Which of the following is the GREATEST risk associated with using AI in audit planning?

The greatest risk associated with using AI in audit planning is incomplete data. AI models are fundamentally data-driven; their ability to identify risks, patterns, and anomalies for audit planning depends entirely on the quality and completeness of the input data. If the data used to train or run the AI is incomplete, biased, or inaccurate, the resulting audit plan will be inherently flawed. This "Garbage In, Garbage Out" (GIGO) principle can lead auditors to focus on the wrong areas or, more critically, to miss significant risks, thereby undermining the entire audit's effectiveness and reliability.

A. Increased planning costs: While AI implementation has initial costs, it is often adopted to increase efficiency and reduce long-term costs. This financial risk is less severe than one that invalidates the audit's conclusions.

B. Scope creep: This is a project management risk. The audit team retains ultimate control over the audit scope and can manage suggestions from the AI, making this a controllable issue.

D. Limited knowledge: This is a consequence of incomplete data. The AI's "knowledge" is derived from its data; therefore, incomplete data is the more precise and foundational root cause of this risk.

1. ISACA. (2023). Auditing Artificial Intelligence. Section 3.2

"Data and AI Models." This section emphasizes that the quality of data is a primary determinant of an AI system's performance and reliability

stating

"The quality of the data used to train and test AI models is critical to their performance... Biased or incomplete data can lead to inaccurate or unfair outcomes."

2. Alles

M. G. (2015). "Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession." Accounting Horizons

29(2)

439-449. The paper discusses that a key obstacle for data analytics (a precursor to AI) in audit is the availability and quality of data

highlighting that "the main obstacles to the evolution of Big Data in the audit profession are data-related" (p. 444).

3. Yoon

K.

Hoogduin

L.

& Zhang

L. (2015). "Big Data as a Complement to Audit Evidence." Accounting Horizons

29(2)

431-438. The authors note that the utility of big data and associated analytics in auditing is contingent on the data's integrity. A lack of complete and reliable data poses a significant risk to the validity of any conclusions drawn.