Finance leaders are under pressure to manage more data, make faster decisions, and do it all with fewer resources. That’s not changing anytime soon, which makes it even more critical to take action now.
Most finance leaders know that AI can help improve accuracy, reduce manual work, and free up teams to focus on higher-value initiatives. But knowing where to start is another story. Not every finance workflow is ready for automation, and not every solution fits.
To help, our team wrote this guide to help make sense of it all. We break down what Large Language Models (LLMs) and AI agents can realistically do today…and highlight six finance workflows, all possible using Ledge, where automation delivers real, lasting impact.
What are LLMs and why should they matter in finance?
Large Language Models (LLMs) are a type of generative AI trained to understand and generate human language. They can analyze context, follow instructions, and produce outputs that previously required manual interpretation—like drafting journal entries or classifying transactions.
Unlike traditional automation, which relies on rigid rules, LLMs can flex across workflows—turning disconnected tasks into intelligent systems. This makes them uniquely valuable in finance, where judgment and adaptability matter.
AI agents in finance
LLMs are evolving into something more powerful: AI agents that don’t just suggest but also act. Agents handle full workflows, learn from past decisions, adapt logic in real-time, and escalate only when necessary.
One way to think of agents is as digital teammates: agents are capable of tasks such as resolving exceptions, applying month-over-month logic, following up with vendors, and keeping humans in the loop for final reviews. This shift isn't just about efficiency—it's about redesigning how finance teams work.
LLMs vs. Agentic AI: What’s the Difference?
Here’s how to spot the right workflows to automate now—and prepare for what’s next.
1. Automated remittance matching
AI analyzes incoming payments, suggests invoice matches, and even follows up with customers—so your team doesn’t have to.
Remittance matching is one of the most time-consuming, error-prone workflows in high-transaction environments. When customers don’t include invoice details with payments, the result is back-and-forth emails, detective work, and month-end delays.
This work often falls to accountants, bookkeepers, or payment ops specialists, who spend dozens hours each month matching payments to invoices. But it doesn’t stop there. Controllers and finance leads feel the impact too, especially when reconciliation delays ripple into reporting and month-end close. That’s why remittance matching is one of the most immediate areas where AI can deliver results.
When remittance matching is automated with AI using solutions like Ledge, finance teams reduce the manual burden of matching payments to invoices, which leads to faster reconciliation and fewer support escalations. Accountants spend less time on detective work. Controllers gain timely visibility into payment flows with less manual follow-up and clearing. Finance leaders see fewer downstream delays in month-end close.
With LLMs: Using tools like Ledge, AI matches payments to likely invoices using context clues.
With AI agents: Workflows can contact the customer, confirm remittance details or payment reference, and apply payments automatically.
2. Reduced operational burden of journal entry preparation and posting
AI drafts entries using rules, context, and history — so your team can close faster with fewer errors.
Journal entry prep is one of the most repetitive, time-intensive parts of the close. Whether it’s allocating processor fees, preparing foreign exchange adjustments, or handling intercompany transfers, finance teams spend hours hunting down data, recreating past entries, and formatting spreadsheets for import…only to run into vague ERP error messages or last-minute corrections.
Context lives in inboxes. Supporting docs are scattered. Approvals happen over Slack. And the burden of stitching it all together falls on already stretched teams.
Solutions like Ledge change this dynamic.
With LLMs: AI suggests journal entries using transaction data, business context, and accounting logic, accelerating prep while flagging gaps or anomalies before they reach review.
With AI agents: Agents generate and post recurring entries automatically, reuse logic from prior periods, and escalate only when review is truly needed, which reduces manual effort without sacrificing control.
3. Journal entry creation from recurring transactions
AI learns patterns in your financial operations and drafts journal entries each month with no manual duplication required.
Recurring transactions like payroll, rent, and software subscriptions generate predictable journal entries. Yet teams still spend time recreating them manually each month.
