Artificial intelligence helps lead decisions over intelligent automation. Intelligent automation can be thought of as a combination of "robotic process automation and artificial intelligence," according to an article on the topic in HR Dive. HR Dive is a publication designed for human resources professionals. Organizations that embrace intelligent automation may experience a return on investment of 200% or more, according to an Everest Group report cited by HR Dive. However, that doesn't mean organizations can automatically anticipate a reduction in headcount.

Although Artificial Intelligence (AI) has been around for decades, both its hype and adoption have grown exponentially in the past few years. Enterprises are recognizing the value of AI as a competitive differentiator and value creator, and many are already investing in AI. In our recent survey of IT heads of 200 global enterprises, 65% have already invested in some AI project. However, while interest and investment are high, AI is far from pervasive in most enterprises' business strategies. In fact, more than 80% of the enterprise executives told us that they were unable to adopt AI at scale or achieve any significant business outcomes.

Artificial intelligence AI takes the lead over intelligent automation IA. Intelligent automation is the combination of "'robotic process automation and artificial intelligence to automate processes,'" according to a recent article on the topic in HR Dive, a publication for human resources professionals. Organizations that embrace intelligent automation may experience a return on investment of 200% or more, according to an Everest Group report cited by HR Dive. However, that doesn't mean organizations can expect a reduction in headcount, according to the report. In fact, projections of a reduction in workforce thanks to intelligent automation may be "highly exaggerated," the Everest Group noted.

The QA landscape has been evolving over the past decade and there is a clear shift from independent testing of applications to a more holistic brand assurance of business processes and outcomes. Even as enterprises adopt Agile and DevOps and continue their journey of integrated testing, service providers are strengthening their capabilities to deliver end-to-end QA orchestration to their clients. Providers are also investing in Artificial Intelligence (AI) and Machine Learning (ML) to expand their test automation suite of tools and IP, and even include performance and security testing into the continuous delivery model, thus enabling brand assurance for their clients' business.