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Agentic AI represents the next stage in artificial intelligence, moving beyond systems that only respond to prompts toward systems that can independently take action. Unlike traditional AI or early large language models that focus on generating text, summaries, or predictions, agentic AI can trigger workflows, perform multistep tasks, and interact with other systems with minimal human input.
Experts from McKinsey explain that the real shift lies in accessibility and autonomy. According to Stephen Xu, agentic AI allows people to complete complex tasks through natural conversation instead of rigid software interfaces. Dave Kerr adds that while traditional machine learning predicts outcomes, agentic AI is designed to act on those predictions, such as searching the web, sending emails, or executing transactions.
Michael Chui highlights that this combination of AI models, machine learning, and automation could soon make AI feel invisible, much like the internet today. Rather than being a novelty, it may become an expected capability embedded into daily work.
However, the experts also caution against excessive hype. While the potential is significant, real-world impact is currently concentrated in specific use cases. The challenge ahead is identifying where agentic AI delivers genuine business value, while managing risks, reliability, and human oversight.
