Building strong relationships with business stakeholders starts by understanding their needs and challenges and aligning your AI and data initiatives to address them. This approach not only demonstrates business value but also builds trust and engagement. A data and analytics leader who is a business stakeholder should focus on developing a strong relationship with their Chief Financial Officer (CFO) because they:
- Oversee overall corporate performance
- Control budgets and decide which capital requests get funded
- Have the power and influence to remove organizational obstacles
In this blog I look at seven common areas of financial analysis, why they are important to business performance, ways AI can provide value to the CFO, and why data quality and observability are important.
1. Income statement
How did the business team score? Where was the performance strong or weak?
Why it’s important: Understanding the team’s financial performance provides insights into the overall success of business strategies and identifies areas that require improvement. By assessing strengths and weaknesses, companies can replicate successes and address underperforming areas, ultimately enhancing profitability and efficiency.
How AI can help: AI can analyze historical income statement data and predict future performance trends. By automating the identification of revenue patterns, cost drivers, and profitability, AI can help pinpoint areas of strong or weak performance. AI tools can also generate natural language responses, such as “Revenue increased by 10% this quarter due to higher product sales in region X.” This capability makes it faster to interpret the data and understand performance.
Why data quality matters: Accurate revenue and expense data are essential for AI to properly assess financial performance and identify areas of strength and weakness. Inaccurate data can lead to incorrect conclusions about business performance, misinforming management decisions.
Role of data observability: Data observability tools monitor real-time data flows and flag discrepancies (e.g., missing, duplicated, or incorrect entries) in income statement data. This ensures AI systems have a reliable foundation for analysis, improving the accuracy of financial insights and enabling early detection of data quality issues.
2. Drill-down variance
What causes changes in financial performance?
Why it’s important: Identifying the drivers behind financial fluctuations enables businesses to adjust strategies proactively. Knowing the causes of positive or negative variances helps prevent future issues, optimizes processes and fine-tunes resource allocation, ensuring consistent financial stability and growth.
How AI can help: AI can automate drill down analysis of financial data to uncover underlying causes of performance fluctuations. Machine learning (ML) models can analyze multiple financial variables simultaneously, identifying anomalies, correlations and root causes for variance between planned and actual performance. This can include factors like sales trends, cost fluctuations or external economic impacts.
Why data quality matters: For AI to pinpoint the causes of financial performance variances, it needs high-quality transactional, operational and financial data. Poor data quality can distort the variance analysis, leading to flawed root cause identifications.
Role of data observability: Data observability provides end-to-end visibility into the data lifecycle, ensuring completeness, consistency and accuracy in the financial datasets that AI models rely on for variance analysis. This prevents skewed results and supports the continuous validation of data against expected benchmarks.
3. Operational plan variance
How do we best support, coordinate, and manage the delivery of meaningful plans?
Why it’s important: Effective coordination of financial and operational plans ensures that business units are aligned and working toward common goals. By managing these plans efficiently, companies can improve resource allocation, meet targets and drive long-term success.
How AI can help: AI can streamline the process of comparing operational plans against actual results. AI-based predictive analytics can simulate different scenarios, allowing finance teams to adjust plans based on real-time data and anticipate future operational challenges. AI can also suggest optimal plans by identifying historical patterns and guiding the development of more accurate forecasts.
Why data quality matters: AI models need reliable operational and financial data to evaluate how actual performance compares to the operational plan. Inconsistent or incorrect data can lead to poor planning decisions and misalignment with business objectives.
Role of data observability: Data observability tracks data across multiple systems, highlighting any discrepancies in the operational plan data (e.g., changes in sales forecasts or production targets). It ensures that AI models have access to consistent, up-to-date data for reliable planning and forecasting.
4. Cash flow and working capital
How do we manage working capital, collect accounts receivables and monitor cash use effectively?
Why it’s important: Proper management of working capital ensures liquidity, operational efficiency and the ability to meet short-term obligations. Efficient cash management and timely collection of receivables are critical for sustaining operations, investing in growth and avoiding financial strain.
How AI can help: AI can enhance cash flow management by predicting cash inflows and outflows, optimizing working capital, and automating tasks such as payment processing and collections. AI can track accounts receivables, analyze customer payment behaviors and recommend strategies to reduce DSO (Days Sales Outstanding). It also monitors cash reserves, flagging potential liquidity risks well in advance.
Why data quality matters: Accurate cash flow data is critical for AI to predict future liquidity and optimize working capital. Low-quality data could result in missed forecasts, leading to liquidity shortfalls or excesses.
