How AI Will Affect Accounting: 5 Changes Finance Teams Should Prepare For

Many finance functions are experimenting with AI in some capacity, yet remain caught between early tests and scaled deployment. That may be the reason “how AI will affect accounting” has become an operational planning issue.

And AI will indeed affect teams, changing accounting workflows by moving spend visibility earlier and embedding policy enforcement within the workflow. Controllers, CFOs, and accounting managers should plan for five specific changes now.

Why Accounting Teams Are Testing AI but Struggling to Scale

For T&E accounting, scaling AI depends on staffing, workflow readiness, and integration depth. These are the factors that determine whether AI moves beyond experimentation.

Limited staffing, manual processing costs, and reliance on manual workflows are accelerating the shift within travel and expense (T&E) accounting. The State of Corporate Travel and Expense 2026, a report from Skift and Navan, found that 29% of T&E managers surveyed still process expenses manually, up from 23% two years ago — a signal that the problem is growing even as tools mature.

The broader shift is about more than adding automation to back-office steps. In AI-powered T&E, the model is to solve problems at the source, before they become downstream administrative work for accounting. That approach depends on connected data. Navan’s unified T&E data core connects travel intent with final spend across 130-plus data elements, giving finance teams more context than disconnected travel, card, and expense systems provide.

5 Changes in How Finance Teams Handle T&E

Accounting work is moving closer to the moment money moves, with automatic data capture and policy enforcement happening as employees spend. These changes are already underway at organizations that have moved beyond small experiments.

Here’s a closer look at the changes, which include:

1. Expense Processing Moves From Manual Data Entry to Automatic Capture

Automatic data capture at the point of swipe can replace the manual expense report, which creates one of the biggest time demands in T&E accounting. The time cost is spread across the organization as employees fill out forms and finance teams chase down missing receipts before review.

AI-powered tools change this workflow by capturing spend details at the source. When an employee swipes a corporate card, the system pulls merchant details, location, amount, and category without anyone having to type a word. Calendar integration adds meeting attendees. Receipt data is extracted through OCR and matched to the corresponding charge.

Data captured automatically at the point of swipe includes details such as:

• Merchant name, location, and category
• Transaction amount and currency
• GL codes and cost centers
• Meeting attendees from calendar integration
• Receipt line items via OCR extraction

Navan Expense automatically captures 130-plus data elements per transaction, including GL codes, cost centers, and attendee information. Its Expense Agent reads every line item on a receipt and applies the correct GL code based on company policy, which can reduce the manual work required before transactions move into the general ledger. A Forrester TEI report commissioned by Navan and based on a composite organization found that organizations using Navan saved 24 minutes per expense report submission.

Less time spent on data entry can give your accounting team more capacity for analysis and exception handling that require human judgment.

2. AI Audit Tools Replace Sample-Based Reviews With a Review of Every Transaction

AI audit tools now scan every charge against a consistent set of rules. That capability gives finance teams an alternative to sample-based reviews, which leave systematic blind spots in expense programs. Traditional expense auditing relies on reviewing a small percentage of submitted reports, typically selected at random or by dollar threshold. Out-of-policy charges and duplicate submissions may not be selected for review, especially when split transactions stay under approval limits.

AI changes the math of reviews by making it feasible to check every transaction. These tools scan the entire population and send only exceptions to a human reviewer after automatically clearing compliant charges.

The Forrester TEI study found that organizations using Navan reduced the time their finance teams spent on expense management by 40%. The coverage increase is just as significant as the time savings: Reviewing every transaction can surface policy violations and anomalies that sample-based auditing may miss. An Audit Agent powered by Navan Cognition checks each transaction against configurable audit rules and surfaces only the spend that needs attention. Navan Cognition is an enterprise-grade agentic AI framework built for mission-critical operations, which helps explain why finance teams evaluating AI weigh reliable, at-scale rule enforcement alongside speed.

For your team, the shift from sampling to full review can mean a stronger compliance posture with less manual effort, a combination that matters — especially during audit committee reviews.

