mole’ nature of constant trouble tickets.

All of this will change the nature of the human manager’s contribution to the enterprise, and yes, some jobs will be eliminated. But as we’ve seen in the past, this creates new opportunities for the workforce to operate at a higher level of efficiency and productivity. This will only enhance their value to the enterprise, leading to higher-paying jobs that are less-stressful and more creatively satisfying.

For networking professionals, this is a tremendous opportunity. No longer will they be looked at as cost centers that must be minimized, but as vital contributors to the core, revenue-generating, aspects of the business mode. But only those who embrace AI will successfully make this transition. Those who do not will likely be swept aside – not by AI but by someone using AI to make themselves more productive.

About the Authors

Bob Friday is group vice president and chief AI officer at Juniper Networks' AI-Driven Enterprise business unit that develops self-learning wireless networks using artificial intelligence. He was a co-founder, vice president, and chief technology officer at Mist, a part of Juniper Networks.

Jeff Aaron serves as VP of Enterprise Marketing at Juniper Networks, where he is responsible for global promotion of the company's groundbreaking AI-Driven Enterprise portfolio. Prior to Juniper, Jeff served as Head of Marketing at Mist Systems (acquired by Juniper). Jeff has over 24 years of experience marketing in the high-tech space, having worked at various software, networking and telecommunications companies including Silver Peak Systems, Airespace (acquired by Cisco), Turnstone Systems, and Newbridge Networks (acquired by Alcatel).