Agentic AI: The Real Cost Beyond Initial Savings

Agentic AI systems often promise faster processing and lower costs, but the real calculation of their value is much more complex. Atul Arya, CEO of Blackstraw.ai, highlights that only about 5 percent of generative AI pilots actually reach production with real impact. He says many boards don't define what "working" truly means for these systems. Before scaling an agentic AI, companies need a clear baseline of current process costs, business metrics like cycle time and customer impact, and defined triggers for human intervention. Enterprises often mistakenly treat agentic AI as a one-time software build, overlooking ongoing operational costs. These include integration, continuous monitoring, maintenance, and the costs of human intervention for exceptions and rework. A common mistake is focusing only on labor savings and faster processing. The bottom line is that true return on investment for agentic AI requires considering the full operational cost over several years, not just initial gains. This insight could save companies significant money and help them make better investment decisions.