Benchmark Your Firm Across Six Levels of Testing Automation. Get a realistic look at where your firm stands today and how to evolve your AI usage effectively, from foundational automation to autonomous agentic testing.
Saved per week across finance teams with AI — but 69% is lost to rework and inefficiencies
While the market conversation has quickly moved toward agentic AI, the reality inside most audit teams is more nuanced.
Broad research shows that poor data readiness and integration are leading causes of AI project underperformance.
It’s not just about adopting AI, but levelling up effectively.
Manual testing, sampling, and paper-based evidence
Structured calculations, but disconnected workflows
Centralized systems and structured engagement management
Automation embedded within connected audit workflows
Agents execute approved procedures within defined controls
Adaptive agents that sequence and plan procedures dynamically
An audit AI readiness assessment evaluates how prepared your firm is to adopt and scale AI across audit workflows. It measures key areas like data quality, workflow connectivity, testing standardization, and governance to determine whether your team can move beyond experimentation into production-grade AI.
Most AI initiatives fail to scale due to weak data foundations, fragmented workflows, and unclear governance. In audit, these gaps are even more critical because AI must be explainable, traceable, and defensible during inspections. Readiness ensures AI improves audit quality—not just efficiency.
AI readiness in audit is typically assessed across core pillars such as data and evidence foundations, testing coverage, workflow integration, audit trail traceability, and AI governance. Firms are scored to determine their maturity level from manual processes to fully autonomous, agent-driven audit workflows.
Audit AI readiness progresses through six stages: manual testing, spreadsheet-driven workflows, cloud-based systems, AI-assisted testing, trained agents, and autonomous agents. Most firms are still in early to mid stages, where foundational improvements are required before scaling AI effectively.
A high AI readiness score indicates your firm has strong data infrastructure, standardized testing processes, connected workflows, and clear governance. This positions you to safely adopt advanced AI capabilities, like trained agents, while maintaining audit quality, oversight, and regulatory compliance.
