Massachusetts’ sales tax system

Find out interesting insights with Dave Sackett, VP Finance , Persimmon Technologies

Moderated by Srishti, Digital Transformation Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Srishti: Hello, everyone! My name is Srishti Rajvir, and I’m a digital transformation consultant at Hyperbots today. I’m delighted to have Dave Sackett as my guest. Thank you so much for taking the time, Dave.

Dave Sackett: Yeah. Thanks, Srishti.

Srishti: So a little bit about Dave for our viewers. He is the VP of Finance at Persimmon Technologies, and today we will be discussing the Massachusetts sales tax system. So whenever you’re ready, Dave, we can get started.

Dave Sackett: Okay, sounds good. I’m ready.

Srishti: Alright, to start with, could you explain how Massachusetts’ sales tax system stands out, especially in terms of the rates and the products that are taxable?

Dave Sackett: Sure, Massachusetts has a statewide sales tax rate of 6.25% on most goods and services. This is relatively straightforward, but there are some exceptions. For example, there’s tangible personal property that’s taxable, while food and clothing are generally exempt. Prepared food, like meals in restaurants and hot food sold in grocery stores, is taxable. It’s important for businesses to understand these distinctions to avoid errors when they file their taxes.

Srishti: That’s helpful. Speaking of exemptions, could you provide some specific examples of goods or services that are exempt from sales tax in Massachusetts?

Dave Sackett: Yeah, sure. Certain goods and services are exempt from sales tax, including clothing, most food items, and prescription drugs. For example, if you buy a pair of jeans or a sweater, it won’t be taxed. However, if you dine at a restaurant or buy food outside, that is taxed at the 6.25% rate. Another example is prescription medications. These are exempt, but over-the-counter drugs, vitamins, and cold medicines—you’ll be paying tax on those.

Srishti: That sounds like businesses really need to stay on top of the details to remain compliant. So what challenges do businesses face in handling sales tax with these exemptions and different rates, especially those with multiple locations?

Dave Sackett: Yeah, there is complexity in applying the correct tax rates, especially when dealing with exemptions. One of the biggest challenges is knowing whether something is taxable or not. Businesses that operate across municipalities face additional complications, as some localities impose their own taxes on top of the state rate. Tracking which items are taxable or exempt requires a system. For example, differentiating between grocery store food and prepared meals—a grocery store might sell both a cold salad, which is exempt, and a hot meal, which is taxable. Distinguishing between these two for every transaction requires a tool.

Srishti: I see. How frequently do Massachusetts sales tax rates or exemptions change, and what strategies do businesses use to keep up with these updates?

Dave Sackett: Massachusetts generally has a stable tax rate, but exemptions can change. It’s politically driven in some cases, and there are occasional updates to the regulations. For example, changes can occur in the types of food services that are exempt from sales tax. Companies need to monitor updates from the Massachusetts Department of Revenue to ensure compliance with any changes in the laws. Having an automated system that can track these changes and apply them across transactions is a super helpful tool. For instance, if a state changes its policy on sales tax exemption for certain food items, businesses would need to update their point-of-sale system immediately to reflect that change and ensure they’re collecting the correct tax.

Srishti: Understood. And in your experience, how can AI and automation help businesses maintain compliance with Massachusetts’ sales tax rules?

Dave Sackett: AI and automation can play a huge role in improving efficiency and accuracy in tracking sales tax. AI can automatically determine the correct tax rate for transactions based on the customer’s location, the product type, and whether it’s taxable or exempt.

Srishti: Additionally…

Dave Sackett: There are AI tools to keep track of regulatory changes, ensuring businesses update their systems whenever tax rates or exemption rules change. If Massachusetts were to modify the taxability of certain digital products, AI could update the system instantly to reflect the new rule, ensuring businesses don’t miss it.

Srishti: Understood. Can you share a specific example of how AI has helped a business navigate Massachusetts’ sales tax rules?

Dave Sackett: Yes. A company that sells both tangible goods and prepared food had issues ensuring the correct sales tax was applied at checkout, especially when operating in multiple regions. With AI automation, the system is now automatically classifying food items as either taxable or exempt based on the preparation method. For example, a customer buying a cold sandwich from a deli would pay no tax, but a hot sandwich would be taxed. AI in the background ensures the correct tax is applied each time. This streamlined the process, reducing manual effort and errors.

Srishti: That’s an amazing example. Now, when businesses have to handle multiple exemptions or taxability rules, how can they streamline their processes to avoid errors?

Dave Sackett: To avoid errors, businesses need correct classifications and automated tracking of tax. AI can help by instantly classifying products at the point of sale and ensuring the correct tax rate is applied to each transaction. Businesses should also invest in systems that integrate directly with the state tax authority for updates. For example, if Massachusetts changes the definition of prepared food or creates new exemptions, the system should automatically update without manual intervention by the finance team.

Srishti: Understood, and this is really helpful. That brings me to the last question: what advice would you offer to businesses finding it difficult to keep up with sales tax rules in Massachusetts?

Dave Sackett: My advice would be to implement a reliable sales tax automation solution that uses AI. These tools can handle complex calculations, track changes in regulations, and apply the right tax rate in every situation. Staying informed is critical, so combining automation with a proactive approach to monitoring state regulations will reduce the risk of non-compliance. Also, maintain a strong relationship with your tax advisor to ensure your business stays on top of Massachusetts-specific rules. Additionally, businesses must consider not just sales tax but also Massachusetts use tax on items they buy and use in the business.