3. Month-End Close Becomes a Continuous Process

Continuous reconciliation, powered by AI, can spread month-end close work across the entire period, making the close day itself a confirmation step with less deadline pressure. The traditional close compresses reconciliation, journal entries, variance analysis, and sign-offs into a narrow window, often requiring overtime and leading to errors under pressure. AI can distribute the close workload across the month, so the final day carries far less friction.

AI reconciliation tools monitor account activity throughout the period, surface discrepancies as they arise, and propose resolutions while context is still fresh.

Continuous processing absorbs tasks such as:

• Matching transactions to bank statements and booking records
• Surfacing discrepancies as they arise during the period
• Proposing resolutions while the context is still fresh
• Pre-coding transactions with GL codes and cost centers for ERP export

Navan’s Reconciliation Agent matches personal card payments to corresponding travel bookings. This helps create a fuller financial picture across transaction types. Pre-coded transactions then flow into your ERP through direct integrations with systems such as NetSuite, QuickBooks, and Xero.

4. Pre-Transaction Controls Replace Post-Trip Policy Reviews

AI makes it possible to enforce expense policy at the point of swipe. That shift moves T&E controls from post-trip review to the moment spending happens. Under the traditional model, most policy enforcement happens after money has already been spent. An employee books a hotel, submits the receipt weeks later, and a reviewer flags the rate as out of policy. At that point, the company is deciding how to handle spend after the fact. Earlier controls give teams a chance to guide spend before reimbursement review.

At the pre-booking stage, AI can surface compliant options first and flag potential violations in real time. When an employee uses a corporate card, controls auto-approval, flags for review, or declines charges based on configurable rules, such as spending limits, merchant categories, or whether the employee has an active trip on file.

This shift from reactive review to proactive control adds discipline before spend occurs, which keeps out-of-policy costs from reaching the bottom line. Navan’s policy system operates at the point of swipe. It applies spend controls that auto-approve compliant charges, flag borderline transactions for review, or decline out-of-policy purchases before the money leaves the company.

Pre-transaction enforcement also tends to produce cleaner downstream data. When out-of-policy spending is prevented before the charge goes through, your close process tends to have fewer exceptions to resolve and fewer adjustments to record.

5. Data Entry Gives Way to Oversight, Analysis, and AI Governance

Accounting roles are shifting from routine processing to oversight, analysis, and governance of AI itself, as automation absorbs data-entry tasks. As automated workflows take on more coding, matching, and first-pass review, the work left to people becomes more focused on judgment, interpretation, and control.

When AI handles GL coding, receipt matching, and first-pass auditing, your team can move toward responsibilities such as:

• Reviewing exceptions and edge cases that automation flags but cannot resolve
• Validating AI outputs and confirming that automated coding matches company policy
• Interpreting patterns that AI surfaces across spending data
• Building governance frameworks for AI agents before automation scales further

Data literacy — the ability to understand how AI models reach their conclusions and when to question them — is becoming a baseline competency. As finance teams give automation a larger role in day-to-day workflows, governance becomes more important. Building a solid oversight structure now, before AI handles more of your expense management workflow, helps keep controls strong as it grows.

From Reactive Cleanup to Continuous Control

Together, these five changes move accounting work toward continuous, forward-looking control. Each one, from automatic data capture to pre-transaction policy enforcement, shifts the point at which your team engages with spending data closer to the moment the money moves.

Teams can implement these changes one workflow at a time. A practical rollout can start with high-friction workflows, such as reconciliation or receipt processing, then build confidence in the AI’s accuracy before expanding. Treat AI as a change in how work is structured across the finance function.

For many teams, the differentiator will be whether the AI system is built for employees and supported by connected travel and expense data. That’s what allows automation to work earlier in the workflow instead of after the fact. It also helps explain why production-ready platforms matter more than generic AI claims: The more finance depends on automation for coding, review, and policy enforcement, the more reliability and context matter.

Your close can get faster. Your audit coverage can get broader. And the time your accounting team currently spends on data entry can go toward the judgment calls and analysis that AI cannot do on its own.