Artificial Intelligence (AI) has spent the past two years proving that it can assist people. It can draft reports, answer customer queries, summarise documents, and generate code in seconds. But a more profound shift is now underway. AI is no longer simply helping employees perform work. It is beginning to perform work on their behalf.
Across enterprises, autonomous AI agents are moving beyond chat interfaces and into day-to-day operations. They investigate security incidents, review compliance evidence, retrieve information across multiple business systems, coordinate workflows, and resolve customer requests with minimal human intervention. Unlike traditional software, these systems can reason through a task, decide what comes next, and take action across applications.
As organisations increasingly hand over operational responsibilities to AI, one question deserves far more attention than the technology itself: If an AI agent has the access of an employee, does it have the governance of one?
The question is becoming harder to ignore. According to EY's AIdea of India 2026 report, 34% of senior leaders are actively implementing agentic AI, while 94% of enterprises are concerned that AI sprawl is increasing complexity and security risks. AI is rapidly becoming part of the workforce, but governance has yet to catch up.
Every employee enters an organisation through a structured lifecycle. They receive an identity, role-based access, mandatory training, managerial oversight, and periodic reviews. Their actions are logged, their responsibilities are defined, and when they leave, their access is revoked. Decades of enterprise governance have been built around ensuring that every employee is accountable.
{{/usCountry}}Every employee enters an organisation through a structured lifecycle. They receive an identity, role-based access, mandatory training, managerial oversight, and periodic reviews. Their actions are logged, their responsibilities are defined, and when they leave, their access is revoked. Decades of enterprise governance have been built around ensuring that every employee is accountable.
{{/usCountry}}AI agents increasingly perform comparable work, yet they rarely undergo a similar process. Many are deployed with broad API permissions, access to multiple enterprise systems, and the ability to execute workflows without the same visibility or oversight organisations expect from human employees.
Good governance is not about requiring human approval for every AI action. Routine, low-risk tasks can be executed autonomously, while decisions with financial, regulatory, or operational consequences should continue to require human oversight. The objective is not to limit autonomy, but to ensure it operates within clearly defined guardrails.
Most organisations can tell you exactly how many people they employ. Increasingly, far fewer can answer the same question about AI agents. As these systems proliferate across departments, maintaining an inventory of AI agents, their permissions, ownership, and responsibilities may become just as fundamental as maintaining an employee directory.
The governance challenge is not simply that AI is autonomous. It is that organisations continue to govern AI as software even as it begins to behave like a workforce.
Unlike conventional applications, AI agents evolve with changing context, interact with multiple systems, and influence business decisions long after deployment. Governance, therefore, cannot end when an agent goes live. It must continue throughout its operational lifecycle.
India's emerging approach to AI governance reflects this reality. Rather than prescribing detailed rules for every AI system, the government's draft governance principles place human accountability at the centre while giving organisations the flexibility to innovate responsibly. That flexibility also places greater responsibility on enterprises to establish their own guardrails. They must know what their AI agents can access, what actions they can take, where human approval is required, and whether every significant decision can be explained during an audit.
Governance is often viewed as something that slows innovation. In reality, it is what makes innovation sustainable and scalable.
Organisations adopt new technologies with confidence when responsibilities are clearly defined, permissions are controlled, and accountability is never in doubt. The same principle now applies to agentic AI. The goal is not to place humans in the loop for every decision, but to ensure that autonomous systems operate within well-defined guardrails, with greater oversight as the level of risk increases. This is also the direction global AI governance is taking, with initiatives such as the EU AI Act and standards like ISO/IEC 42001 placing greater emphasis on accountability and responsible oversight.
The enterprise workforce is changing. It will soon include not only people, but also intelligent systems capable of making decisions and taking action. The organisations that succeed will not necessarily be those with the most AI agents, but those with the strongest governance around them.
Because if AI is becoming part of the workforce, it deserves to become part of the governance model too.
(The views expressed are personal)
This article is authored by Raghuveer Kancherla, co-founder, Sprinto.