In the fast-moving digital world of today, delivering high-quality software at an accelerated pace has become a major challenge for corporations. The World Quality Report 2021 claims that testing is a significant bottleneck as documented by its finding that only 15% of tests are automated. This manual approach does not allow scale and meet changing business needs resulting in delayed releases and poor quality of software. Nonetheless, there has emerged a new era of AI-driven test automation which fosters innovation while propelling efficiency and quality to unprecedented heights.
The Bottleneck Challenge
Testing bottlenecks stem from various factors, including manual processes, a lack of coordination between teams, and the scarcity of automation. These bottlenecks impede developer productivity, resulting in burnout and talent shortages, ultimately hindering the ability to deliver quality software at the desired speed. The primary bottlenecks identified by IDC include:
- The need for business integration
- Manual processes
- Testing and quality assurance
- Lack of coordination between business teams, software development, quality teams, and DevOps teams
The AI-Powered Solution
AI-powered test automation has emerged as a game-changer, enabling enterprises to achieve unprecedented levels of automation, defect reduction, and accelerated release cycles. Companies like Chipotle, Cisco, and USPS have leveraged this technology to achieve remarkable results, including:
- 90% automation rates
- 80% reduced defects
- 3x faster release speed
The AI-powered test automation can effectively overcome testing bottlenecks that once hindered software delivery process. Thus, the four steps outlined in this whitepaper will help enterprises connect and consolidate their test management assets, migrate and automate their test automation assets, maximize their test automation coverage along with expanding their testing footprint with new value-added services. This method addresses the challenges associated with manual processes, business integration problems as well as lack of synchronization thereby opening up possibilities for creativity, effectiveness, and quality assurance. Therefore, embrace AI-driven test automation for your QA process transformation.
