Your First Real AI Win: How to Choose the Right Use Case

This week, we’re taking the next step in your AI adoption journey.

If you've already explored how to use AI for summarizing documents or drafting communications, you're likely asking the right next question:
What should I automate first that actually saves me time?

That’s exactly what we’re covering today.

We’ll look at how to identify a high-impact, low-risk use case that you can implement on your own. We’ll also examine real-world examples—both successful and unsuccessful—to give you a clear picture of what works and what to avoid. 

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In this session, I’ll break down the biggest barriers holding finance back and explore real-world strategies to accelerate AI adoption.

Your First AI Automation: What Works, What Fails, and Why It Matters

Last week’s newsletter covered how to start your AI journey from scratch—learning the tools, experimenting safely, and building confidence through low-risk applications. This week, we move a step further.

If you’ve already been using AI for personal productivity—drafting emails, summarizing reports, analyzing internal memos—then the next step is clear: identify and implement your first real automation.

The key is choosing wisely. A poorly selected use case can create confusion or disappointment. A well-chosen one can save hours, establish credibility, and set the foundation for future adoption. To help you navigate this step, I often refer to a simple but effective framework that separates good candidates from bad ones.

A Framework for Selecting the Right First Use Case

This framework provides a strong filter when you’re deciding where to apply AI. In the sections that follow, I will share several real-world examples—some successful and others less so—that illustrate how this framework works in practice.

Successful First Automations: What Worked and Why

1. Contract Review Assistance

A CFO implemented an AI-based tool to assist with recurring contract reviews. The AI was instructed to extract and summarize key clauses, including payment terms, renewal conditions, liability caps, and governing law, and compare them against internal policy standards.

Why it worked
The review process was already well-defined and repetitive. The CFO was not seeking to delegate decision-making but to reduce time spent manually scanning documents for a known set of elements. The AI served as a first-pass filter, with all outputs reviewed by the CFO before approval.

Lesson
AI is well-suited for structured extraction tasks where expectations are clearly defined and outcomes are easy to verify. Automating the initial review stage reduced effort without compromising control.

2. Cash Flow Forecasting

A finance leader previously built weekly cash flow projections manually, combining judgment with spreadsheets and source data from accounts payable, accounts receivable, and bank records. The process was transitioned to GPT, which analyzed uploaded data, identified historical patterns, and generated short-term projections.

Why it worked
The data inputs were clean, regularly updated, and already structured for analysis. More importantly, the CFO maintained a consistent review cycle, validating GPT’s projections against actuals and adjusting inputs accordingly. This created a feedback loop that improved accuracy over time.

Lesson
AI can accelerate structured forecasting processes, but human oversight is critical. By treating AI as an analytical assistant rather than a decision-maker, the CFO preserved accuracy while reducing time spent on routine projections.

3. Financial Report Conversion Across Languages and Currencies

In a multinational organization, the CFO consolidated monthly financial reports submitted in various languages and local currencies. This required translating documents, converting currency values, and reformatting them into a unified structure for consolidation.

The CFO created an AI-based automation where reports were uploaded with instructions to translate content to English, convert currencies to USD, and generate clean, standardized output for use in Excel.

Why it worked
The task was repetitive, structured, and did not involve confidential or strategic content. The AI's role was limited to formatting and surface-level transformation—an ideal entry point for automation.

Lesson
The most effective first automations often target administrative friction. Converting formats, cleaning inputs, and streamlining repetitive preparation tasks are areas where AI can provide substantial efficiency gains.

When It Doesn’t Work: Lessons from Missteps

1. Forecasting Revenue Using Unreliable CRM Data

A CFO attempted to automate revenue forecasting in a services company by using client activity data stored in the CRM system. The AI model was tasked with projecting revenues based on the status of client engagements and expected pipeline.

Why it failed
The underlying data lacked consistency and completeness. Client managers were not updating the CRM regularly, and the information feeding the forecast was often outdated or missing entirely. As a result, while the AI produced a forecast, it was based on flawed inputs and led to misleading projections.

Lesson
AI models are only as reliable as the data they are given. Forecasting tasks that depend on human-entered systems must be preceded by rigorous data discipline. Automating a process without trustworthy inputs amplifies uncertainty rather than reducing it.

2. Budget vs. Actual Analysis Without Accurate Actuals

A company implemented AI-generated budgeting and sought to automate plan-versus-actual comparisons as part of its financial review process. However, when comparing AI-generated budgets to actual results, there were large and unexplained variances.

Why it failed
The company lacked a monthly closing process and was not accruing routine expenses. As a result, the reported actuals were incomplete and inconsistent, making it impossible to generate meaningful comparisons. The AI was performing calculations correctly, but the inputs were not representative of reality.

Lesson
AI cannot compensate for the absence of fundamental financial processes. Budgeting and forecasting workflows depend on a reliable actuals baseline. Attempting to introduce AI without accounting rigor leads to false confidence and confusion rather than insight.

Final Takeaway

AI works best when it's introduced into a process that’s already functioning, and where the goal is to reduce manual effort, not replace judgment.

The best first automations are small, structured, and easy to validate. They save time on tasks you already understand well: reviewing contracts, preparing recurring reports, or transforming data formats. These are the use cases where AI can take something that used to take 60 minutes and turn it into a 10-minute task, without disrupting your workflow.

What doesn’t work is trying to automate chaos. If a process is inconsistent or if the data is unreliable, AI won’t fix it.

The key is to pick one task that’s already working and make it more efficient. Build your confidence there. Learn how to check the AI’s work. Get familiar with what it can do well—and where it needs oversight.

From there, you’ll know where to go next.

5 Smart AI Automations for CFOs

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.

Closing Thoughts

Your first automation doesn't need to be perfect.
But it does need to be useful.

A 30-minute task reduced to 5 is a meaningful win, and more importantly, it's proof to yourself and your team that AI can be applied with intention.

In future issues, we’ll continue to explore how finance leaders are building on these early successes, moving from isolated wins to structured, repeatable workflows. Until then, focus on one thing: implement something real, small, and valuable this month.

And if you’ve recently automated something, hit reply and tell me about it. I’d love to hear what’s working.

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO