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.

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.

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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.

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