Finance and accounting (F&A) are critical to the operational efficiency and strategic decision-making of any business. The advent of artificial intelligence (AI) presents a transformative opportunity for these functions. This article analyzes manual, analytical, and strategic activities within these functions and determines the most optimal AI adoption roadmap.
The following table estimates the volume of manual, analytical, and strategic activities in these functions as high, medium, or low:
Functions | Manual | Analytical | Strategic |
Procure to Pay | High | Medium | Low |
Order to Cash | High | Medium | Medium |
Expense Management | High | Medium | Low |
Tax and Compliance | Medium | High | High |
Treasury | Medium | High | High |
Financial Planning & Analysis | Low | High | High |
Mergers & Acquisitions | Low | High | High |
The next-generation AI technologies are mature and can be applied well in Finance and Accounting with a significant financial impact. We recommend prioritizing the Procure-to-Pay, Order-to-Cash, and Expense Management functions for AI adoption.
AI techniques for interpreting unstructured data have advanced significantly in recent years. These techniques now permit what was previously considered human-level intelligence tasks.
Transformer-based frameworks allow for unstructured content understanding, language generation as well as predictive tasks. Large Language Models (LLMs) accelerate the ability of AI systems in language understanding, information retrieval, summarization, text generation, and conversational AI. Data-driven econometrics models for forecasting and trend analysis enable numeric and financial data analysis.
In finance automation, this is how these AI techniques can radically transform each of these tasks:
The structured and orderly nature of finance processes, underpinned by a robust ERP knowledge base, provides a solid foundation to leverage sophisticated machine learning and AI methodologies. Now is an opportune moment to invest in the adoption of AI-native strategies for a substantial positive business impact.
The P2P function involves numerous repetitive and manual activities where AI can significantly increase efficiency and reduce errors.
AI Capabilities | Readiness |
Uses machine learning and LLMs to achieve straight-through processing for 80% of invoices. This includes automated invoice extraction, understanding, validation, matching, GL coding, and posting | Short-term |
Uses forecasting systems to automate accruals | Short-term |
Uses predictive and prescriptive models for optimal vendor payment timings | Short-term |
Uses advanced ML techniques to detect fraudulent and duplicate invoices | Short-term |
Uses classification techniques to classify expenses for capitalization | Short-term |
Uses AI models and tax dictionaries to verify the sales and other types of applicable taxes | Short-term |
Builds company and F&A-specific conversational AI models to provide chatGPT-like analytics | Medium-term |
Optimizes vendor selection using predictive analytics | Medium-term |
The O2C function is also highly manual and prone to AI automation.
AI Capabilities | Readiness |
Uses machine learning and text processing techniques to extract and validate purchase order information from customers PO documents and contracts and auto-uploads information into the ERP system, resulting in 95% plus automation | Short-term |
Uses advanced AI techniques to generate customer invoices based on purchase orders, customer master, inventory, and shipment information to generate 100% First Time Right (FTR) invoice. | Short-term |
Uses recommender systems to create a daily/weekly priority list of customers for collections. | Short-term |
Uses dynamic models to enhance customer credit scoring | Short-term |
Uses advanced data science techniques for cash management including discovery of discrepancies, over and under-payments | Short-term |
Uses generative AI to automatically communicate with customers on invoices and payments, including follow-ups | Short-term |
Uses generative AI for conversational analytics on O2C data | Medium-term |
Employee expense management continues to be tedious for employees and the finance teams. This process can make use of AI to achieve a very high degree of automation.
AI Capabilities | Readiness |
Uses image and text processing techniques to automatically extract information from receipts and bills, and validate and auto-create expense reports for employees. | Short-term |
Uses machine learning techniques to verify expense reports against policies and proofs. | Short-term |
Uses advanced ML techniques to detect fraudulent and duplicate expenses. | Short-term |
Uses classification techniques to identify the correct GL code for each expense | Short-term |
Uses generative AI to communicate and answer employee queries | Medium-term |
AI has a high potential to optimize and streamline the tax and compliance function.
