From information to action: Why the future of enterprise AI is agentic
By Arun ‘Rak’ Ramchandran, CEO QBurst
Living in the heart of Silicon Valley, it is easy to be blinded by the glare of billion-dollar ‘World Models’ and the daily hype cycle of tech giants. But beyond the Valley, I see a different reality. My work with global clients, augmented by the incredible engineering momentum coming out of India, reveals that for the enterprise, the ‘wow’ factor is irrelevant without the ‘why.’
Today, CIOs and CTOs are under immense pressure to move past the novelty of Generative AI and demonstrate tangible value. In the real world, adoption and ROI remain the only true benchmarks of success. We are transitioning from a period of AI fascination to a period of AI accountability.
For the past two years, the enterprise narrative has been dominated by chat interfaces. While these are low-hanging fruit, they are fundamentally limited. A workforce does not spend its day merely answering questions; it executes multi-step workflows involving physical actions, digital steps, and intelligent decision-making. The real value lies in augmenting these elaborate processes with agentic workflows.
The technology has arrived; what is required now is a fundamental shift in thought process—moving from “chatting” to “doing”. According to McKinsey’s 2025 Global AI Survey, 23% of organisations are already scaling AI agents, while another 39% are moving beyond simple chat projects. The transition from information to action has begun.
Why Chat Interfaces Are No Longer Enough
The initial days of Gen AI were all about providing generic chat interfaces, driven by prompt engineering and RAGs. While they did a good job of demonstrating the capability of AI systems, the real value add in terms of productivity gains was minimal. Chats are reactive and human-dependent; they are great for ‘asking, but they don’t ‘do’.
The shift to Agentic AI is the shift from Information to Action. Human work is driven by policy and outcomes, multi-step and often spanning multiple systems. Consider heavy industries like mining or manufacturing. A chatbot can explain a contract clause, but it cannot perceive the physical world, verify raw materials against delivery standards, or automatically trigger a supply chain alert. Agentic AI can. These ‘digital workers’ operate within strict guardrails to plan, react, and collaborate with humans to achieve specific goals.
The Missing Piece: Managing Agents At Scale
As organisations move toward this agentic mode, a new challenge emerges: orchestration. IDC projects a whopping 1.3 billion agents across organisations by 2028. Without unified governance, enterprises face a fragmentation crisis where agents operate in silos, creating a ‘Shadow AI’ economy that bypasses IT oversight
With agents being in the loop of highly critical decision-making within an organisation, it is important that they are controlled, free from security breaches, cost overruns, and compliance failures. There is a significant technical risk: when Agent A (Supply Chain) talks to Agent B (Procurement), how do you prevent an infinite loop of API calls that burns your token budget in minutes? This is the “Day After” reality. Without a unified governance layer, these agents operate in silos, creating a ‘Shadow AI’ economy that bypasses IT oversight.
Another aspect is the cost of “Being Smart”: Organisations must consider token optimisation and latency as a first-class design principle. Intelligence comes at a cost, both financial and temporal. A CIO in a heavy industry cannot afford a 30-second latency for a production-line decision, regardless of how ‘smart’ the agent is. The trade-off between model complexity and execution speed becomes a critical business decision.
To navigate this landscape, enterprises need to move toward a ‘Managed Agents’ approach. Managed agents platforms bring disparate agents under a single umbrella for orchestration, monitoring, and control. This facilitates the human-in-the-loop paradigm, allowing employees and agents to work in a synchronised, controlled environment.
The Future Of The Landscape
The success of an organisation’s foray into the agentic workflow world will depend upon three factors.
Capturing the transient knowledge within the employees. These represent the ways of doing work and navigating the business environment.
The capability to utilise humans in the loop effectively, in other words, is how well one is able to integrate agents into an environment dominated by humans.
Organisations’ capability in implementing standardisations, setting up guard rails and compliance frameworks
As agents achieve near first-class employee traditional pricing mechanisms will evolve towards outcome-oriented models. Metrics like reduction in human effort, change in cost per transaction, reduction in compliance violations, and improvement in SLA can become popular. Human agent ratio can also become a key metric that will be tracked in the future.
The question for the next decade is no longer whether to build an agent, but whether you can scale and operate an entire organisation of them. Irrespective of the specific metrics chosen, the enterprises that master the art of managed agents, balancing innovation with governance, will define the next decade of digital operations.