How to Build AI Controls for High-Stakes Workflows

In this week’s newsletter, we’re exploring one of the most critical aspects of AI adoption in finance: building controls for high-stakes workflows.

As AI systems find their way into budgeting, forecasting, and financial reporting, their ability to streamline complex processes is undeniable. But with this power comes a new layer of responsibility. Mistakes in financial processes are costly, not just in numbers, but in trust and compliance. That’s why designing control points that safeguard against errors is not just a best practice; it’s a necessity.

This week, I’m walking you through exactly how to build these controls step by step, ensuring that your AI-driven processes are both efficient and airtight.

The Balanced View: How to Design and Implement AI Controls

Finance professionals are traditionally conservative, and for good reason. Errors in financial statements, reconciliations, or forecasts can be catastrophic. Compliance issues, shareholder distrust, and financial penalties are just some consequences of inaccurate data. When AI enters the picture, it amplifies both opportunity and risk.

Unlike many business functions, finance operates with very little room for error. A miscalculation in forecasting or a flaw in reporting could ripple out, impacting investment decisions, regulatory compliance, and overall financial stability. Therefore, integrating AI into financial processes requires a strategic focus on controls and checkpoints that maintain this critical accuracy.

The key to successful AI integration is understanding where and how to place controls that ensure reliability without stifling innovation.

AI can perform some of these controls using the prompt examples provided below. However, many of these critical checkpoints still require human oversight, particularly in high-risk or compliance-heavy areas.

Effectively designing these checkpoints is a significant part of the AI implementation process—one that must be addressed case by case, depending on the specific financial workflow and risk exposure.

When designing an effective control framework, we need to consider three main areas:

1. Input Validation:

The saying “garbage in, garbage out” is never more true than with AI. Flawed or incomplete data leads to flawed predictions and reports. Input validation routines should be implemented to check for:

• Data Completeness: Ensuring that all required fields are filled and there are no gaps in the dataset.
  Prompt: "When processing the dataset, check for any missing fields or incomplete rows. Flag any discrepancies for review."

• Correct Formatting: Verifying that financial figures, dates, and currency formats are consistent.
  Prompt: "Analyze the dataset and ensure all dates follow the format YYYY-MM-DD, currency figures are formatted with two decimal points, and all numerical data is correctly aligned."

• Integrity of Imported Data: Making sure there are no missing transactions or discrepancies when importing data from accounting software or spreadsheets.
  Prompt: "Cross-reference imported financial data against source reports. Identify any transactions that are missing or do not align with the original records."

Human Oversight: AI can never validate whether your data truly makes sense, if you are comparing apples to oranges, or using inconsistent data. Therefore, when implementing AI in your workflow, think critically about whether your analysis is sound. AI will never replace your unique knowledge of the company, data, and processes, so remember to utilize it.

2. Process Checkpoints:

Financial workflows are not a single step but a series of interconnected processes. AI must move through these processes without compounding errors for it to perform effectively. Key checkpoints should include:

• Data Reconciliation Checkpoints: AI can be configured to pause and cross-check balances at various stages of processing.
  Prompt: "At each stage of data processing, compare account balances against the previous stage to identify any discrepancies."

• Variance Analysis: Automated variance analysis that flags deviations from expected patterns, prompting a manual review.
  Prompt: "Compare forecasted values with actuals. Highlight any variances greater than 5% for further analysis."

• Cross-Referencing with Historical Data: This helps identify outliers by comparing real-time data with historical records.
  Prompt: "Analyze current data trends against historical financial performance. Flag any outliers with deviations above the threshold.

Human Oversight: AI helps a lot with automated verification and flagging; however, it remains up to your judgment to identify the acceptable thresholds, tell AI how exactly the data should be verified (compared to the previous year, previous quarter, or forecast), and design these validation steps.

3. Output Verification:

The final output must be verified, even with robust input validation and process checkpoints. CFOs should implement:

• Sampling of AI-Generated Reports: Randomly select sections of the AI-generated financial statements for manual inspection.
  Prompt: "Select random samples from the final report. Cross-check figures and summaries for consistency with original data inputs."

• Comparison Against Historical Performance: Ensuring the AI-driven reports align with expected outcomes based on historical data.
  Prompt: "Analyze key financial metrics against historical performance for anomalies. Highlight any unexpected changes for manual review."

• Audit Trails: Every AI-driven report should have a traceable path back to its original data source for validation.
  Prompt: "For each financial output, create an audit trail that maps data back to its source document, including date, time, and version."

