Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hello, everyone! This is Emily, and I’m a digital transformation consultant at Hyperbots. I am very pleased to have Jon on the call with me. Jon is a seasoned CFO. With experience in larger companies as well as startups and public companies. I’m so glad to have you on the call with us, Jon.
Jon Naseath: Appreciate it.
Emily: So the topic for today, Jon, is purchasing without purchase orders. This is an area that many organizations grapple with, and I’m looking forward to your insights. So let’s just dive into the 1st thing that I’d want to ask you is, what are the common reasons that you’ve observed? That leads to an organization, you know, to make purchases without using a purchase order.
Jon Naseath: Yup. A lot of times it comes down to using a purchase order may not be a key control, and it’s just an additional process. So they find ways to work around it if they need to. A lot of times. The ERP systems, whatever one you’re using, have complicated interfaces and so I’ve seen companies avoid not wanting to have to give everyone logins to those systems and looking for others. Well, they could be open to other ways. But that’s a key challenge to the system itself.
Emily: Got it. Can you provide examples of situations where you know bypassing the PO process might seem justifiable?
Jon Naseath: Well, I would argue that there are times when it is justifiable. I usually work and figure out where the threshold is. And so, if purchases are needed, they are below a threshold. There could be times when you decide as a company. We’re not going to do PO’s there. In those situations. What I usually do also is we’ll either provide a credit card for individuals for those smaller purchases that have a set limit to still control it or you could do kind of what we call a blanket PO for certain areas. So it’s not specified to a given vendor, but it allows them a defined budget that they can use within that function or project.
Emily: Got it, and any risks that you see associated with making purchases without a purchase order?
Jon Naseath: Well the purchase order itself is a tool, and I remember a situation where it was very difficult for projects and for different departments to understand how much budget they had, and they were constantly going over budget. And so we needed to find a way that enabled direct visibility to how much budget they had, and how much they were within budget to spend something and allow them the authority to control their own department or function or project budgets. In that example, we found that Pos could be a powerful tool because they’re already doing all this work to figure out the budgets and their projects. They want to feel like they own that budget so allocating that budget out into different POs for what the project says. Vendors are going to need, or for a blanket PO to catch. The remainder was a powerful tool.
Emily: Got it. And from your experience, Jon, how does the absence of a PO process affect vendor relationships and negotiation power?
Jon Naseath: Yeah so especially when there’s a large vendor or a strategic vendor you’ll negotiate terms, and there’s so much work that goes into establishing those contracts and all the terms around it. And so then opening up the PO, based on that contract, is a nice way to control and keep everything transparent and open. Now there are often situations where you might not use all of the PO, so it’s important to have visibility to how much is being used, and it’s not just purchase orders, but the purchase requests that then feed into those and then at the same time being able to see for vendor management how much available capacity is still on that old purchase order. For when you need to do the next project, and maybe you have to discuss it. Can we roll that over into the next purchase order that wasn’t used? Or is that money needed to be held back for the budget for another department?
Emily: Got it understood. And so how do you view the cost and complexity of implementing a PO system, particularly for smaller businesses?
Jon Naseath: Sure. I don’t know if it’s about the size, but also the operational maturity of the organization. You know, if you’re just starting. It’s not your biggest priority. You just have to get customers in the door and make sales or if you’re just in the process of scaling and you have tight you you feel like you’re in control of the spending. That’s happening. Then that’s fine. But when you reach that point where you, as an organization, are so big and so complex that you don’t know or have control over the demands of spending. And you people need to be able to, in different departments, own their budgets, instead of having kind of a centralized finance, be approving every spend as it goes out. The nice thing about pos is that kind of pre. It’s kind of a pre-approval for future spending. And so it’s allocating out budgets in an approved way so I’d look for the complexity when it becomes when you need a solution, and you’re looking for a way to control your spending. The whole process of that procurement process can be very helpful.
Emily: Got it makes sense. So, Jon, do you believe there is a you know. One-size-fits-all approaches to whether organizations should adopt a peer-driven process And why or why not?
