A recent survey encompassing 1,775 IT and business executives reveals a significant trend: 71% of organizations have integrated some level of artificial intelligence (AI) and generative AI into their operations. Notably, over a third (34%) are utilizing these technologies to enhance quality assurance processes. This survey, conducted by Sogeti—a division of Capgemini—along with OpenText and the research firm Coleman Parkes, indicates that another 34% of organizations are drafting strategies to improve quality engineering, building on the foundations laid by successful AI pilot projects. Meanwhile, 19% are still in the pilot phase of AI implementation.
Challenges in AI Adoption
Despite the enthusiasm for AI, several challenges impede its broader adoption in software testing. The primary concerns include:
- Data breach worries: 58%
- Tool integration issues: 55%
- Effort and resources required: 53%
- AI hallucinations (misleading outputs): 47%
- Unexpected costs: 43%
Organizations also face internal hurdles, such as a lack of a clear AI strategy (56%), a shortage of skilled personnel (53%), and an undefined testing approach (50%). In terms of test automation, 29% of respondents indicate that their organizations have successfully adopted generative AI, while 42% have engaged in experimental initiatives.
Benefits of AI in Testing
The survey highlights several advantages of implementing AI in software testing, with the top three benefits being:
- Faster automation: 72%
- Easier integrations: 68%
- Reduction in testing resources/efforts: 62%
In terms of practical applications, the most common use cases for AI in testing include:
Test automation script conversions: 50%
Test reporting: 56%
Defect analysis: 56%
Knowledge management: 54%
Test data generation: 52%
Below is a graph explaining the basic obstacles to Test Automation Adoption that organizations face while choosing a company for their testing requirements.
