Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi, everyone. This is Emily, and I’m a digital transformation consultant with hyperbots. Really pleased to have Dave Sackett on the call with me. Who is the VP of finance at Persimmon Technologies. Thank you so much for joining us, Dave.
Dave Sackett: Yeah. Thanks, Emily.
Emily: So, Dave, the topic that we’d be discussing today is AI-driven sales tax verification strategy. And I’d like to kick things off by asking, how exactly does hyperbots AI-driven sales tax verification strategy ensure compliance with varying tax laws?
Dave Sackett: Sure. Hyperbots’ AI strategy automatically verifies sales tax rates by extracting the relevant data from invoices, purchase orders, and such as origin and destination addresses. These details are cross-referenced against local, state, and federal dictionaries to make sure compliance is maintained. For example, if a company in New York buys goods from a vendor in California, the AI pulls California as the origin, New York as the destination, allowing it to accurately assess sales tax based on these locations.
Emily: Understood. So can you also explain how hyperbots automatically categorize line items on invoices for accurate tax treatment?
Dave Sackett: It uses NLP and machine learning. Hyperbots categorizes each line item on an invoice into relevant tax categories. For example, an invoice with software licenses, computer hardware, and consulting services would be categorized as digital goods, physical goods, and services respectively. Each category may have a different tax requirement or exemption ensuring applied tax is accurate based on the item types.
Emily: Got it. And how does Hyperbots tracking of purchases by origin and destination exactly add value to sales tax verification?
Dave Sackett: Tracking purchases by origin and destination allows hyperbots to monitor total purchases over time for given locations, which is crucial for jurisdictions with specific thresholds. For example, if California requires tax to be collected only after total purchases exceed a certain threshold, hyperbots can identify when this threshold is met and apply the correct tax treatment automatically, preventing overpayment, noncompliance, and saving time.
Emily: Understood. Dave, can you also explain to the viewers how hyperbots reference sales tax dictionaries to verify the accuracy of applied taxes?
Dave Sackett: Yes, hyperbots leverage comprehensive sales tax dictionaries that can be accessed by AI, which include state, local, and product-specific sales tax rates. For example, if a vendor applies a 7.5% tax on a transaction where 8% is the correct rate, the system will flag this discrepancy by comparing it against the updated dictionary for that state. This ensures that the tax on the invoice is precisely aligned with local regulations and helps the accountant understand where that gap is in compliance and make the correct move.
Emily: Got it. Do you mind walking us through a specific example of how hyperbots would handle a tax exemption on a service item?
Dave Sackett: Sure. Let’s suppose an invoice includes software as a service or SaaS provided by a California vendor to a company in New York. Hyperbots identifies the SaaS as a category potentially exempt from New York sales tax by referencing New York’s tax guidelines. The system flags any incorrectly applied tax, recommending its removal to maintain compliance and avoid unnecessary costs, and again, to save time.
Emily: Got it. How are discrepancies in applied sales tax rates handled by hyperbots?
Dave Sackett: Hyperbots flags discrepancies automatically. If the AI detects that the rate applied by a vendor differs from what’s listed in the tax dictionary, it sets it aside and alerts the finance team, suggesting the correct rate. For example, if a vendor charges 9% tax when 8.875% is applicable, hyperbots notes that discrepancy, enabling quick correction before payment is made, which is far more difficult to reverse. This also ensures a clear audit trail, saving time.
Emily: Understood. Just a couple more questions, Dave. How does hyperbots ensure that its tax dictionary remains accurate and up to date?
Dave Sackett: Hyperbots regularly updates its tax dictionaries with the latest rates and regulations at all jurisdiction levels. This is especially important because tax laws are dynamic and change all the time. By integrating periodic updates, hyperbots guarantee that they apply the most accurate tax information, whether it’s for a physical good, a digital good, or a service.
Emily: Got it. And how exactly does Hyperbot’s AI-powered system operate autonomously to identify and correct tax application errors?
