Moderated by Emily, Digital transformation consultant at Hyperbots
Emily: Hi, everyone. This is Emily. I’m a digital transformation consultant at Hyperbot Sync, and I’m pleased to have John, who is the VP of FP&A at Extreme Reach, on the call with me. So thank you so much for joining us today, John, really appreciate it.
John Silverstein: No problem. Thank you for having me.
Emily: Of course. So, John, the topic that we’d be discussing today is the impact of GL coding on financial reporting and auditing and I want to begin things by asking why GL coding is so important for financial reporting and auditing.
John Silverstein: Yeah, GL coding is critical for your financial reporting and audit, otherwise it becomes very difficult to report correctly, and you have to go to the transaction level, and then it defeats the purpose of the GLs. So it’s critical. It’s also extremely hard to audit something when it’s mixed, and you’re also generally not compliant if you’re not coding things properly in the financials. So it is critical both from a reporting and audit perspective and also for management to make proper decisions and everything to make sure that selling and marketing expenses are selling and marketing expenses and then it’s easily tracked. And you know who the owners are, and you know what it is.
Emily: Got it. So let me ask you this, how does a well-structured GL coding scheme enhance decision-making for the management?
John Silverstein: Yeah, if you well structure the GL coding, the decision-making, you have to align your GL coding to not only align from an accounting and GAAP standpoint, but also to make sure that you can make proper decisions on pricing or do proper ratios and things like that based on how you break your financials out. GL coding allows you to get segment reporting and figure out how profitable certain areas, products, or service lines are. If you don’t break it out and you don’t have the right granularity, it gets complicated to figure out the important information to make decisions in the business.
Emily: Got it. So just out of curiosity, John, can you provide an example of how GL coding affects compliance with regulatory and tax requirements?
John Silverstein: Yeah, GL coding is critical. If your transactions aren’t accurately classified with GAAP, it can affect your tax calculations, anything from sales tax to VAT to corporate income tax. You can end up overpaying or facing audit issues on the tax side as well. You must follow proper accounting, have the right GL coding, and minimize penalties for noncompliance. If VAT is consistently coded under a specific GL account, it becomes much easier to prepare accurate VAT returns and comply with local tax authorities.
Emily: Got it. So what are some of the common mistakes that organizations make with their GL coding schemes, and how can they be avoided?
John Silverstein: The biggest mistake is over-complication of the GL, where you start making GL codes for everything. But then there’s the opposite side, where you don’t break things out at all, and it’s all lump amounts, which makes decision-making hard. It’s critical to find a balance between detail and summary-level data. Make sure you have proper hierarchies so you can go more granular if needed and have a proper roll-up. There are also tools now that help with different ways of reporting from a management perspective that can help this as well.
Emily: Understood. Also, John, how can a GL coding scheme be designed to provide real-time or near-real-time financial reporting?
John Silverstein: If your GL coding is proper, then as transactions are happening in your GL, ERP, or even CRM, you can see that data at the right levels in near real-time. This allows you to see where you might end up from a financial standpoint and make decisions like ramping up production or slowing down sales based on live data. It’s important to align GL coding with ERP, CRM, and procurement systems to get live financial analysis instead of waiting for month-end close.
Emily: Got it. John, would you provide an example of how GL coding alignment with business strategy can improve performance monitoring?
John Silverstein: Sure. If a company aligns its GL coding schema with key performance indicators, it can monitor and optimize these metrics more effectively. For instance, a SaaS company might use specific GL codes for different components of customer acquisition costs and retention expenses, which gives insights into performance against goals. By doing this, you can generate financial reports focusing on metrics like customer acquisition costs, helping to make more strategic decisions in real time.
Emily: Makes sense. So a little bit about the audit process, John. How does a well-structured GL coding scheme simplify the audit process?
John Silverstein: A well-structured GL coding scheme simplifies the audit process by providing a clear and consistent trail of the transactions. This allows auditors to quickly trace entries, verify accuracy, and ensure compliance with accounting standards. For example, if an organization uses separate GL codes for office supplies at HQ and regional offices, auditors can efficiently sample and analyze expenses related to different locations. However, you need to be careful to ensure your schema isn’t overly complicated.
Emily: Got it. Just to wind things up, one last question, John. What would be your key recommendations for organizations looking to optimize their GL coding scheme?
John Silverstein: First, design a hierarchical structure that goes down to a detailed level but allows summarization. Use multi-dimensional analysis so you can get different insights like company roll-ups, cost centers, departments, and product lines without mixing everything into the GL. Balance granularity and simplicity, and align with the business strategy because strategies evolve. It’s important that GL coding reflects the current business direction. Integrate with other systems like ERP to avoid silos, and regularly review your coding schema to ensure it complies with current regulations and organizational structures.
Emily: Got it. Thank you so much, John, for sharing your insights on the critical role of GL coding in financial reporting, auditing, and decision-making. Your examples and recommendations will certainly help organizations better structure their GL coding schemes to achieve more actionable financial reports. Thank you so much for being here.
John Silverstein: No problem. Glad to be here.
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 Claudia Mejia on the call with me. Claudia is the managing director at Ikigai, and the topic that we’d be discussing today is that why the industry is struggling to achieve straight-through processing of invoices honestly, there could have been no better person to speak about this. So glad to have you on board, Claudia.
Claudia Mejia: Thank you, Emily. It’s a pleasure to talk with you this morning. Thank you for having me.
Emily: Amazing. So to quickly start things, Claudia. Let’s start with the 1st question, What is the straight-through processing of invoices?
Claudia Mejia: Well, basically, it’s the process where the invoices come in. And then we automatically can process them to the ERP system. It includes not only the data capture of the invoices but the validation of the data and also the integration with the accounting systems.
Claudia Mejia: So it’s usually in a conceptually, I see process, but it doesn’t work as simple as it sounds.
Emily: Understood. So, Claudia, have you seen anywhere wherein, straight-through processing of invoices is being achieved?
Claudia Mejia: Honestly, with my experience, I usually consult small and medium-sized companies, and I have not said a thorough end-to-end process that is seamless, it usually is fragmented. You have various stages to the process, and you have a lot of manual manipulation and validation of the data. So it has been one of the challenges that I have seen with the CFOs. The procure-to-pay process is challenging and very manual. It requires a lot of effort. However, with hyperbots, we have seen that this is the solution that has been able to bridge all these gaps and bring the invoices from beginning to end very seamlessly.
Emily: Great. So because you mentioned, Claudia, that the process is a little fragmented, especially in the small to mid-sized businesses. What are the primary challenges that you have seen in achieving straight-through processing of invoices?
Claudia Mejia: Well, there are several issues, right? When you receive invoices, you receive them from different sources. You have portals, you have email IDs, all kinds of sources of information, and different types of formats which means you have structured data, and in structured data, it is very hard to control the variability in one aspect, and taking all that data and integrating it into legacy systems that they are not flexible. And so those have been some of the challenges, and on top of that, you have the resistance to change. Corporations don’t want to change their workflow. They’re fearful of technology. And the new technology that we see with AI is new. But it’s very powerful. I have seen it and is amazing for this process but those are the challenges that most companies have regarding it.
Emily: Understood. So, Claudia, if any organization is, say, PR-PO driven. Is it a better fit for straight-through processing of invoices as in what should a company do, if most of its purchases are still without a purchase, requisition, or purchase order?
Claudia Mejia: Well, PO, some PR structures are very important for this process. The more you can standardize this process at the beginning of the process the better it’s gonna be on the data validation through the process. So we want for that process to be very standardized, the templates, the formats but it’s not as easy, right? But my recommendation for the companies that don’t have a PR structure is just to try to standardize those processes, because once you bring the technology, on top of that, then it will make the whole process a lot easier.
Emily: Understood. So, Claudia, since the chart of accounts and GL codes in each company is different, you know. GL coding of each invoice is difficult to automate. Why is it so? And what can be done to handle it?
Claudia Mejia: Well, deals have all kinds of structures for different companies right, so it’s very difficult to just standardize one charge of accounts but the solution that now we have with Hyperbots is that the system now can learn by itself, so something that invoice, we can code in particular GL accounts. The system will learn that over time, and it will be very accurate and will place that spending into that GL account in the past with other processes. This is not as simple you will require manual manipulation and somebody validating that particular GL code. So the technology is there, let’s use it.
