In the AI era of finance, an audit readiness checklist provides a framework for maintaining trust across automated financial workflows.
Blaise Radley
Editorial Strategist
Workday
As AI, automation, and connected systems become more deeply embedded in finance, audit readiness increasingly depends on whether organisations can provide full transparency into the technology-enabled workflows producing their financial results.
Adoption is accelerating, but assurance capabilities are lagging. KPMG’s 2026 Global AI in Finance study found that active AI use across finance increased from 30% to 75% over the last two years. Yet only 42% of organisations are fully assurance-ready for AI-enabled finance processes, meaning they can produce audit evidence efficiently and explain how outputs were generated.
That readiness gap becomes especially apparent when an audit begins. Supporting evidence is often scattered across systems, control ownership may be unclear, and unresolved issues from prior periods can carry forward. By the time auditors request information, finance teams may already be working against the clock.
An audit readiness checklist turns audit preparation into a coordinated, year-round discipline and supports continuous planning. It helps finance teams surface gaps earlier, strengthen accountability, and respond with confidence when scrutiny arrives.
Report
AI use in finance increased from 30% to 75% over the last two years, yet only 42% are fully assurance-ready for AI-enabled finance processes.
Audit readiness refers to an organisation's ongoing state of preparedness to undergo an external or internal review. To qualify as “audit ready”, a company’s financial records, compliance data, and internal controls must be accurate, up-to-date, and easy to access in real-time, to ensure an audit can begin without delay.
As finance workflows become more automated, auditors increasingly need to consider how system-generated information can provide reliable audit evidence.
Under AUASB standards—including ASA 500 Audit Evidence and the revised ASA 315 Identifying and Assessing the Risks of Material Misstatement—auditors must evaluate the relevance and reliability of information used as audit evidence, including its accuracy and completeness where necessary.
They must also understand relevant information systems, identify risks arising from the use of IT, and evaluate the design and implementation of relevant automated and general IT controls.
An automated feed, AI-generated recommendation or system-produced report cannot be treated as reliable solely because it comes from an established platform. In this environment, audit readiness means maintaining traceable and supportable evidence over the data, processing and controls relevant to financial reporting and the audit, such as:
This changes the timing of audit preparation. Evidence must be generated and retained as the process operates because approvals, system states, and exception histories can be difficult to reconstruct later. Audit readiness therefore becomes an ongoing test of whether finance can show the path behind their numbers and demonstrate that controls remained effective from source data through final reporting.
A traditional audit checklist often begins with documents the auditor is expected to request. In an AI-enabled finance environment, that is no longer enough. The checklist must also capture how financial information moves through the organisation, which technologies shape it, where controls operate, and how people oversee the result.
That requires finance teams to build the checklist around material workflows rather than individual files. Each workflow should be traceable from the original data through processing, review, and final reporting. The following six steps create that structure:
1. Map material financial workflows
2. Identify evidence points across each workflow
3. Define what constitutes sufficient evidence
4. Assign ownership across the business
Start with the processes that produce material balances, transactions, and disclosures across finance operations. Prioritise areas involving significant judgment, high transaction volumes, new technology, or prior audit findings.
Map each process from the originating event through the systems, transformations, controls, and approvals that lead to final reporting. If AI supports an accounting classification, estimate, or other decision, include that activity. The resulting map defines the financial path the checklist must be able to support.
Identify where evidence must be retained as data moves through each workflow. Focus on moments when information is transformed, a control operates, an exception occurs, or professional judgment affects the result.
Each evidence point should demonstrate at least one element of the defensible chain established earlier: data lineage, system logic, control execution, human oversight, or final reporting.
Connect every requirement to a specific financial risk, accounting judgment, control, or reported result so teams understand why the evidence is necessary.
State exactly what must be provided before each checklist item can be considered complete. Replace broad requirements such as “provide revenue support” with specific deliverables, such as a transaction population, reconciliation, approval record, or contract sample.
Record the reporting period, source system, and storage location for each item. System-generated evidence should include enough information to reproduce the output. For an AI-enabled process, documentation should identify the technology and source data used, the output produced, and how that output was validated.
Workday research found that 71% of organisations using intelligent automation closed their books in six days or fewer compared with 23% using minimal automation.
Assign one person to produce each item and another to review it. Ownership should follow the workflow instead of defaulting every requirement to finance.
Finance may own the accounting conclusion, while IT maintains access or configuration records. Business teams may own the underlying transaction, and data teams may manage integrations between systems.
For automated processes, distinguish between the person administering the technology and the person accountable for validating its financial output. Add deadlines and an escalation path so unresolved requirements surface before fieldwork.
Separate evidence that can be captured throughout the reporting period from work dependent on final balances. Access reviews, control testing, configuration monitoring, and exception resolution can often occur continuously, reducing the need to reconstruct evidence later.
Automation can support this process through standardised approvals, automated reconciliations, and embedded audit trails. Workday research found that 71% of organisations using substantial intelligent automation close their books in six days or fewer, compared with 23% using minimal automation.
The checklist should also make sure a review is triggered whenever a system integration, model, or configuration changes. That way, periods of transformation don't lead to systemic disarray.
Test the completed checklist by tracing a material transaction, balance, or control from its original source through final reporting. The record should make the basis for the final reported outcome clear to an independent reviewer.
If the reviewer cannot follow the path without additional explanation, identify the source of the gap. The issue may lie in the checklist, the evidence retained, or the underlying workflow. Resolve it before fieldwork, then use questions raised during the audit to strengthen the process for the next reporting period.
An audit readiness checklist embeds evidence collection into financial workflows so teams can trace and support results without reconstructing them when an audit begins.
An audit readiness checklist embeds evidence collection into financial workflows so teams can trace and support results without reconstructing them when an audit begins.
The same visibility that simplifies fieldwork also supports faster closes and more reliable financial reporting. It gives leaders a stronger foundation for adopting automation and AI in finance.
As financial workflows become more complex, trust depends on the organisation’s ability to explain how information was produced and demonstrate that the right controls operated along the way.
A checklist built around that full chain of evidence helps finance teams meet that standard while reducing audit disruption and strengthening control across financial operations.
Report
Discover how to surface insights faster, redefine how work gets done, and operate with confidence—with AI that's built in, not bolted on.