Dave Sackett: Hyperbots runs systems without manual intervention. It’s automatic, analyzing and validating tax applications as invoices and POs are processed. For example, if an invoice includes an incorrect tax on consulting services due to location-based exemptions, hyperbots automatically flag that mistake, providing recommendations. This ensures real-time accuracy without a heavy workload for the finance team.
Emily: All right. Do you mind giving an example of how hyperbots handle multi-state or multi-jurisdiction tax complexities?
Dave Sackett: Sure. Hyperbots handle multi-jurisdiction complexities by analyzing both the origin and destination information and referencing those state-specific tax laws. For example, if a company in New York purchases from a vendor in Texas, hyperbots considers both Texas and New York’s state laws. If New York mandates a different tax treatment than Texas, hyperbots applies the appropriate rate based on the destination-specific requirements, ensuring compliance.
Emily: Got it. And just one last question, Dave. What would you say are the primary benefits of using Hyperbot’s AI-driven approach to sales tax verification?
Dave Sackett: Hyperbots offer several benefits in this area. It enables compliance by applying accurate jurisdiction-specific sales tax rates, reduces manual intervention and potential for human errors, and saves time by automating these complex tasks. For example, the AI-driven approach identifies and corrects tax errors autonomously, freeing up resources in your finance department for more strategic activities. By tracking cumulative purchases, hyperbots can adjust tax treatment based on purchase thresholds, which is particularly beneficial for organizations with high transaction volumes across multiple locations.
Emily: Got it. Thank you so much, Dave, for talking to us about AI-driven sales tax verification strategy, especially in regards to hyperbots and how the various nuances are solved. It’s really insightful. Thank you so much.
Dave Sackett: Yeah. Thanks, Emily.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi, everyone! This is Emily, and I’m a digital transformation consultant with Hyperbots, Inc. I’m pleased to have Dave on the call with me. Dave Sackett is the VP of Finance at Persimmon Technology, thank you so much for joining us today, Dave.
Dave Sackett: Yeah, thank you, Emily.
Emily: So, Dave, the topic we’ll be discussing today is debit or credit entry postings and the different nuances across ERP systems like QuickBooks, SAP S/4HANA, and NetSuite. To keep it brief, my first question is, how do the postings of debit and credit entries differ across these ERPs specifically when a vendor invoice is posted?
Dave Sackett: Okay, to answer that, I’ll categorize by small, medium, and large data requirements. QuickBooks might have the simplest and shortest journal entries in terms of debits and credits, followed by NetSuite, then SAP. SAP has a lot more complexity and required fields to post a transaction, so it’s a bigger effort in SAP compared to QuickBooks. NetSuite falls somewhere in between.
Emily: Understood. Thank you, Dave. Moving on, what are the main differences you’ve seen in tax handling among QuickBooks, SAP, and NetSuite when posting vendor invoices?
Dave Sackett: Similar to the number of fields each system requires, when it comes to tax compliance, there are tools that each ERP can leverage for tax assistance. QuickBooks, which I’m currently using, is quite simple. NetSuite offers more functionality and good APIs for tax services, while SAP is the most complex and thorough ERP, especially in handling international taxes.
Emily: Understood. So, Dave, what unique features does SAP S/4HANA offer for handling vendor invoices that differentiate it from QuickBooks and NetSuite?
Dave Sackett: With SAP, there’s a far greater opportunity for granularity in data tracking. You can track by vendor, region, or almost any business-specific criteria, which allows for detailed outbound reporting. It’s very complex, with many required fields, which support robust data tracking and reporting. So, if in-depth tracking is important, SAP might be the best choice.
Emily: Understood. Since you mentioned you’re currently using QuickBooks, what limitations does QuickBooks have in handling complex vendor invoices compared to SAP or NetSuite?
Dave Sackett: QuickBooks is generally geared toward small businesses, treating data somewhat like a checkbook. It doesn’t offer the depth of analytics found in NetSuite or SAP, which are better at tracking vendor assignments, purchase history, and FP&A analytics. So, compared to the other two, QuickBooks is limited in data gathering and analysis.
