AI Automation Won’t Replace Jobs – It Will Replace Processes
When it comes to new AI automation, the Software 2.0 movement can be seen as akin to the auto industry’s recent evolution. Beginning in the 1990s and rapidly progressing into the 2000s, the auto industry saw a huge rise in AI automation such as digital vehicle diagnostics.
Throughout this, people worried that digital transformation might take jobs away, when instead it led to “greater profits, productivity, and competitiveness,” according to a 2008 study by the journal of Technological Forecasting and Social Change. In short, these innovations didn’t replace jobs, per se; they simply changed the processes of jobs from the manufacturer’s assembly line down to the mechanic’s garage.
An auto diagnostic specialist used to perform tedious tasks such as counting flashes and converting them to error codes on printed-out tables. Computerized diagnostic tools made this process faster, more reliable and, you might imagine, far less tedious. According to diagnostic specialists Helmut Frank and Uwe Schmidts, digital transformation went from being “a necessary evil to being a key to new, interesting and innovative functions.”
You might liken the above example to the innovations that AI automation and machine learning (ML) have offered to software developers, who until recently, were tasked with a much more hands-on approach to deploying and maintaining applications. In many ways, DevOps practices have heroically come in to break them free of this minutiae, automating many of the repetitive tasks involved in application management. By design, it’s increasing efficiency and security while decreasing tedium and the chance for human error.
Speaking to tedium, Mike Loukides and Ben Lorica, in their article “The road to Software 2.0” noted, “Up until now, we’ve built systems by carefully and painstakingly telling systems exactly what to do, instruction by instruction. The process is slow, tedious, and error-prone; most of us have spent days staring at a program that should work, but doesn’t.”
In short: Much like digital transformation brought efficiency to the life cycle of automobiles, AI automation is offering efficiency to every stage of the software development cycle.
Don’t Change the Players, Change the Game
Indeed, a new generation of AI automation and ML tools is now emerging, shaping what some call a Software 2.0 movement. But before you begin to fear that these tools will lead to a “they took our jobs!” revolt, consider that they may be celebrated instead as a means of not only augmenting the work of software developers but also helping offset the ongoing developer drought. According to a 2019 Stack Overflow survey, over a third of developers named “not enough people for the workload” as their primary challenge to productivity. Rather than replacing jobs, automation should be viewed as offering efficiency in an industry that desperately needs it.
As an example, the process of writing code, though more streamlined today than it was 25 years ago, has remained a mostly manual process. This has presented an excellent opportunity for the automated processes we are beginning to see, which bring in AI to automate aspects of code development such as program synthesis, smart code completion and static code analysis.
Assaf Araki spoke to this in the article, “Software 2.0 takes shape.” Araki described how automation is filling gaps in many other areas, such as regression testing, code analysis, AI for vulnerability detection and observability tools to monitor software health.
From Monolith to Microservices
Just like the recent, mysterious disappearing monolith in the Utah desert, the relevancy of monolithic development may be on its way out.
In the past, software cycles were longer. Teams updated monolithic-style deployments only monthly or yearly. Nowadays, companies are under pressure to push much more rapidly, often turning to microservices and progressive release strategies. This new paradigm requires much automation to increase release efficiencies and enable a seamless CI/CD.
As Araki pointed out, “Automation in software is best viewed through the lens of Continuous Integration (CI) and Continuous Deployment (CD).”
How to Know When AI Automation is Worth Its Salt
It’s smart to consider the investment of both money and time when deciding whether an AI solution is worth adopting. “AI systems need to add value that exceed any new costs and requirements (such as the need for labeled data, hiring new staff, etc.),” Araki said.
You should also be asking if the solutions are wide-ranging enough to meet the needs of your developers and anyone else on the team who may be employing them. Key considerations for AI automation, Araki noted, are focusing on tasks and systems that are frequently used, adjusted or tuned.
4 Examples of Versatile AI Automation and ML:
Data management systems from simple systems that employ ML to more complex systems that enable self-driving data management.
Dashboards that are automatically created, ideally with intuitive and visually appealing UX, that use AI automation and ML to bring up the right elements—more progress is expected in these areas in the coming years.
Websites and mobile apps that use AI automation to become highly personalized interfaces, such as those that employ real-time data for online and reinforcement learning—these are also expected to progress in the coming years.
UX and design tools that use simple automation to design and revise digital media, with ML being a promising way to kick the automation up a notch, such as deep learning for game developers.
AI Automation and ML to Grant Opportunities
In short, Software 2.0 doesn’t currently appear poised to threaten jobs or decrease developer need. Instead, it seems to be changing the processes engineers are using and creating efficiencies in their work, at a time when their expertise is in increasing demand. Demand for novel roles, like the Cloud Economist, demonstrates the emergence of new opportunities for humans with cloud knowledge who work alongside AI.
The new evolution of AI and ML also could create a unique subset of developers with skills keeping par with these new innovations—much like digital transformation in the auto industry led to evolving specialties in computerized vehicle systems.
Not to mention that just as low-code has paved the way for citizen developers and AutoML led to citizen data scientists to fill large gaps in demand, AI and ML solutions have the potential to further broaden and democratize software and code development.