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.
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:
Because finance outputs must be accurate, explainable, and auditable, AI tools used in these workflows must prioritize control and transparency over autonomy or speed.
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.
AI vendors must clearly define the exact finance tasks their tools support.
A finance-appropriate AI tool should explicitly state:
AI tools that cannot be mapped to a defined finance function introduce ambiguity and operational risk.
AI systems in finance fall into two categories:
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.
Financial data governance is mandatory.
AI vendors must clearly disclose:
Financial data must remain protected, isolated, and accessible only to authorized users.
This is a critical risk consideration.
AI vendors must explicitly state:
For finance workflows, tools that do not train on customer data are generally preferred.
Finance outputs must withstand audits and regulatory review.
AI systems used in finance must provide:
Unexplainable outputs are incompatible with regulated finance environments.
AI systems are probabilistic by nature.
Finance-grade AI tools must:
Systems that obscure uncertainty introduce financial risk.
Finance workflows require embedded controls.
AI tools must support:
AI tools that bypass or weaken internal controls are unsuitable for finance use.
AI tools rarely operate in isolation.
Vendors must specify:
Integration fragility is a common source of AI implementation failure in finance.
AI vendors operating in finance must meet recognized compliance standards.
This typically includes:
Lack of compliance documentation is a disqualifier for finance use cases.
AI does not replace accountability.
Finance AI tools must clearly define:
AI should augment professional judgment, not replace it.
Finance organizations must be able to verify accuracy.
AI vendors should provide:
Unmeasured performance cannot be trusted in finance workflows.
Finance environments evolve over time.
AI tools must support:
Scaling limitations introduce future operational risk.
AI systems must fail safely.
Vendors must define:
Finance operations must remain functional during AI outages.
AI does not shift responsibility.
Finance organizations must understand:
Accountability must remain explicit and documented.
Finance AI adoption must be deliberate.
Vendors should clearly define:
Undefined onboarding plans often lead to failed adoption.
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.