Srishti: Understood. Thank you so much for sharing your expertise today. It has been really insightful. I’m sure our audience will find these tips very helpful. That brings us to the end of our discussion. Thank you so much, Dave, for being a part of this.

Dave Sackett: Yeah. Thank you. Happy to help.

Srishti: Of course. Thank you so much, and to our viewers, we’ll stay connected and see you next time. Bye-bye. Have a good one!

GL coding in the Chart of Accounts(COA)

Find out interesting insights with Jon Naseath, CEO/Founder Cantu Capital Inc

Moderated by Sherry, Financial Technology Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Sherry: Hello, and welcome to all our viewers on CFO  Insight. I am Sherry, a financial technology consultant at Hyperbots, and today we are speaking with Jon Naseath, who is an accomplished executive with expertise in AI, machine learning, and computer vision driving impactful technology solutions in education, healthcare, and business. Thank you so much for joining us today, John. Today we’ll be discussing the importance of GL coding in the chart of accounts, common mistakes companies make, and how technology can help maintain coding integrity. So let’s dive right in. Can you briefly explain what GL coding is, and why it is so important in the chart of accounts?

Jon Naseath: So just briefly, what GL coding is, and I’ll tell you an example of the importance. GL throughout the entire business. People are doing their work. They’re spending money, earning revenue, buying things, or investing in product development. Whatever you’re doing throughout the entire business. That translates into equity. You’re making money for investors or making customers happy. Whatever is your role, there’s a representation of that in the GL. We have to make sure that what you’re doing, what you’re spending, and what revenue you’re making is accurately represented in what’s going to turn out later to be the P&L or your balance sheet. You know, what did we invest in on the balance sheet? And what did we spend, and how much revenue did we make on the P&L? If we don’t map things correctly to the GL, then the rolled-up reporting numbers will be wrong. There are all these very formal GAP accounting rules over how things exactly have to be mapped. The story example I wanted to raise real quick was just a few hours ago. I got an email from a very good friend of mine. I used to work at a large organization, and he’s a leader in that organization. He spends his whole day talking to big companies around the world about how to reduce the cost of IT and cloud spending, and how to help them with strategy. He pinged me and asked me, “Where can I find the right definition of revenue?” I chuckled when I saw that because, I mean, there are people’s whole careers spent helping companies figure out how to define revenue for their business. He asked, “What’s the definition of net revenue?” I spent a year working with a company where there were actually seven definitions of revenue just within that company. They called it gross revenue, sales revenue, RevOps, net revenue, and all these different things. But the reality is, if you ask an accounting person, there’s only one definition of revenue, and there are actual policies around how that’s defined. The reason I bring it up is, that if you’ve mapped all these different things that you do in your business to the GL, and then you map that GL to management reporting, different management reporting people will want to see how what they’re doing impacts revenue in this example. They can make up their definitions, but they should define that there’s one thing that is accounting GAP, you know, audited revenue. All the other stuff is management reporting. If you’ve mapped stuff in wrong, then the core number is wrong. If you map stuff wrong, then the other pieces are wrong. Just bluntly, if you do it intentionally and you’re trying to hide things, people go to jail. So that’s why it matters. But also so investors can understand what they’re doing, and management can have clear views of how they’re managing the business. Long answer to a short question, but it was a pretty loaded question.

Sherry: And in your opinion, what are some common coding schemes that companies should follow when setting up the chart of accounts? Can you provide examples from different industries?

Jon Naseath: Most simply, think about it as your P&L and your balance sheet. As you walk down your P&L, you know, revenue, cost of goods, operating expenses, whatever you have in your P&L. As you walk down your balance sheet, assets, liabilities, etc. All it is is a number that represents walking down your P&L and balance sheet.  The root of your question, though, is within different industries, there’s become quasi-standards around how they’re doing their business. Every company within that industry is also going to try to find its competitive advantage, so they’ll do something unique to them. When they do roll up their P&L and balance sheet, I remember I was in a job where my job was to take that from the accounting team, roll it up into these investor analyst reports, and then we’d hit submit to go live at quarter-end. There was an army of like 60 investor analysts whose whole job seemed to be to find any errors we had in any of those numbers. What they’re doing is mapping that financial statement to other ones in the same industry and seeing if we’re different, wanting to compare them. So to some degree, you want to be similar to your peers in the industry, but for others, you want to innovate. I was at Equinix, which is an innovator in space. We impacted how the industry looks at metrics like FFO and other REIT-related revenues. We made sure we were compliant with revenue. There were lots of things besides just data center buildings that we had as revenue, and we had to account for them correctly.

Sherry: What are some common mistakes you’ve seen companies make with their GL coding structures? Can you provide examples from various industries?

Jon Naseath: One mistake is in the way companies add dimensions to the GL, like region, department, or sales channel. You might see the initial chart of the account code, and then different dimensions get added as “dash something else.” This lets you slice and dice to understand costs by department, region, or product type. But bloating the GL by adding too much detail, like putting all your product SKUs into the chart of accounts, can make it unmanageable. Over time, old codes and new codes can create complications. Another issue is the lack of standardization. It’s important to align with industry standards so reporting can be compared. Insufficient detail is another problem, where management wants specific insights to reduce costs or invest more, but all they have is a generic code for product costs. You need to break things up by what should be capitalized and what should be expensed. Finally, there’s inadequate training and documentation. If people aren’t trained well, they can tag transactions incorrectly, which impacts the rolled-up reporting. That’s why visibility and proper training are key.

