Internal Audit and Artificial Intelligence Resources Available for Download:

The Next Phase: AI and Human Collaboration Powering Internal Audit Transformation

Look to AI technology enablement as a catalyst to empower CAEs and their teams to deliver stronger strategic oversight and value-added input for the business. 

AI Competencies for Internal Auditors in the Public Sector

Examine the critical integration of artificial intelligence (AI) competencies within the realm of internal auditing, particularly in the public sector.

Use of Artificial Intelligence and Audit Analytics in Internal Audit Processes in the Public Sector

Leverage digitization to optimize data management and fast-track the adoption of artificial intelligence in public sector internal audits and risk management.

The Business Case for Internal Audit Software

Explore how internal audit software improves workflows, collaboration and decision making while delivering clear ROI.

AI and Internal Audit

The Internal Audit function is increasingly moving from manual, sample-based work to data-driven continuous assurance, thanks in large part to the assistance of AI.

This information is timely as AI adoption accelerates across industries, reshaping talent strategies and operational methodologies. The Next Phase: AI and Human Collaboration Powering Internal Audit Transformation highlights the importance of building an AI-ready workforce skilled in adaptability, critical thinking and technology fluency. Practical advice includes conducting comprehensive skills inventories, leveraging AI-driven analytics for workforce planning, and piloting AI use cases to assess impact.

Key areas that are changing as a result of AI include:

• Continuous Auditing and Monitoring: AI is used to scan larger or even full data sets, so anomalies and issues can be detected earlier.
• Automating Routine Work: Common tasks like document extraction and administrative tasks are being automated.
• Risk Assessment and Planning: AI is now used to prioritize audit areas and spot emerging patterns to refine audit plans.
• Report Automation and Insights: AI is used to draft audit reports, summarize evidence, and generate management and executive-ready narratives.

AI and Internal Audit Procedures

Although AI is currently being used to assist with an increasing number of internal audit procedures, there are several that are gaining traction.

If you are just starting to use AI, begin by starting small. Pick one repeatable audit procedure, clearly define the data and exceptions, test the model based on historical cases, and perform a human review before relying on the audit file.

Below are several AI and internal audit procedures to consider:

Audit Planning and Execution (Including Fieldwork)

AI is being used to make both audit planning and fieldwork more data-driven and efficient, while leaving final judgment to human auditors. It does this by surfacing risks from both historical and external data, drafting scoping materials, and prioritizing engagements. While in the field, it can perform full population testing, flag anomalies, and group and prioritize exceptions. Because human auditors retain responsibility for judgments and conclusions, teams should validate outputs and model guidance, which typically include explainability, bias checks and traceability. If you’re just starting to use AI, start with a small, well-controlled pilot.

Risk Assessment

AI can assist with risk assessment activities because of its ability to continuously analyze large volumes of both structured and unstructured data to capture anomalies and patterns that humans may miss. In addition, these tools can score and prioritize risks in real-time, suggesting where mitigation may have the greatest impact with the broadest coverage.

During periodic or continuous monitoring, AI tools can raise real-time alerts, make investigations more efficient by pointing out focus areas and by supporting root-case analysis, hypothesis and scenario simulations.

More importantly, AI provides decision support and not decisions. Auditors should validate model outputs, assess evidence and apply professional judgement.

Testing

Many organizations are leveraging AI for testing. Internal audit practitioners are using AI to prepare data, highlight higher-risk items, and expand the coverage area to increase the number of test cases. AI tools have become so helpful in data analysis that they can ingest data from ERP feeds, bank feeds and unstructured sources to enable near-complete population testing, rather than smaller samples.

Additionally, with this increased amount of data, AI can apply rule-based checks, fraud detection, user behavior analytics and other unusual activity to notify human reviewers faster.

Lastly, AI can draft observations, recommend remediation plans, and summarize narratives, while human auditors validate the output, review evidence, and judge the quality of all documentation.

Monitoring

Similar to the way AI tools leverage large data sets to assist with testing activities, these same capabilities can be used to assist with monitoring. By continuously ingesting streams of both structured and unstructured data, AI tools can detect deviations from expected patterns, determine the priority and severity of potential risks, and deliver real-time alerts to stakeholders so that issues can be immediately investigated.

