The AI Revolution: IDC Spotlight Reveals Game-Changing Impact on Development

In order for organizations to maintain a competitive edge, developers must be able to innovate and deliver applications faster than ever. They are the crux of digital transformation, and as such, they are turning to AI technologies such as AI coding assistants to enhance their productivity. 

It’s easy to understand why. IDC predicts that by 2027, AI will dramatically increase developer velocity by automatically generating code to meet functional business requirements for 80% of new digital solutions. This prediction comes from Katie Norton’s IDC Spotlight paper, “Governing AI: The Impact of AI-Assisted Development on Software Delivery and Security,” published in September 2024. 

What Does This Mean for the SDLC? 

IDC Research Manager Katie Norton believes that to harness the benefits of AI coding assistants fully, the entire SDLC must evolve to accommodate the increased volume of code produced. Essentially, if existing pipelines are not designed to handle such increased development, the increased code production can cause bottlenecks and inefficiencies. So, what does this mean for different parts of the software development lifecycle? 

• Testing and Quality Assurance: Increased code production necessitates a shift towards more automated testing, as manual testing processes become inadequate for ensuring comprehensive coverage and timely execution.
• Continuous Integration/Continuous Delivery: Scaling CI/CD infrastructure through enhanced automation, intelligent resource allocation, and parallelized testing to manage increased code production and prevent bottlenecks.
• Release Orchestration and Deployment: Accelerates feature readiness, demanding a more streamlined release management process with enhanced coordination, robust risk management, and efficient rollback mechanisms to handle the increased frequency and complexity of releases.
• Quality and Security: AI coding assistants can inadvertently introduce bugs and security vulnerabilities due to their reliance on potentially outdated, flawed, or biased training data and their lack of true semantic understanding. This poses risks to code quality and organizational reputation. 

Automation, Governance, and Platform Engineering 

According to Norton’s research, AI-driven development necessitates a holistic approach to optimize the entire software development lifecycle. Organizations can leverage AI to enhance software quality and testing, with developers recognizing its potential in these areas even more than in code writing. Automation plays a crucial role in streamlining build, test, and deployment processes, while AI’s predictive capabilities can optimize resource allocation and mitigate risks. 

Robust governance and automated policy enforcement are essential to ensuring high-quality, secure code. Compliance standards must be embedded into workflows, and consistent quality must be maintained across AI-generated and human-written code. 

Platform engineering emerges as a key strategy, with 80.8% of organizations expanding, using, or piloting internal developer platforms to provide security guardrails and standardized DevOps workflows. This approach consolidates tools and technologies, reducing fragmentation and creating a smooth, secure development workflow that empowers developers to write code rapidly while adhering to best practices. 

The Digital.ai Difference 

AI coding assistants promise accelerated development cycles and competitive advantages. Still, their potential can only be fully realized with proper integration of AI and automation throughout the entire software development lifecycle (SDLC). Organizations must adopt holistic solutions that optimize the SDLC from development through deployment, ensuring that AI-generated code enhances efficiency without compromising quality or security. 

With AI in our name, our end-to-end solution is designed to address the complexities of AI-driven development. We provide various tools and capabilities with these challenges in mind, like our Change Risk Prediction tool, which enables teams to predict the probability of failure of each application change and helps eliminate problems before they appear.  

We can also generate test cases through AI and employ ML for self-healing, allowing users to improve the testing process’s efficiency and reliability and reduce the need for highly skilled QA resources. This journey towards AI-driven development requires continuous adaptation and a commitment to leveraging cutting-edge technologies and methodologies for excellence in software delivery.