When “Close Enough” Becomes a Liability: The Real Test for Agentic AI in Risk Management

AI is everywhere in the enterprise. It drafts content, summarizes documents, flags anomalies, and surfaces recommendations. By most measures, adoption is accelerating. According to McKinsey, 88% of respondents reported regular AI use in at least one business function. 

But here’s what that number obscures: most of that AI still isn’t held accountable for anything. 

In the majority of deployments, AI plays an assistive role where humans remain in the loop, make the final call, and own the outcome. In this model, the stakes are low and AI recommendations that are “close enough” are more acceptable because the human can override them. But agentic AI is moving into a new, higher-stakes territory where the output must be auditable because the decision is consequential and someone else’s name is on it.  

That’s a fundamentally different environment, and risk management is one of the places where the gap between what AI promises and what it can actually be trusted to do is most exposed. 

Most Organizations Are Further Behind Than They Admit 

A study by MIT Sloan Management Review and Digital Applied found that while 79% of organizations report adopting AI agents, only 11% run them in full production mode — meaning they operate independently, outside of experimental or pilot conditions. The rest are still hedging. 

Risk and compliance professionals are not skeptical of agentic AI because they don’t understand it. Most of them understand it well enough to know what could go wrong. They’re skeptical because the tools haven’t consistently demonstrated that they’re reliable, explainable, and controllable in environments where the stakes are real.  

Earlier AI was advisory in that it surfaced information and left the decision to a human. Agentic AI drives workflows, completes tasks, and influences outcomes in real time. That shift, from informing decisions to making them, changes the accountability structure entirely: A flawed recommendation can be ignored, whereas a flawed action may already be in motion before anyone catches it. 

Why Third-Party Risk Management (TPRM) Is the Hardest Test 

Third-party risk management is one of the clearest use cases for agentic AI, and yet one of the most unforgiving environments to deploy it in.  

Operationally, risk teams manage thousands of suppliers, review massive volumes of documentation, and track evolving regulations across fragmented systems. Most of that work still runs on manual processes, periodic reviews, and repetitive administrative tasks.  

At a certain point, the volume and complexity outpace what those processes were designed to handle. 

But scaling those decisions is only part of the challenge. From a compliance standpoint, supply chain and vendor risk decisions must also be defensible under audit, often for years after they are made. When a regulator asks why a decision was made, “the agent decided” is not an acceptable answer.   

This is what makes TPRM a genuine test of whether agentic AI is ready for high-stakes environments. The work is data-heavy, time-sensitive, and tied directly to business outcomes where the margin for error is narrow. There are steep financial, legal, operational, and reputational costs for acting on false positives or dismissing false negatives. 

It’s why most current TPRM deployments stop short of autonomous decision-making. Vendor risk scoring, adverse media monitoring, and sanctions screening are meaningful capabilities, but they still automate workflows and provide inputs for human decisions. They are not yet systems that can own a decision.   

The Right Foundations. Not the Fastest Deployment 

The organizations who will get agentic AI right in risk management are the ones that are investing in the foundations first. 

Agentic AI is only as good as the data it works from. If it’s running on generalized, disconnected, or ungoverned inputs, the outputs won’t be reliable enough to act on, and they certainly won’t be defensible under audit. Before evaluating any AI capability, ask whether you have a structured, governed system of record that gives the technology real context. If the answer is no, that’s the first problem to solve.  

The other mistake most organizations make is removing the human from the loop too early.  Giving people visibility into what the AI is doing at every step, and the ability to step in when something looks wrong, is what builds the confidence that allows autonomy to expand responsibly. Every AI-generated output should be searchable, retrievable, and auditable to its originating data and logic.  

The same discipline should apply to scope. Start with workflows where the inputs, outputs, and decision criteria are well-defined. Get confident there before widening what the AI is trusted to do. The temptation to scale quickly is real, but shadow AI already taught us what happens when adoption moves faster than governance. Agentic AI is a higher-stakes version of that same lesson. 

Autonomy also deserves more attention than it usually gets. How much do you delegate, and when? Get it wrong in one direction, and you’ve removed the human oversight that catches errors. Get it wrong in the other, and you’ve built an expensive tool that still requires a human to do everything. Neither outcome justifies the investment or the risk. 

What “Trusted” Actually Requires 

The periodic review model that most risk programs still rely on is becoming unsustainable. A supplier’s security posture, financial health, or regulatory exposure can change overnight, and it doesn’t wait for the next assessment cycle.  

Agentic AI can change this, but only if it is deployed in a way that earns the trust it requires to function. Here’s what that looks like in practice:  

• Outputs must be reliable without requiring continuous human validation. The efficiency case is gone and the accountability gap doesn’t close if risk professionals have to check every AI-generated recommendation before acting on it. 

• Decisions must be explainable to internal stakeholders, regulators and auditors who will ask hard questions. The reasoning has to be traceable, even when it happens across multiple steps and inputs. 

• Accountability cannot disappear into the system. Someone still needs to own the outcome. As agentic AI matures, the role of risk professionals will evolve where they spend less time chasing information and more time on judgment and exception management. But accountability doesn’t transfer to the machine. 

The conversation about agentic AI is met with either enthusiasm or alarm. Neither is particularly useful for the professionals who have to make real decisions about how to deploy it.  

Adopt agentic AI or don’t, but be honest about whether your program is built to use it responsibly, and whether the tools you’re evaluating are built for environments where accountability is real, not assumed. 

The same discipline that makes a good risk program makes a good agentic AI deployment. Know what you’re working with. Understand where the exposure is. Build the controls before you need them. And don’t let the pressure to move fast outrun the judgment to move well.