Emily: Got it. Also, I’ve seen there are so many solutions in the industry specifically OCR. Why do you think the current OCRs are not, you know, sufficient to understand the content of an invoice and is it a big handicap?
Claudia Mejia: Well, it is. Let’s describe what OCR is, which is optical character recognition. This technology is very good for converting PDFs and images into text but it has a little bit of a struggle with consistency, on the other hand, natural language processing is not only able to recognize and transfer the images to text but also understand the context behind that images on text, so it can learn by itself, which you will never find in the OCR technology, because it’s not. It wasn’t meant to be like that. So that’s what, now with AI we will be able to kind of do the end-to-end process in a way that is not as fragmented as we talk about.
Emily: Got it. So you know, diverse formats, unstructured data and lack of standards is it the reality? So is that the primary challenge for straight-through processing, and what exactly can be done to address all of these?
Claudia Mejia: Well, you have. This is something that I say. I am a process person, usually. So when I look at coordinate states or processes. I always start with the process and then we bring the technology that can fix the process. Okay, help the process not to get fixed process. But in this particular case, the technology is the one leading the process because of the technology. Now we’re able to push through the data from end to end and so my recommendation is, to make sure you understand your processes in the beginning and standardize those formats, making sure your vendors understand those formats, and then use the technology to push through the data, validate data, capture the data, and make sure it goes directly to the ERP without much manual intervention, unless you want it to be right. There are pieces that you say no, I don’t want to go directly to my ERP until I approve certain expenses so those are the main points that you can put through the system to make sure that you have the controls that you want.
Emily: Got it also a few obstacles for straight-through processing of invoices are inaccurate data in the invoice, or, you know, supplier side error, duplicate invoices. How to address these challenges?
Claudia Mejia: Well, the beauty about hyperbots specifically, because I have seen it is that the technology can not only read the data, understand the errors make sure it stops any invoices that have the errors, and then also provide recommendations. So there are a lot of good stages through the process that somebody can say, Oh, here I have the error, here I have inaccuracies. So that’s the beauty of having 1st a robust standardization, but also a good process through the book.
Emily: understood and from a regulatory standpoint, Claudia, you know what regulatory and compliance aspects should be evaluated for straight-through processing of invoices?
Claudia Mejia: Well, from an accounting point of view, there are a lot of controls that you need, right? So you need the all details, to make sure there is transparency in the transactions. You need the tax regulation that when you have invoices from different countries and different tax regulations, the system also has the flexibility to grab those types of regulations, and any inaccuracies through the process also can grab them or stop them. right? and data security. We are all concerned about data security, and make sure that all the data that goes through the system is secure and protects sensitive information.
Emily: Got it. And just to wind things up, the last question that I wanted to ask you, Claudia, is, what advancements in AI can help achieve straight-through processing to the highest possible degree?
Claudia Mejia: Well, AI has different levels of technology, right? So we have the machine learning algorithms which will help extract the information from the invoices, then we have the natural language processing which will be able to learn by itself and predict, and make recommendations and so does the magnificent view of this technology, right? This is something that we were not able to do before. And then we have the advanced analytics and so now we combine all these factors into a system like hyperbots, and we will be able to truly do it end to end. That’s what I said in this particular process technology leads it and I’m very happy to see that Hyperbots has been able to put it all together for us, and you’ll see the ROI come through.
Emily: Alright. Thank you so much, Claudia, for talking to us about the different challenges that the industry is facing with the straight-through processing of invoices, and also suggesting a couple of different measures. It was a fruitful discussion, an insightful one. So thank you so much for joining us today.
Claudia Mejia: No, thank you, Emily, thank you for having me.
Moderated by Moderated by Emily , Digital Transformation Consultant at Hyperbots
Emily: Hi everyone! In this segment, we’ll be discussing strategies for alignment and optimization. Mike, thank you so much for walking us through the approval workflow and organizational structures in our last section. To delve deeper into this segment, what strategies do you recommend for designing approval workflows that align with an organization’s hierarchical structures?
