Artificial intelligence (AI) is reshaping internal audit, but not in the way many initially expected.
Early conversations framed AI as a replacement for human auditors, particularly for lower-level, repetitive tasks. Today, that narrative has changed. Effective implementations typically demonstrate that AI does not replace the auditor. Instead, it augments the auditor and allows for a more efficient and valuable audit function.
The focus for internal audit leaders has shifted from whether or not to adopt AI, to how to integrate it in ways that improve efficiency without sacrificing quality, judgment, or accountability.
Where AI Delivers Measurable Value
Across internal audit functions, AI has proven very effective in handling structured, repeatable activities, including control testing, evidence collection, and first-pass workpaper drafting. These use cases allow organizations to accelerate audit execution while maintaining strong oversight through human review.
Early adopters are already seeing tangible results. In pilot programs, organizations have reported meaningful reductions in audit cycle time, particularly in areas such as information technology general controls (ITGCs) and standardized business process testing.
Notably, these gains are not coming at the expense of quality. In fact, many report improved outcomes when AI outputs are paired with human evaluation.
This reinforces a critical reality that the value of AI in internal audit is speed with enhanced consistency and scalability, coupled with human oversight.
The Importance of Foundational Control Clarity & Scalable Audit Design
Foundational control clarity is the disciplined effort to clearly define, document, and align the organization’s key controls to risks, processes, and ownership. It allows everyone involved to understand what the control is, what risk it mitigates, who owns it, how it operates, and what evidence proves it worked.
A consistent theme across both industry guidance and practitioner experience is that AI performance is only as strong as the controls it is built to evaluate. When risk and control matrices (RCMs) are vague, inconsistent, or poorly documented, AI tools may not be as effective.
On the other hand, when controls are clearly defined with explicit objectives, attributes, evidence sources, and evaluation criteria, AI can be set up for success to execute testing accurately and built to scale.
Organizations with forward vision are responding by elevating the RCM into a structured “audit test framework” that:
- Standardizes control attributes
- Defines acceptable deviations
- Encodes reviewer judgment into reusable rules
- Enables machine-readable audit logic
This investment in control clarity strengthens audit quality and creates the foundation for scalable AI adoption across the audit life cycle.
How To Align Organizations With Industry Guidance & Rising Expectations
Industry bodies and regulators are reinforcing that AI is becoming an expected component of the modern audit function. While AI can and should be used, accountability remains firmly with the auditor.
The Institute of Internal Auditors (IIA) has outlined frameworks for responsible AI adoption, emphasizing governance, ethics, and risk management. These frameworks point to practical starting points, such as scoping memos, evidence extraction, and draft workpapers, that align directly with how organizations are currently using AI.
At the same time, regulators are evolving their standards to address the use of AI-driven analysis. Updates to PCAOB standards (AS 1105 and AS 2301), approved by the SEC, clarify expectations around audit evidence and planning when AI is involved.
Transitioning From Point-In-Time Testing to Continuous Assurance
One of the most significant implications of AI adoption is streamlining continuous auditing and monitoring. Traditional audit models rely heavily on point-in-time testing, often resulting in delayed identification of control failures and reactive remediation.
As AI helps to reduce the cost and effort of testing, organizations can shift to ongoing control validation throughout the year.
This change enables:
- Earlier detection of control breakdowns
- Faster remediation cycles
- Greater visibility into emerging risks
- Reduced audit disruption during formal review periods
Technologies such as continuous controls monitoring (CCM) are making this shift increasingly feasible, allowing internal audit and management to work in tandem to deliver real-time assurance rather than retrospective insight.
Elevating the Role of the Auditor
As AI offers auditors the opportunity to automate repeatable work, the role of the auditor will also evolve.
The most valuable audit work remains inherently human. Humans are needed to:
- Connect isolated findings to broader business risk,
- Interpret patterns across functions,
- Translate technical issues into business impact, and
- Advise leadership on forward-looking risk mitigation.
Industry discussions highlight that audit findings alone rarely drive action. What matters is the ability to understand and articulate the risk, why it matters, and how to affect change within an organization.
Balancing the Use of AI in Internal Audit
While the opportunity is apparent, successful AI adoption in internal audit requires more than deploying new technology and piloting use cases. Organizations should take a deliberate, structured approach that includes:
- Strengthening foundational controls and RCMs,
- Identifying high-impact, repeatable use cases,
- Establishing governance frameworks for AI use,
- Integrating AI outputs into existing audit workflows, and
- Upskilling audit teams to interpret and validate AI-driven insights.
By automating repeatable processes and enabling continuous assurance, AI allows internal audit teams to move faster, operate more efficiently, and focus on delivering insight, identifying risk, and driving better business outcomes.
For organizations that invest in strong control frameworks and embrace a human plus AI operating model, internal audit has the potential to become a meaningful source of competitive advantage.
How Forvis Mazars Can Help
The internal audit team at Forvis Mazars helps organizations strengthen risk management, enhance control environments, and modernize audit functions. We work alongside clients to help build a more efficient, insight-driven internal audit function prepared for what’s next.
For further information, please reach out to a professional at Forvis Mazars.