[By Prashanth Nanjundappa, VP, Product Management, Progress]
In the last decade, the codification and spread of DevOps best practices have transformed the day-to-day workings of businesses worldwide. Where development and operations were once needlessly siloed, they are now more closely aligned for the most part. Consequently, the delays, inefficiencies and communication failures that were once par for the course in software development have been drastically reduced. Security is now being integrated into the process from the onset as well.
But it would be inaccurate to say that all the problems have been solved completely. Development and operations teams remain over-reliant on one another, to the detriment of innovation. After all, a reduced delay is still a delay.
Enter platform engineering. This emerging discipline and a complementary org structure is dedicated to building, constructing and maintaining the platform tools needed to speed up digital solutions. It centers on building scalable and reusable self-service platforms that software developers can use to streamline the development cycle—also known as internal developer platforms (IDP). The role represents a synthesis of tasks that are becoming more crucial for modern software development: platform engineers are now fluent in system administration, cloud technologies and automation.
The importance of IDPs lies in their ability to abstract infrastructural complexities. Instead of waiting for operations teams to spin up a new environment, developers can get straight to work. The result is that more and more organizations are splitting into product and platform teams. The platform teams implement DevOps practices and the product teams leverage them to focus on enhancing the customer experience through new features.
According to a recent IDC report, 60% of organizations attempting to scale DevOps will adopt IDPs by 2025. This underlines the critical role platform engineering has assumed in the modern software development process, with ClickOps fast supplanting the old code-first approach. Across the board, businesses that adopt platform engineering are seeing increased efficiency and reduced employee burnout.
The Rise of Intuitive “Click-First” Tools
Efficient development practices have relied on coding since the dawn of computing. But as DevOps tools grow more user-friendly, we’re seeing that start to change. “Click-first” tools are more commonplace, with low-code/no-code platforms drastically reducing a user’s barrier entry. In fact, according to Gartner, 70% of new business applications will likely be deploying low-code/no-code technologies by 2025.
This paradigm shift can largely be attributed to two things. One, ClickOps dramatically speeds up the software development cycle through increased automation—sometimes by up to 90%. Two, ClickOps appeals to businesses whose workforces may be unequipped for more intensive coding-centric approaches.
Generative AI plays a major role here. It can automate tasks, optimize workflows and even analyze data patterns to suggest potential improvements. Consequently, businesses can innovate on a tighter timeline. According to a recent McKinsey report, generative AI (GenAI) allows software developers to work twice as fast on things like coding documentation, code refactoring and code generation.
The Human Element Still Matters
There’s no question that the vast quantity of data generated by the average business far outstrips the ability of even the most robust operations team to manage it. There is simply too much information for operations to get any kind of meaningful handle on it, let alone make the kinds of analyses and predictions that can increase efficiency.
But it’s important to note that—in this and other contexts—the role of AI isn’t to replace human employees. Rather, it’s to spare those employees the drudgery of repetitive, unstimulating tasks. In deploying AI, they can apply themselves to more complex, creative tasks. Navigating tricky coding issues and examining existing code for bugs will always require some degree of human oversight. And AI also plays a useful role in validating this. Businesses increasingly use AI tools to detect discrepancies with higher precision and promptly take preventative actions.
What we’re talking about, then, is a kind of fusion: human ingenuity and the bleeding edge in GenAI working in tandem to maximize productivity. It’s an exciting convergence, one with significant implications not just for DevOps and platform engineering, but for the technological landscape.