Jon Naseath: I don’t think that there is just a 1 way. Many businesses are different. One example is you have recurring, spending that’s happening, or if all of your stuff is just transactional, and one-time spending there are also opportunities to do a lot of similar controls through different types of credit cards, and where you can get cash back from credit cards but especially with larger spends. You know, doing a purchase order, or purchase request process is a powerful way to do it. Again, I want to call out that concept of an open PO or a blanket PO for things that you. You can still allocate it to a department or a project, but then they can spend it against that blanket PO where it’s not exactly known how much it’s going to be. The other one, I’d call out, is for different utilities, or or recurring subscriptions. You might not need a Po, for, because you’re not getting an invoice, you’re just paying a recurring bill, but still creating that blanket open PO for your internal controls and budgeting can be helpful. You don’t need to tell the customer or the supplier about that PO but it’s just a way to control budgets.
Emily: Sure understood. And what alternative approaches can organizations take if they find a full peer-driven process? You know, too rigid or cumbersome.
Jon Naseath: Yeah again, it’s based on the size, the size, and the complexity of the organization. If you feel like you’re controlling it through your own active communication of people in real-time. Then that’s fine. It’s just when it becomes too complex. Like, I remember a situation where I had a product manager, set up a strategic planning workshop with one of his best friends who was a vendor, and they were amazing at doing what they were doing. It was an awesome strategy and marketing workshop over a few days. You know, they flew a large team in, and there were dinners that we thought they were paying for and I only got invited to one of the dinners. When I said that, we thought that this product manager was being paid by them. I attended one of the dinners, and I started talking to them at the dinner and realized that they were going to be charging us for everything back through that ended up being like 1.5 or almost 2 million dollars for their advisory consulting project that didn’t result in any transformational change that we did. So that was problematic. So avoiding those is kind of like spending. And just, it’s just a way of controlling spending is a nice way of thinking about it, and there can be. Usually, it gets put in place when something happens that shouldn’t have happened. Oftentimes, when I see it come into place it’s a way to tighten up controls.
Emily: Makes sense.
Jon Naseath: Sorry. One more point there. It’s oftentimes not the key control for Sarmeans, Oxley, or something. So it’s something that might not be required, for you know what accounting would push for but as , I think that as a finance leader is a very helpful tool from an FP and a perspective and again, just watching for where the demand is. That’s where it adds value.
Emily: Okay that’s pretty insightful. So in your opinion, what should organizations prioritize when deciding whether to implement a PO-driven process?
Jon Naseath: I like to look at finance from the perspective of a finance business partner, and so a lot of times where there are lots of departments or lots of projects that are all asking for money literally. The terms are purchase, request, and purchase order. So I remember one time sitting down with a product development team and an organization where we had many different teams doing lots of different development. And they all had requests and I sat down with them, and we did a lean 6 Sigma style process map and mapped out the current state. And then we looked at it and looked for waste in the process, and we looked at how we could optimize what was happening and through that process of discussing, how can we help these teams fix their purchase request problems. I wrote in the box on the future state diagram. I said, this is a purchase request, and you guys make a purchase request when you need money, and then that gets approved. And this is a purchase order, and when it’s approved becomes a purchase order, and then you get a spend. And they were like, Wow! That’s a great idea. We should do that all the time, and if I had just gone in from the beginning and said, Oh, your answer is, you need personal purchase orders, and let’s put it in place. I’m sure there would have been pushback, but when you approach your app, your question is like, When is it needed? And my answer is when your different departments and teams your business partners in the business are asking for support, getting the funding that they need. This is a nice way of helping them feel like they’re at least participants and have a process for requesting funds. That’s literally what it is.
Emily: Funny. So just one last question to summarize everything. Jon. you know AI is the birth of the town. So how can AI help automate the PO process?
