According to a recent survey by AppDirect, half of IT decision-makers are losing sleep over security. It's no surprise, given that 45% of respondents have experienced a security breach in the last year. The threat of vulnerabilities and cyber security attacks is on the rise, and the impact of an incident can be devastating. Having the right DevSecOps strategy helps, but getting the right toolchain in place is crucial.

Today, mobile apps are no longer a luxury but a necessity for businesses striving to stay competitive. Whether it’s an e-commerce platform, a banking app, or a fitness tracker, the expectation is clear: the app must perform flawlessly and scale effortlessly to meet growing user demands. The success of your mobile app hinges not just on its initial launch but on its ability to scale and perform consistently as your user base expands. Selecting the right tools to support this growth is critical.

What do developers really think about artificial intelligence (AI)? To many, AI is synonymous with innovation. While some noisy naysayers on the sidelines may cry the promise of generative AI (GenAI) is overhyped, the reality experienced by actual developers and leaders in the digital trenches working with GenAI and large language models (LLMs) tells a clear story: AI is a top priority in the enterprise today because it has already delivered real, tangible business benefits.

‍ The rise of artificial intelligence (AI) in testing is enabling more predictive and intelligent test generation, execution, and defect analysis. This shift aims to reduce the time and effort required for manual testing to enhance test coverage and improve the overall software quality. Justifiably, key insights from Fortune Business project the growth of the AI-enabled testing market from USD 736.8 million in 2023 to USD 2,746.6 million by 2030.

Every business has key customer behaviors it aims to drive — whether it's encouraging repeat purchases, promoting product upgrades or boosting add-on services. Yet, despite access to advanced marketing technology and rich customer profiles, most businesses still rely on broad, generalized lifecycle marketing campaigns that fail to engage with customers.

Not too long ago, businesses tended to downplay the importance of integrating analytics into their operational strategies and embracing digital big data. The formidable challenges of hefty budgets and a need for more qualified digital transformation professionals were major barriers, deterring companies from fully engaging with the big data era.

It’s nearing the end of the summer in North America, and one report has been a staple on my reading list for more than a decade: the Flexera State of the Cloud Report. The annual survey of hundreds of global IT decision makers assesses cloud strategies, migration trends, and important considerations for companies moving to the cloud or managing cloud environments.

What drives organizations to make the tough choices between speeding up releases, cutting costs, boosting product quality, and sparing time for innovation? One of the critical reasons is the time consumed in maintaining failed test cases and the inability to leverage AI in testing. Test automation is proving ineffective and outdated due to the high cost and time needed for maintenance.