Sherry: How do these mistakes impact a company’s financial management and reporting?

Jon Naseath: From a financial perspective, it creates a lot of unnecessary work. Ideally, you could just roll things up and have it reconciled, and everything makes sense. But often, you do plan vs. actuals or month-over-month, and something’s off. You might have a gut feeling that a number isn’t right, and sure enough, you unpack it, find a miscode, and need to reclassify. The impact includes inaccurate or inefficient financial statements, upset executives, and hours of rework. It can lead to compliance risks, resource wastage, and worse, damage a company’s credibility. If a CFO has too many reporting errors, they could lose their job.

Sherry: How do you think technology can help maintain GL coding integrity and reduce these mistakes?

Jon Naseath: Technology is excellent at reconciling across different dimensions and sources. It can tie everything together, but it’s not perfect—AI can sometimes hallucinate. There are automation tools and AI that help, but there needs to be a balance between understanding numbers and producing accurate outputs. For example, I was talking to the controller of a large global organization. They have a complex ERP system and are transitioning to a new version. They’ve gone through the business planning, and they know what they want that future state GL and reporting metrics to look like. And they finish that. But it’s gonna be another at least a year and a half, maybe 2 or 3 years, until they get this full ERP fully implemented, all trained, and are in the new future state system. In the meantime, they’ve got a year and a half and 2 years or more, because things always go wrong in those projects. There’s always some reporting that’s missing, even if they say they go live. It’s never right at first. However, even with what I just said, it’s never right at first. I believe there’s an opportunity here where if you define what you want your future state reporting to look like, and you have that data coming in. And you’ve created this mapping thing that you’re gonna give off to some developers and they’re gonna rebuild the system based on that new mapping tables and then you have to extract the data from the old table, load it into the new table once it’s developed and then wait. Maybe the reports work, but they don’t. And it’s huge UAT testing.  Anyone who’s been through that knows it’s painful. I think AI can do a lot of that stuff. I think you can take from your legacy system, your current system. You can say, here’s what I want my master tables to look like. Here’s what I want my reporting outputs to look like and it can help produce those. Now, again, it’s going to hallucinate. You have to code it the right way to make sure that it gives you the right outputs. But a lot of the pain, which is real pain, or a lot of the late nights because there’s an error, and there’s rework. You have to go through a lot of the cost of hiring an army of people to fix an error that was in there historically and rebuilding reporting.  A lot of that, I believe, will be able to be fixed by AI. And it’s no longer for me just a belief. I know it’s real, because I’m seeing it happening in different companies I’m talking to or working with and it’s fun. And frankly, I’ll just give you guys a shout-out. You’re on your track with the products you guys develop. I’m seeing good things. It’s exciting to see what you’re building, and where this will lead to.

Sherry: Thank you so much, Jon, and from your experience in the finance industry, can you provide an example of how automation might improve GL coding practices in a specific industry?

Jon Naseath: Yeah. The manufacturing industry is complex a bit, because it’s not all just kind of in the cloud, SaaS, and I’ll say relatively easy. So there are so many different stages of raw materials, work in progress, finished goods, making sure you’re coding things all to the right place and then, making sure it’s current. I think that speed aspect is really important, because then, if you don’t get it right in time, you’re making accruals, and you have to fix them later. So I think that can reduce a lot of human error and complexities when things go away. And I think automation will fix a lot of that stuff.

Sherry: And what best practices would you recommend for companies, or for our viewers looking to implement or improve their GL coding system?

Jon Naseath: Sure. Don’t get overloaded is the way I like to describe it in your GL. Keep it relatively simple. Think about what is the core dimension of your GL, and then what the sub-dimensions, and sub-ledgers that tie into that. Make sure people have the training they need so that they’re not screwing your stuff up. I remember a friend of mine was in accounts payable, and he had a paper on his screen. He would just keep, and all he was doing was coding things to those key numbers, and that master table. When I come into a new company, I’ll, based on that, go around and ask the people in these types of roles: “Show me your kind of cheat sheet. What’s that master thing for mapping that you rely on?” And they all have them. They all pull out, “Well, this is what I look at, this is what I rely on.”I like to take those cheat sheets, standardize them into policy, and make them real. Use technology wherever possible. I do think AI is great, but I think that there’s lots and lots, you know, throughout my whole career there’s been automation of things. So there are lots of things that are proven as technology automation, use them. We don’t need to reinvent AI just to do something that’s already fully automated. And then use AI for stuff that couldn’t have been automated previously. That is now enabled. And the combination of those two things is where you get really powerful results and then just basic, I’ll call them controls. Reconciliations, making sure that things are coded correctly, doing budgeting, and making sure you’re doing those plans versus actuals. The best call out here is to partner with FP&A, finance, and accounting. Accounting wants to get their numbers right. They’re very proud of that. But then it’s FP&A that is creating the management reporting a lot of times and a lot of the forecasting. So if those numbers are off, work together to make sure they’re right before you go spread it all over the business and tell them that they’re idiots because they screwed up some number when it was an accounting-finance disconnect. That never happens, but just hypothetically.

Sherry: Thank you so much, Jon, for these valuable insights on GL coding practices, and how technology can play a crucial role in maintaining financial integrity.

Jon Naseath: My pleasure, always fun.

