Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hey, everyone, this is Emily and I’m a digital transformation consultant with Hyperbots. And today we have the privilege of speaking with Dave Sackett, who is the VP of Finance at Percy Moon Technologies. So thank you so much for joining us today, Dave.
Dave Sackett: Yeah. Thank you, Emily. A pleasure to be here.
Emily: The topic we’d be discussing today is interrelationships and dependencies between GL codes and different ERP solutions. To start, Dave, can you please explain the importance of understanding the interrelationships and the dependencies between different GL codes in an organization’s financial reporting?
Dave Sackett: Yeah. It all starts with the chart of accounts and the way you structure it. You’ll have interrelationships between them. For example, revenue, COGS, expenses, assets, liabilities, and taxes. These are fundamental to accurate financial reporting and management. Revenue and COGS directly impact gross profit, while assets and liabilities are crucial for maintaining the balance sheet. Mismanagement or misclassification of any of these can lead to inaccurate financial reporting.
Emily: Got it. So, Dave, how do different ERP systems like SAP S/4HANA or Microsoft Dynamics 365 help maintain these interrelationships effectively?
Dave Sackett: ERP systems like SAP and Microsoft Dynamics are designed with a robust financial model that enables these relationships to be maintained accurately. SAP uses a universal journal that consolidates financial and managerial accounts into a single data source, making it easy to track dependencies and dimensions and advanced account structures to categorize and report financial data flexibly, ensuring that all interrelated GL accounts are consistently updated through the workflows and approval process.
Emily: Understood. Could you also please elaborate on these specific features in NetSuite and Sage Intacct that help maintain interdependencies between these GL codes?
Dave Sackett: Certainly. NetSuite provides a segmented chart of accounts and real-time reporting, which allows clear tracking of management interdependencies. Its revenue management module automates revenue recognition and ensures alignment with expenses, which is critical for accurate financial reporting. On the other hand, Sage Intacct is known for its dimensional chart of accounts, allowing for a more granular and flexible approach to managing GL codes. It supports automatic allocations and offers robust audit trails, ensuring that changes in GL codes are documented and those dependencies are maintained accurately by the accounting staff.
Emily: Understood. And, Dave, what about ERP solutions like QuickBooks, which are, you know, often used by small to medium-sized businesses? How do they handle these interrelationships?
Dave Sackett: With QuickBooks, it’s a very simple system, but there are essential tools to manage these interrelationships between GL codes. It uses linked accounts to automatically update related GL codes when transactions are recorded, ensuring the basic dependencies are still maintained. For example, when an invoice is generated, it automatically updates the revenue account and accounts receivable. This simplicity is beneficial for small businesses that don’t have complex financial needs and still require accuracy in their financial reporting.
Emily: Got it. So, talking a little bit about, you know, regulated environments such as companies in the government or contracting sector, how does ERP like Deltek Costpoint help maintain these interrelationships?
Dave Sackett: One of the specialties of Deltek Costpoint is that it’s specifically designed for government contractors and heavily regulated environments. It offers project-based accounting that links revenue, COGS, and expenses to specific projects and contracts, ensuring compliance with strict government regulations. Costpoint’s multi-entity and multi-currency management features help keep accurate interdependencies across different entities and countries, while its automated billing and revenue recognition ensures that revenue and related costs are properly matched and reported. Many government-type jobs are cost-plus, meaning that the government is going to have access to your cost records. So, you need to have it set up logically and structured for that audit and that review as part of your sale to the government.
Emily: Understood. Also, how important are third-party AI tools in maintaining these GL codes and the interrelationships for various ERPs?