Mike: Thank you, Emily. Before developing the workflow, we need to consider the organization hierarchy. One key thing is to think about the best way to structure it. We need to establish a robust organizational hierarchy that includes the reporting lines, levels of authority, and other training and documentation. This impacts the decision-making process and ensures efficient collaboration within the department. Here are some strategies for developing a workflow:
Proper Reporting Lines: Clearly define who reports to whom. This is crucial because the workflow needs to move smoothly from one step to another.
Levels of Authority: Determine the authority level for each position. Not every decision needs to go through the same hierarchy. Define limits and responsibilities accordingly.
Clarity of Roles and Responsibilities: Specify who approves what and why they are involved in the workflow. Not everyone needs to be involved in every step.
Approval Levels: Establish clear approval levels and maintain an audit trail to keep track of the approvals.
Standardization: Standardize the approval process whether the organization is centralized or decentralized. This ensures consistency.
Utilization of Tools: Use appropriate tools like workflow management systems or AI to facilitate the workflow.
Training and Support: Ensure proper training for those involved in the approval process to prevent inefficiencies.
Collaboration and Coordination: Foster cross-functional collaboration and ensure accountability in the approval process.
Alignment with Organizational Objectives: Ensure that the workflow aligns with the overall organizational objectives, not just departmental ones.
Emily: Got it. So, Mike, how do you strike a balance between centralizing control for oversight and decentralizing decision-making authority in approval workflows?
Mike: Striking a balance between centralizing control for oversight and decentralizing decision-making authority is essential for optimizing efficiency, maintaining accountability, and fostering innovation. Here are some strategies to achieve this balance:
Clear Hierarchy: Establish a clear reporting structure that defines who is responsible for approvals, whether centralized or decentralized.
Key Decision Points: Identify critical decision points to determine the required authority level for approvals. For example, the marketing department may approve budgets, but cross-functional collaboration might be necessary for other decisions.
Delegate Authority Appropriately: Delegate routine tasks to lower levels and critical tasks to higher levels, empowering employees with the necessary training and tools.
Escalation Protocols: Establish clear protocols for escalating decisions that exceed certain authority levels.
Technology and Transparency: Implement technology solutions like workflow management systems to provide transparency and ensure all stakeholders are informed.
Cross-Functional Collaboration: Foster collaboration across departments since workflows often span multiple areas.
Training and Documentation: Provide thorough training and maintain documentation to ensure everyone understands their roles and responsibilities.
Emily: How can organizations ensure that approval workflows are flexible enough to accommodate changes in the organizational hierarchy?
Mike: Flexibility in workflows is crucial for accommodating changes in the organizational hierarchy. Here are a few strategies to ensure flexibility:
Modular Design: Design workflows in a modular fashion so changes can be made easily without disrupting the entire process.
Role-Based Approvals: Implement role-based approvals instead of individual-based ones. This allows for smooth transitions when people change jobs.
Dynamic Routing: Use dynamic routing to handle situations where someone is unavailable, enabling delegation and preventing bottlenecks.
Centralized Policy Management: Maintain centralized policies to ensure consistency and compliance across the organization.
Regular Review and Monitoring: Continuously review and monitor workflows to identify areas for improvement and ensure they remain adaptable to changes.
Emily: Gosh, so Mike, how can organizations leverage technology to automate routine approval tasks and streamline workflows? Can you share a few examples as well?
Mike: Before leveraging any technology, organizations need to understand their current processes and identify areas where technology can be beneficial. Here are a few examples of how technology can be used to streamline workflows:
Workflow Management Systems: These systems automate routing, track progress in real-time, enforce processes, and provide notifications and reporting capabilities.
Electronic Document Systems: These systems store data in one place, provide version control, and reduce manual intervention.
Electronic Signatures: Legal electronic signatures can replace physical signatures, streamlining the approval process.
ERP Integration: Integrating workflows with ERP systems ensures data consistency and seamless operation.
AI and Machine Learning: Implementing AI solutions can enhance workflow efficiency by automating routine tasks and providing insights for process improvements. These technologies help automate routine tasks, reduce manual errors, and ensure that workflows are efficient and adaptable to organizational needs.
Moderated by Niyati Chhaya, Co-Founder at Hyperbots
Niyati: Hi everyone, good morning, good afternoon, and good evening. I’m Niyati, Co-founder and AI Lead at Hyperbots Inc. Today, we have Mike Vaishnav with us, a CFO, consultant, and strategic advisor to many privately owned organizations.