Chart of Accounts (COA) and GL coding for SAP

Find out interesting insights with John Silverstein, VP of FP&A, XR Extreme Reach

Moderated by Emily, Digital Transformation Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Emily: Hi, everyone. This is Emily, and I’m a digital transformation consultant at Hyperbots. I’m very pleased to have you on the call with me, who is the VP of FP&A and taxes at Extreme Reach. Thank you so much for joining us today, John. It’s great to have you.

John Silverstein: Thank you for having me.

Emily: So, John, we are here to discuss the best practices and common challenges related to GL coding and the chart of accounts in SAP, especially when dealing with multiple legal entities across different countries. So let’s just dive right in. Why is it important for an organization to have a well-structured GL coding scheme in SAP?

John Silverstein: Yeah, thank you. It’s really important to have structured GL coding and controls around it because it provides a standardized way, as you said, for multiple legal entities across the globe. If you have different structures, it’s going to be impossible for systems to know what’s in those different entities, and consolidations become very difficult. This structure enables clear financial reporting, reduces reliance on mapping tables, and ensures more consistency across the globe. You’ll also have accurate decision-making, as it helps avoid confusion between different types of accounts. Additionally, it ensures compliance with local and international accounting standards. A consistent coding scheme also allows for quicker consolidation of financial statements. Without it, taxes and audits can become quite complicated.

Emily: Got it, understood. Moving forward, what are some of the best practices you would recommend for designing a GL coding structure in SAP?

John Silverstein: The biggest thing is consistency. Ideally, you want one chart of accounts from a global standpoint, with rules and descriptions that everyone can use. This consistency should apply across the globe, not just within one legal entity. At the same time, you need granularity—maintaining an appropriate level of GL codes to categorize your assets, liabilities, and expenses properly. For example, using consistent standards like 1,100 for cash or 1,200 for accounts receivable. This helps roll up financials smoothly without overcomplicating the structure. You also need to align the structure with business needs, which can be challenging. Don’t use GL accounts for everything. Sometimes, companies confuse GL accounts with cost centers or product codes, making things unnecessarily complex. For instance, if you’re a retail company with multiple sales channels, separate revenue accounts for each channel, like online sales versus in-store sales, help track profitability more accurately. It’s also crucial to regularly review and update the coding structure to meet business requirements, especially as new lines of business or clients emerge. I’m currently working on a project to clean up our chart of accounts to ensure it’s updated and reflects our business structure properly. Automation and validation within SAP can further reduce errors, especially through the automatic allocation of expenses to the correct cost centers.

Emily: Thank you so much for that answer. It adds a lot of value. What are some of the common errors or mistakes companies make when managing their GL coding structure?

John Silverstein: A common mistake is using different coding or number schemas across entities. Sometimes, even within one entity, you see inconsistent number schemas. For example, marketing expenses might be coded differently across departments, which can create confusion during consolidation. Another mistake is overcomplicating the GL structure, such as creating a GL account for every vendor or customer to track profitability. This is not scalable, especially as the business grows. Using dimensions like cost centers or product codes to map out profitability is much more effective. Improper account mapping is also common. Travel expenses, for instance, may be scattered across different categories like car rentals, creating unnecessary confusion. Duplicate accounts are another issue; it’s common to see multiple office expense accounts or consulting fees that should be consolidated. Neglecting legal requirements, such as VAT accounts in European countries, can also lead to non-compliance.

Emily: Got it. How do you handle multiple legal entities of the same company in different countries from a GL coding perspective?

John Silverstein: You need to assign unique company codes for each legal entity in SAP, which allows for separate ledgers and easier consolidation. For example, you might use US01 for the US entity and UK01 for the UK entity. Ensuring that the codes are consistent is critical for proper roll-up and reporting. Multiple charts of accounts are necessary for local statutory compliance, but there should be a group chart of accounts for consolidation. For instance, cash might be represented as 1,000 across all entities, but VAT receivables might be specific to the UK, and sales tax receivables might apply to the US. Intercompany transactions also need to be managed carefully with distinct GL codes for intercompany payables and receivables. Automation through SAP’s intercompany reconciliation tools helps minimize errors.

Emily: Got it. Can you also provide an example of how a company can ensure compliance and accuracy when dealing with multiple currencies across different entities?

John Silverstein: Sure. Let’s say a US-based company has subsidiaries in the UK and Japan. SAP can handle transactions in local currencies, such as GBP and JPY, and simultaneously record them in group currency (USD) and possibly a global currency like EUR. This allows for consistent financial reporting at a consolidated level while ensuring local compliance. 

SAP’s foreign currency valuation tools help adjust GL balances according to current exchange rates. For instance, if the Japanese subsidiary records a sale in JPY, SAP can convert that into USD for group reporting and automatically adjust balances for exchange rate fluctuations.

Emily: That makes sense. So, how can AI help prevent common mistakes in GL coding for SAP?

John Silverstein: AI can help in several ways. It can automate data entry, ensuring consistency and accuracy while reducing human errors. AI can learn from past transactions and suggest GL codes for new ones, improving over time with continuous learning. Anomaly detection is another big area—AI can flag transactions that don’t align with established patterns or rules. For example, if a travel expense is coded to an unusual account, AI can alert the user immediately. Natural Language Processing (NLP) tools also help categorize documents like invoices and receipts, automatically assigning them to the correct GL accounts. This is particularly helpful when dealing with diverse formats and languages. Predictive analytics can help forecast potential errors based on historical data, and continuous learning means that AI will get better at detecting and correcting errors over time.