Dave Sackett: The more complex your system, the more important it is for your ERP system to maintain GL code interrelationships. While ERPs like SAP, NetSuite, and Microsoft Dynamics come with built-in automation and analytic tools, third-party AI tools offer advanced capabilities, such as predictive analytics, anomaly detection, intelligent automation, and flux analysis. For example, AI tools can automatically identify patterns and anomalies in transactions that could affect multiple GL accounts, such as unusual spikes or unexpected expense increases, and suggest corrective actions. So that’s your flux analysis where it’s looking and saying, “Hey, this data doesn’t belong here. It’s not in the normal spec.” That’ll help users of that AI zero in and figure out why it’s doing that. Then, once they find that anomaly, they can train the bot to process it normally or still keep it as an exception, depending on the nature of the anomaly.
Emily: That’s amazing. So, Dave, can you provide an example of how an ERP system might handle a complex transaction that affects multiple GL codes?
Dave Sackett: Yeah, certainly. Let’s take an example of a sales transaction involving inventory in SAP. While the inventory is sold, the system automatically decreases the inventory asset account, increases the COGS expense, increases the revenue, and at the same time, calculates and records tax liability based on the tax rules that are set up in the system. All of these entries are done automatically, ensuring that every GL code involved in this transaction is accurate, and it’s being updated in real-time. Not only does it maintain interdependencies, but it also ensures compliance with accounting standards as it’s programmed specifically that way.
Emily: Got it. So finally, to wind things up, one last question, Dave. How can CFOs leverage these ERP systems to ensure continuous improvement in financial management and reporting?
Dave Sackett: CFOs can leverage ERP systems by utilizing advanced features like real-time reporting, automated workflows, and data analytics to continually monitor and improve financial performance. Regularly reviewing configurations, such as account structures, financial dimensions, and chart of accounts, ensures alignment with the company’s evolving needs. Additionally, investing in training and leveraging ERP vendor support can help maximize the use of these systems. By doing so, CFOs can ensure that financial management processes remain efficient, accurate, and compliant with regulations. Your ERP system is an investment, and you want to constantly check it to make sure it’s meeting your needs and your future needs. So, it’s not a one-time investment. It’s something that you want to have to grow with the company and continue to invest in that technology to give you better and better reporting.
Emily: Got it. Thank you so much, Dave, for sharing your insights on this complex yet essential topic. It was great having you, and this discussion was truly fruitful. So, thank you so much.
Dave Sackett: Yeah, thank you, Emily.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi, everyone! This is Emily, and I’m a digital transformation consultant at Hyperbots. I’m happy to have Claudia on the call with me. Who is the managing director at Ikigai. It’s great having you on Claudia.
Claudia Mejia: Hi, Emily, thank you for having me.
Emily: Claudia. The topic we’d be discussing today is redundant and duplicate gl codes in the chart of accounts without, you know, wasting everybody’s time. I’ll just dive right into the 1st question, which is, why do redundant and duplicate gl codes exist in a company’s chart of accounts?
Claudia Mejia: Yeah. Well, I will say that one of the major reasons is the lack of standardization in the creation of the GL Codes. Many companies don’t have a standardized process, or this is a very important process. So some departments might just create codes on their own without a centralized team, a process that directs them how to create them. Another reason why we have redundant, I will say, is mergers and acquisitions. You have 2 different companies that they merged. Now you have 2 charts of accounts, and you need to have a very good process to map the accounts and make sure that you don’t have redundancy when you combine these companies.
Emily: Got it so, Claudia, would you be able to provide some examples of common redundant, or duplicate gl codes that you have encountered?
Claudia Mejia: So some examples that are probably the most common are office supplies. You have, for example, office supplies administrative and calls printing supplies stationary. So a lot of these descriptions are the same. But they’re created multiple times by different departments. And then you have the entries, putting in different accounts. That creates a lot of complexity for teams in the FP. And A when they’re doing reporting, because now we have several accounts that mean the same. Another example is travel expenses, right? If you break it out now too much, then you’re probably not very accurate with the actual expenses. For example, entertainment. You have domestic traveling and international traveling. So sometimes it’s just that simplicity is the best. You can have one line. But if you want to go down to have subcategories. You can be very mindful of what those subcategories should be.
