But, he added, LLMs tuned or trained for business are not in decline. “We are moving to the world of agents that can work against multiple models for specific tasks,” he said. ServiceNow is smart enough to know that its models should focus on the tasks most specific to its customers’ processes and data, and the more common stuff can be handled with a frontier model.
Sanchit Vir Gogia, chief analyst at Greyhound Research, said that this move is happening now because the enterprise AI conversation has crossed a line from assistance to accountability. “Until recently, ServiceNow could credibly argue that business tuned models were sufficient, because the AI was largely augmentative,” he said. “It summarized tickets. It drafted responses. It helped agents move faster, but humans still carried responsibility.”
Harsh internal reality at play
That boundary “has now collapsed. Customers want AI that opens cases, triggers approvals, escalates incidents, interacts with legacy systems, and increasingly operates through voice and agents rather than a structured UI,” he noted. “Once AI is expected to act, reasoning quality and generalization depth stop being nice to have and start becoming operational risk variables.”