Accountants handle the bulk of this repetition, which can consume hours that could be spent on more strategic tasks. Controllers rely on the accuracy of these entries for reliable close processes. Finance leads need confidence in month-over-month consistency. When these entries are missed or inconsistent, the ripple effects show up in reporting and forecasting.
We’ve built Ledge to simplify this challenge using AI.
Our platform’s AI eliminates repeat manual data entry for recurring transactions, which improves accuracy and consistency. Accountants spend less time replicating past work. Controllers face fewer discrepancies at review. Finance leaders benefit from cleaner books and fewer manual adjustments.
With LLMs: AI, using a solution like Ledge, suggests entries based on prior transactions and suggests journal entries based on established rules.
With AI agents: An agent can identify a new transaction, analyze how similar items were previously booked in NetSuite, and auto-post a new journal entry, escalating only when the logic is unclear.
4. Continuous exception resolution
AI flags issues early, investigates discrepancies, and resolves routine mismatches before they cause delays.
Unmatched transactions and reconciliation breaks are inevitable—but resolving them doesn’t have to be so time-consuming.
In most finance teams, accountants chase discrepancies, controllers triage open issues, and finance leads wait on final numbers to complete reporting. During the month-end close, this often means long hours, delayed insights, and high-pressure chaos.
AI, using a solution like Ledge, shifts the process from reactive to proactive.
By handling matches and exceptions continuously rather than at the last minute, finance teams reduce end-of-month backlogs and operate with greater predictability.
With LLMs: We’ve built the Ledge AI to surface unmatched transactions and prioritizes them for review based on context, transaction size, or other risk factors.
With AI agents: Agents investigate discrepancies across internal and external sources, identify root causes, and resolve common mismatches automatically.
5. Immediate answers to payment status queries
AI surfaces real-time payment data so teams can get answers instantly, without interrupting finance processes.
Payment status inquiries can overwhelm finance teams with internal escalations—especially when only a few employees have access to bank portals.
Accounts Payable teams get pulled into constant chases. Controllers field escalations. Finance leaders are forced to context-switch away from strategic work to track down updates.
We’ve built Ledge’s AI-driven solution to shift this dynamic by reducing context-switching.
AP teams avoid repetitive questions. Controllers gain time back from unnecessary status checks. Finance leaders can focus on strategic initiatives without being pulled into day-to-day tracking.
With LLMs: AI, built into a solution like Ledge, can integrate live payment status into internal tools, letting business users get answers without involving AP.
With AI agents: Agents can proactively respond to questions, share updates, flag delays, and initiate vendor outreach if needed.
6. Intercompany transaction reconciliation
AI matches entries across entities, flags mismatches, and drafts summaries, so teams can close with confidence.
Intercompany reconciliation is one of the most tedious and high-stakes workflows in finance. It spans multiple teams, systems, and time zones—and even small mismatches can delay consolidated reporting or trigger audit issues.
Accountants spend hours coordinating across entities to track down support. Controllers bear the cleanup and compliance burden. Finance leaders are left managing delays and defending accuracy to auditors and the board.
We’ve built Ledge’s AI-powered platform to reduce manual coordination by unifying data, identifying mismatches early, and recommending resolutions…speeding up close and improving audit readiness.
Ledge centralizes inter-company data, flags mismatches automatically, and supports resolution workflows—streamlining consolidation.
With LLMs: AI detects and matches intercompany transactions across ledgers, flagging inconsistencies by amount, date, or memo field.
With AI agents: Agents follow up with counterparties, apply logic from past resolutions, and generate reconciliation summaries for review.
Building for automation success
A strong automation strategy starts with the workflows AI can reliably improve today, while laying the groundwork for what’s next.
Each automated workflow builds the data structure, audit trail, and process integrity needed to scale over time. Teams that prioritize clean data and repeatable logic now will be ready to integrate more intelligent systems later.
For finance leaders, the opportunity is clear: focus on the tasks your team already spends too much time on. Start small. Automate the friction. Let AI handle the repetitive work—so your people can focus on what truly moves the business forward.
