In recent years, the field of machine learning has witnessed remarkable advancements, yet its rapid growth has also brought to light critical concerns regarding opacity and interpretability. The increasing complexity of AI models, particularly deep learning algorithms, has led to a pressing demand for more explainable AI systems.

Explainable AI or XAI refers to techniques and models that make the output of AI algorithms understandable to humans. This has become pivotal, especially in industries like healthcare, finance, and autonomous driving, where understanding AI decisions is crucial for compliance, trust, and safety.

A significant driver of this trend is regulatory pressure. Governing bodies have started to emphasize transparency in AI systems to ensure ethical standards and accountability. Regulations such as the General Data Protection Regulation (GDPR) in Europe already mandate explainability as a component of automated decision-making. These regulations have propelled businesses to adopt XAI practices, enhancing the relationship between AI systems and stakeholders.

Moreover, industry leaders are spearheading initiatives to incorporate explainability into AI models. For instance, Google and IBM have been at the forefront of developing toolkits that offer insights into model predictions. Google's "What-If Tool" and IBM's "AI Explainability 360" provide developers with user-friendly interfaces to explore model behavior, offering transparency to both technical and non-technical users.

Real-life applications showcase the significance and impact of XAI. In the financial sector, for instance, banks employ explainable models to justify credit decisions to applicants, thus fortifying customer trust. Similarly, in healthcare, interpretable AI systems assist doctors in understanding diagnostic outputs, which is crucial for validating life-critical predictions.

The implementation of XAI supports ethical AI practices by minimizing bias and discrimination risks. Transparent models enable stakeholders to identify inherent biases and take actionable steps to rectify them, thus aligning AI deployments with societal values and ethics.

Despite its potential, explainable AI still encounters challenges. The trade-off between explainability and accuracy remains a hotly debated topic among practitioners. Often, more interpretable models compromise predictive performance, a challenge professionals continuously strive to balance.

To conclude, the rise of explainable AI marks a pivotal evolution in the machine learning landscape. As AI becomes an indispensable part of various sectors, transparency and interpretability will remain cornerstones in building trust with users and ensuring ethical AI use. Organizations that prioritize XAI not only advance technological innovation but also champion ethical responsibility, reinforcing the bridge between humans and intelligent systems.