AI Capabilities | Readiness |
Automates collection and validation of data required to file tax returns, ensuring higher accuracy and reduced human effort | Medium-term |
Applies the correct withholding rates based on payer and recipient jurisdiction, reducing errors | Medium-term |
Helps organize documentation related to taxation for audit purposes | Long-term |
Tracks applicable sales and use taxes across jurisdictions, ensuring accurate application to transactions | Long-term |
Uses generative AI to map financial statements to the latest reporting standards. Facilitates SOX compliance | Long-term |
AI can provide substantial value by automating routine activities and improving decision-making in treasury management.
AI Capabilities | Readiness |
Machine learning models analyze historical cash flow patterns to predict future cash needs. | Medium- term |
Monitors cash balances across accounts and recommends the most efficient pooling techniques. | Medium- term |
Compares fee structures across banks, helping to negotiate better terms. | Long-term |
It uses advanced predictive models to assess market risks and helps optimize long-term portfolio allocation. It also recommends low-risk, high-return, short-term investment opportunities. | Long-term |
Predicts currency fluctuations to help develop effective hedging strategies. Identifies forex arbitrage opportunities. | Long-term |
Builds models to identify market and operational risks using various internal and external debts. | Long-term |
FP&A involves many analytical and strategic activities. AI can help improve decision-making for these activities.
Automates the data extraction from structured and unstructured sources like documents and ERPs | Long-term |
Analyzes the historical data to build predictive budgets and rolling forecasts | Long-term |
Simulates scenarios and recommends outcomes | Long-term |
Helps in variance analysis between planned and actual budgets | Long-term |
Analyzes capital allocations and predicts ROI using historical data | Long-term |
Predicts future cashflows based on historical trends | Long-term |
AI can play a significant role in M&A, improving efficiency and strategic decision-making.
AI Capabilities | Readiness |
Analyzes financial reports and news articles to assess potential targets. | Long-term |
Builds sophisticated financial models using machine learning to provide a more accurate valuation of the target companies. | Long-term |
Analyzes contracts and other financial statements for risks and liabilities. | Long-term |
Identifies and predicts potential risks. | Long-term |
Provides data-backed insights into potential negotiation points. | Long-term |
Now that we have analyzed the specific AI-based automation of the above finance functions, we can estimate the financial impact it can create.
Having evaluated the financial impact on all F&A functions, we can recommend the AI adoption roadmap.
The next-generation AI technologies are mature and can be applied well in Finance and Accounting with a significant financial impact. We recommend prioritizing the Procure-to-Pay, Order-to-Cash, and Expense Management functions for AI adoption.
Moderated by Niharika Marketing Manager at Hyperbots.
Niharika: Good morning, good afternoon, good evening, everyone, depending on wherever you are. I am Niharika, and I take care of marketing at Hyperbots. Today, we have with us Mr. Ayo Fashina, the CFO of Kobo 360, Africa’s leading integrated logistics solution provider. Ayo brings a wealth of experience and insights, having operated in the domain for a little more than 20 years now. It’s a pleasure to have you on board, Ayo.
Ayo Fashina: Thank you. Happy to be here.
Niharika: Today, we will be discussing a very interesting topic: how AI can not only improve but also revolutionize cash outflow management. To set the stage, can you help us understand what cash flow is and why businesses must manage it effectively?
Ayo Fashina: Thank you, Niharika. Let me start by defining cash outflow. Cash outflow refers to the movement of money out of a business for various needs like expenses, investments, debt repayment, or salary payments. Managing it effectively ensures that a business can meet its obligations while pursuing growth opportunities. Managing both cash outflows and inflows is essential. The timing of money inflows and outflows determines a business’s solvency. If a business does not manage its cash flow properly, it may become insolvent, and unable to meet its obligations, leading to potential closure. Effective cash flow management is crucial for businesses, including banks, which also face challenges in managing cash flows.
Niharika: How does AI fit into this picture, especially for those new to this concept?