Before we delve into our discussion on how AI complements ERP systems, Mike, could you introduce yourself?
Mike Vaishnav: Thank you, Niyati. I’ve worked in Silicon Valley for almost 30 years across diversified industries in various roles, including controllership, FP&A, treasury, tax, investor relations, and operational roles. In my last two CFO positions, I managed fund, IT, legal, HR, and procurement functions. I’ve covered all aspects of finance and operations in different industries.
Niyati: Wow, that’s a broad range. Today, we’ll address our topic in three broad categories: the efficacy of ERP systems, how AI and ERP work together, and the actual integration of AI into ERP systems.
Niyati: You have been part of several large and medium-sized organizations. What kind of ERPs and business processes have you worked with?
Mike Vaishnav: I’ve used both small ERPs and large ERPs like Oracle and SAP. I’ve been involved in every module for ERP, including procure-to-pay, accounting, sales, and inventory processing. I’ve implemented ERP systems globally over the past 20 years.
Niyati: What gains do you see in companies through effective ERP implementation?
Mike Vaishnav: Key gains include process automation, process improvement, audit trails, and data security. ERPs provide detailed analysis and streamline financial information, moving away from manual processes.
Niyati: What are the challenges despite effective implementations?
Mike Vaishnav: Challenges often arise during data migration and integration with old systems. Proper testing and documentation are crucial to ensure successful ERP implementation. Companies should conduct parallel test runs in a test environment for about two to three months to ensure data accuracy before going live.
Niyati: Let’s now discuss how AI and ERP systems complement each other.
Mike Vaishnav: AI is complementary to ERP. It provides add-on solutions that make data analysis more effective. While ERP systems collect and process data, AI enhances the ability to make timely and informed decisions, especially in mid-size or small ERPs that may lack advanced data analytics capabilities.
Niyati: Can you give an example, like invoice processing?
Mike Vaishnav: Sure. In large ERPs, the entire procure-to-pay process is automated. However, mid-size or small ERPs might lack such automation. AI can automate processes like opening and approving POs, providing real-time answers to specific queries, and creating customized dashboards for different departments. This enhances efficiency and privacy.
Niyati: Why is it better to use AI to complement an existing ERP rather than upgrading to a bigger ERP?
Mike Vaishnav: Upgrading to a bigger ERP is a complex and costly process. AI add-ons can enhance the existing ERP’s capabilities without the need for a complete overhaul. This approach is more efficient and less disruptive.
Niyati: Where will the budget for AI come from?
Mike Vaishnav: Companies need to work smartly, balancing their budgets. AI can help automate high-volume transactions, improving accuracy and timeliness. In the long run, AI provides better return on investment by enhancing process and operational efficiency, ultimately adding to the bottom line.
Niyati: How should a company assess the need for AI in its various use cases?
Mike Vaishnav: It’s case-by-case. AI is customizable, so companies need to evaluate their specific requirements, budget, and departmental needs. SMBs, in particular, can benefit from AI add-ons to enhance their existing ERP systems.
Niyati: Do you see ERP vendors integrating AI modules themselves?
Mike Vaishnav: Some top-tier ERP vendors are incorporating AI solutions, but mid-tier and lower-tier ERPs are slower to adopt these technologies. AI can help enhance these existing systems, especially for SMBs.
Niyati: When does it not make sense for organizations to adopt AI?
Mike Vaishnav: For companies with low transaction volumes or extremely small operations, AI may be unnecessary. In such cases, manual processing by a single person might suffice.
Niyati: To summarize, AI is a good friend to finance professionals, complementing ERP systems. While AI will not replace ERP, it enhances the capabilities of ERP systems, especially for SMBs and mid-tier ERPs.
Mike Vaishnav: Absolutely. AI adds significant value to ERP systems, making processes more efficient and helping companies make timely decisions.
Niyati: Thank you, Mike, for sharing your insights on how AI complements ERP systems.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, good afternoon based on where you are. I’m Emily, a digital transformation consultant at Hyperbots, and today I have Mike Vashnum with me. We are going to discuss a practical toolkit to bring efficiency in accruals. But before we get into details, Mike, would you like to introduce yourself?
