Finance is a strange place to be told AI will change everything. The function runs on controls and sign-offs designed to stop clever new things from happening unapproved. Yet adoption is real: Gartner’s 2025 survey of 183 finance leaders found that 59 percent of finance functions now use AI, up from 37 percent in 2023, though growth has flattened.
The use cases are telling. Knowledge management led at 49 percent, then accounts payable automation at 37 percent and anomaly detection at 34 percent. The six platforms below cover different jobs, so the right pick depends on where your team loses the most time.
What is an AI agent for finance?
It’s software that handles a finance task from start to finish, such as coding an invoice, matching it to a purchase order, or preparing a reconciliation. A person reviews the exceptions instead of doing every step.
Unlike older automation systems, it can handle messy inputs, such as invoices in different formats.
Why finance is adopting agents now:
Finance teams are asked to close faster and give better forecasts without adding headcount. Much of the work is repetitive, rule-based, and document-heavy, which suits AI well.
Gartner also found that most teams saw only low or moderate early impact, so expectations should stay realistic.
Where finance teams use agents:
- Knowledge: Finding policies, contracts, and past decisions.
- Payables: Processing and approving invoices.
- Spend: Enforcing policy and coding expenses.
- Close: Reconciliations, journal entries, and intercompany.
- Accounting work: Tax prep, audit testing, and month-end for firms.
Quick comparison
| Platform | Best for |
|---|---|
| Glean | Finding policies, contracts, and precedent across tools |
| Vic.ai | High-volume accounts payable |
| Stampli | Procure-to-pay with collaboration around invoices |
| Ramp | Spend management and expense policy |
| BlackLine | Financial close and reconciliation |
| Basis | Accounting firms |
1. Glean
A finance team’s work rests on policy documents, vendor contracts, approval matrices, and the long email chain explaining why a journal entry looks odd. Glean’s AI assistant searches across more than 250 work apps and returns sourced answers, with permissions carried over so sensitive data stays protected.
It doesn’t process invoices or post entries. Think of it as the knowledge layer beside the transactional tools below.
Best for: finding policies, contracts, and precedent fast.
2. Vic.ai
Vic.ai focuses on accounts payable: invoice processing, PO matching, approvals, bill pay, and a vendor portal. It integrates with major ERPs such as SAP, NetSuite, Workday, and Sage Intacct.
Its automation claims are bold, so ask for numbers from a customer similar to you.
Best for: high-volume payables teams.
3. Stampli
Stampli covers more of procure-to-pay, including procurement controls, AP, vendor management, and payments, sitting on top of your ERP. Its AI agent codes, matches, and routes invoices, and keeps the conversation attached to each one.
Best for: teams that value collaboration and context around invoices.
4. Ramp
Ramp began as a corporate card and now offers agents that enforce expense policy, approve low-risk spend, and code transactions to your ERP. It says no money moves without human confirmation.
You’re choosing a spend platform as much as an agent, so involve procurement and treasury.
Best for: companies willing to run spend through Ramp.
5. BlackLine
BlackLine is the incumbent for the financial close. It automates reconciliations, journal entries, consolidation and intercompany, with an AI layer called Verity.
It can be more than a growing company needs.
Best for: large, complex organizations where the close is the bottleneck.
6. Basis
Basis builds AI agents for accounting firms, covering month-end close, tax return prep, and audit testing. People start at review instead of data entry.
It’s early-stage and demo-led, so ask for references of your size.
Best for: accounting firms, or teams that outsource to them.
Working with your ERP
Most platforms here sit on top of your ERP rather than replacing it. The ERP remains the system of record, and the agent reads from and writes to it.
Ask exactly which actions go through the integration and which are manual. That boundary decides how much time you actually save.
Security and fraud
Security comes up in every finance AI conversation, and for good reason. The same technology that speeds up reconciliation also helps attackers craft convincing phishing emails and forge invoices.
TaxLeopard’s article on how AI is fueling cybercrime while also powering better defenses is useful reading for leaders weighing automation against exposure.
A sensible rollout
Start with one process, such as AP coding or expense review. Run the agent beside your people for a month, compare results, then expand.
Keep an audit trail from day one. Auditors will ask for it, and it makes tuning easier.
Common mistakes
Automating a broken process is the classic one. Fix the workflow first, then add the agent.
Another is skipping the exception path. Decide who handles what the agent can’t, and how fast.
Metrics worth tracking
Measure processing time per invoice, the share handled without human touch, error rates and days to close. Capture a baseline before the pilot so you can compare fairly.
Also track exceptions, because a rising pile of them is the early warning that something is off.
Questions to ask every vendor
- How does the agent handle an invoice it doesn’t understand?
- Who can override its decisions, and is every override logged?
- Can auditors see why a transaction was coded a certain way?
If a vendor can’t answer those plainly, the demo is doing the selling.
How to choose
Pull a month of finance team requests and sort them. How many were questions about policy, how many were transactions, how many were exceptions needing judgment?
The first group points to a knowledge layer like Glean. The second points to Vic.ai, Stampli or Ramp, depending on whether the pain is invoices or spend. The third is where human review should stay.
Scope tightly, run the agent alongside your people for a month, and compare error rates and speed. In finance, a fast wrong answer costs far more than a slow right one.