AI tools support the tracking of remediation activities and fixes that have been implemented, so regression testing can surface issues. For monitoring, these regression tools can feed dashboards and alerts that can show trends, reopen any relevant regression test cases, and trigger follow-up testing to ensure that all fixes are stable.

Reporting

Reporting, one of the more popular areas for which internal audit professionals are leveraging AI, can assemble and generate narratives faster while maintaining consistency. AI tools can generate natural-language summaries, executive summaries and observations. They can produce tailored outputs for different audiences, such as technical appendices, executive board summaries, control gap explanations for operational owners, and heat maps for program managers.

As with all procedures, AI should be treated as decision support, with human review required before publishing.

AI and Internal Audit Standards

AI and internal audit standards have shown an increasing level of maturity, enabling organizations to lower risks, strengthen assurance, and scale internal audit capabilities faster to meet the scale at which AI technologies are being deployed.

Without standards, audit measures can become fragmented with different teams using different definitions of risk, running different tests, and obtaining approvals via separate paths.

Discover three insightful articles that emphasize the integration of AI within the Internal Audit function, each accompanied by key takeaways to enhance your understanding and application of these concepts.

AI Competencies for Internal Auditors in the Public Sector

Integrate artificial intelligence (AI) competencies to bridge skill gaps and empower internal auditors, especially in the public sector. Trace the evolution of AI to understand its growing influence on audit methodologies and recognize the urgent need to adapt, embrace new skills, and respond proactively to technological advancements.

Key Takeaways:

• Bridge gaps in AI-related skills for internal auditors.
• Adapt quickly to emerging technologies and evolving audit practices.
• Apply practical AI competencies to enhance audit effectiveness.

Use of Artificial Intelligence and Audit Analytics in Internal Audit Processes in the Public Sector

Embrace digitization to reshape management processes, redefine roles, and meet expanding objectives within the public sector. Manage complex data generated by public services and expedite the integration of AI into internal audits, enhancing risk management and audit frameworks.

Key Takeaways:

• Streamline data organization and processing amid growing complexity.
• Accelerate AI adoption in internal audits for improved risk management.
• Enhance audit frameworks by leveraging advanced analytics and models.

The Business Case for Internal Audit Software

Automate workflows and centralize processes using internal audit software to boost efficiency, foster collaboration, and strengthen risk management capabilities. Equip audit teams with predictive analytics and real-time tracking to navigate regulatory pressures, improve operational efficiency, and build stakeholder trust through enhanced governance.

Key Takeaways:

• Automate repetitive tasks for greater audit efficiency and consistency.
• Enable predictive analytics to inform better decision making.
• Strengthen risk management and transparency with centralized audit solutions.

AI and Internal Audit Tools

Internal audit tools that leverage AI add value to the Internal Audit team by allowing them to expand coverage and to produce clearer, more consistent results. From an organizational perspective, this means improved risk management and remediation.

Here are a few common tool types:

• Audit Management Platforms: These tools help the Audit team manage the audit lifecycle. They support consistent planning, risk assessment, tracking and status reporting.
• Audit Analytics Tools: Analytics tools can analyze large data sets to surface exceptions, trends or unusual patterns. They can turn raw data into actionable insights by identifying outliers and threshold breaches.
• Document Automation Tools: These tools can reduce manual effort in collecting and reviewing evidence by extracting data from invoices, contracts, statements and other source documentation.
• Generative AI Tools: Generative AI tools can support auditors with researching, drafting and rewriting tasks. They can turn notes into clear and consistent narratives, as well as prepare draft audit observations.

AI and Internal Audit Practices

AI is increasingly being used by internal audit practitioners to improve planning, testing, reporting and continuous monitoring. Below are some best practices to consider when combining AI and internal audit:

• Leverage AI to identify higher-risk areas, suggest priorities, and generate planning notes and interview guides when scoping and planning an audit.
• Leverage standard templates, especially for AI-supported work, to improve consistency and thoroughness through the use of AI and internal audit templates.
• Analyze large transaction sets, flag anomalies, and support more comprehensive testing by leveraging AI's analytical capabilities.
• Use AI to assist with creating first drafts of findings and reports, regardless of the audience, to streamline these time-consuming tasks.
• Tailor a pilot use case focusing on one or two narrow workflows if recently starting to use AI, and observe and measure the outcome.