AI and governance: Rethink IT control and accountability

Agentic AI is forcing a conversation many organizations have postponed for too long. As systems move from executing predefined instructions to pursuing goals autonomously, traditional models of IT governance start to show their limits and think about AI and governance. 

What used to work for deterministic systems and human-driven processes no longer scales when software can decide, act, and iterate on its own.

For IT and business leaders, the challenge is no longer whether AI can increase efficiency. The real question is how to govern systems that act independently without losing control, accountability, or trust.  This shift is subtle at first, but its impact on governance, risk, and operations is profound.

Understanding this shift requires rethinking how autonomy, permissions, and oversight work together in modern IT environments.

From instructions to objectives

Traditional AI behaves like an advanced assistant. You give it a task, and it executes within clear boundaries. If you ask it to optimize a SQL query, generate documentation, or summarize incidents, the scope is explicit and the outcome predictable.

Agentic AI operates under a different model. Instead of receiving instructions, it receives objectives.  How it achieves those objectives is not fully predetermined.

That distinction matters. When an agent is tasked with maximizing efficiency or optimizing costs, it may identify actions that technically achieve the goal but violate organizational expectations. An agent optimizing costs might cancel high-value support contracts. An agent optimizing a database might remove data it considers unnecessary.

In both cases, the agent is competent. The problem lies in how autonomy is governed.

This shift from execution to intent changes the nature of risk and responsibility. And it sets the stage for why traditional governance models begin to break down.

Why traditional IT governance falls short

Most IT governance frameworks were designed in an era of predictable systems and human decision-making. Change management, approvals, and segregation of duties assumed that behavior could be reviewed and approved before execution.

Agentic systems do not fit neatly into that model. You cannot anticipate every path an autonomous agent may take, just as you cannot predict every line of code a developer will write. Trying to force agentic AI into rigid approval structures quickly becomes unworkable.

At the same time, removing controls altogether creates unacceptable exposure. The result is a governance dilemma. Too much control slows innovation and pushes teams toward shadow solutions. Too little control introduces systemic risk.

This tension is not theoretical. It already appears when organizations experiment with agents across IT operations, service management, and data access.

To resolve it, leaders need to shift focus away from process-heavy control and toward structural safeguards.

The real governance risk is permissions

One of the most practical governance risks with agentic AI is not what agents can reason about, but what they are allowed to do.

In many organizations, agents are created quickly and granted broad permissions for convenience. Multiple agents may share the same service account, or receive access well beyond their intended scope. When something goes wrong, the blast radius is large and accountability is unclear.

Governance starts with identity and permissioning. Each agent should have a clearly defined purpose, its own identity, and only the minimum privileges required to perform its function.

An agent that analyzes customer tickets does not need permission to delete them. An agent that reads production data does not need write access. When permissions align with intent, risks become contained and manageable.

This principle is well known in security. With agentic AI, it becomes foundational to governance.

Once permissions are treated as a first-class design decision, the conversation naturally shifts to how organizations monitor and supervise autonomous behavior.

From predictability to observability

Traditional governance emphasizes predictability. Leaders want to know exactly what will happen before it happens. With agentic AI, that expectation must evolve.

You cannot fully predict how an autonomous agent will solve a problem. You can, however, observe what it does, trace its actions, and understand how decisions were made.

Observability becomes more valuable than pre-approval. Logs, action histories, and decision traces provide the evidence required for audit, compliance, and continuous improvement.

This distinction is especially important in regulated environments. Compliance does not require humans to manually approve every action. It requires that processes are documented, consistent, and reviewable.

Agentic systems can support these requirements if governance is designed around transparency rather than rigid control.

This same logic applies to one of the most sensitive governance areas in IT: Change Enablement.

Change management without bureaucracy

Change management is often cited as incompatible with autonomous systems. In practice, the issue is not change management itself, but how it has been historically implemented.

Many organizations still equate governance with meetings and manual approvals. That approach does not scale, regardless of AI.

A more effective model is based on thresholds. Low-impact, reversible actions can be automated. High-impact or irreversible actions require human visibility or approval.

