Systems | Development | Analytics | API | Testing

In March 2024, the European Union passed the AI Act. This sweeping regulation reshapes how organizations deploy and manage AI systems. The law addresses AI risks that could affect both individuals and businesses, from hiring biases to critical infrastructure failures. Similar rules have started taking shape across the world, including several state-based regulations in the US. But the regulation is only the floor. We all share the responsibility of creating a safe, responsible AI future.

Katalon officially signed a Memorandum of Understanding (MOU) with RMIT University Vietnam. This collaboration aims to bridge academic learning with real-world software development and software testing expertise, equipping students with an adaptive mindset, future-ready skills and AI tools. Leaders and representatives from RMIT Vietnam and Katalon at the MoU signing ceremony.

The era of generative AI is upon us, and large language models (LLMs) like Claude are demonstrating incredible potential to revolutionize how businesses operate and interact with customers. However, to truly unlock this potential, AI needs secure and standardized access to the wealth of information and services locked within enterprise systems. This is where standards such as Model Context Protocol (MCP) come in, offering a powerful way to make enterprise resources AI-consumable.

In a new Forbes Insights report, 500 C-suite tech leaders share how they’re navigating data complexity, scaling AI, and prioritizing governance — and why modern integration is now mission-critical.

Enterprise adoption of large language models (LLMs) is surging. According to Gartner, more than 80% of enterprises will have deployed generative AI (GenAI) applications or used GenAI APIs by 2026, up from just 5% in 2023. That stark increase paints a telling picture: LLMs have evolved from a fringe technology to a cornerstone of business development and productivity. But as with any new technology, competition is fierce.

AI is a groundbreaking technology that is ready to modernize the way federal government agencies operate. By automating tasks and optimizing workflows, artificial intelligence (AI) promises to enhance efficiency, minimize errors, and boost productivity without adding resources. But as with any change—and especially one as transformative as AI—leaders need to take deliberate and cautious steps to ensure a smooth integration of these innovations and to gain the buy-in of government employees.