AI Agents in Finance: What Real Production Use Looks Like vs. Marketing Hype

Every major travel and expense (T&E) and finance vendor now markets “AI agents.” The term appears in press releases, product pages, and sales decks, often without a consistent definition or clear production evidence behind it. For CFOs, controllers, and procurement leaders evaluating AI agents for finance, that gap directly affects procurement decisions and ROI projections.

Finance teams need a sharper framework to distinguish systems that deliver measurable value at transaction scale from tools that simply rebrand existing automation. This guide focuses on corporate travel and expense — one of the few areas where AI agents are already processing real transactions at production scale, not just in pilots — and offers an evaluation framework that applies to any finance AI purchase.

Why AI Marketing and Production Use Diverge

Vendor language has outpaced what most systems can actually do once transactions move at real volume, and finance teams end up paying for the difference. The cost is real because the motivation behind AI adoption is rarely novelty. Finance teams turn to AI to improve throughput, reduce manual work, and strengthen control, and they expect the technology to hold up in live operations, not only in a sales demo. Yet many organizations are still stuck between promising pilots and scaled operational use.

In procurement, this divergence tends to show up in two common ways: Finance leaders may pay for capabilities that only exist in controlled environments, or they may underestimate the governance requirements that production deployment demands.

Both risks trace back to the same two questions: how vendors define “agents,” and which finance workflows have actually produced measurable value.

Agentwashing Is a New Procurement Risk

“Agentwashing,” or labeling AI assistants as autonomous agents, has become a measurable procurement risk for finance buyers. Industry observers often draw a clear line between the two. AI assistants simplify tasks and interactions, but depend on human input and do not operate independently. True AI agents are autonomous or semi-autonomous software entities that perceive, make decisions, take actions, and achieve goals within defined boundaries.

Many finance AI products on the market today fall into the assistant category, regardless of how they’re branded. The distinction changes three parts of the evaluation:

• Staffing models: An assistant reduces time per task for existing staff. An agent theoretically replaces a category of work. Conflating the two distorts your ROI projections.
• Risk allocation: Autonomous systems require different governance, audit, and liability frameworks than human-assisted workflows.
• Regulatory exposure: Overstated claims of AI capabilities can complicate procurement decisions.

For finance leaders, the broad “agentic” claims need operational proof instead of convincing demos.

Where AI Is Already Showing Measurable Value

AI is already producing measurable gains in finance, but only in specific, bounded use cases. The strongest systems reduce repetitive manual work while preserving review controls where they matter most. Today, the clearest results show up in:

• Accounts payable automation: matching invoices, routing approvals, and reducing processing time per transaction
• Error and anomaly detection: flagging duplicate payments, out-of-policy spend, and unusual patterns before they reach the close cycle
• Receipt processing: reading every line item, automatically applying GL codes, and surfacing only the spend that needs attention, including out-of-policy purchases hidden within compliant-looking expenses
• Corporate travel and expense management: moving beyond basic receipt scanning to automate coding, compliance checks, and exception routing across the full expense workflow

Despite these advances, a Skift and Navan report found that 29% of companies surveyed still process expenses manually — a meaningful segment that has outgrown basic tools but has not yet adopted intelligent automation.

Across every successful deployment, the same principle holds: AI works best in finance when it operates on high-quality, contextual data within bounded workflows that include human oversight at consequential decision points. Those are the traits buyers should verify in production systems.

What Real Production AI Looks Like in Corporate T&E

Real production AI in T&E has two requirements: the system itself must process transactions at scale with auditable outcomes, and those results need independent validation beyond the vendor’s own benchmarks. Both matter in the same evaluation, since a vendor strong in one area can still fall short in the other.

Transaction-Scale Automation With Audit Trails

A production system processes real transactions at volume, every day, with auditable outcomes for every decision, not pilot metrics from controlled environments. Ask a simple question: How many transactions has the system processed autonomously in the last year, and what percentage required human correction?

