97% of finance leaders are willing to pay more for AI-driven audits, but they still want people in the loop

AI and the audit: Finance leaders are rewarding firms that get tech right

Finance leaders are voting with their budgets. In a recent survey of 210 senior finance executives, 97% said they're willing to pay more to work with audit firms that use advanced technology. Trust is rising too: 81% now have greater confidence in firms that invest in and actively use AI and similar tools - an 18-point jump from last year.

The expectation has flipped. It's no longer "does your auditor use advanced tech?" It's "how well do they use it, and does it show up in audit quality and delivery?"

What finance leaders expect - and what they're getting

• 93% say their audit firm's technology is progressive.
• 85% say their experience with audit tech met or exceeded expectations (up from 77%).
• 34% would consider switching firms if the auditor's use of advanced technology is limited (second only to poor project/deliverable management at 36%).

Bottom line: technology isn't a nice-to-have. It's a selection criterion and a retention risk.

Where the concerns are real

Adoption is up, but risk worries are loud and valid. Finance leaders flagged six areas:

• Cybersecurity risks (82%)
• Data privacy risks (80%)
• Regulatory risks (74%)
• Incorrect AI output informing company decisions (71%)
• Overreliance on technology (71%)
• AI bias (68%)

The takeaway: AI can move the audit forward, but it doesn't replace judgment. People who ask better questions, validate results, and explain the "why" still create the most value.

How to evaluate your audit firm's use of AI (fast checklist)

• Scope: Where does the firm apply AI today (risk assessment, journal entry testing, anomaly detection, substantive analytics)? What's on their roadmap?
• Quality controls: How do they validate models and data sets? What human review steps exist before conclusions make it into deliverables?
• Security and privacy: Which data leaves your environment, and how is it protected? Do they support data minimization and masking by default?
• Explainability: Can they show the logic behind flagged anomalies, risk scores, and conclusions in plain language?
• Regulatory alignment: How do their methods align with professional standards and emerging AI guidance?
• Change management: What training do their teams (and yours) receive to safely and effectively use these tools?
• Project hygiene: Do tech gains translate into better timelines, fewer PBC rounds, and clearer status updates?

Guardrails every finance leader should insist on

• Model governance: Require documented testing, drift monitoring, and periodic revalidation of AI-enabled procedures.
• Human-in-the-loop: Ensure experienced auditors review AI findings and challenge outliers before they reach management or the audit committee.
• Data discipline: Limit sensitive data exposure, log access, and define clear retention policies.
• Error pathways: Establish a written process for handling incorrect AI output, including escalation, impact assessment, and rework.
• Bias checks: Ask for evidence of bias testing and controls over training data.

90-day action plan

• Week 1-2: Map your current audit data flows and identify which sets can be safely used with AI-assisted procedures.
• Week 3-4: Meet your audit partner to review their AI roadmap and request concrete metrics (cycle time, test coverage, error rates).
• Week 5-8: Pilot targeted AI use cases (e.g., journal entry outlier testing) with clear success criteria and human review steps.
• Week 9-12: Formalize guardrails, update your audit committee materials, and align on next-year scope and budget impacts.

What "good" looks like in practice

• Broader risk coverage with fewer manual hours and cleaner PBC cycles.
• Transparent explanations for every flagged item - not black-box answers.
• Documented controls around model performance, data use, and change management.
• Measurable improvements year over year (cost, speed, quality, and insight depth).

If you're building internal capability alongside your external audit, consider practical training for your team on AI tools used in finance. Curated options can save time and prevent common mistakes.

AI tools for finance: curated options and use cases

For risk frameworks and controls, this overview is a useful reference point: NIST resources on AI risk management.

The bigger picture

Firms investing in people, data, and technology are pulling ahead. The finance teams that match that energy - with clear guardrails and sharper questions - will extract more value from the audit without adding risk. That's the standard to set this year.