Emily: Got it. What strategies would you suggest for aligning GL coding practices across multiple countries while ensuring compliance with local regulations?

John Silverstein: Centralize the group chart of accounts, but allow for local flexibility to meet statutory requirements. This means having a global chart of accounts for consolidation while still letting local entities maintain country-specific charts for regulatory compliance. SAP’s localization features, like country-specific tax codes, can help meet regulatory requirements. Standardizing processes across the organization, such as using consistent templates for financial reporting, is also essential. Regular training and reviews are crucial to keep up with changes in business needs, regulations, and technology. Maintaining clear communication between global and local finance teams ensures alignment across the board. At some scale. So you need to make sure that the technology changes. So you need to ensure that people know what they’re using. They’re not using the old versions of all your software and technology that you have out there.

Emily: Got it. And just to wind things up. One last question, John. So how does a well-structured GL coding scheme contribute to faster financial reporting and better decision-making overall?

John Silverstein: Yeah. So this is you. If you screw up the GL coding, you sometimes can’t report on things until you’re done with consolidations and going through mapping tools and all this other stuff, it gets very complex. And you end up catching errors during the close process, or too late in the process that you can’t, that you end up going back and forth. It adds days and unnecessary work. During your close process, your data will be inconsistent. You’re gonna make mistakes, and you’re gonna catch these too late. Is a big part of it. So, one of the most critical things you can do is to make sure that you are consistent and the automated reporting, if it’s manual reporting today and there are inconsistencies all the time, you end up doing corrections all the time, and then your accounting is not correct. So if you don’t do this right, you’re gonna make mistakes, and it can cause restatements, even in tax restatements and all that stuff as well. So very critical.

Emily: Got it. Got it. Thank you so much, John, for joining us today again. It was an insightful discussion, you know, talking to you about the chart of accounts and the GL coding for SAP in particular. So thank you so much for joining us.

John Silverstein: Yeah, no problem.

Vendor visibility in the invoice processing workflow

Find out interesting insights with Claudia Mejia, Managing Director, Ikigai Edge

Moderated by Mayank, Marketing Manager at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Mayank: Hi, everyone I am Mayank, the marketing manager at Hyperbots. I am pleased to have Claudia Mejia on the call. Claudia is the managing director at Ikigai Edge. Thank you for joining us today. We are here to discuss the level of visibility companies should provide to the vendors during the invoice processing workflow. Let’s start with the basics. So why is vendor visibility in the invoice processing workflow important for a company’s financial operations?

Claudia Mejia: Hi, Mayank, thank you for having me. Well, it’s very important, because it impacts vendor relationships in any company. We want to make sure that our vendors are content, and for that, we need to provide efficient processes and make sure that processes align with the terms set with the vendors. So we just need to make sure that the process remains smooth and timely.

Mayank: Awesome. So basically, there are two primary approaches to vendor visibility: full transparency versus high-level updates. Could you explain these two approaches?

Claudia Mejia: Full transparency means you communicate to the vendor the stages of the invoice process, from the moment it’s received, through reviews, approvals, and finally payment. The vendor knows exactly where they are in that process. However, high-level status is more about giving them key updates, like “We’re in the review stage” or “You’ll be paid on this date.” It doesn’t delve into all the details, just gives them a sense of when they can expect payment. Ultimately, vendors want to know when they’re going to get paid.

Mayank: Awesome. So, what do you see as the primary advantage of providing full transparency to the vendors?

Claudia Mejia: It’s about trust at the end of the day. Transparency builds trust, and that’s crucial in any organisation, especially in vendor relationships. When you’re transparent, it reduces the number of inquiries you might receive from vendors. If you have a system in place to provide that information, it also saves time for the team processing invoices since they deal with fewer inquiries.

Mayank: Got it. So, what are the potential risks or downsides of this full transparency approach?

Claudia Mejia: Well, there’s always a balance. Full transparency can lead to information overload, exposing processes to vendors that might not need to be shared. This could reveal vulnerabilities in your systems or processes. Sometimes it’s just unnecessary to show all the details. There’s always a balance between what the vendor needs and what they don’t need to know.

Mayank: Got it. Conversely, what are the benefits of sticking to high-level updates?

Claudia Mejia: It minimises the effort required from the team to process the invoices. The key is to communicate around important milestones. As long as you fulfil the terms of the contract, that’s what matters most. 

Mayank: Got it. Do you think there’s a risk of vendors feeling dissatisfied with high-level updates due to a perceived lack of transparency?

Claudia Mejia: Honestly, I haven’t seen that in my experience. As long as communication is clear and expectations regarding terms and payments are laid out, most vendors are satisfied. Issues tend to arise when you go beyond those terms and fail to explain a delay in payment. Being proactive when you can’t meet the terms is key. Otherwise, I’ve found that vendors generally understand the process as long as it’s efficient.

Mayank: Got it. So, in terms of communication and collaboration, what methods do you recommend to ensure effective interaction with vendors regarding invoice status?

Claudia Mejia: One effective method is having a vendor portal. Through the portal, vendors can view their invoices and where they are in the process. Of course, this requires a system that tracks those stages. Alternatively, providing key milestones, making sure you meet the terms of the contract, and offering clear lines of communication—such as a contact person, phone number, or email address—are essential. Vendors should always have someone to reach out to for inquiries and like I said, be proactive. If there’s an issue with payment, let them know in advance.

Mayank: Got it. Finally, how can AI play a role in making the invoice processing workflow more efficient for both companies and vendors?

