Artificial intelligence (AI) has made rapid advances in the past decade. On a global scale, the AI market is predicted to have a compound growth rate of 38.1% a year. By 2030, the market is expected to reach 1,811.8 billion USD. These predictions have sent several proprietors leaping into the industry, and existing companies are using the new advancements to aid their tech processes. From software architecture to API integrations, AI growth has a hand in the technology industry’s expansion. Its impact on custom software development is among the most transformative–and there’s still much progress to be made.
Enhanced Custom Software Development via AI
AI can be trace back several decades. From the advent of the Roomba to IBM Watson’s introduction into the world, it has influenced modern technology in several ways. However, most people will interact with AI through their personal devices– Siri, Alexa, Google, and other virtual assistants are primary examples. AI’s relationship with custom software development goes further than that. There are two primary purposes it can serve: automation of tasks and machine-enabled decision making.
Task Automation
AI has helped software developers automate processes that previously relied on manual completion. For example, writing code is a labor intensive, time consuming task that custom software development companies would rely on developers to take care of. With the implementation of AI, developers have a guaranteed level of automated code quality via code optimization and code review AIs. AIs also have the capability to make strategic decisions based on data alone, so there is no chance of personal bias or human error.
AIs have been develope to automate testing services for programs, websites, and applications to catch and identify bugs for developers to quickly fix. This is especially useful for software architecture implementation. During the testing phase of UI/UX design, AI can quickly catch potential errors and save the development team time and money prior to product launch.
Machine-Enabled Decisions
Machine learning is a type of AI wherein the machine makes decisions and implements them on behalf of the team. For any additional upgrades or new versions for software architecture improvements, AI can assist with ensuring a smooth transition and reduced failure rates. Machine learning actually enables the AI to automate DevOps, to an extent.
The learning algorithms can examine a product’s deployment process and collect data on how certain users behave while interacting with the product. The machine can then use this learned behavior to respond to a variety of user actions by producing variable content and accommodating user preferences (think buttons, font size, and other page specific elements).
This type of AI creates a dynamic product experience that uses current user data to improve the software and record preferences for developers. Having this type of ongoing AI development and user feedback reduces friction areas and increases satisfaction (or success) rates.
Custom Software Development With AIs & APIs
API integrations have grown in popularity as the amount of data in the world continues to boom. Boosting API performance is just another example of how AI has pioneered its way into almost every corner of the custom software development world.
We’ve already discussed AI’s usefulness for task automation, and this is especially true when it comes to the development of APIs. Throughout every step of an APIs lifecycle, AI can use machine learning to automate processes and reduce the amount of human labor needed for production. The bigger picture for AI & API integration includes a higher support for API scalability, more stable APIs, and a larger data management system with fewer errors or hiccups. APIs supported with AI leads to a more positive user experience and less work for the development and management teams.
Both API integrations and AI implementation share a common goal: streamlining processes. APIs do most of the legwork for users, developers, and consumers alike. Their usefulness and accessibility does make them more vulnerable, however. AIs can alleviate this pain point of APIs by offering automated security measures: authentication measures, static security checks, and throttling capabilities being the most prominent.
Where Will AI Lead Software Development?
Every year, the role of AI in custom software development expands. AI consistently proves its usefulness and value in the industry, and the role of “developer” is quickly evolving to keep up with an uptick in AI usage. Currently, AI can be thought of as a developer’s apprentice. Developers teach AI how to complete specific tasks and use their extra time to focus on other tasks or projects. As the tech industry works to cultivate more intuitive and complex AI systems, machine learning may eventually reach the level of writing code by itself.
The potential is exciting! Some might think that AI could be decreasing the need for software developers, but it’s quite the contrary. More than ever, there’s a high need for software developers who can work collaboratively with AI systems–and write their code.