Jon Naseath: Yeah. I think it’s exciting what AI will be able to do for it because a lot of you know, even if companies don’t have what an accountant might call a purchase, order, purchase, request, procurement process. Every company is doing procurement. Every company is figuring out how to buy things in their way. So the idea of taking that current state process and saying, Well, how can AI enable that procurement process in whatever way it is? Maybe there’s a future state where, based on different insights that AI can do, you can make it so that AI will be able to approve more of the purchase requests without needing so much manual approval or manual touches. And certainly, there’s a threshold where you have to have the manual touch and manual check. But I think that AI could alert for risks when it needs to be double-checked or I look at AI as providing a kind of that 1st sanity check on things. And then it’s good at identifying problematic areas. But overall, it should be able to simplify and expedite that purchase request process. You never want to end up in a situation where the business departments feel like they’re not able to spend money because they’re waiting on a PO. They’re waiting on a purchase request, approval. And so whether it’s helping the AI could help identify the risks related to that purchase request, or it could help identify threshold approvals where certain ones can just be approved without manual touches. I think of it similarly to how I mean just a parallel example. Similarly to how, when you do your taxes say you’re using TurboTax as an example, and you’ll come. You’ll prepare your taxes. Think of that as kind of quasi, like a purchase request, and then, when TurboTax, it will go through everything, and it will say, Oh, we don’t see any issues in this, and if there are issues we’ll support you in mitigating them. But we think this will be fine and I’m a Cpa. I’ll confess I still use TurboTax, advanced stuff for my accounting in different ways and I think that similarly AI could be used for purchase requests and purchase orders to do that risk profile and help the person who’s submitting it. Usually, it’s stupid, simple things that might be wrong or might not be appropriate in a purchase order. I think that AI can help identify those for the person submitting it so they can modify them if needed, and get everything to go through faster.
Emily: Got it. Got it. Thank you so much, Jon, for sharing your insights, you know it’s clear that a peer-driven process offers many benefits. It’s not without its challenges. So your perspective on finding a balanced approach and leveraging AI to streamline the process will certainly help organizations make more informed decisions. So thank you so much for joining us today. It was great having you.
Jon Naseath: Pleasure have a great day.
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.
Creating a comprehensive and efficient purchase requisition, invoice, and payment approval process is crucial for organizations to maintain operational efficiency and financial control. Given the diversity in practices across companies, its beneficial to consolidate best practices that can serve as a guideline for establishing or refining these processes. This blog aims to outline these best practices, incorporating examples and illustrations to provide clear insights.
An approval authority matrix is a framework used by organizations to define who can approve expenditures and at what thresholds. The complexity of these matrices can vary based on the organizations size, structure, and operational needs. Here are some foundational best practices:
A common practice is to implement multiple levels of approval based on the value of the purchase. For example, purchases under $1,000 might only require approval from a direct manager, while those exceeding $10,000 require additional sign-off from a department head or even the CFO. This tiered approach ensures that higher-value transactions receive more scrutiny.
PURCHASE VALUE | PURCHASE VALUE | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
Up to $1,000 | Direct Manager | N/A | N/A |
$1,001 – $5,000 | Direct Manager | Department Head | N/A |
$5,001 – $10,000 | Direct Manager | Department Head | CFO |
Over $10,000 | Department Head | CFO | CFO |
Some organizations adjust approval levels based on the department making the purchase or the type of expense. For instance, IT hardware purchases might follow a different approval path than marketing expenses due to the specialized knowledge required to evaluate such expenses.
DEPARTMENT | EXPENSE TYPE | PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 |
IT | Hardware | Any | IT Manager | CFO |
Marketing | Advertising | Up to $10,000 | Marketing Manager | CFO |
Operations | Supplies | Up to $5,000 | Operations Manager | Department Head |
Tracking gross purchases from the same vendor across multiple requests helps in negotiating better terms and identifying opportunities for bulk discounts. This also ensures better internal financial control. This approach requires a more sophisticated tracking system but can lead to significant cost savings.
VENDOR PURCHASE TOTAL ACROSS MULTIPLE PURCHASES | APPROVAL REQUIREMENT |
Up to $5,000 | Direct Manager |
$5,001 – $20,000 | Department Head |
Over $20,000 | CFO |
This can be additional authority metrics in addition to 1 or 2 outlined as above.
