Is it time to audit the value of artificial intelligence?
Artificial intelligence insights for internal audit
While the internal audit profession continues to explore the risk considerations of artificial intelligence (AI), assessments of its value reveal new opportunities for efficiency and productivity. Explore how you can design your AI strategy and accelerate its impact on your auditing practice.
Clearing a path for AI
Digital and AI-driven processes can help internal audit teams introduce a higher level of quality, assurance, and productivity to your day-to-day operations. However, what roadblocks might impede AI implementation?
To help anticipate and manage delays, you can conduct a readiness check to assess your capacity for AI. Examine your team’s AI readiness with the following six questions.
How can you break down barriers with digital competency?
Start engaging with learning and development now. You do not need everyone to become data scientists, engineers, or digital experts. However, being familiar with the terminology, capabilities, and the potential of these tools will help accelerate adoption.
Does your digital strategy go beyond AI?
Determine how AI can help enable your strategy and achieve desired outcomes. However, do not limit your digital strategy to just AI but explore the right technology tools for a given job.
There are many digital solutions and use cases relating to machine learning, such as natural language processing, sentiment analysis, and neural networks. Additionally, you can leverage audit management systems, embrace analytics, and incorporate visualizations to round out your digital strategy.
Are your AI goals backed by your tech infrastructure?
Collaboration with your technology team can help you understand your organization’s stance toward AI, both from a data privacy and security perspective. This can help internal audit teams assess their appetite for shaping existing solutions within the safety of an organization’s ecosystem.
Data is currency—but is it current?
The caliber of AI performance, both in terms of its training and its outputs, is dependent on data quality. Many internal audit functions have poor data quality, ineffective version control, and outdated documents that have remained in their system for years.
As digital tools become more accessible, getting your data in order will pay dividends for your AI strategy. For example, to create a tokenized database of internal audit content, analyze your risk and control frameworks, scope documents, findings, and recommendations. This could help turn untapped information into a goldmine of knowledge and insight.
Risk begets more risk?
While the benefits of AI are becoming more apparent, so are the risks associated with it. Bias, privacy concerns, and intellectual property infringement are among the risks come with AI use. Consequently, regulations are on the rise to mitigate these risks—and new regulatory risk to account for.
Good governance is critical to managing the risks of AI. Internal audit professionals should challenge themselves to put in robust governance processes and controls around the use, development, testing, access, and ongoing monitoring of AI.
How can you further your AI ambitions with the right culture?
The limits of what Generative AI (GenAI) could be used for are often contained by the imagination of individuals. Functions that have a culture of innovation and curiosity have usually fared better than those that were less willing to embrace change. By investing in culture-building programs and encouraging experimentation, internal audit leaders can nurture the right behaviors needed to execute AI-centered initiatives.
How can GenAI accelerate the internal audit life cycle?
To help evaluate your organization’s readiness for GenAI, explore how the solution can bring value across the internal audit lifecycle:
- Risk assessment: GenAI can help auditors research and understand the risks of a specific industry.
- Plan development: Leaders can leverage GenAI to suggest scheduling and resource allocations based on known constraints, such as the number of staff, needed skills, and seniority.
- Engagement planning: Internal audit teams can use GenAI to suggest data sources and analytics tests based on their needs.
- Execution: Auditors can use GenAI to produce the initial draft of workpapers and conduct the first round of review and quality assurance.
- Reporting: Professionals can review reports, test for quality assurance, simplify language, and generate summaries with GenAI.
Perform more efficiently with AI
Internal audit leaders have already identified ways to apply AI to drive performance, from data management to reporting. Organizations can take their performance a step further with the right precautions, such as weaving controls and governance into the digital strategy This can help your internal audit function drive value with AI and keep risk at bay.
Sarah Fedele
