We are now 3 years into the generative AI era, and many organizations have dramatically embraced AI to transform their operations. Yet many internal audit functions remain slow to embrace the technology. AuditBoard’s 2026 Focus on the Future Report highlights the gap. Only 25 percent of internal auditors say they are actively using AI or automation tools as 2025 comes to a close. About 50 percent say they are experimenting or piloting these capabilities. Seventeen percent expect to adopt AI within the next two years. The remainder say they have no plans to use AI within the next 12 months.

That last group should concern internal auditors and their stakeholders around the world. Internal audit cannot keep pace with rising risks and stakeholder expectations if it avoids the tools the rest of the organization is adopting. Still, resistance is real, and it deserves examination. When auditors explain why they are not ready to use AI, the same reasons appear repeatedly.

Below are the top five reasons auditors cite, along with practical steps your team can take to overcome them.

1. Lack of in-house expertise

Many audit functions say they simply do not have the skills to use AI. Their teams lack data literacy. They lack familiarity with AI tools. They fear missteps. They worry they will not know how to validate outputs. Without confidence, they default to what they know.

This challenge slows many teams, but it is solvable. You can build capability through deliberate learning.

Strategies to address the gap:

• Start with essentials. Train your team in basic AI concepts, risks, and use cases.
• Use pilot projects that require limited technical depth. Let internal auditors learn by doing.
• Create internal champions who share insights and guide others.
• Partner with IT or data science teams for support.

Skill development does not require advanced coding. It requires curiosity and structured practice. When your team gains confidence, adoption increases.

2. Limited understanding of AI capabilities

Many internal auditors do not know what AI can do for them. They still view AI as futuristic or abstract. They do not see its relevance to audit planning, fieldwork, or reporting. They do not recognize that AI is already reshaping analytics, documentation reviews, and continuous monitoring.

This lack of understanding creates a real barrier. If internal auditors cannot connect AI to their daily responsibilities, they will not use it.

Strategies to bridge the knowledge gap:

• Demonstrate practical use cases such as contract reviews, anomaly detection, or trend analysis.
• Highlight documented gains in speed, accuracy, and coverage from teams using AI.
• Encourage internal auditors to experiment with small tasks, such as summarizing evidence or analyzing simple datasets.

Once the team see AI ease workloads and improve insight quality, skepticism fades.

3. Concerns about data privacy or security

Internal auditors are trained to protect sensitive information. Many worry that AI tools will expose confidential data or increase security risks. They fear regulatory scrutiny by others in their organizations or even external regulators. They fear breaches. They fear that placing data into AI systems will violate internal policies.

These concerns are legitimate. They are also manageable.

Strategies to strengthen confidence:

• Work with IT and legal to establish clear rules for approved AI platforms.
• Require data minimization. Use only the information needed for the task.
• Apply strong access controls and document all safeguards.
• Conduct risk assessments on any AI tool before adoption.

When auditors see a structured governance framework in place, they are more willing to engage.

4. Executive leadership is not prioritizing AI

Some internal audit functions say their organizations’ leadership is indifferent or uncertain about AI. When executives do not champion innovation, internal audit often waits. Without sponsorship, CAEs may avoid proposing AI investments. Staff may assume the organization is not ready. This leads to paralysis.

The challenge here is cultural. It reflects an organization that has not yet connected AI to strategic value.

Strategies to build support:

• Present leaders with examples of how AI improves assurance quality and efficiency.
• Start low-cost pilots that demonstrate value with minimal disruption.
• Use evidence to show how competitors and peer organizations are advancing.
• Tie AI adoption to enterprise risk management and strategic goals.

Leadership does not oppose AI. They often lack clarity. You can help them see its relevance.

5. A “wait and see” mindset

Many internal auditors admit they prefer to watch others pioneer the way. They want clearer standards. They want proven methodologies. They want certainty before they act. They believe the risks of early adoption outweigh the benefits.

This mindset slows progress. It also creates vulnerability. When auditors delay learning, they fall behind the risks they must evaluate.

Strategies to shift the mindset:

• Start with safe, controlled pilots that carry minimal risk.
• Use lessons from early adopters to guide practice.
• Reinforce that experimentation does not require full-scale deployment.
• Track and communicate small wins to build momentum.

You can take incremental steps without jeopardizing audit integrity. Waiting for perfect clarity prevents growth.

What these barriers mean for the profession

When internal auditors decline to adopt AI, they do not avoid risk. They create it. The risk is slower insight. The risk is reduced coverage. The risk is misalignment with what boards and executives expect.

Internal audit functions that avoid AI will struggle to address bourgeoning risks. They will struggle to process the volume and complexity of data their organizations generate. They will struggle to deliver timely assurance in a volatile environment.

AI will not replace internal auditors. It will amplify the value of those who use it. It will help auditors focus on judgment, relationships, foresight, and ethical decision making. These are human strengths. AI supports them by reducing routine tasks and surfacing insights that would be difficult to identify manually.

The profession has navigated similar transitions before. Data analytics, automation, and cybersecurity all began as intimidating domains. Today they are foundational. AI is on the same path.

It’s not a matter of if, but when

Adoption will continue to rise. The pace, however, depends on how leaders respond to the barriers above. CAEs who help their teams build skills, understand capabilities, address security concerns, engage leadership, and experiment safely will position their functions for relevance and impact.

You do not need to become an AI expert to start. You need awareness, structure, and a willingness to learn.

The risks facing your organization move fast. AI can help you keep up. The sooner you begin building your team’s capability, the stronger your impact will be.