For example, changes to non-critical systems may be fully automated. Changes affecting revenue systems, customer operations, or financial data should require explicit human involvement. A simple rule applies. If an action cannot be undone, a human should be involved.

This approach preserves autonomy while maintaining accountability. It also aligns governance with business impact rather than technical detail.

When designed this way, governance becomes an enabler rather than an obstacle.

-> Read more: What AI failures teach us about building smarter systems

Governance as a catalyst for scale

Poorly defined governance often slows AI adoption. Teams either move cautiously or bypass controls altogether. Clear governance does the opposite.

When boundaries are explicit, teams can experiment safely. Sandbox environments that mirror production but use read-only or replicated data allow agents to be tested without endangering real systems.

Maintaining a documented inventory of agents prevents duplication and reduces risk. In large organizations, it is common for multiple teams to independently build agents with similar goals. Visibility enables reuse and coordination.

You cannot govern what you cannot see. Discovery and documentation are prerequisites for scale.

As agents proliferate beyond IT into business functions, governance also starts to reshape organizational relationships.

Why AI governance brings IT and business closer

Decisions about what agents are allowed to do are not purely technical. They reflect business priorities, risk tolerance, and regulatory obligations.

As agentic AI spreads across departments, IT governance cannot operate in isolation. Business teams define objectives and acceptable outcomes. IT teams translate those expectations into architecture, permissions, and controls.

This mirrors earlier shifts seen with DevOps and DevSecOps, where silos between development, operations, and security had to be broken. Agentic AI accelerates this convergence.

Governance becomes a shared responsibility. IT is no longer a downstream enforcer, but an active partner in shaping how autonomy is applied.

This shift also clarifies a recurring question about accountability.

-> Read more: How to establish flexible and predictable execution rhythms for AI projects

Autonomy does not remove responsibility

When an agent deletes data or makes a harmful decision, the agent is not accountable. Responsibility lies with the organization that designed, authorized, and deployed it.

This is no different from a human error caused by excessive permissions or weak controls.  The difference is scale and speed.

Human roles evolve accordingly. Instead of executing tasks, humans design systems, define boundaries, and supervise outcomes. They decide what agents cannot do, monitor behavior, and intervene when actions cross defined thresholds.

Autonomy increases the importance of governance design. It does not reduce it.

Three things leaders must get right about AI and Governance

For executives, the challenge is not mastering the technical details of agentic AI. It is making the right governance decisions early. Three priorities consistently stand out.

First, visibility

Leaders need a clear view of what agents exist, what they do, and what permissions they hold. Without this visibility, governance is impossible. Documentation and discovery are not overhead. They are the foundation of control.

Second, least privilege

Agents should only have access strictly necessary for their function. Broad permissions create unnecessary risk and blur accountability. Limiting access reduces impact when things go wrong and simplifies oversight.

Third, separate technical capability from organizational judgment.

Agentic systems can produce highly accurate outputs and still lack awareness of broader business, legal, or ethical consequences. That gap is not a failure of the technology, it is a natural characteristic of autonomous systems operating toward defined objectives. 

Governance plays a critical role in bridging this gap by ensuring that decisions with material impact remain aligned with business intent, risk tolerance, and accountability.

Conclusion

Agentic AI challenges long-standing assumptions about how control, accountability, and oversight work in IT environments. As systems gain autonomy and the ability to pursue objectives, governance models built around predictability and manual intervention begin to lose effectiveness.

The path forward requires a shift in emphasis. Organizations need to design governance that accounts for autonomous behavior by defining clear boundaries, permissions, and escalation points. Observability, documentation, and intent become central to maintaining trust and compliance as agents operate at scale.

Leaders who approach AI and governance as a structural design problem, rather than a tooling or policy exercise, are better positioned to scale agentic systems responsibly. When governance is treated as part of the architecture, not an afterthought, autonomy becomes manageable and aligned with business goals.

As agentic AI continues to expand across IT and business operations, the organizations that succeed will be those that intentionally govern systems capable of acting, learning, and evolving within clearly defined limits.