Claudia Mejia: AI can automate many tasks that are currently done manually. AI can automatically send notifications and, through predictive analytics, anticipate certain events. AI can handle standard inquiries via chatbots, providing information that doesn’t necessarily require human intervention. Ultimately, it’s about making sure vendors have the information they need, whether through AI or by speaking to a person when necessary. AI can streamline processes significantly, but there will always be situations where human interaction is needed. AI won’t solve all our problems, but it can definitely make processes more efficient.

Mayank: Totally agree with you, Claudia. Thank you for sharing your insights. It’s clear that balancing transparency with efficiency is key to maintaining strong vendor relations while protecting the company’s interests. Thank you so much, Claudia, for your time. It was really insightful.

Claudia Mejia: No, thank you very much. Thanks for having me.


Mayank: Thank you.

Dealing with date format variations in financial documents

Find out interesting insights with Claudia Mejia, CFO & Strategic Advisor

Moderated by Sherry, Digital Transformation Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Sherry: Hello, and welcome to all our viewers on CFO Insights. I am Sherry, a financial technology consultant at Hyperbots, and I’m very excited to have Claudia Mejia here with me, an experienced finance and operations leader, with over 15 years in finance, operations, project management, and driving businesses towards efficiency, innovation, and strategic growth. Thank you so much for joining us today, Claudia. Date format variations in financial documents can lead to significant challenges in global operations. Let’s delve into this topic with a focus on understanding the issues, best practices, and how AI can help. Could you explain how date formats vary across different financial documents and regions?

Claudia: Hi, Sherry, thank you for having me. Yes, this is an important subject, especially now with the era of AI. I think let’s explain a little bit about how the formats are different across regions. In the United States, we usually use the format of month, date, and year, while in Europe or Latin America, they use the date, month, and year. In Asia, they use year, month, and day. So with all these variations of format, it’s complicated for people in general who manage documents. Data entry becomes complicated and is prone to errors. It’s important to make sure there’s standardization because these inconsistencies can create issues with accuracy and all kinds of consolidation problems.

Sherry: And before we move on to best practices, let’s touch upon the obstacles. What are the primary challenges organizations face when dealing with these variations in date formats?

Claudia: Well, there’s the risk of misinterpretation, right? I can interpret one format as the month, the year, or vice versa. This creates not only payment process issues, but also contractual, legal, and compliance issues. The first step is understanding how we’re going to interpret formats and how we can standardize them in a way that the whole company, especially global companies, can execute properly.

Sherry: From your experience, can you share any real-world examples where date format issues caused significant problems?

Claudia: Yes, for example, in a global company, let’s say a vendor in Europe sends an invoice to be processed using their format. They might put the date first, then the month, then the year, like 8/7/2024. In the United States, we might interpret that as August 7th, while they meant July 8th. This creates late payments, penalties, and issues with cash management, which is crucial for any company. It’s important to ensure there’s standardization across all regions to avoid such issues.

Sherry: To overcome these challenges, what best practices do you recommend for managing date format variations in financial documents?

Claudia: One standardization that’s widely used is the ISO 8601 format, which follows year, month, and day. This eliminates ambiguity. It’s also important to create validation rules in financial systems to correct issues before invoices and documents are fully processed. Training everyone who handles documents, including contracts, is crucial. Educating vendors about the formats your company uses also helps establish standard practices.

Sherry: Since AI is the future, how do you see AI playing a role in addressing these challenges?

Claudia: AI can detect and monitor date formats, automatically correcting them. As systems learn, AI can catch errors before data enters the financial system. AI can also address issues like when people accidentally enter the wrong year, for example. It can correct these mistakes automatically, which is something humans often miss.

Sherry: What are the potential risks if an organization fails to address date format inconsistencies?

Claudia: If companies don’t address this, they risk missing payments or deadlines, which damages trust with vendors. This can create compliance issues, complicate audits, and waste time resolving unnecessary problems, adding no value to the company.

Sherry: Could you share some insights into how AI-driven solutions are currently being used to manage date formats in global financial operations?

Claudia: Many companies use OCR (optical character recognition) to capture data, which is a machine learning technology. AI can learn and predict potential errors, helping to mitigate issues before they arise. AI can also correct problems before the data enters the ERP system, ensuring accuracy.

Sherry: Makes sense. Looking forward, how do you see the role of AI evolving in managing financial document processes, particularly concerning date formats?

Claudia: AI will drive efficiencies in data capture and system learning for document processes. Companies adopting AI will see increased automation, with AI capturing, correcting, and pushing data into ERP systems without human intervention. Human checks can still be incorporated, but AI can handle most tasks end-to-end, which wasn’t possible before because the technology wasn’t advanced enough to learn independently. AI can bring great efficiencies and accuracy, but companies must also maintain proper controls.

Sherry: Thank you so much for being here, Claudia, and for sharing your insights. It’s clear from this conversation that addressing date format variations is crucial for maintaining financial accuracy, and that AI offers promising solutions to these challenges.

Claudia: Thank you very much, Sherry, for having me. It’s always a pleasure.

Best practices in structuring expense heads in COA

Find out interesting insights with Anthony Peltier, Coast to Coast Finance

Moderated by Pat, Digital Transformation Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Pat: Hello, and welcome to CFO insights by Hyperbots. Today we have Anthony Peltier, a seasoned CEO, with extensive experience in financial management across various industries. We’ll be discussing the structuring of expense heads in the chart of accounts, best practices, common mistakes, and the role AI can play in this area. Thank you for joining us, Anthony.