Ayo Fashina: AI enhances decision-making and operational efficiency. In finance, AI can process vast amounts of data to forecast and manage cash flows. It can identify savings opportunities and automate transactions, making cash flow management more efficient. AI can also connect with APIs to consolidate all your bank information, eliminating the need for manual data entry and enabling seamless financial analysis from a single interface tools generate detailed reports and perform in-depth financial analyses, providing the insights needed to track financial performance and identify improvement opportunities.
Niharika: How can AI improve the accuracy of forecasting and budgeting compared to traditional methods?
Ayo Fashina: AI algorithms can analyze historical data, and market conditions, and predict future cash flow needs more accurately than humans. AI can synthesize a vast amount of data quickly, creating more realistic budgets, financial plans, and forecasts. It provides real-time comprehensive forecasting, offering complete visibility into cash flows and enabling better-informed financial decisions.
AI also aids in scenario analysis and planning by simulating various what-if scenarios, helping businesses understand potential future changes and their impacts. This capability allows for more accurate financial forecasting and decision-making.
Niharika: Can you explain how AI enhances operational efficiency and expense management?
Ayo Fashina: AI tools streamline expense management by identifying patterns and anomalies in spending, helping businesses cut unnecessary costs and negotiate better terms with suppliers. AI optimizes cash flow by monitoring payment terms, taking advantage of discounts, and delaying payments when appropriate can also increase visibility into procurement data, ensuring that purchase orders and invoices are properly matched. This enhances cash flow forecasting accuracy and enables efficient payment scheduling. Overall, AI significantly reduces the time required for financial tasks, improving operational efficiency.
Niharika: How does AI contribute to risk management and fraud detection?
Ayo Fashina: AI is adept at identifying irregularities and spotting slight changes that humans might overlook. In fraud detection, AI can monitor transactions and flag unusual activity, such as sudden large transactions on a credit card, potentially preventing fraud. By checking trends and identifying irregular transactions, AI enhances risk management and protects company finances.
Niharika: With AI playing such a big role, how do companies ensure compliance and ethical use?
Ayo Fashina: AI providers must adhere to international standards and regulatory requirements, ensuring ethical data handling and management. Compliance involves following regulations around personally identifiable information and confidential data. AI tools should have access rights and data classification to maintain trust and reliability. Ensuring compliance with these standards is crucial for the ethical use of AI in financial management.
Niharika: Absolutely. Thank you for answering that, Ayo. I think we’ve covered fraud detection and risk management well. But are there other examples where AI has successfully optimized cash flows?
Ayo: Certainly. At our organization, we are an e-logistics platform matching transporters with goods owners. We manage payments between transporters and goods owners. Initially, managing these cash flows was manual and prone to errors. To optimize this, we adopted an AI solution. By connecting our systems to banks via APIs, we automated payments, eliminating duplication and ensuring timely payments. AI also optimized our cash outflow reporting, providing automated and accurate financial reports. On the accounts receivable side, AI generates and tracks invoices, sending automated reminders to customers about due payments. This has significantly reduced our cash-to-cash cycle the time between money going out and coming back in. For example, we reduced our cash-to-cash cycle from 45 days to about 10 days. Some customers even make partial advance payments, further improving our cash flow. These improvements allow us to conduct more business with the same amount of cash, demonstrating AI’s impact on financial efficiency.
Niharika: Thank you for that insight, Ayo. It’s wonderful to hear how AI has been implemented successfully. However, I’m sure integrating AI comes with challenges. Could you share your experience with that?
Ayo: The primary challenge with adopting any technology, including AI, is people. There’s natural resistance to change. Convincing staff and even senior management can be tough. The second challenge is ensuring the quality of data and outputs from the AI. It’s crucial to monitor and clean the data used by AI systems to ensure accuracy. Being a startup, our resistance to change wasn’t as pronounced as it might be in larger, more established organizations. In such companies, where processes have been done a certain way for a long time, resistance can be stronger. Building a culture that embraces change is essential for successful AI integration.