About fifteen years ago, DevOps radically overhauled the world of software engineering. Previously, the development process had been defined by sometimes maddening delays, as development teams waited for operations teams to deploy and run new applications or add a new server. By effectively combining these teams—evenly spreading the responsibility for development and underlying infrastructure—the DevOps paradigm brought a new degree of flexibility to the process. Many of the incredible technological leaps we’ve seen in the last decade-plus can be attributed to the accelerated development cycles that DevOps facilitates.
But this paradigm brought problems of its own. For one thing, developers had to take on new skill sets, leading to cognitive overload. For another, DevOps practices could lead to a troubling lack of standardization within a given company: three teams might be using entirely different platforms to deliver and run their applications. And then there’s the fact that a decade and a half since DevOps became the industry standard, the complexity of the infrastructure underlying new applications has become far more complex, dependent on countless microservices, each of which DevOps teams must keep in mind as they attempt to iterate new products and generate value for their company.
In recent years, these challenges have led to a wide-scale rethinking of software engineering best practices—a process further accelerated by advancements in machine learning and AI.
The rise of platform engineering is one result of this reevaluation. Effectively, platform engineering standardizes the underlying infrastructure—the cloud platforms, databases and security measures developers need to iterate new digital solutions speedily. Internal developer platforms (IDP) are at the center of this process: scalable, reusable self-service platforms that software developers can use to streamline the development cycle. These IDPs abstract formerly intractable infrastructural complexities, allowing developers to get down to work quickly.
These IDPs are spreading rapidly: according to a recent Port report, 85% of those surveyed indicated they have either already begun implementing IDPs or will do so by 2025. This development dovetails neatly with the rise of ClickOps, rapidly replacing the old code-first approach. In tandem, these automation-heavy tools are helping businesses get more done while reducing levels of employee burnout.
The Rise of ‘Click-First’ Tools
Typically, a contractor hired to build a house doesn’t make the wood themselves; they work with prefabricated materials. The same logic applies to ClickOps. Instead of reinventing the wheel every time, emergent low-code/no-code tools allow software developers to focus more energy on things that add value. They also reduce the barrier of entry for people who have engineering backgrounds. It’s no surprise, then, that—according to Fortune Business Insights— between 2021 and 2028, the global low-code development platform market will grow from 14 billion dollars to 95 million dollars.
The benefits here are twofold. First, the automation permitted by ClickOps dramatically shortens development windows: their user-friendly interfaces can reduce development time by up to 90%. Second, simplifying some coding processes allows employees unfamiliar with coding-centric approaches to contribute meaningfully to development processes.
The Role of Generative AI
The rise of platform engineering and ClickOps has been accompanied—and helped along by—rapid advancements in Generative AI (GenAI). Because GenAI can automate tasks, optimize workflows and use pattern detection to suggest potential improvements, it can rapidly speed up development—and turbocharge innovation. A recent McKinsey study shows that software developers can complete important coding tasks (like code documentation, code generation and code refactoring) up to twice as fast by deploying GenAI tools.
These tools aren’t merely “useful.” Given the unprecedented vast stores of data now generated by even small businesses daily, they’re necessary. The most robust IT team cannot analyze this data efficiently or to keep track of the complex workflows and integrations demanded by contemporary software engineering. In this emerging software paradigm—defined increasingly by ClickOps and platform engineers—AI is an indispensable tool, taming internal sprawl and creating the conditions for developers to do their best work.
Which is to say that AI is not replacing developers. Instead, the tools we’re discussing here are designed to cut down on busy work and allow developers to apply their ingenuity to more complex, creative tasks. This convergence of human and AI capabilities promises to revolutionize the landscape of DevOps and quicken the pace of overall technological advancement.
About the Author
Prashanth Nanjundappa is VP of Product Management at Progress. He has spent his entire career of over 20 years in the tech world, managing cross-functional high-performance teams, focused on building and launching enterprise and consumer products globally.
In the first 12 years of his career, Prashanth worked as a developer, technical lead and architect for mobile, video-broadcast and OTT, SaaS and PaaS products. Prior to joining Progress, he led the product management teams for high-tech B2B and enterprise products at companies like Cisco and Knowlarity. He has spent time working in Italy, France and South Korea.
Prashanth has an engineering degree in Electronics & Communication from Bangalore University and an MBA from the Indian School of Business (ISB) Hyderabad. Learn more about Progress here https://www.progress.com/
