AI agents are no longer experimental. They are processing transactions, managing customer interactions, making operational decisions, and executing workflows with increasing autonomy. The business case is compelling: efficiency, scalability, and the ability to operate continuously without fatigue.

But a pattern has emerged in enterprise AI deployments that should concern every executive. Organizations invest heavily in what their AI systems can do while underinvesting in what those systems should do. Capability gets the budget. Governance gets a checklist.

Gartner estimates that more than forty percent of enterprise agentic AI projects may be cancelled by 2027 due to escalating costs, unclear value, or inadequate risk controls. Capability is rarely the cause of failure. Governance is. Here are five questions every executive should be asking before signing off on their next AI deployment.

1. Who is accountable when the agent makes a bad decision?

This sounds basic. It is not. In most AI deployments, accountability is diffuse to the point of being meaningless. The engineering team built it. The product team defined the requirements. The compliance team reviewed the policy. The vendor provided the model. When something goes wrong, every team can point to another.

AI systems cannot be accountable. They are tools. When an agent approves a transaction it should not have, or sends a message that damages a customer relationship, or accesses data outside its intended scope, the question is not what went wrong with the AI. The question is: who authorized the AI to take that action, and were the boundaries adequate?

Before deploying any agent, the executive team should be able to answer a simple question: for every action this system can take, who is the human responsible for defining whether that action is appropriate? If the answer is unclear, the governance is not ready. And “the governance committee” is not a specific enough answer. Accountability that cannot be traced to a named individual is accountability in name only.

2. Can you explain what your AI did and why it was appropriate?

Regulators are going to ask. Customers are going to ask. Your board is going to ask. And “the model decided” is not an acceptable answer.

The EU AI Act imposes transparency and accountability requirements for high-risk systems. The NIST AI Risk Management Framework emphasizes governance, mapping, and measuring AI risks throughout the system lifecycle. These are not hypothetical future requirements. There are current expectations that will only intensify as AI systems take on greater operational responsibility.

Explainability is not just a compliance box to check. It is a precondition for trust. If your organization cannot explain why an AI agent took a particular action in a particular context, then you cannot verify that the action was appropriate. And if you cannot verify appropriateness, you are operating on faith. Faith is not a governance strategy.

3. Does your governance operate at the speed of your AI?

This is the question most organizations have not thought to ask, and it may be the most important one.

Most AI governance today operates on a human timescale. Policies are reviewed quarterly. Audits happen annually. Incident response activates after something breaks. This worked when software was deterministic and predictable. It does not work for agentic systems that make thousands of decisions per minute, adapt to conditions in real time, and take actions that no one specifically anticipated at configuration time.

If your governance evaluates requests before execution and reviews outcomes after execution, but is absent during execution itself, you have a gap precisely where failures occur. The agent acts in milliseconds. Your review process takes hours. That temporal mismatch is not a minor inconvenience. It is the difference between preventing a problem and performing a post-mortem.

The organizations that will deploy AI most successfully are the ones building governance that participates in runtime, not just at the gates.

4. When AI surfaces a problem, who has the mandate to act on it?

Most organizations have invested heavily in the insight side of AI: dashboards, analytics, forecasts, and anomaly detection. Far fewer have codified who has the authority to act when those insights demand a response.

This is the gap that AI exposes. Not a technology gap, but an authority gap. When an AI agent flags structural risk in a workflow, or recommends stopping an initiative mid-execution, or contradicts the judgment of a senior stakeholder, what happens next? Is there a defined resolution path, or does the organization default to whoever has more influence in the room?

Ask your technical team a direct question: if an AI agent begins executing a workflow that turns out to be problematic, can you stop that specific action, that specific agent, or that specific workflow without bringing down the entire system? In most organizations, the honest answer is no. And even where the technical capability exists, the organizational mandate to use it often does not.

The ability to intervene is what makes deployment a managed risk rather than a leap of faith. Circuit breakers do not prevent the use of electricity. They make it safe to use at scale. The same principle applies to AI governance. When you can intervene surgically, you can deploy more broadly, because you retain control when the unexpected happens.

But intervention requires more than a kill switch. It requires predefined authority: who can stop an action, under what conditions, and what the downstream consequences are. Organizations hesitant to give agents meaningful authority are often the ones with the least clarity on these questions. They restrict the scope precisely because they have not designed the override conditions. Building real intervention capability, both technical and organizational, is the investment that unlocks confident deployment.

5. Is trust something you configured once or something you measure continuously?

Most AI deployments treat trust as binary. The system is either deployed or it is not. Access is either granted or withheld. Permissions are configured at launch and revisited only when something forces a review.

This is not how trust works in any other domain. You do not give a new employee the same authority as a ten-year veteran on their first day. You do not grant a contractor the same access as an internal team member without additional verification. Trust is earned through demonstrated behavior and adjusted when circumstances change.

AI governance should follow the same logic. An agent that has consistently operated within its boundaries might warrant expanded authority. An agent exhibiting anomalous behavior should face increased scrutiny automatically, not three hours later when someone notices a dashboard alert. Trust should be a continuous, evidence-based signal that adapts to what the system is actually doing, not a static permission that assumes good behavior indefinitely.

The Architecture Question

These five questions share a common thread. They are not about technology. They are about architecture. Specifically, they are about whether your organization treats governance as a foundational design decision or as a compliance layer added after the real work is done.

Most organizations have authority structures that were never explicitly designed. They emerged from org charts, informal norms, and institutional habit. That worked when decisions moved slowly enough for those norms to absorb ambiguity. AI compresses time. It surfaces risks and recommendations faster than informal negotiation can resolve them. When the tension between AI output and human judgment needs to be resolved in seconds rather than meetings, authority that was assumed rather than designed will fail.

The distinction determines outcomes. When governance is architectural, it clarifies authority, enables explainability, operates at execution speed, provides intervention capability, and adapts trust based on evidence. When governance is an afterthought, it creates friction without providing control, satisfies auditors without preventing failures, and leaves the organization hoping nothing goes wrong at a speed too fast for anyone to respond.

Boards do not need deeper dashboards. They need clarity on stop authority, override conditions, consequence ownership, and intervention rights. They need to know whether their AI governance is embedded in policy or merely embedded in hope.

Every discussion of what your AI systems can do should include an equally rigorous discussion of what they should do. The capability question is exciting. The governance question is what determines whether that excitement turns into value or liability.

The organizations that get this right will not be the ones with the most advanced AI. They will be the ones with the most thoughtful governance. And that starts with the executive team asking the right questions before deployment, not after the incident report.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller Infailible and Customer Transformation, and has been recognized as one of the Top 30 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, will be published in 2026.