Emily: All right. So moving ahead, Claudia, what are the best practices to avoid creating redundant or duplicate GL codes?
Claudia Mejia: I will say, implementing a chart of account governance framework, basically standardizes the process to who? What is the centralized team that is putting together the codes? Who’s doing it when they’re doing it? how they’re doing it, making sure that they’re out. It’s either quarterly, semi semi-annual to make sure that there are no redundancies with the codes. A centralized team is important, and a system can have the governance of these codes. Make sure also naming conventions. Right? It’s important to make sure that there is a standard, not only for the numbers but also for the descriptions. So now we just don’t have these long descriptions that are not easy to follow and manage from a reporting perspective.
Emily: Got it. So, bringing AI into the equation quickly, how can AI help in identifying and eliminating duplicate or redundant GL codes of the chart of accounts?
Claudia Mejia: Well, AI can understand the descriptions, understand partners, and also make recommendations. So you can use large language models, and to make sure that the chart of accounts is analyzed. And by doing that you can determine which charts of accounts are duplicates and which ones can be consolidated. For example, clustering reclassification. So AI can tell you which accounts are very similar and can be consolidated. For example, marketing, digital marketing, and other types of marketing can be consolidated into one also it can give you semantic similarity detections. So for those descriptions that are very similar can say, Hey, these 2 accounts are very similar you might be able to consolidate. But again, AI provides recommendations, and is very important for that team to follow up. If that recommendation makes sense.
Emily: Got it so just to utilize your expertise, Claudia, how would you recommend implementing an AI solution for a company that is looking to reduce redundancy in its chart of accounts?
Claudia Mejia: Well, you can have AI system solutions that connect with your ERPs and they can have that audit done automatically. Another way to do it, making sure that you follow the policies of the companies is using the large language models like Chatgpt Gemini, but making sure that when you’re loading that data into the system, it follows the company’s privacy for the data and all the policies. But by doing that you can, as a model, basically say, can you analyze my charts of accounts? Tell me which accounts are duplicates. Tell me which ones I can consolidate. You have to be very specific and clear about the prompts. But these models are very good at analyzing this type of data, and they will provide a recommendation that tells you which one you can combine and which ones you can eliminate. So by doing that, you can have that information and move it through a governance team that can decide if these recommendations make sense, and which was which charge of account which goes, can be eliminated, and consolidated and have an audit on that recommendation and make sure that once you have a sign-off from the leaders and the users of the information, then basically, you communicate the changes and make sure that you monitor this information often. Joseph accomplished something that is not seen as an important process, but it is a very important process because it triggers all the financial reporting from there. So if you have a bad setup in your charge of accounts, then your financial reporting can become very complex, and it creates manual efforts from the IFP team and all the reporting teams afterward.
Emily: Got it. Got it. Thank you so much, Claudia, for these insightful answers. It’s clear that you know, with the right tools and best practices. Companies can significantly reduce redundancies and maintain a clean and efficient chart of accounts. So thank you so much for speaking about it. It was great having you.
Claudia Mejia: No, thank you, Emily, as usual.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi, everyone. This is Emily, and I’m a digital transformation consultant at Hyperbots, I’m very pleased to have Jon Naseath on the call with me. Jon is a chief operating officer at Osmo. The topic that we’d be discussing today is why matching invoices with purchase orders and goods receipt notes is tedious, and also how AI solves it. Thank you so much for joining us, Jon. To start off, can you please explain some of the major challenges that organizations face with invoice matching to purchase orders and goods receipts?
Jon Naseath: The fundamental issue is, vendors want to get paid. You’ve got all this purchase order process upfront to get approval for payments, and accounting isn’t going to release the funds until you’ve verified that the services have been done or the goods receipts have been received. The hold-up is usually vendors calling up their contacts within the company and saying, “Where’s my money?” And then you have to verify, “Well, did you get the work or the goods?” Then you can pay them. Accountants love paying people, but they want to make sure the boxes are all checked.