The process for approving invoices can differ for purchase order (PO) based and non-PO-based transactions. PO-based approvals typically follow a more streamlined process since the purchase has already been pre-approved at the requisition stage. Non-PO transactions may require additional verification steps to ensure they are legitimate and necessary.
INVOICE TYPE | PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
PO-Based | Any | Pre-approved* | N/A | N/A |
Non-PO-Based | Up to $1,000 | Direct Manager | N/A | N/A |
Non-PO-Based | $1,001 – $5,000 | Direct Manager | Department Head | N/A |
Non-PO-Based | $5,001 – $10,000 | Direct Manager | Department Head | CFO |
Non-PO-Based | >= $10,000 | Not permitted | Not permitted | Not permitted |
* PO-Based invoices are considered pre-approved at the requisition stage but may require final verification through system based matching logic..
While a few companies combine invoice approval and payment authorization into a single step, most others separate these processes to add a layer of control. Separating these steps can help in identifying discrepancies before payments are made.
For example for company A the invoice approval could be as per the following table:
INVOICE TYPE | PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
PO-Based | Any | Pre-approved* | N/A | N/A |
Non-PO-Based | Up to $1,000 | Direct Manager | N/A | N/A |
Non-PO-Based | $1,001 – $5,000 | Direct Manager | Department Head | N/A |
Non-PO-Based | $5,001 – $10,000 | Direct Manager | Department Head | CFO |
Non-PO-Based | >= $10,000 | Not permitted | Not permitted | Not permitted |
And for the same company the payment approval would be as follows:
PURCHASE VALUE | APPROVAL LEVEL 1 | APPROVAL LEVEL 2 | APPROVAL LEVEL 3 |
Up to $1,000 | Direct Manager | Department Head | Finance Controller |
$1,001 – 5,000 | Department Head | Finance Controller | N/A |
$5,001 – 10,000 | Department Head | Finance Controller | CFO |
>=$10,001 | Department Head | CFO | CEO |
Organizations must decide whether the approval hierarchy should mirror the organizational structure or if it should be decoupled to allow for more flexible and efficient processing. Decoupling can be advantageous in organizations where cross-departmental purchases are common.
APPROVAL STRUCTURE | PURCHASE VALUE | APPROVAL ROLE 1 | APPROVAL ROLE 2 |
Hierarchical | Up to $5,000 | Direct Manager | Department Head |
Hierarchical | Over $5,000 | Department Head | CFO |
Decoupled | Up to $5,000 | Project Manager | Finance Controller |
Decoupled | Over $5,000 | Procurement Specialist | CFO |
Implementing the best authority metrics does not automatically make a companys approval process optimal and efficient. The following factors play a critical role in that.
To conclude, with the right mix of policy, process, and technology, organizations can ensure that their procure-to-pay approval cycles are both efficient and effective, paving the way for fiscal responsibility and long-term success.
The shift from traditional workflow automation to AI-enhanced processes in finance represents a leap toward more intelligent, adaptive, and strategic financial management. AI’s ability to learn and improve over time, predict future trends, and provide deep insights into financial data offers CFOs powerful tools to drive efficiency, compliance, and strategic decision-making. While traditional automation streamlines tasks based on rules and workflow, AI introduces a level of sophistication and analytical depth that transforms finance functions into proactive, strategic pillars of the organization.
The table below highlights the evolution from traditional automation, to AI-led automation.
