How AI Will Transform the Future of Work in IT

Taking central stage as a conversation topic for businessmen, techies, and politicians alike, the role of artificial intelligence has become a controversial topic with many theories on how it will impact work. To some, AI appears to be a holy grail, unlocking productivity and letting people focus on putting their creativity to a more just use, while others use it to paint a dystopian picture of the world. As we remain stuck in the middle of the converging trends of AI taking hold in IT and DevOps taking hold in IT processes, we thought it was apt to weigh in on the fundamental shift that we believe IT jobs are headed towards.



The Barriers to Entry

In today’s age, coding is hailed as the equivalent of literacy and is becoming an ever increasing priority for students and professionals alike. Though the message that the jobs of the future will require everyone to code is pervasive, we think that view is bit extreme and can’t be generalized across all sectors. However, IT jobs had a different anthology, where technical expertise and coding skills were the difference between the haves and have nots. Since the Dotcom bubble, developers have been idolized as the most valuable resources in an organization and are treated as such, leaving teams that support developers like manual testers, business analysts, and technical writers to become overlooked. However, people in these roles have started to become discontent, often learning to code half way through a career to keep up.



Explosive Complexity in IT Systems

While today’s workforce is becoming ever more technical to keep up with the demands of the rapidly changing IT landscape, the reality is that application landscape have grown in size and complexity at an accelerating rate, with teams having to manage more moving parts than ever before, many of which they don’t control. To solve these problems, companies are going to need to build or adopt products that abstract many of the tasks we deem value-add today, turning to new technologies like AI to simply and scale the jobs development teams do today and avoid getting bogged down by these systems. In fact, we already see the seeds of this change take place in the adoption of cloud technologies and DevOps initiatives. And just like we don’t see modern IT jobs centering around kernel development, the majority of IT jobs future won’t focus on writing code but rather managing large software factories. IT has no choice but to move away from hand crafted, unmaintainable systems into industrial software development teams, and to get there, intelligent systems akin to software factories will lead the way and change the type of work teams do.



Redefining Jobs & Converging Roles

To enable the software factories of the future, jobs will need to morph to keep up; however, they won’t be purely coding based. Rather, they will focus on managing large systems and understanding how the system works, working with an intelligent system to focus on outcomes and not tasks. Though jobs will require a rudimentary idea of how computers work and require some technical expertise, the leverage great process and scalable technology creates will be extraordinarily impactful, enabling teams to pair great developers with lean but savvy operations teams, dramatically simplifying the development process and allowing it to scale. The technology fabric that weaves together these business and operations jobs will be powered by artificial intelligence and allow teams to redefine value-added activity from building software well to creating well designed and smart systems that scales over many platforms. Many jobs we see today will converge to a few roles that are not based on tasks but knowledge.


Let’s look at a concrete example of how autonomous testing is impacting the role of testing.

  1. Low code: We built a system that only requires the user to have a basic understanding of how requirements need to translate into user stories, eliminating the need to write hundreds of lines of code a day to test and instead interacting with the intelligent system, telling the machine what to do and letting it do the hard work.


  1. Smart Systems: Because the system is intelligent and people aren’t writing code, they aren’t having to spend all the time reworking the system and taking a large portion of time geared toward maintenance today towards looking for other places to test. Moreover, they act in tandem with the rest of IT’s processes, having the context from triggering new builds to know what to maintain.


  1. More Participation: As AI bridges the technical gap required in many jobs, more people will be able to add tremendous value to development teams without needing to have a computer science PhD. In the case of autonomous testing, developers, BAs, and product managers alike can be more productive than the best automation engineers on the market today, allowing those great at understanding quality to fully test software, the great automation engineers to focus on developing more critical applications, and teams to let intelligent systems eliminate a critical bottleneck while focusing on the important details without getting lost in the noise.


As AI powered intelligent systems begin to take hold across the development lifecycle, enterprises will have to evolve to keep up and focus on attracting the right talent to lead the way and transform the skills of their workforce to scale up intelligent systems, focusing on outcomes and not activity.