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The future of Audit

AI and the Next Evolution of Auditing: Enhancing Accuracy, Efficiency, and Insight

Note: Further reading and sources later on page

Paper on Internal Audit & Risk Commentary — October 2024

Artificial intelligence is reshaping both external and internal auditing by enhancing accuracy, efficiency, and insight. Major audit firms and academic researchers are accelerating innovation in the field, and AI’s capabilities are now accessible to practitioners across all industries. The technology provides unprecedented analytical capacity, enabling auditors to deepen their examination of data and strengthen assurance without proportional increases in cost or time.

This paper draws on professional audit experience and wider research, including ongoing work with international policy bodies.

Areas of Impact

External Audit

AI improves audit quality by:

  • automating data ingestion and analysis,
  • identifying anomalies and irregularities,
  • supporting judgement formation with predictive analytics,
  • creating transparent audit trails.

Auditors can now investigate significantly more information, in greater depth, with greater consistency.

Internal Audit

AI supports internal auditors through:

  • continuous monitoring and real-time risk visibility,
  • data-driven audit planning,
  • intelligent document review,
  • benchmarking across units or periods,
  • automated workpaper generation.

This shifts internal audit towards more strategic, forward-looking activity and clarity over operational performance.

Continuous Operational Improvement

AI enables:

  • real-time performance analysis,
  • process optimisation,
  • predictive maintenance and incident forecasting,
  • automated compliance checks,
  • customer or stakeholder feedback analysis.

This improves agility, speeds remediation, and drives reliable operational discipline.

Challenges and Ethical Considerations

Programmatic Limitations

AI can support complex judgement but may remain inconsistent in performing multi-step processes without appropriate controls and structure.

Data Quality and Bias

High-quality data is essential. Both AI and humans introduce biases; these need explicit mitigation.

Security and Confidentiality

While more manageable than in early AI generations, data protection and access control remain essential for trust.

Transparency and Accountability

AI must be explainable, and human oversight must be properly balanced to avoid overreliance or blind trust.

Regulatory Compliance

Organisations must keep pace with evolving regulation and emerging governance standards for AI use.

Future Directions

1. AI Audit Standards & Explainability

Develop robust internal standards for AI-supported audit work, including transparent models, explainable outputs, and consistent traceability.

2. Improved AI–Human Collaboration

Define how technology and professional judgement interact, whether individually or in mixed teams. This supports careful management of cognitive load and prevents analysis fatigue.

3. Training & Education

Provide ongoing awareness, capability-building and scenario-based training to help teams understand AI behaviours, limitations, and safe-use boundaries.

A Strategic Framework: Turning Internal Learning into Customer and Organisational Value

A structured approach enables organisations to strengthen internal capabilities while building a cycle of continuous learning that benefits the wider business and its stakeholders.

1. Data Collection and Insight Generation

  • Implement comprehensive analytics across logs, audit outputs, operational systems, and documentation.
  • Create a knowledge engine that learns from internal patterns, issues, and effective solutions.

2. Continuous Feedback Loops

  • Capture structured feedback from users, auditors, and operational teams.
  • Establish review panels to interpret findings, extract lessons learned, and propose improvements.

3. Adaptive AI Models

  • Use real-world data to refine AI models so they become more predictive and aligned with genuine operating conditions.
  • Apply structured “method-statement learning” to tune AI behaviour based on accumulated performance and feedback.

4. Customer-Centric Solution Enhancements

  • Develop configurable features that support diverse industries and regulatory requirements.
  • Introduce predictive tools that detect potential issues early and suggest proactive mitigation strategies.

5. Transparent and Explainable AI

  • Ensure AI methods provide clear, comprehensible reasoning to support assurance and trust.
  • Educate users on how AI models learn and evolve.

6. Partnerships and Collaborative Development

  • Work with customers, partners, regulators, and standards bodies to shape emerging industry norms.
  • Contribute operational insights to strengthen sector-wide governance.

7. Ethical and Responsible AI Practices

  • Conduct periodic ethical audits of algorithms and learning processes.
  • Continuously review compliance with data protection and regulatory requirements.
  • Ensure fairness, integrity, and accountability in every AI-supported activity.

AI & The Evolution of Auditing — Sources & Further Reading

This curated list brings together industry, academic, and governance perspectives on how AI is reshaping internal and external audit, assurance, and algorithmic oversight.

1. Big Four & Industry Leaders

2. Academic and Peer-Reviewed Research

3. AI Oversight, Risk and Governance

4. Regulators & Professional Bodies

5. Industry & Media Perspectives