The AI Revolution: IDC Spotlight Reveals Revolutionary Impact on Development
Posted: Wed Apr 23, 2025 5:27 am
For organizations to maintain a competitive edge, developers must be able to innovate and deliver applications faster than ever before. They are the crux of digital transformation and as such, they are turning to AI technologies like AI coding assistants to increase their productivity.
It’s easy to see why. IDC predicts that by 2027, AI will dramatically increase developer velocity by automatically generating code to meet business austria mobile database requirements for 80% of new digital solutions. This prediction comes from Katie Norton’s IDC Spotlight article, “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 fully leverage the benefits of AI coding assistants, the entire SDLC must evolve to accommodate the increased volume of code produced. Essentially, if existing pipelines are not designed to handle this 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: Increasing code production demands a shift to more automated testing as manual testing processes become inadequate to ensure comprehensive coverage and timely execution.
Continuous Integration/Continuous Delivery: Scaling CI/CD infrastructure through improved automation, intelligent resource allocation, and parallelized testing to manage increased code production and avoid bottlenecks.
Release Orchestration and Deployment: Accelerates feature readiness by requiring a more streamlined release management process with improved 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AI coding assistants can inadvertently introduce bugs and security vulnerabilities due to their reliance on potentially outdated, flawed, or biased training data and their lack of true semantic understanding.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 requires 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 writing code. Automation plays a crucial role in streamlining the 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 built into workflows, and consistent quality must be maintained across both 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 protections and standardize DevOps workflows. This approach consolidates tools and technologies, reducing fragmentation and creating a smooth, secure development workflow that empowers developers to write code quickly while adhering to best practices.
It’s easy to see why. IDC predicts that by 2027, AI will dramatically increase developer velocity by automatically generating code to meet business austria mobile database requirements for 80% of new digital solutions. This prediction comes from Katie Norton’s IDC Spotlight article, “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 fully leverage the benefits of AI coding assistants, the entire SDLC must evolve to accommodate the increased volume of code produced. Essentially, if existing pipelines are not designed to handle this 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: Increasing code production demands a shift to more automated testing as manual testing processes become inadequate to ensure comprehensive coverage and timely execution.
Continuous Integration/Continuous Delivery: Scaling CI/CD infrastructure through improved automation, intelligent resource allocation, and parallelized testing to manage increased code production and avoid bottlenecks.
Release Orchestration and Deployment: Accelerates feature readiness by requiring a more streamlined release management process with improved 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 requires 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 writing code. Automation plays a crucial role in streamlining the 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 built into workflows, and consistent quality must be maintained across both 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 protections and standardize DevOps workflows. This approach consolidates tools and technologies, reducing fragmentation and creating a smooth, secure development workflow that empowers developers to write code quickly while adhering to best practices.