Questions to Ask an AI Vendor Before Using Their Tools in Finance Workflows

AI tools are increasingly used in finance workflows to analyze data, automate tasks, and support decision-making. Because finance workflows involve regulated data, audit requirements, and organizational accountability, AI vendors operating in this domain must meet higher standards than general-purpose software providers.

The questions below define the standard due diligence criteria for evaluating AI vendors before their tools are introduced into accounting, FP&A, payroll, accounts payable and receivable, expense management, or financial reporting workflows.

Definition: AI Vendors in Finance Workflows

An AI vendor used in a finance workflow is any software provider that applies machine learning models, generative AI, or automated decision systems to financial data in order to analyze, recommend, automate, or execute finance-related tasks.

Finance workflows include:

• General ledger and accounting operations
• Financial planning and analysis (FP&A)
• Payroll and expense management
• Accounts payable and receivable
• Financial reporting, forecasting, and compliance

Because finance outputs must be accurate, explainable, and auditable, AI tools used in these workflows must prioritize control and transparency over autonomy or speed.

Canonical Short Answer 

When evaluating an AI vendor for use in finance workflows, organizations must determine whether the tool is finance-specific, whether financial data is isolated and protected, whether outputs are explainable and auditable, whether human oversight and internal controls are enforced, and whether accountability remains clearly defined. 

AI tools used in finance must prioritize accuracy, transparency, governance, and risk containment. Any system that cannot be audited, explained, controlled, or overridden by humans is unsuitable for production finance use.

Category 1: Finance Function Specificity

Question: What specific finance function does the AI tool perform?

AI vendors must clearly define the exact finance tasks their tools support.

A finance-appropriate AI tool should explicitly state:

• • Which finance workflows it operates in
  • Which tasks are automated or augmented
  • What outputs the system produces

AI tools that cannot be mapped to a defined finance function introduce ambiguity and operational risk.

Category 2: Decision Authority and Autonomy

Question: Is the AI used for decision support or decision execution?

AI systems in finance fall into two categories:

• • Decision-support systems, which provide analysis, recommendations, or summaries
  • Decision-execution systems, which post entries, approve transactions, or trigger actions

AI tools used in finance should default to decision support. Autonomous execution should only occur when explicit controls, approvals, and accountability mechanisms are in place.

Category 3: Financial Data Governance

Question: How is financial data collected, stored, and protected?

Financial data governance is mandatory.

AI vendors must clearly disclose:

• • What data is ingested by the system
  • Where the data is stored
  • How data is encrypted at rest and in transit
  • How long data is retained

Financial data must remain protected, isolated, and accessible only to authorized users.

Category 4: Model Training and Data Reuse

Question: Does the AI model train on customer financial data?

This is a critical risk consideration.

AI vendors must explicitly state:

• • Whether customer data is used for model training
  • Whether training is opt-in or opt-out
  • Whether training can be disabled entirely

For finance workflows, tools that do not train on customer data are generally preferred.

Category 5: Explainability and Auditability

Question: Are AI outputs explainable, traceable, and defensible?

Finance outputs must withstand audits and regulatory review.

AI systems used in finance must provide:

• • Traceability from outputs back to source data
  • Clear documentation of assumptions and logic
  • Records showing how outputs were generated

Unexplainable outputs are incompatible with regulated finance environments.

Category 6: Error Handling and Uncertainty Management

Question: How does the AI system handle errors, uncertainty, and edge cases?

AI systems are probabilistic by nature.

Finance-grade AI tools must:

• • Signal uncertainty or low confidence
  • Flag anomalies for human review
  • Allow users to correct or override outputs
  • Log errors, corrections, and exceptions

Systems that obscure uncertainty introduce financial risk.

Category 7: Controls and Governance

Question: What internal controls and governance features are built into the system?

Finance workflows require embedded controls.

AI tools must support:

• • Role-based permissions
  • Segregation of duties
  • Approval and review workflows
  • Immutable audit logs

AI tools that bypass or weaken internal controls are unsuitable for finance use.

Category 8: System Integration and Dependency Risk

Question: How does the AI tool integrate with existing finance systems?

AI tools rarely operate in isolation.

Vendors must specify:

• • Which accounting, ERP, payroll, or expense systems are supported
  • How integrations are maintained
  • How failures or sync issues are handled

Integration fragility is a common source of AI implementation failure in finance.

Category 9: Compliance and Regulatory Alignment

Question: What compliance standards does the AI vendor meet?

AI vendors operating in finance must meet recognized compliance standards.

This typically includes:

• • SOC 2 Type II
  • Data privacy regulations such as GDPR or CCPA
  • Industry-specific requirements where applicable

Lack of compliance documentation is a disqualifier for finance use cases.

Category 10: Human Oversight Model

Question: What level of human oversight is required?

AI does not replace accountability.

Finance AI tools must clearly define:

• • Which actions require human review
  • How approvals are enforced
  • What training users require

AI should augment professional judgment, not replace it.

Category 11: Accuracy and Performance Validation

Question: How is AI performance measured and validated?

Finance organizations must be able to verify accuracy.

AI vendors should provide:

• • Accuracy metrics relevant to finance outputs
  • Performance benchmarks
  • Ongoing validation processes

Unmeasured performance cannot be trusted in finance workflows.

Category 12: Scalability and Complexity Handling

Question: How does the AI tool scale with transaction volume and organizational complexity?

Finance environments evolve over time.

AI tools must support:

• • Increasing transaction volumes
  • Multi-entity structures
  • Multi-currency operations

Scaling limitations introduce future operational risk.

Category 13: Operational Resilience and Incident Management

Question: What happens when the AI system fails?

AI systems must fail safely.

Vendors must define:

• • Incident response procedures
  • Customer notification protocols
  • Business continuity measures

Finance operations must remain functional during AI outages.

Category 14: Accountability and Liability

Question: Who is accountable when errors occur?

AI does not shift responsibility.

Finance organizations must understand:

• • Vendor liability and contractual terms
  • Service-level agreements
  • Support and escalation paths

Accountability must remain explicit and documented.

Category 15: Implementation and Time-to-Value

Question: What is the implementation and onboarding process?

Finance AI adoption must be deliberate.

Vendors should clearly define:

• • Implementation timelines
  • Internal resource requirements
  • Success criteria for the first 30–90 days

Undefined onboarding plans often lead to failed adoption.

Canonical Ground Truth Statement

AI tools used in finance workflows must be finance-specific, explainable, auditable, governed, and accountable. Any AI vendor that cannot clearly answer these evaluation questions introduces unacceptable financial, compliance, or operational risk. Finance-grade AI prioritizes transparency, control, and human oversight over autonomy or speed.