As AI becomes an integral part of web applications, developers face unique challenges in its implementation. Let's explore some common hurdles and possible solutions: Challenges Data Privacy and Security: Handling sensitive user data for AI training requires robust security measures. Compliance with regulations like GDPR or CCPA adds complexity. Scalability: AI models, especially those requiring large datasets, can strain server resources, affecting application performance during peak traffic. Integration Complexity: Merging AI models with existing tech stacks can lead to compatibility issues, especially in legacy systems. Real-Time Processing: Achieving low-latency predictions is critical for a seamless user experience in applications like chatbots or recommendation systems. Skill Gap: Teams may lack expertise in AI/ML, leading to slower adoption and implementation.