Human Oversight: Everyone who has been in a finance leadership role long enough and managed teams has some system for validating the information. After all, we are responsible as CFOs for something that our teams have done. Use the same logic with AI-generated outputs—treat them as if a recent college graduate has been working on them—scan for discrepancies, formula integrity, and consistency. Always read your reports and add your unique perspective to them.

Common Pitfalls to Avoid:

• Assuming AI will always be accurate. Even the best-trained models can fail if the underlying data changes or the context shifts. Always pair AI outputs with contextual human analysis.

• Overlooking the need for human intervention. Some steps, especially those requiring nuanced judgment, must always involve a human expert.

• Implementing controls without assessing their relevance. Not all AI-driven processes need the same level of oversight. Customize controls based on the process's risk and criticality.

• Focusing solely on automation without accountability. Even with automated checks, there must be a clear line of responsibility for oversight and error handling.

In next week's newsletter, we'll explore how to take these principles a step further by designing a Control Matrix—a structured approach to mapping AI workflows against control points and risk levels. This will allow CFOs to fully understand where human intervention is crucial and where automation can operate safely without compromise.

How I Build Investor Reports Using AI and Get Zero Mistakes

Preparing investor reports is one of the most critical activities for startup CFOs. Transparency and accuracy can directly impact investor trust and future funding.

Over the last four quarters, I have used an AI-driven investor reporting workflow that combines automation with strategic checkpoints to deliver near-perfect reports every time.

This process maintains high accuracy and saves me around ten hours per quarter for every startup I cover. Here’s how I do it.

Stage 1: Data Collection and Initial Validation

The first step in my process is to gather three critical pieces of information:

• Financial information for the current quarter

• Recent company updates, covering major milestones and strategic changes

• Market updates that reflect industry trends and macroeconomic factors

I upload all this information to my custom GPT model, which is specifically configured to validate it before it’s even considered for the report.

When the data is uploaded, my custom GPT model automatically:

• Compares the current quarter's financials against the previous quarter.

• Highlights any variances that are above 15%.

• Presents a summary of these variances before proceeding to the next step.

In many cases, I have identified discrepancies right at this stage, often due to data entry errors or misalignments in the reporting software. This early validation saves me a massive headache down the line because these errors are caught before they make it into the report.

Stage 2: Drafting the Investor Report

Once the data is validated and discrepancies are corrected, I proceed to draft the report. This step is largely automated but done in step-by-step segments to ensure clarity and accuracy:

1. Revenue and Expense Breakdown – AI generates a detailed breakdown of revenue streams, operating expenses, and net income.

2. Variance Analysis – Key changes are highlighted with explanations automatically generated based on uploaded company updates.

3. Key Performance Indicators—Metrics like burn rate, runway, and profitability are presented in a clean, readable format.

Although the AI handles the drafting, I perform a manual cross-verification of the financial figures.

• I selectively check revenue numbers, cash positions, and expense lines against the original documents I uploaded.

• I don’t check every number but focus on high-impact areas—like top-line revenue and major expense categories.

Stage 3: Final Review and Executive Summary

The last stage is to combine the report sections and craft the Executive Summary. This part is crucial because it’s where narrative meets numbers.

• I read the entire report word for word to catch any inconsistencies or areas that may require rephrasing.

• I cross-check selected numbers that are critical for investors, such as quarterly growth rates, total revenue, and major expense shifts.

This is also the stage where I add my unique perspective. My experience in the business, my understanding of market conditions, and the strategic moves the company has made are translated into the Executive Summary. I view this as non-negotiable—AI can draft the numbers, but it cannot interpret the subtleties of market position, strategic risk, or executive decisions.

I have been running this process for four straight quarters and have not had an error in the final report that required major correction. The reason is simple: the initial validation process catches 90% of discrepancies upfront, and my manual review at the end ensures that nothing slips through.

Closing Thoughts

Building bulletproof AI controls isn’t just about error prevention—it’s about trust. When you can confidently rely on your AI-driven reports, forecasting, and budgeting processes, you unlock the real value of automation: more time for strategic thinking and better decision-making.

As finance leaders, our role is not just to adopt new technologies but to adopt them responsibly. By combining the efficiency of AI with well-placed human oversight, we can transform high-stakes financial workflows into models of accuracy and reliability.

If you’re looking to improve your investor reporting or financial automation or see how AI can seamlessly integrate into your workflows without sacrificing control, I’m here to help.

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO