--- title: Finance AI Maturity Diagnostic --- [Skip to content](https://info.cshco.com/how-ai-ready-is-your-finance-function#main-content) 5-Minute Diagnostic # How AI-Ready Is Your *Finance Function?* Benchmark your AI Governance, Controls, and Data Readiness across three finance-critical pillars. Start My Assessment → For CFOs, Controllers, and Internal Audit teams navigating AI adoption, this 5-minute assessment helps you benchmark where you stand and identify gaps before they become risks. Pillar 01 AI Strategy & Financial Alignment Are your AI initiatives tied to measurable financial outcomes? Pillar 02 Governance, Controls & Audit Readiness Are your policies, monitoring, and documentation audit-ready? Pillar 03 Data Integrity & Security Foundations Can you trust the data driving AI decisions? ### After completing this assessment you'll receive: Your AI maturity score across strategy, governance, and data readiness Identification of potential control, audit, and reporting risks Practical next steps to strengthen AI oversight in finance Start My Assessment → Section 1 of 3AI Strategy & Financial Alignment Question 1 How would you describe your organization's current use of AI or automation in finance processes? - AI is embedded across multiple finance functions with defined ownership and measurable outcomes. - AI tools are in active use across several areas, though adoption and oversight vary by team. - AI is used in isolated areas, primarily through vendor-provided tools such as an ERP or Excel. - We are evaluating AI use cases but have not deployed meaningful capabilities yet. - We have not introduced AI or automation into our finance processes. Question 2 When a new AI tool or feature is introduced into a finance process, who is involved in evaluating it? - Finance, IT, and Audit participate in a formal review process prior to deployment. - Finance and IT collaborate on evaluation, though the process is informal. - IT leads the evaluation with limited involvement from Finance or Audit. - Adoption decisions are made by individual users without a formal review. - There is no evaluation process — tools are adopted as needs arise. Question 3 After an AI tool is deployed in finance, how do you assess whether it is delivering value? - Realized benefits are tracked against defined metrics and reported on a regular basis. - We check in periodically, but success criteria are not formally defined. - We measure adoption or user feedback rather than financial or operational outcomes. - Value is assumed unless issues are raised. - We do not have a method for measuring post-deployment impact. Please answer all questions before continuing. ← Back Section 1 of 3 Next Section → Section 2 of 3Governance, Controls & Audit Readiness Question 4 Do you have documented policies governing AI use in financial processes? - Formal policies exist, including approval workflows and accountability assignments. - Policies exist but are inconsistently enforced. - Governance is IT-led with limited finance ownership. - AI use in finance is informal with no standards. - No governance framework exists. Question 5 If auditors requested documentation tomorrow, how prepared would you be? - Complete documentation exists: data lineage, model documentation, validation, and system logs. - Documentation exists but requires manual compilation. - Documentation is incomplete or siloed across teams. - Evidence is informal and difficult to substantiate. - We would struggle to provide defensible documentation. Question 6 When an AI-generated output in a financial process appears incorrect or unexpected, what is your process? - A defined escalation process exists with documented review and sign-off requirements. - Review occurs, but the process depends on who identifies the issue. - Issues are flagged informally, typically by the individual using the tool. - We rely on the vendor to identify and communicate errors. - No formal process exists for catching or reviewing AI-generated errors. Question 7 If an auditor requested an explanation of how an AI-generated number in your financials was produced, how prepared would you be? - Fully prepared — we can trace inputs, logic, and source data completely. - Mostly prepared — we can explain the general process but not every detail. - Partially prepared — our ability to respond depends on the tool and who produced the output. - Not well prepared — we would need to rely on the vendor or the individual who ran the output. - Unprepared — we do not have visibility into how AI-generated outputs are produced. Please answer all questions before continuing. ← Back Section 2 of 3 Next Section → Section 3 of 3Data Integrity & Security Foundations Question 8 How prepared is your finance data environment to support AI-enabled financial processes? - Finance data is centralized, standardized, and routinely used by analytics or automation tools. - Core finance data is centralized, but non-finance sources require manual integration. - Data exists across multiple systems and requires manual consolidation. - Significant data silos and inconsistent definitions limit automation. - Data fragmentation prevents meaningful AI use today. Question 9 Do you have a defined strategy for integrating, preparing, and maintaining data for AI use in finance? - A documented data pipeline strategy exists and aligns with prioritized finance use cases. - A partial roadmap exists, with implementation currently in progress. - Integrations are developed on an ad hoc, project-by-project basis. - Data integration is handled reactively as needs arise. - No defined data integration or pipeline strategy exists. Question 10 How clearly defined are data ownership and stewardship responsibilities within finance systems? - Formal owners and stewards exist with documented responsibilities. - Roles are defined but inconsistently executed. - Ownership is understood but not formally documented. - Responsibilities are unclear across systems. - No ownership structure exists. Question 11 How confident are you in tracing financial data from source systems to reporting outputs? - Automated lineage tools provide full traceability. - Lineage can be reconstructed manually when needed. - Visibility is partial or dependent on specific systems. - Reconciliation is largely manual and reactive. - There is little transparency into data origin or transformation. Question 12 How mature are your data access and segregation-of-duty controls in AI-enabled financial processes? - Role-based access, SOD controls, and activity logging are enforced and regularly reviewed. - Controls exist, but periodic reviews are inconsistent. - Access controls exist at the system level but are not centrally governed. - Controls are informal and minimally documented. - Access governance is fragmented or outdated. Please answer all questions before continuing. ← Back Section 3 of 3 Almost Done → One Last Step Tell us a bit about yourself ### Contact Information First Name * Last Name * Email Address * Company Name Role / Title How did you hear about this? Select... LinkedInColleague / Referral Email NewsletterSearch / Website Event / WebinarOther Comments / Questions (optional) Please fill in your first name, last name, and a valid email address. ← Back Step 4 of 4 View My Results → Your Assessment Results # Finance AI Readiness Report Personalized results based on your responses ## Overall AI Maturity Score — / 48 pts ### Your Score by Pillar ### Turn Your Results into Action Our Technology & Risk and Controls experts can help you close the gaps identified in your assessment. AI Financial Alignment Workshops Governance Framework Design Data Governance & Security Assessments Download PDF Report Request a Free Consultation