Mike: Sure. Thank you, Emily. I’ve been working in Silicon Valley for about 30 years and have had the opportunity to work in a diverse range of industries. I’ve been fortunate enough to touch every aspect of finance, including controllership, FP&A, treasury, tax, and investor relations. In my last two roles as CFO, I also managed non-finance and operations departments like legal, HR, IT, facilities, and procurement. So, I bring a very diversified and wide-ranging experience in finance and operations.
Emily: That’s great, Mike. I have divided this session into two parts. In the first part, we will discuss the current accrual landscape, and in the second part, we’ll dive into AI and accruals. So, for the first part of this discussion, Mike, I’d like to ask you to highlight the importance of expense accruals for companies and how they generally impact financial reports.
Mike: Absolutely. Not just expense accruals, but any accruals are essential for a company. If you look at basic accounting principles, you have to match the revenue with your costs. If you’ve booked the revenue and incurred the cost but haven’t received the invoices or bills, your profitability will be inconsistent. One month you might show higher profitability because of lower expenses that weren’t booked, and the next month you might have all those expenses catching up, but no matching revenue. This mismatch doesn’t present an accurate picture of profitability. By accruing expenses for which you haven’t received invoices, you match your revenue with the associated costs, giving a more accurate representation of profitability. This is crucial for investors and analysts to ensure the company’s profitability for a specific period is correct.
Emily: Understood. Based on your experience, Mike, what is the current process used in companies for accruals?
Mike: Different companies use different processes. Most accrual processes are manual because you have to collect information from respective departments. I would categorize accruals into inventory accrual and expense accrual. Inventory accrual can be automated in sophisticated ERP systems, where goods received notes allow for booking received but not invoiced items as accounts payable. Expense accruals, however, are very manual and time-consuming. Accountants typically send emails to department heads to check for pending invoices and services rendered. They also look at open POs to follow up on whether services or expenses have been incurred. Despite some ERP systems offering recurring accruals, accurate accruals still require manual processes and a lot of back-and-forth communication.
Emily: Can you also share the categories of accruals and how does the reversal happen?
Mike: Sure. As I mentioned, there are inventory and expense accruals. Expenses can vary depending on the company’s size and nature. The reversal process is necessary to avoid duplication of expenses. When you book an accrual at the month-end and then receive the invoice in the following month, you don’t want to double-book the expense. So, the best practice is to book the accrual, reverse it, and rebook it at the next month-end.
Emily: Can you highlight the pain points in the accrual process?
Mike: The main pain point is the manual process. Collecting data, ensuring the accuracy of accruals, and coordinating with various departments are time-consuming tasks. Ensuring all necessary accruals are booked correctly is critical because auditors will not accept general accruals they need specific purposes and processes.
Emily: Thank you, Mike, for explaining accruals and the current landscape. In the next section, we will cover AI and its role in accruals.
Mike: Sure, thank you.
Emily: Welcome back, Mike. Thank you for taking us through the current landscape of accruals. Now, let’s discuss the role of AI in accruals. What role does AI play in accruals, and can you provide some examples to help us understand it better?
Mike: Absolutely. AI can tremendously speed up the accrual process. While ERPs can handle many tasks, AI enhances them, especially in communication. For example, AI can look at open POs and automatically send messages to the respective departments asking if services have been rendered. This eliminates the need for human intervention. By setting AI to check for open POs on specific days, it can collect and follow up on all necessary information automatically. Let’s take the legal department as an example. AI can send automated messages to attorneys asking if services have been rendered, which replaces the manual process where accountants send emails and wait for responses. AI can also help with inventory by identifying received items that haven’t been invoiced and processing those accruals. Essentially, AI can handle the communication aspects of accruals, ensuring accurate and timely data collection without human intervention. This streamlines the month-end close process, allowing accountants to focus on analysis rather than data gathering.
Emily: That gives me a clear vision of how AI helps in accruals. Thank you so much, Mike.
Mike: You’re welcome. I’m looking forward to seeing AI develop further to make the accrual process faster and simpler, ultimately allowing more time for analysis and less on data gathering.
Emily: Thank you, Mike, for being a part of this discussion on AI and accruals. It was great having you here.
Mike: Thank you. Glad to be here.