Agentic AI has captured the attention of enterprise leaders, but many organizations remain stuck between promising pilots and scaled deployment. The challenge is no longer simply proving that the technology works in controlled environments; it is understanding what agentic AI can realistically do, where it still falls short, and what conditions are required for it to deliver value in production. Moving from experimentation to scale demands clear expectations, strong governance, effective integration with existing workflows, and a sharper understanding of where autonomy can create value. How can organizations move beyond pilots and identify the use cases where agentic AI can genuinely scale? What misconceptions about the technology are slowing adoption or creating unrealistic expectations? And how should leaders design the governance, workflows, and oversight needed to turn agentic AI into measurable enterprise value?
