How Is AI Used in Finance Today?

Finance teams use AI today to forecast faster, catch reconciliation exceptions before period-end, assemble reporting packages, review contracts, and answer leadership questions without another export to Excel. Every one of those outputs has to reconcile to actuals.

That's where most projects stall. A model can look accurate in a pilot and fall apart at month-end if it runs on unreconciled actuals or can't see period locks. The teams getting real value put AI on the same governed data they close on, so the output is something a controller will sign and an FP&A lead will defend in front of leadership or the board.

AI in Finance is the use of machine learning (ML), automation, and predictive analytics on financial data so teams spend less time collecting and more time deciding. In the Office of the CFO, it means forecasts, reconciliations, reports, and analysis that sit on the same actuals used to close the books.

Finance can reuse the same modeling techniques, and it still needs outputs that can be traced, audited, and tied to a period. If the model can't show why it moved a forecast or flagged a balance, the work stalls in review.

Most projects fail at the connections around the model. Data is siloed, incomplete, or exported into a side tool. Models are easy to stand up and hard to govern at scale. There's no explanation an auditor will accept, and the people who understand the business can't reach the tools because data science lives outside Finance.

A pilot can look precise and still be useless in month-end. A forecast on unreconciled actuals inherits the same gaps, and an assistant that can't see period locks will invent a number the books won't support.

The uses that last plug AI into a defined workflow, on governed data, with a Finance-owned measure of success. People still approve the output, and the system does the first pass.

These are the Office of the CFO uses that show up when AI is embedded in the close, the plan, and the report, rather than sitting in a side tool. The five below are the ones that hold up in those processes.

Forecasting Finance can explain

Planning teams are asked to re-forecast when prices, demand, or rates move, then defend the change. Spreadsheet rebuilds can't keep that pace, and a black-box model won't survive the next board pack.

Purpose-built ML time-series forecasting, run on the same data used for close and reporting, is the use that has the clearest published results. OneStream SensibleAI Forecast is machine-learning forecasting inside the Finance process, with explainability dashboards and narrative summaries behind each prediction. Across customers, forecast accuracy has improved 25% or more on average, and time to create forecasts has dropped 82% or more. No coding or external toolkit is required.

Review planning software to see how forecasting sits next to budgeting and scenarios.

Stake Center Locating has to forecast variable project work and the crew capacity behind it, across roughly 10 million locating tickets a year. After moving that work onto SensibleAI Forecast, the team reported:

• 15% to 20% better accuracy on ticket-volume forecasts
• A weekly forecast cycle in place of a quarterly one
• Millions in savings, tied to staffing against real demand

The accuracy gain is what produces the savings, because headcount and equipment are planned off that forecast.

Exceptions earlier in the close

A typical close still waits until period-end to match transactions, reconcile accounts, and hunt for the missing entry. AI can move matching and exception work earlier in the period so less of it piles up at month-end.

Used well, models watch reconciliation data for unusual balances, missing support, and repeated process issues, so the team reviews exceptions instead of every account. Matching can run through the period so more reconciliations are already clean by the time certification starts. People still sign off, and the audit trail stays on.

OneStream applies this inside account reconciliations through SensibleAI Studio, on the same data used for consolidations and reporting, so the match and the statement share one set of numbers.

Reports without a second extract

Routine packs still consume days when someone has to collect, re-key, and reconcile versions: income statement, balance sheet, cash flow, and management reports. AI and automation help when they assemble the pack from the same model as the close, then draft commentary the team can edit.

Cox Enterprises had Finance spread across divisions and systems, so people spent the cycle reconciling data instead of explaining it. They put OneStream underneath as one source of truth first, then deployed SensibleAI Agents on three accounting workflows:

• Trusted data retrieval, about 10 minutes saved per query
• Self-service reporting, about eight minutes saved per query
• Contract review with risk flagging, about 10 hours saved per contract

Those figures are measured per task, so they should not be read as a company-wide hours total.

Document review on exceptions

Contracts, invoices, and support files hold terms Finance needs and rarely has time to read line by line. AI can pull dates, amounts, and clauses so people review the outliers. Cox's contract-review use is the Finance version of that job: large-volume review, with risk flagged for a person.

The extract still has to stay attached to the record, and an approver still owns the call.

Purpose-built Agents with Financial Intelligence

AI agents are useful when they turn a question into a calculation or a controlled workflow, not when they summarize a slide. In Finance that means the agent uses the consolidation, planning, and reporting engines already in place, and it inherits role-based access, approvals, period locks, and audit trails.

OneStream SensibleAI Agents are built with financial intelligence, they know your hierarchies, dimensions, and metadata so they understand the context of how you run your business.Teams can use them inside OneStream, or through Copilot, ChatGPT, Claude, or other tools that support Model Context Protocol (MCP), without exposing raw data or dropping governance.

The uses above only hold up if the data underneath them is trusted. Check these five things before you scale.

1. Actuals, plans, and reports should come from the same model. If they don't, every gap between them shows up in the output.
2. A controller or FP&A lead should be able to see why the system flagged a balance or moved a forecast.
3. The agent should respect the same permissions and period locks the rest of Finance already uses, so governance is part of the architecture.
4. The agent should understand your hierarchies, dimensions, and metadata, so it answers in the terms your business already runs on.
5. Success should be a Finance-owned measure such as time to forecast, exception rate, or hours pulled out of a named workflow, rather than whether a pilot launched.

OneStream's SensibleAI portfolio is built for that: Forecast, Studio, and Agents on the same data model, security, and metadata Finance already uses, with dashboards, audit trails, and transparent reasoning on the output. Customers have deployed it in weeks without having to spend months developing and maintaining their own AI solutions.

Three places to go next:

• Explore agentic AI for how agents run on governed close and plan data
• Compare AI Finance software if you are building a shortlist
• Read enterprise AI examples for more planning use cases

AI in Finance is already in production. Finance teams use it to forecast faster, surface close exceptions, assemble reports, and answer leadership questions from governed data.

The companies that get value treat AI as part of the Finance process. They keep one version of the numbers, they require an explanation, and they put people on the exceptions. That is how the work moves from a pilot into a number someone will defend in review.

What is AI in Finance?

AI in Finance is machine learning, automation, and predictive analytics applied to financial data. Finance teams use it for forecasting, close support, reporting, and analysis.

How is AI used in Finance today?

The uses that hold up are the ones tied to a real workflow: explainable forecasting, exception detection in reconciliations, automated reporting on governed actuals, document review, and assistants that inherit Finance controls. Banks also use AI for fraud, credit, and customer service.

Where is AI actually saving Finance time?

Published OneStream examples: SensibleAI Forecast customers have seen 25% or more average forecast-accuracy improvement and 82% or more less time to create forecasts. Stake Center Locating improved accuracy 15% to 20% and moved from quarterly to weekly. Cox Enterprises saved about 10 minutes per trusted data query, eight minutes per self-service report, and 10 hours per contract review with SensibleAI Agents.