Anthony Peltier: Yeah, thanks for giving me the pleasure to be here.

Pat: Alright. So before we dive in, Can you explain why the proper structuring of expense heads in the chart of accounts is important for an organization?

Anthony Peltier: Absolutely. Yeah. Expense heads are crucial in the chart of accounts because they directly impact the financial reporting analysis decision making right? So when you have a well-structured chart of accounts that ensures expenses are accurately categorized. It makes it easier to track costs, control budgets, and identify areas for cost savings super important. That also facilitates compliance with accounting standards and regulations which is vital for maintaining financial integrity.

Pat: Right. So what are some of the best practices for structuring these expense heads in the chart of accounts, regardless of industry?

Anthony Peltier: Yeah, I would always recommend aligning those categories with the core business activities and using a standardized nomenclature that’s helpful for consistency, that nomenclature can balance the granularity in your accounts, and then regularly review those.

Pat: So what are some of the stories? What are some of the best practices for structuring expenses at the start of accounts, regardless of industry?

Anthony Peltier: Yeah, I would say, the best practices are aligning the expense categories with the core business activities that way you can use a standardized nomenclature for consistency, for balancing granularity, and for regularly reviewing and updating the chart of accounts to reflect any changes in operations that way you can separate the fixed and the variable costs, and start to group expenses by function or department or by bucket, and that’ll enhance clarity  and accountability. So those practices can help maintain a COA that’s both useful for management and compliant with external reporting requirements.

Pat: Okay, so could you provide some examples of how different industries, such as manufacturing or the SaaS industries, might structure their expenses differently?

Anthony Peltier: Absolutely, the focus is gonna be on direct production costs, raw materials, and labor factory overhead, while Sas companies are gonna emphasize technology-related expenses like software development, hosting, customer support, and so on. Each industry is going to have unique cost drivers. So their COA structure needs to reflect those differences, and that’ll ensure accurate cost tracking and financial analysis. A retail company may have expense heads for inventory purchases and store utilities, while a construction firm would include direct material costs, equipment, rentals, and subcontractor fees, stuff like that.

Pat: So all these different industries right? What are some of the common mistakes that you see an organization make when they are structuring their expenses in the chart of accounts?

Anthony Peltier: Yeah, this happens quite often. Some of the main mistakes, I see, are overlapping and redundant categories. This confusion causes inaccuracies in reporting and then another mistake is over. Granularity: Too many categories in the COA are going to become cumbersome and difficult to manage, and inconsistency in naming conventions, that’s gonna cause errors. It’s not gonna reflect changes in the business operations and that’s gonna lead to inefficiency overall. So as a result the expenses are going to get misclassified. They’re going to mix direct and indirect costs and it’s going to distort the financial analysis and decision making.

Pat: Okay, so how do you think AI can help organizations better manage the expense structure in their chart of accounts?

Anthony Peltier: Yeah, AI can help a lot in this regard. It can automate the classification of the expenses. It’ll reduce manual errors, and it’ll increase accuracy, so it can suggest optimizations by identifying those redundant categories and proposing consolidations, also detecting, you know, unusual spending patterns that might indicate errors or fraud and even it can extract data from invoices like hyperbots does and other documents enhancing accuracy reducing the workload for the finance team. So overall AI can provide dynamic, continuous learning capabilities that are going to adapt to the evolving needs of the organization.

Pat: So could you give me a specific example of how AI might be used in the practice to optimize the expense structure in a company?

Anthony Peltier: Yeah. A retail company could use AI to automatically categorize expenses related to marketing. It can analyze the invoice descriptions and the vendor names right? Then those AI algorithms can learn from historical data to distinguish between different types of marketing expenses, such as digital advertising versus print, and that will allow for a more accurate categorization. That’ll also help create a more precise chart of accounts, and it can alert management to any unusual spending patterns, such as a sudden spike in a particular category.

Pat: Right, that makes sense. So what steps should an organization take to integrate AI effectively into their expense management process?

Anthony Peltier: Well, it’s gonna come down to the starting point, making sure their data is clean and well structured. So AI tools, it’s the common saying, garbage in garbage out, right? So if you want quality data to function effectively then they can define specific areas where the AI can add value such as expense, classification, or fraud detection. You want to choose the right AI tools that align with their needs and integrate them with the existing financial systems. Finally, AI is a continuous learning machine learning. So ongoing training and adjustment are essential to refine those algorithms over time and ensure they continue to meet the organization’s requirements.

Pat: Right. So final question looking ahead, how do you see the role of AI evolving in the context of managing expenses and the chart of accounts?

Anthony Peltier: Yeah, I see it becoming more proactive and predictive. Instead of just purely automating tasks which are valuable. I see AI providing strategic insights, identifying cost savings, and opportunities, and predicting future expenses based on trends. It can also play a role in enhancing collaboration across departments by providing real-time, data and analysis and then that should enhance faster decision-making. So as these tools continue to evolve their capabilities are going to expand, and that should offer more comprehensive solutions to complex financial challenges. I think overall finance teams need to embrace these tools and see how it’s gonna make their life easier and allow them to have a more positive impact on the organization as a whole.

Pat: I think I very much agree. Thank you so much, Anthony, for sharing these insights. Structuring the expenses in the chart of accounts and the integration of AI can bring significant benefits to an organization across all industries.

Anthony Peltier: Yeah, thanks for having me. I look forward to more companies adopting AI and helping with their chart of accounts.