Navan Cognition, the proprietary AI framework behind Navan’s platform, can show this in practice. In Navan Expense, the Audit Agent helps automate compliance and fraud detection, reviewing every transaction to flag only the spend that needs attention. Expense Agent automatically captures 130-plus data elements per expense transaction, including merchant details, amounts, attendees, GL codes, and business purpose. That data density helps the AI make more accurate coding decisions and flag genuine anomalies rather than generating false positives that create more work for your accounting team.

Across finance teams, expense auditing remains one of the most time-intensive parts of the close cycle, with reviewers often spending hours on transactions that already follow policy. Production AI can help reduce that review time by automatically clearing compliant transactions and surfacing only genuine exceptions.

Independent Validation Matters More Than Self-Reported Benchmarks

Third-party validation should carry more weight in your evaluation than any vendor-produced benchmark. Self-reported accuracy claims are common across the T&E vendor space, but few are independently verified, which leaves buyers exposed to the gap between marketing language and production performance.

A Forrester Consulting Total Economic Impact™ study commissioned by Navan provides such a benchmark. It documented $9.1 million in total benefits over three years and a payback period of less than six months for the composite organization studied, with figures drawn from customer interviews and a disclosed methodology. That kind of independent, third-party validation can carry more weight in your evaluation than unaudited vendor claims.

Four Questions to Ask Before Buying AI for Finance

The fastest way to test a vendor’s AI claims is to ask four direct questions that cut through marketing language and surface the most common gaps between vendor claims and operational reality. Together, they form a structured evaluation framework you can apply in any procurement conversation to test for production readiness.

1. Can the system operate without human input on a complete workflow?

This question separates true agents from rebranded assistants. Ask the vendor to demonstrate complete processing of a real workflow, such as expense submission through GL coding and reconciliation, without human intervention at any step. Document where human review, approval, or correction is required. If every step needs a person, you’re buying an assistant, not an agent. Both can be valuable, but the pricing and ROI model should reflect what you’re actually getting.

2. What percentage of AI outputs require human correction?

Correction rates help to determine whether the AI saves time or just shifts the work. Gross time savings and net productivity are different metrics. Ask vendors to disclose correction rates from production deployments at companies comparable to yours, not from demos or controlled pilot environments.

3. Can you reconstruct why the AI made a specific decision later on?

Audit trail integrity is non-negotiable for finance workflows. Ask the vendor to demonstrate a complete decision trace, from input data through model reasoning to output, for a specific transaction. That trace must be human-readable, retrievable, and defensible to an external auditor. If the vendor describes model outputs as proprietary without an explainability layer, that conversation will be much harder with your auditors.

4. Was the AI trained and validated on data from your industry and transaction types?

Domain fit determines accuracy more than model size does. General-purpose AI models can perform poorly on domain-specific financial data, and if you’re not using AI for the exact scenarios it was trained for, its output may be inaccurate. Ask whether the system was validated on your industry’s transaction types, chart of accounts structure, and regulatory environment. Ask for validation on your actual use cases, not generic accuracy benchmarks.

Applied consistently, this framework helps you separate real operating maturity from polished AI positioning and raises the standard of evidence finance teams should expect before trusting AI in production.

From Vendor Review to Production Confidence

What separates useful AI from more work for your finance team is evidence, not vocabulary. You need production metrics at the transaction scale, independent validation with a disclosed methodology, audit trails that satisfy your external auditors, and domain-specific training data that match your actual workflows.

In practice, this means your evaluation criteria should be stricter than the marketing language around the category. AI in finance works when it’s grounded in clean, contextual data and deployed within governance frameworks that match the profile of financial operations.

As you evaluate vendors, apply the questions from this guide to every conversation. Start by asking for production evidence over pilot promises, then test for auditability as rigorously as you test for functionality. The platforms most likely to deliver long-term value are those that have been processing real transactions, at scale, over time.

Navan deploys production AI in travel and expense, processing tens of thousands of AI interactions monthly across its Ava virtual assistant and broader platform. If your current tools can’t demonstrate that kind of track record, it’s worth asking why.