Chatbots and virtual assistants have existed for years, but large language models have transformed what they can do. Modern conversational AI can handle open-ended questions, maintain context across long exchanges, adapt its tone to different situations, and perform tasks like booking appointments, answering product questions, or walking you through a troubleshooting process. The best systems feel genuinely helpful rather than frustrating. Businesses are deploying these widely - for customer support, internal knowledge management, and as general-purpose assistants that sit alongside your existing tools. The improvement over the rigid, script-based chatbots of a few years ago is dramatic. But conversational AI still has significant blind spots. It can hallucinate information with complete confidence. It struggles with tasks requiring precise factual accuracy, nuanced judgement, or real-time data. And the appearance of understanding can mask the fact that these systems have no real comprehension of what they are saying. The most effective deployments set clear boundaries - using AI for tasks where mistakes are low-cost and easy to catch, while keeping humans in the loop for anything consequential.