Artificial Intelligence (AI) has transformed the world of technology, enabling systems to learn, adapt, and make decisions without explicit programming. From autonomous vehicles to medical diagnostics and flight control systems, AI promises unprecedented efficiency and capability. However, when it comes to safety-critical systems—where failure could result in injury, loss of life, or significant damage—the use of AI introduces profound challenges that go far beyond traditional software engineering. Unlike conventional software, which behaves predictably according to its programmed logic, AI is built on learning and training. Its decisions and outputs depend heavily on the data it has been trained on and the patterns it recognizes during runtime. This adaptive, data-driven behavior means that an AI system’s responses may vary with changing inputs or environments, often in ways that are not explicitly defined or foreseen by developers. While this flexibility is a strength in many applica...
Scrum has become one of the most widely adopted frameworks for managing complex projects in software development and beyond. Its success, however, hinges on the proper execution of its three core roles: Product Owner (PO), Scrum Master (SM), and Development Team (Dev Team) . While Scrum prescribes responsibilities and practices for these roles, organizations often experience deviations from the intended behavior. These deviations, known as anti-patterns , can impede team performance, diminish transparency, and reduce the value delivered to stakeholders. This post explores the most common anti-patterns associated with each Scrum role and offers insights to mitigate them.