Emily: Can you provide an example of how complex matching requirements can affect the invoice processing workflow?
Jon Naseath: Sure. It’s usually data disconnects. There was a plan when the PO was created, and then the invoice had something slightly different. With goods receipt, it should be straightforward. For example, the invoice lists 100 units of product, and the PO specifies 90, but the goods received say 85. They’re trying to charge you for 100, but you approved 90, and they only sent 85. So what are you going to pay them? It usually takes effort instead of flowing through automatically.
Emily: Understood. What are some of the common format differences between invoices, POs, and GRNs that complicate the matching process?
Jon Naseath: A lot of times, especially in international transactions, there are differences like month-day-year versus day-month-year formats. There are also differences in units of measure whether it’s quantities or services provided. Sometimes the invoice might be for work performed, and you have to verify if they completed the work. Did they do what they were supposed to, or are they just saying that? Also, is the person signing off on the work holding the vendor accountable, or just saying “pay them”?
Emily: Got it. So, Jon, how does data entry error impact the accuracy of invoice matching?
Jon Naseath: If it’s intentional, it’s a fraud, but if it’s an error, it can be small things like entering the amount in euros when you’re expecting US dollars. Data entry errors like this can cause issues with reconciling numbers. For new vendors or publishers, it can be a lot of work to chase down little data points. Meanwhile, vendors are asking, “Where’s my money?” Another example is when a customer uses a DBA (doing business as) name, and they send a slight variation of their name, like Vendor Inc. instead of Vendor LLC. Data quality matters.
Emily: It sounds incredibly overwhelming. So how can AI help in automating the data extraction and normalization process?
Jon Naseath: It’s two-fold. First, avoid the issue in the first place. AI can help by reconciling the data against the PO to catch discrepancies before sending it. This helps vendors get paid faster. On the receiver side, AI can flag errors quickly so they can be resolved before reaching accounts payable. Ideally, it flags the issue and sends it to the business owner of the account to fix it before accounts payable is even involved.
Emily: Got it. What role does AI play in detecting and correcting errors in invoice processing?
Jon Naseath: AI can identify common errors in documents like typos, incorrect item codes, or mismatched numbers. It also looks at historical data trends to detect patterns. If an accounts payable clerk is manually processing hundreds or thousands of invoices, they can easily miss these issues. I remember joining a company where the accounts payable clerk was buried under a mountain of invoices. We automated some of it, but it was still painful. AI can help people in these situations and reduce their workload.
Emily: Can you explain how AI algorithms detect anomalies and discrepancies in invoice matching?
Jon Naseath: AI is very effective at identifying patterns and spotting discrepancies in quantities, prices, or item descriptions. AI does this across hundreds of variables and can instantly flag issues that a human might miss. A typical accounts payable clerk might not be motivated to catch these anomalies, especially if they’re overwhelmed by the volume of work. AI helps mitigate those risks.
Emily: How does AI handle the challenges of matching invoices that reference multiple purchase orders or involve partial deliveries?
Jon Naseath: In accounting, it’s easy to think everything should line up perfectly in a two-way or three-way match, but in reality, you often have invoices referencing multiple POs or partial deliveries. You don’t want to delay payments by asking vendors to reissue invoices. AI can reconcile these discrepancies and help keep everything in order across big POs and multiple transactions.
Emily: To wrap things up, what are the key benefits of integrating AI into the invoice-matching process for an organization?
Jon Naseath: Integrating AI into invoice matching automates repetitive tasks, reduces manual errors, improves data accuracy, and enhances anomaly detection. It helps you get the job done faster and protects you from costly errors, like overpaying a vendor or missing a payment. AI is like having an extra set of eyes to help you avoid mistakes.
Emily: Got it. Thank you so much, Jon, for talking to us about why matching invoices with purchase orders and goods delivery notes is tedious, and how AI can help. It was great having you today.
Jon Naseath: Great, my pleasure.
