In a recent CIO.Inc interview, QBurst CEO Arun ’Rak’ Ramchandran explores a question many enterprises have yet to fully confront: what happens to technology budgets when AI agents become responsible for executing work?

According to Rak, most organizations still evaluate AI through a software lens, focusing on model costs, licensing, and infrastructure. But as enterprises move toward agentic systems capable of reasoning, coordinating, and acting autonomously, the economics changes. The challenge is allocating capital across an expanding ecosystem of digital workers.

A key theme of the discussion is that many organizations remain in the early stages of the agentic journey. While automation and AI-assisted workflows are becoming more common, truly autonomous agents remain relatively rare. Rak notes that success depends not only on deploying these systems, but also on establishing the governance, organizational structures, and knowledge frameworks needed to support them at scale.

Research from organizations such as Salesforce, Gartner, and IDC highlights the growing scale and complexity of agent adoption, making visibility, accountability, and orchestration essential. QBurst has seen this firsthand through engagements ranging from retail basket optimization—which increased average order value by 25% while reducing returns by 30%—to an AI-powered resident support agent that reduced complaint resolution times by 40% and improved satisfaction scores by 25%. 

These outcomes underscore a broader point Rak makes throughout: the challenge is no longer simply deploying intelligent systems, but governing them effectively at scale. Through its Managed Agents™ offering, QBurst helps organizations build, govern, and scale autonomous systems while maintaining control and oversight.

As Rak notes, the enterprises best positioned for the future will be those that learn not only how to adopt autonomous systems, but also how to measure, manage, and maximize the value they create.