A plain-English definition for finance leaders who need evidence before they fund the next AI workstream.

An AI Audit is a fixed-scope diagnostic that maps the AI already running across a finance firm: approved tools, Shadow AI, embedded SaaS AI, internal agents, spend waste, risk exposure, and the adoption outcomes to fund next.

An AI Audit turns usage into evidence.

The work starts with visibility. Finance leaders need to know which AI tools are approved, which tools appeared outside procurement, which internal agents are live, and which workflows are creating value or risk.

Inventory approved AI tools, embedded SaaS AI, internal agents, and Shadow AI.

Map ownership, data exposure, policy coverage, spend, usage depth, and workflow impact.

Return an operating read that names what to expand, contain, consolidate, or defer.

The audit scope follows the AI estate.

A useful audit does not stop at the model. It follows where people and systems actually touch AI: browser tools, copilots, SaaS features, developer agents, internal applications, and production traces.

Approved AI tools and license utilization.

Shadow AI and unmanaged MCP or developer-tool exposure.

Internal agents, eval coverage, incident history, and baseline gaps.

Board-ready summary of AI value, AI risk, and the next workstream.

The deliverable is a sequenced operating read.

The output is not a generic maturity score. It is a funded sequence: what to do first, what to fix before scale, which teams need fluency work, and where governance evidence is too thin.

Build when a workflow is ready for an agent.

AI Governance when risk or evidence gaps block scale.

AI Fluency when teams use tools but cannot turn usage into outcome.

AI Audit questions, answered plainly.

Questions buyers actually ask.

An AI Audit is a diagnostic that maps the AI already running across an organization and returns evidence on value, risk, usage, governance gaps, and the next work to sequence.

Finance firms need one when AI tools, copilots, or internal agents are already in use but leadership cannot answer what is approved, what is risky, and what is creating value.

No. The AI Audit produces the operating read. AI Governance is one follow-on workstream when the audit finds evidence, policy, or control gaps.

Keep the audit path moving.

Run the Audit

See the two-week AI Audit service from Tensile AI.

Checklist

Use the checklist to judge whether an audit scope is complete.

Shadow AI

Read the discovery path for unmanaged AI exposure.

Enterprise AI Audit

Scale the audit read across business lines and functions.

Turn the AI read into a decision.

Bring one workflow, vendor, or AI portfolio. We will map the evidence needed for finance leaders to fund, ship, or stop it.

Related links and sources

• Checklist Use the checklist to judge whether an audit scope is complete.

• Enterprise AI Audit Scale the audit read across business lines and functions.