Pat: Perfect. Thank you so much, Anthony.

AI, friend or foe to finance?

Find out interesting insights with Mike Vaishnav, CFO & Strategic Advisor

Moderated by Emily, Digital Transformation Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Emily: Hi everyone. Good morning, good afternoon, good evening, depending on where you are. I’m Emily, a digital transformation consultant at Hyperbot Systems, and I’m very pleased to have Mike Vaishnav on the call with me. Mike is a CFO, consultant, and strategic advisor to various privately-held organizations. Before we get started on our discussion on how AI can be a friend rather than a foe to companies, Mike, could you tell us a little more about yourself?

Mike Vaishnav: Of course, thank you, Emily. I’ve been working in Silicon Valley for close to 30 years in various roles, ranging from controllership to FP&A, treasury, tax, significant M&A transactions, and process improvement system implementations. I’ve worked with companies of different sizes, from $60 million to $22 billion. In my last two roles as a CFO, I also managed HR, legal, and IT functions. So, that’s my overall background. Let’s focus on our topic rather than my background.

Emily: Thank you so much for the introduction, Mike. Today’s discussion will cover three broad categories: technology evolution in finance, the perceived threats of AI, and the benefits of AI. Starting with technology evolution, Mike, as you mentioned, you’ve spearheaded different finance functions in various organizations of varying sizes. Would you like to briefly share your key experiences?

Mike Vaishnav: Of course. I’ve seen technology evolve from mainframe computers in the early ’90s to the latest cloud-based technology. The speed and analysis of data have changed significantly. Automation and process improvements have been tremendous. We’re now entering a stage where AI can further evolve technology, especially in the finance industry.

Emily: You’ve been part of different waves of technology in finance, from manual bookkeeping to advanced ERP systems. What technological evolution have you seen over the years?

Mike Vaishnav: Automation has progressed from manual processes to cloud-based systems. Adding AI and other solutions to existing ERP systems can automate processes and make finance functions more efficient and effective.

Emily: These days, there’s a lot of buzz around AI. How do you see AI affecting the finance function?

Mike Vaishnav: AI can significantly enhance the finance function. AI is essentially human intelligence on a computer, helping finance take the next step. AI can gather and analyze large amounts of data, complementing human efforts. It can provide real-time, accurate data, improving decision-making and operational efficiency. AI can help finance executives focus on detailed analysis to improve profitability and efficiency.

Emily: Thank you, Mike. In the next part, we will discuss the potential threats of AI.

Emily: Welcome back, Mike. Here, we’ll talk about the threats of AI. AI is seen as a threat by some and a friend to others. Why are the perceptions so different?

Mike Vaishnav: People see AI as a threat mainly due to fears of job losses, data security, and privacy issues. There’s also a concern about people becoming too reliant on AI and potential biases in data. Since AI is still evolving, these perceptions persist.

Emily: Is the perception of threat real? What can companies do to change this perception?

Mike Vaishnav: The threat isn’t entirely real. While some routine jobs may be impacted, AI will create opportunities for more analytical roles. Companies need to educate their employees about AI, showing that it can complement human intelligence rather than replace it. People doing routine jobs can be redeployed to learn new skills.

Emily: We just spoke about job security. How real is this threat, or do you see it as an opportunity?

Mike Vaishnav: I see it more as an opportunity. While some entry-level positions may be affected, AI will create chances for employees to learn new skills and take on more analytical roles. The perceived threat can be mitigated through proper education and redeployment of resources.

Emily: Another threat you mentioned is data security. How real is it, and what can be done to mitigate it?

Mike Vaishnav: Data security is a real concern, but it has become more manageable with sophisticated AI systems. Ensuring data privacy and security involves everyone interacting with the data, not just the data administrators. Companies need to maintain high ethics, integrity, and trust in data handling to mitigate this threat.

Emily: That’s quite concerning for companies considering AI-driven processes. Thank you for your inputs, Mike. In the next part, we will cover the benefits of AI.

Emily: Welcome back, Mike. In the previous sections, we discussed the evolution of technology in finance and the threats posed by AI. Now, let’s explore the benefits of AI. Can you share some examples where AI simplifies the life of finance professionals?

Mike Vaishnav: AI can collect data, assist in decision-making, eliminate human error, simplify complex information, and reduce costs. It provides real-time data for analysis, making the finance function more efficient. AI helps finance professionals by automating data collection and analysis, saving time, and improving accuracy.

Emily: What skills should finance professionals acquire to take advantage of AI technology?

Mike Vaishnav: Finance professionals don’t need specific new skills because they are generally system-savvy. The key is to be open-minded and understand how to interpret and use AI-generated data. Trust in AI is built on understanding how data is collected and algorithms are written.

Emily: Can AI be a trusted friend, or should you always keep a watch on it? Can you give an example where AI can be fully trusted and another where its output must be reviewed?

Mike Vaishnav: AI can be a trusted friend for finance professionals if the data collection and algorithms are accurate. For instance, AI can reliably process and analyze large datasets. However, for complex decision-making, it’s essential to review AI outputs to ensure accuracy and relevance. Trust in AI comes with proper data handling and algorithm design, but human oversight remains crucial.

Emily: Thank you so much, Mike, for the insightful discussion. I’m sure this will provide our audience with clarity on embracing AI in their finance processes while avoiding potential threats.

Mike Vaishnav: Absolutely, thank you so much. It was a great discussion.