What Does the AI Revolution Mean for Compliance Employees?

Where AI is used in Compliance

AI is already being used across several parts of Compliance, including training, monitoring, reporting, investigations and policy administration. 

Our 2026 State of Risk & Compliance survey results indicate AI use was highest in training, at 42%. This was followed by monitoring and surveillance at 33%, and program reporting and analytics at 32%. Investigations support and policy administration both stood at 29%. 

The impact on employees in Compliance roles depends on the task being supported, including finding information, preparing summaries and identifying records that need closer review. In each area, AI can take on part of the process while human experts remain responsible for checking the output and applying context. 

Policy guidance and creation 

AI-assisted policy search can help employees find relevant wording across approved documents and return answers with citations. AI-generated summaries can also show reviewers what has changed between policy versions without requiring a line-by-line comparison. 

This reduces time spent retrieving guidance and identifying revisions. Employees can reach approved information more quickly, while policy owners and approvers focus on whether proposed changes are accurate, complete and appropriate. 

Research suggests AI can help reviewers identify relevant information more quickly. A 2026 scoping review in healthcare publishing found that GPT-4 feedback overlapped with human reviewers by around 30-35%, which was comparable with agreement between human reviewers. The authors also suggest AI helped reduce review fatigue by identifying and organizing relevant points. 

The study examined scientific publishing rather than compliance as a function, but the same division of work is useful here, as reliable source material underpins both uses: AI supports document review, while people should make the final judgment. Policy writers and managers need to keep documents current, confirm that the system has selected the right source and check that any summaries are accurate. 

Reviewers also need the expertise to recognize when the available guidance is not enough because local law applies, policies conflict or the circumstances fall outside the policy. In those cases, the issue may need further interpretation or escalation. 

AI can make policy guidance easier to find and review, but employees still need to decide how it applies when the answer is unclear or the situation falls outside the policy. 

Investigations 

Investigation teams are handling a steady volume of reports while cases are taking longer to close. Our 2026 Whistleblowing & Incident Management Benchmark Report recorded a median reporting rate of 1.65 Reports per 100 Employees in 2025, alongside an increase in median case closure time from 21 to 28 days. Taken together, the record levels of reporting and a 33% increase in case closure time show the pressure on teams to keep investigations consistent, timely and aligned with global whistleblowing regulations. 

AI can help by summarizing reports, suggesting categories, routing cases and identifying similar matters. Purpose-built compliance platforms can support these tasks within established case management workflows, retain the original submission and keep sensitive information in an approved environment. Investigators can then focus on gathering evidence, assessing credibility and reaching findings they can support. 

Using general-purpose AI to summarize case information or organize evidence creates a different risk. Sensitive information may be entered into an unapproved service, and the output may not preserve the original record or show how it reached its conclusion. General-purpose AI also lacks the compliance-specific context needed to fully understand the nuance of investigations. Clear rules should define which tools can be used, what information can be entered and where human review is required. 

The information reaching an investigator may already have been shaped by AI. The 2026 Safecall Whistleblowing Benchmark Report notes growing signs that employees are using public AI tools to draft reports. This may help them structure a concern, but it can also alter the wording, omit useful detail or expose personal and confidential information. Reporting guidance should encourage people to explain concerns in their own words, provide as much detail as possible and use available in-language reporting options. 

Investigators should compare any summary with the original submission and contact the reporter when clarification is needed. AI can help prepare information for review, but investigators still need to assess the evidence and decide how the case should proceed. 

Risk analysis and third-party review 

Risk analysis depends on connecting information from across therisk and compliance program, including reports, disclosures, training records, audits and previous risk assessments. AI can compare those records over time and flag changes or overlaps that may warrant closer review, reducing the time analysts spend collecting information from separate sources. 

One example comes from recent U.S. Department of Justice guidance on AI governance. As we explore in our article on AI risk assessment and antitrust risk, the DOJ expects organizations to understand how AI is used, assess the risks it introduces and apply the same compliance disciplines they would to any other business risk, including policies, training, due diligence, testing and ongoing monitoring. AI can support that work by bringing information together and helping reviewers decide where to focus first. The analyst’s role then shifts from assembling the evidence to testing it, explaining it and deciding how the organization should respond. 

A rise in reports could indicate more misconduct, greater awareness of reporting channels or a change in classification. Regional differences may reflect local exposure, reporting culture or uneven use of the compliance program. Analysts need to test those possibilities against the underlying records before changing a risk assessment or recommending additional controls. 

The same shift applies to third-party screening, monitoring and due diligence. AI can screen large volumes of external information, identify possible matches and prioritize records for review. Reviewers still need to confirm the correct person or organization, examine the reliability and currency of the source and decide whether the finding changes the organization’s assessment of the relationship. Also important to bear in mind are training cutoffs for LLMs, which could result in third-party screening that might exclude more recent events. 

The NIST AI Risk Management Framework takes a similar approach. It recommends that organizations define where human oversight is required, understand the provenance and quality of the data and AI systems they rely on, and document the controls used to monitor AI throughout its lifecycle. Those checks become especially important when external data, third-party AI services or automated recommendations form part of the process. 

AI can help bring together the information needed for a risk assessment, but reviewers still need to determine whether the evidence supports a finding and whether further action is warranted.

Why human-in-the-loop compliance is needed when using AI

Human review is needed whenever AI contributes to a policy answer, an investigation, a risk assessment or a third-party decision. Employees need to be able to check the evidence behind the output, understand how it was produced and decide whether it is appropriate to rely on. As organizations adopt AI, and regulations on AI usage continue to develop, human oversight helps ensure decisions can be reviewed, explained and supported. 

Research into AI-assisted knowledge work helps explain why that oversight is needed. A study by Fabrizio Dell’Acqua and colleagues found that AI improved performance on some consulting tasks. In a field experiment involving 758 management consultants, participants used GPT-4 for realistic work such as generating ideas, analyzing information and drafting recommendations. Those using AI completed 12.2% more work, finished 25.1% faster and produced higher-quality results. 

The result was different when consultants were asked to recommend a strategy using financial data and employee interview transcripts. The financial data pointed toward one conclusion, but the interviews contained information that changed how those figures should be interpreted. GPT-4 often followed the misleading pattern in the financial data. Consultants using AI were 19% less likely to reach the correct answer than those working without it.  

So, what does this mean when it comes to compliance? 

When can AI get compliance work wrong? 

Many employees are already using AI to search policies, summarize reports and draft documents. Without approved tools and clear guidance, those activities can take place outside established compliance workflows, making it harder to verify the output, protect sensitive information and retain the evidence needed to support decisions. 

An AI tool that performs well in one situation may produce much less reliable results in another, even when the tasks appear similar. Users may not recognize where that boundary lies – and when AI is used outside approved workflows, organizations have little visibility into how those outputs were generated, reviewed or used to support decisions. 

This is a serious limitation of general-purpose AI used for compliance work. A general-purpose AI tool may give answers that appear correct, but fail to use the right policy, preserve the original case record or draw from verified third-party data. Information entered into public AI services may also leave established compliance workflows, creating additional risks around confidentiality, recordkeeping and governance. 

Purpose-built, AI-powered risk and compliance tools can reduce some of these risks because they are designed around the work being performed. They can use approved sources, operate within established workflows and retain the information reviewers need to check the result for accuracy. This gives Compliance teams the context they need to review the output against the underlying evidence before relying on it.