The question at hand
Your compliance team spots a potential red flag in a vendor's regulatory history. Your new AI tool flags it as high-risk and recommends termination. Do you act on that recommendation immediately, or do you treat it as one input among many?
This scenario is becoming increasingly common. More than half of compliance professionals are now using or trialing AI, up from 30% in 2023. As these tools become standard for third-party risk management, you're facing a practical question: How much decision-making authority should AI have in your due diligence process?
The debate divides compliance teams. One side sees AI as a solution for overwhelmed risk programs managing numerous vendor relationships. The other worries about automating judgment calls that require human context. Both perspectives have merit.
The case for AI-driven decisions
Let's start with the efficiency argument. If 83% of executives plan to expand their partner networks over the next three years, manual review isn't scalable. You need technology that can process thousands of vendor profiles and highlight the ones that matter.
AI excels at pattern recognition across massive datasets. It can flag adverse media mentions, cross-reference sanctions lists, and identify regulatory histories faster than any analyst. When approximately 90% of Foreign Corrupt Practices Act enforcement matters between 1978 and 2023 involved a third-party intermediary, you need systems that can spot those risk patterns before they become enforcement actions.
Consistency is another advantage. Human reviewers have off days. They miss things when they're tired or distracted. They apply different standards depending on their mood or workload. AI doesn't. It applies the same criteria to every vendor, every time. That consistency can strengthen your defense if regulators question your due diligence process.
AI also synthesizes data quickly. It can pull together regulatory histories, sanctions listings, adverse media, and relationship-specific risk data in seconds. It can weight these factors according to your risk appetite and generate scores that help you prioritize where to focus human attention. For large enterprises managing hundreds of thousands of third-party relationships, this filtering function is essential.
The case for human-centered oversight
But here's where the counterargument gets strong: AI can't understand context the way humans can.
Consider regulatory and legal histories. Yes, 40% of practitioners view these as critical for due diligence. But not all companies, especially smaller ones, have documented violations. A clean record might mean strong compliance, or it might just mean they haven't been caught yet. Conversely, a past violation that led to genuine remediation might indicate a vendor that takes compliance more seriously now than one that's never been tested.
AI struggles with these nuances. It sees patterns in historical data, but it can't assess whether a company's leadership has changed, whether their compliance program has matured, or whether the context of a past violation is relevant to your current risk profile.
The risk of bias is real. Nearly one-quarter of compliance professionals in recent discussions identified AI bias as their top concern with the technology. If your AI tool learns from historical data that reflects past biases, it will perpetuate them. It might flag vendors in certain countries or industries more aggressively, not because the risk is actually higher, but because your historical enforcement patterns were skewed.
Then there's the overreliance problem. When AI generates confident-looking risk scores, people tend to accept them without sufficient verification. You see a vendor scored at 85 out of 100 for risk, and you move on. But what if that score weighted factors that aren't actually relevant to your business? What if it missed reputational risks that don't show up in structured data?
The DOJ Criminal Division Guidance emphasizes that a well-designed compliance program should apply risk-based due diligence to third-party relationships. That word "risk-based" implies judgment. It means understanding the business rationale for a relationship, assessing the qualifications and associations of partners, and considering reputational factors. These are judgment calls, not algorithmic outputs.
Where practitioners actually land
Most compliance teams aren't choosing between AI and human judgment. They're trying to figure out the right division of labor.
In practice, AI handles the heavy lifting: organizing data, summarizing documents, flagging potential issues based on predefined criteria. Humans make the decisions. They review AI-flagged vendors, investigate causes, corroborate findings with other sources like hotline reports, and apply business context that AI can't access.
The challenge is defining clear handoff points. When does an AI flag require human review? What thresholds trigger escalation? Who has authority to override AI recommendations, and under what circumstances?
Your answer will depend on your organization's size and risk profile. Large enterprises with massive vendor portfolios might set stricter thresholds, using AI to filter out lower-risk relationships and focus human attention on the highest-priority cases. Smaller organizations might use AI primarily for data gathering, with humans conducting most of the analysis.
Our take
AI should inform third-party risk decisions, not make them.
Here's why: The stakes are too high and the context too variable to fully automate these judgments. But you can't ignore AI's capabilities either. The volume of third-party relationships most organizations now manage makes purely manual review unsustainable.
Build your process this way: Use AI to aggregate data, identify patterns, and generate preliminary risk assessments. Treat those assessments as one input, not the final word. Require human review for any decision with significant consequences, whether that's vendor termination, enhanced due diligence, or relationship approval.
Document your decision criteria clearly. If you override an AI recommendation, record why. If you accept one, note what additional factors you considered. This documentation protects you if regulators question your process later.
Invest in data quality before you invest in AI sophistication. As practitioners consistently note, the effectiveness of analytics depends on the quality of underlying data. Clean, well-structured data produces better AI outputs than advanced algorithms working with messy information.
Finally, audit your AI tools for bias regularly. Test whether they're flagging vendors consistently across geographies and industries. Check whether their recommendations align with your actual risk experience. Be willing to adjust algorithms when they don't.
The goal isn't to choose between AI and human judgment. It's to use each where it's strongest: AI for scale and pattern recognition, humans for context and accountability.
