While approximately 80% of Fortune 500 companies have begun experimenting with agentic AI, many organizations struggle to move these initiatives beyond isolated pilot programs. According to Arun Chandra, chief operating officer at NiCE, the primary obstacle to scaling is a lack of alignment between AI deployment and core business strategy. Rather than simply applying new technology to existing processes, firms must first evaluate whether their workflows are efficient and determine clear financial or strategic objectives, such as cost reduction or revenue growth.
Chandra warns that layering AI onto outdated workflows can limit the effectiveness of the technology. To achieve meaningful scale, organizations must treat agentic AI as a unified system. This requires providing agents with the necessary context, data, and access to back-end systems to perform tasks effectively. Fragmented information silos can significantly hinder an agent’s decision-making capabilities, making the quality of ingested data a critical factor in performance.
The shift toward enterprise-wide deployment also introduces significant organizational challenges. As agents take on more consequential responsibilities, companies must prioritize security, privacy, and governance. Chandra suggests that organizations should view their future workforce as a hybrid of humans and AI, holding agents to the same operational standards as human employees to prevent the creation of disconnected, siloed systems.
Ultimately, the transition to scale involves focusing on high-value use cases rather than attempting to overhaul every process simultaneously. By building a connected strategy that integrates workforce changes with measurable outcomes, businesses can enable agents to operate proactively and collaborate to resolve complex operational needs.
Source: MIT Technology Review
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