If you're past the point of testing and ready to start applying AI to real finance work, these use cases are a strong next step. Each one is based on repeatable, structured workflows that benefit from faster preparation, deeper pattern recognition, or better documentation.

1. Anomaly Detection for Internal Reviews and Audit Prep

Use Case: Identifying inconsistencies or potential issues across expense categories, transactions, or audit documentation.

How to do it: Upload expense data, transaction logs, or documentation trackers. Prompt GPT to flag deviations from past trends, missing justifications, or potential policy breaches.

Why it works: You define the audit criteria. GPT handles the first scan—so you can focus on validation and follow-up.

2. Credit Line Evaluation and Risk Flagging

Use Case: Reviewing and comparing credit facility proposals from multiple lenders.

How to do it: Upload all proposal documents and prompt GPT to extract terms, flag key differences, highlight restrictive clauses, and generate a list of follow-up items for legal and negotiation.

Why it works: GPT accelerates the comparison process, giving you a cleaner foundation for internal discussion and external negotiation.

3. First Draft of a Monthly Management Report

Use Case: Producing a narrative and performance summary based on the current month's financials.

How to do it: Upload core reports and last month’s version. Ask GPT to replicate the format with updated data, commentary, and variance notes.

Why it works: The report structure is usually fixed—only the story changes. GPT helps you move faster without losing context.

4. Vendor Review and Negotiation Preparation

Use Case: Preparing for annual vendor renegotiations by consolidating insights across contracts, spend data, and competing offers.

How to do it: Upload last year’s vendor contract, current spend summaries, and the proposed renewal or updated agreement. Optionally include offers from comparable vendors. Prompt AI to summarize key contract terms, identify year-over-year cost changes, and draft potential negotiation points.

Why it works: This is a high-value task where better preparation often leads to better outcomes. AI reduces prep time while giving you clearer insights to support your negotiation strategy.

5. Reconciliation Assistant for Multi-System Data

Use Case: Matching data between two reports, such as a bank statement and your accounting system, or internal billing vs. client records.

How to do it: Upload both reports (e.g., CSVs or exports). Prompt AI to identify unmatched entries, highlight discrepancies in amounts or dates, and summarize missing or duplicate items.

Why it works: These tasks are tedious, detail-heavy, and ripe for error. AI reduces the time spent line-matching and helps surface issues faster for manual verification.

These five automations are high-impact, low-risk, and designed to give you time back in the areas where you already know what you’re doing, without needing to reinvent your systems.

Choose one and test it in your next reporting cycle.

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