Where AI delivers value in third-party risk management

For several years now, conversations about AI in third-party risk management have been oddly lopsided. The technology is discussed as though the central question is whether machines can assess risk as well as people. It is an interesting question, but not the one most organizations should be asking.

The real constraint inside most third-party risk programs has never been a shortage of professional judgment. Risk teams already know how to evaluate critical suppliers, challenge weak controls, and escalate material concerns. Their problem is that too much of the working day disappears into administrative labor that contributes little strategic value. For example, analysts:

• Chase evidence
• Reconcile conflicting records
• Read hundreds of questionnaire responses that differ only slightly from one another

The intelligence exists, but the capacity does not. This explains why AI is beginning to find a practical role in third-party risk management. Recent findings from EY's 2025 Global Third-Party Risk Management Survey reflect the same shift, with organizations increasingly focusing AI investments on improving the efficiency, consistency, and scalability of third-party risk operations rather than replacing expert decision-making.

Technology delivers its greatest value not by replacing experienced analysts, but by removing the friction that prevents those analysts from spending their time where it matters most. Organizations looking for autonomous risk management are likely to be disappointed. Organizations looking to eliminate repetitive, administration-heavy work are finding something far more useful.

This article will cover the following:

• AI addresses operational challenges in third-party risk management
• AI outcomes depend on data quality
• Where AI and automation create third-party risk management value
• AI changes the economics of third-party risk management
• Why human judgment still matters in risk management
• Using artificial intelligence to improve third-party risk management efficiency
• FAQs

AI changes the economics of third-party risk management

Perhaps the most important effect of AI is not what it changes about third-party risk management itself. It is what it changes about the economics of operating a mature program. Historically, scaling a third-party risk function usually meant hiring more analysts. Every additional supplier increased review workloads, documentation requirements, follow-up activities, and reporting obligations. AI changes that equation, though. Analysts spend less time:

• Collecting information because much of that work can be automated
• Organizing documentation because relevant evidence can be surfaced automatically
• Producing summaries because first drafts already exist before they begin their review

The real value lies not in hours saved, but in how those hours are used, providing greater opportunities for deeper analysis. Risk professionals can devote more attention to complex suppliers, emerging threats, concentration risk, fourth-party dependencies, regulatory implications, and business conversations that technology cannot conduct on their behalf. What’s important is that risk management does not change with AI. What changes is the efficiency of the people doing the work.

Why human judgment still matters in risk management

There is one line AI has not crossed, and there is little reason to believe it ever should. Risk management has always been about more than processing information. It is about weighing competing priorities, understanding business context, and making decisions that remain defensible long after the facts have changed.

Risk decisions require context that extends well beyond available evidence. Materiality depends on business strategy, regulatory expectations, operational dependencies, contractual obligations, organizational risk appetite, and other factors that rarely appear together in structured datasets.

A supplier may satisfy every security requirement while presenting unacceptable geopolitical exposure. Another may demonstrate technical weaknesses that become acceptable because replacing the relationship would create even greater operational risk. These decisions involve trade-offs, not calculations.

AI can organize evidence, identify patterns, summarize documentation, and recommend areas for review. It cannot determine whether accepting a particular level of risk aligns with organizational objectives. Judgment remains a human responsibility because accountability remains a human responsibility.

Using artificial intelligence to improve third-party risk management efficiency

The conversation surrounding AI in third-party risk management is gradually becoming more practical, and that is a very welcome development. Organizations no longer need to ask whether AI will eventually replace risk professionals. The more pertinent question is where it removes enough administrative effort to let those professionals do the work that only they can do.

The answer seems obvious. AI performs best where work is repetitive, document-heavy, and constrained by volume. It helps:

• Accelerate evidence review
• Improve consistency
• Summarize information
• Identify changes in controls year over year
• Identify patterns across supplier populations that would otherwise remain hidden

These capabilities are critical because they expand the capacity of risk teams without diminishing the importance of human expertise.

The future of AI in third-party risk management is unlikely to resemble autonomous compliance functions making unsupervised decisions. It looks considerably more useful than that. It looks like experienced analysts who no longer spend their time searching for information but using their expertise to assess it. That is not a replacement for judgment. It is the condition that allows good judgment to scale.