How AI Will Impact Compliance Teams’ Work and Staffing

Understanding AI’s compliance capabilities 

Yes, AI can do a wide range of compliance work, and do it quite well – but doing compliance work is not necessarily the same as executing compliance processes.  

Large language models (LLMs) from OpenAI, Microsoft, Google and Anthropic – often called “frontier models” – power nearly every AI system compliance officers encounter. These models excel at tasks such as matching or mapping data, extracting information, and ranking elements based on risk or urgency. They are great at language translation and pattern analysis. They can summarize long documents and suggest next steps in a course of action. 

Those tasks are all work that compliance teams must get done; if AI can help to do that work faster, that’s great. On the other hand, if you ask compliance officers what they do every day, they’ll rattle off a list of processes: 

• “I review whistleblower reports to identify serious issues that need immediate attention.” 
• “I assess conflict of interest submissions from employees, and consider whether we need more detail before coming up with a mediation plan.”  
• “I assess employee training needs based on risks we have, and confirm that the training materials we provide reflect those issues and work well.” 

Every compliance process consists of multiple smaller steps, and many of those steps are the work that AI can do well. AI, however, still struggles to string all that work together into one self-contained process that it can execute as well as a person. 

So yes, AI can categorize conflict of interest (COI) submissions by issue, region, and so forth; but it struggles to understand what additional detail might help to determine the true risk in a COI submission. It can take a written policy and cook up a training video; but it can’t easily ponder internal hotline and training data to devise a new policy that better addresses employee behavior.  

For now, AI is still very much a tool compliance teams can use to achieve your program objectives. It can’t achieve your program objectives on its own – that still requires people.

Fitting AI into your compliance program 

Even when used simply as a tool, AI unquestionably can help chief compliance officers to improve your program overall. The real question for 2026 is how to integrate AI into your compliance program.  

First, consider the objectives you and senior management want to achieve. AI can help with all sorts of process improvements, but making those improvements does need careful planning. 

For example, you could use AI to build a policy chatbot that answers employees’ questions about compliance policies. Lots of companies are already experimenting with this exact idea. Early evidence suggests employees do engage with policy chatbots often, which is good.  

At the same time, however, you may need to write longer policies, and update them more often, to assure the policy chatbot gives current and correct answers. You might also have more policy escalations to evaluate since employees are asking the bot more questions (sometimes, lots more). 

In other words, policy chatbots can help you build a more engaging compliance program, but that effort might rearrange the work your compliance team does – eliminating some work here, creating new work there. Some of that work, AI will handle; other work, human employees will still need to manage. 

That’s the sort of analysis chief compliance officers will need to perform in 2026 and beyond: “If we introduce artificial intelligence into this compliance process, how will it change that process? And what will that change mean for my own team supporting that process, and what will it mean for others engaging with that process?” 

The list of potential scenarios here is long. AI systems will transform your compliance program, leaving it able to do more things more efficiently. That doesn’t automatically mean AI will make your program more efficient or less complicated – the work of compliance teams will adjust to support these new capabilities.

The other challenges AI introduces 

Compliance officers also need to think about the larger strategic questions of how to “fit” AI into your compliance program and your organization’s IT environment.  

For example, most LLMs can now do most compliance work quite well – but no single LLM does all compliance work consistently well. So, would you want to use one LLM that is good at all your compliance tasks, but not necessarily great at the one or two compliance tasks that matter most to you? Or would you want to use multiple LLMs, each one focused on your most common or pressing needs?  

Using only one LLM would save money and reduce security and operational risks, but may sacrifice performance. Using multiple LLMs may bring better performance, but may introduce more security and operational risk. So how would you, your CISO, your IT manager, and your CFO decide which choice is best?  

The questions about using AI strategically in your compliance program are many. They’ll need to be answered too.  

Compliance officers will also play a crucial role helping the rest of the enterprise as they integrate AI into their operations, too.  

First, business units will need help identifying and assessing the compliance risks that might arise from weaving AI into their processes. Then they’ll need help developing and implementing new controls to make sure their AI-enhanced business processes avoid privacy, security and compliance risks.

What does that mean in practice? Consider a few steps. 

Develop an AI governance model.  

That is, a structured approach for senior executives (ideally, a team including heads of Legal, IT Security, HR, Finance, and of course Compliance) to review and approve AI implementation across the enterprise, so those use-cases properly handle the company’s compliance obligations. Good news: according to the NAVEX 2025 State of Risk & Compliance Report, two-thirds of compliance officers are either “very” or “somewhat” involved in deciding how AI is used within your organization. 

Use frameworks to guide your AI implementation.   

Frameworks can help you identify new regulatory risks, gaps in your existing controls, and new controls that might plug those holes. Two leading frameworks today are the ISO 42001 standard for AI management systems, popular in Europe; and the NIST AI Risk Management Framework in the United States. Australia, Singapore, and other countries are rapidly developing their own regulatory frameworks too, so keep your eyes open globally. 

Prepare to help.

As your organization rolls out new policies or controls for AI, you’ll need new training and communication materials to help employees understand AI risks and behavior. You’ll need policy management capabilities to identify new AI regulations as they arrive to see whether your policies need updating. You’ll need testing, auditing and data analytics capabilities geared for AI risks, too.

2026 prediction for AI in compliance

Concerns about an “AI bubble” will become more pointed –  primarily for the LLMs spending billions on data center infrastructure, but even for other AI businesses too. Executives will want to see clear and specific ROI for proposed AI investments, so team leaders trying to integrate AI into their operations will need good answers for productivity gains, risk management requirements and data management challenges. 

This article is part of our 2026 Top 10 Risk & Compliance eBook. Check out the full eBook for more expert predictions for the year ahead.