FINANCE FUNCTION | TRADITIONAL WORKFLOW AUTOMATION | AI-LED AUTOMATION |
Procure to Pay (P2P) | Automates workflow, payment processing, and basic vendor management tasks based on predefined rules. Each invoice needs to be reviewed by a human. | Uses machine learning to improve invoice processing accuracy over time, predicts optimal payment timings for cash flow management, and detects fraudulent invoices through pattern analysis. Enables straight-through processing for 80% of invoices. |
Order to Cash (O2C) | Focuses on automating order entry, credit checks based on static criteria, and straightforward dunning processes for overdue accounts. | Enhances credit scoring with dynamic models, automates personalized dunning campaigns using customer data to improve collections, and predicts future payment behaviors for credit risk management. |
Financial Planning & Analysis (FP&A) | Streamlines data aggregation for budgeting and forecasting, relying on historical data and linear projections. | Utilizes predictive analytics for forward-looking insights, identifies trends and anomalies, and simulates various business conditions for strategic planning, offering adaptable FP&A. |
Expense Management | Simplifies expense report submission and approval processes, enforcing policy compliance through rule-based checks. Each expense needs to be reviewed by a human. | Employs NLP and machine learning for detailed expense report audits, identifies policy violations and fraudulent patterns, and personalizes reporting guidance to reduce errors. Enables straight-through processing for 80% of expenses, and receipts. |
Treasury | Automates cash management and forecasting based on historical cash flow patterns and executes predefined investment strategies. | Leverages advanced analytics for accurate cash flow forecasting, recommends investment strategies by analyzing market trends and the company’s financial health, and adapts to real-time market conditions. |
Mergers & Acquisitions (M&A) | Supports data room management and basic due diligence automation, relying on manual analysis for valuation and integration planning. | Automates in-depth financial, operational, and market data analysis to uncover insights, predicts integration success, identifies synergies and red flags, and optimizes deal structuring based on strategic objectives. |
Tax and Compliance | Facilitates tax calculation, filing, and basic compliance monitoring through rule-based systems. | Automates complex tax planning strategies, monitors compliance with changing regulations using predictive analytics, identifies potential compliance risks, and adapts to new tax laws and regulations for tax filing and reporting |
This detailed look at AI’s impact across various finance processes underscores the need for CFOs to embrace AI technologies, not just for the operational efficiencies they bring but for their potential to fundamentally redefine how finance supports and drives business strategy.
In the intricate world of financial management and reporting, the Chart of Accounts (COA) stands as the foundational framework upon which companies build their financial narratives. This structured listing serves not just as an organizational tool but as a strategic asset, facilitating the meticulous tracking of expenses, revenues, assets, and liabilities. However, the bespoke nature of the COA, tailored to meet the unique needs and reporting requirements of each company, introduces a set of challenges that, if not properly managed, can lead to significant inefficiencies and inaccuracies in financial reporting.
The COA’s complexity often reflects the complexity of the business it serves. As companies evolve, so too must their COA, but this evolution can lead to bloated, unwieldy lists that confuse more than clarify.
Key challenges include:
A poorly maintained COA can lead to a range of errors in financial reporting, such as:
To avoid these pitfalls, companies should adhere to several best practices:
Standardize the COA structure: Establish a standardized structure that can be easily understood and used across all departments.
Accurately booking expenses against the correct accounts in the COA is crucial for accurate financial reporting. Best practices include:
One of the most labor-intensive aspects of maintaining a COA is the manual work required to map each expense to the correct General Ledger (GL) account. This process is prone to human error, leading to misclassifications that can skew financial analysis and reporting.
Artificial Intelligence (AI) offers a promising solution to many of the challenges associated with COA management. AI technologies can automate the GL account mapping process, significantly reducing the risk of human error. By learning from historical data, AI can predict the correct account for new expenses, streamline the reconciliation process, and even suggest optimizations for the COA structure itself.
The management of a Chart of Accounts is a critical aspect of financial reporting that requires meticulous attention and discipline. By understanding the challenges involved, adopting best practices, and leveraging the power of AI, CFOs and controllers can enhance the accuracy of their financial reporting, streamline their financial processes, and provide strategic insights that drive business decisions. As technology continues to evolve, the integration of AI into financial systems represents a significant opportunity to transform the landscape of financial management.
Are you ready to explore how AI can be brought into action to reduce errors in your chart of accounts? Contact us for personalized assessment and take the first step towards transforming your chart of accounts today.