Role of data observability: Data observability tools ensure the accuracy and timeliness of cash flow data, enabling AI to continuously track accounts receivable, payable and inventory levels. These tools detect anomalies in the data, such as incorrect payment entries or outdated receivables, and correct them to ensure AI can make reliable cash flow predictions.
5. Balance sheet
How do we balance and structure the financial funding options, resources, and risks of the business?
Why it’s important: Balancing financial resources and risks through effective funding strategies helps businesses maintain a healthy capital structure, mitigate risks and ensure long-term financial sustainability. Strategic funding decisions influence growth potential and the company’s resilience during market fluctuations.
How AI can help: AI tools can help balance and structure financial resources by analyzing the company’s asset and liability mix. Machine learning risk models can evaluate funding options, optimize capital structure and project future scenarios, ensuring that the company maintains healthy leverage ratios and liquidity levels. AI can also assist in automating complex reconciliations and improving data accuracy.
Why data quality matters: AI relies on balance sheet data to evaluate the company’s financial health, including assets, liabilities and equity. Inaccurate data can distort AI-driven risk assessments or capital structure optimization.
Role of data observability: Data observability ensures that all asset and liability data is accurate, complete and up to date. It monitors the flow of financial information across departments, such as asset management and debt reporting, ensuring AI models have reliable inputs to guide funding and risk management decisions.
6. CapEx and strategic investments
What are the investment priorities and why?
Why it’s important: Prioritizing investments based on ROI and strategic goals ensures that resources are allocated to projects with the greatest potential to drive growth, innovation and competitive advantage. Clear investment priorities help align the organization’s focus and optimize the use of capital.
How AI can help: AI-powered investment models help prioritize capital expenditures by calculating ROI, assessing long-term impacts and identifying the most strategic opportunities for growth. By evaluating past investments and using predictive analytics, AI can highlight which CapEx initiatives will likely generate the best returns. This ensures that strategic investments are aligned with the company’s broader financial goals.
Why data quality matters: To evaluate ROI on capital expenditures, AI needs high-quality data on both historical performance and forecasted returns. Faulty data can mislead investment decisions and lead to suboptimal allocation of resources.
Role of data observability: Data observability tools track and ensure the consistency of CapEx data across different business units and systems. They monitor financial and operational data streams to ensure that AI models can accurately assess and prioritize investments, highlighting potential returns and risks reliably.
7. Treasury
How can we efficiently manage cash and liquidity requirements?
Why it’s important: Efficient liquidity management ensures that the company can meet its financial obligations while investing in opportunities for growth. It minimizes risks related to cash shortages, improves financial flexibility and enhances overall financial stability, which is crucial for business continuity and resilience.
How AI can help: AI in treasury management can optimize liquidity by forecasting cash requirements and managing short-term funding needs efficiently. AI can also analyze market data to provide real-time insights into currency fluctuations, interest rates and financial markets, helping treasury teams make informed decisions about cash positioning, hedging strategies and investment opportunities.
Why data quality matters: Treasury management requires accurate data on cash flows, liquidity and market conditions for AI to optimize short-term funding and cash management strategies. Inaccurate data can lead to liquidity risks or missed investment opportunities.
Role of data observability: Data observability continuously monitors the integrity of liquidity and market data, flagging any data quality issues that could disrupt AI-driven treasury decisions. By maintaining accurate cash forecasts and currency data, observability tools help AI optimize liquidity management and market positioning.
Secure buy-in for AI initiatives with data quality
By demonstrating a clear understanding of the fundamental questions Finance needs to answer, you show that you recognize the core challenges CFOs face in running the business effectively. Aligning your AI initiatives to directly support these seven key areas proves that you’re not just implementing technology, but offering practical solutions to their real-world problems. This alignment builds credibility with the CFO and other Finance stakeholders, helping secure their buy-in, support and funding for AI initiatives.
Data quality is the foundation for effective AI-driven insights in finance. Without high-quality data, AI systems can produce misleading or incorrect outputs, affecting critical financial decisions. Data observability ensures that AI models receive reliable, complete and timely data by continuously monitoring the entire data pipeline, detecting anomalies and maintaining data integrity. This results in more accurate analyses, improved decision-making, and greater business performance.
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I welcome your thoughts on how to align AI to Finance needs.
- What are your thoughts on AI enhancing financial analysis, such as forecasting, variance analysis, or cash flow management?
- In what areas of finance do you think AI can add the most value, and why?
- What data quality challenges do you see in delivering AI value to Finance? And how would you address them?
To learn more about how to improve your data quality and AI outputs,
Check out our Data Observability Workbook.
