The article discusses the challenges and considerations for scaling agentic AI across enterprises as the technology transitions from experimentation to deployment. Despite adoption by approximately 80% of Fortune 500 companies, progress toward meaningful scale remains uneven, with many organizations still conducting isolated pilots. Arun Chandra, chief operating officer at NiCE, emphasizes the need to move beyond experimentation for its own sake and align agentic AI initiatives with clear business strategies, such as increasing revenue or reducing costs.
Chandra argues that scaling agentic AI requires treating it as a cohesive system, ensuring agents have access to necessary data, knowledge, and context, as well as connections to back-end systems for action. Fragmented information can undermine the effectiveness of AI agents. Additionally, the organizational implications of scaling agents include the risk of creating new forms of fragmentation if teams build isolated systems, and the increased importance of governance, privacy, security, and change management as agents take on more consequential work. Chandra suggests that AI agents should be held to the same standards as human workers, with organizations viewing their workforce as a combination of humans and AI agents. The priority for organizations transitioning from pilots to scale is to develop a connected strategy around high-value use cases, workflows, workforce changes, and measurable outcomes.