Emily: Understood. Also, Dave, how can AI-based external tools help improve the accuracy and automation of GL posting?
Dave Sackett: That’s an excellent question. Each ERP system can support AI integration, and the more complex the system, the greater value you gain from having API connections to third-party solutions for enhanced accuracy and automation.
Emily: Understood. Thank you so much, Dave, for discussing the differences between ERP solutions and explaining how AI can enhance them. It was great having you, and thank you for joining us today.
Dave Sackett: Yeah, thanks, Emily.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi, everyone. This is Emily, and I’m a digital transformation consultant at Hyperbots, Inc. I’m pleased to have Shaun again with us. He is the stock compliance manager at Norfolk Southern, so great to have you on, Sean.
Shaun Walker: Absolutely, thanks for having me.
Emily: Of course. So the topic that we’d be discussing today, Shaun, is ensuring accurate GL coding of expenses. I’d like to begin by asking why it’s so important to ensure that expenses are coded correctly under the right GL codes, and what are the main challenges that companies face in this area.
Shaun Walker: I’d say it’s essential for financial reporting, budgeting, and compliance. It ensures that the financial statements reflect the true state of the company’s finances, which supports better decision-making, compliance, and alignment with regulatory bodies. The main challenge is the volume of expenses, especially in large organizations. There are so many expenses and transactions to go through, which means there’s a risk of human error.
Emily: Got it. Often, what I’ve seen is that sampling is used to check the accuracy of expense coding. Can you explain how sampling works and give examples of its advantages and limitations?
Shaun Walker: Absolutely. As I mentioned, there’s a large amount of transactions, so sampling drills down to the important ones. For example, a company might use a random sampling approach, looking at 5% of all the expense entries rather than all of them. One advantage of the sampling approach is that it’s more cost-effective and time-efficient. However, a limitation is that it may not detect systematic errors or fraud if those errors are not present in the specific transactions sampled.
Emily: Got it, understood. Also, Shaun, how do automation or automation tools in general help in ensuring correct GL coding? What are some examples of their use in companies?
Shaun Walker: Absolutely. Automation tools have predefined rules for certain expense transactions, which reduces the need for the manual process of coding and the associated errors. For example, an ERP system like SAP might automatically categorize all expenses with the heading “hotel” as travel expenses. However, some limitations can be too rigid. For instance, if there’s the word “event” in expense management, some tools might automatically categorize it as marketing, even if it was an internal training session that should have been coded differently.
Emily: Got it. Shaun, what role can AI play in improving the accuracy of expense coding? Can you provide examples of how this is implemented in practice?
Shaun Walker: One technique that AI uses is NLP, which stands for natural language processing. It analyzes transaction descriptions and suggests the most appropriate GL codes. For example, if the description is “client dinner at a restaurant,” AI would correctly code it to entertainment and expenses rather than just food and beverages. AI can also detect anomalies in real-time, helping to catch errors early and maintain accurate records.
Emily: Understood. Just out of curiosity, how can AI help in auditing human-coded expenses, and what are some examples of its effectiveness?
Shaun Walker: AI can learn from historical data to identify what correct coding should look like, comparing it to current entries. It can also flag discrepancies or unusual patterns.
Emily: Got it. Just to round things up, Shaun, one last question: are there any additional benefits of using AI over traditional sampling methods for auditing expenses?
Shaun Walker: The main thing is that humans often do it periodically, like once a month or once a quarter, whereas AI can perform continuous monitoring. AI can detect anomalies immediately, rather than waiting for periodic reviews. It can also analyze a much larger set of data, covering 100% of the expenses, whereas sampling might only cover 5% or less.
Emily: Understood. Got it. Thank you so much, Shaun, for talking to us about GL coding and how accuracy can be maintained. It was great having you, and the discussion was truly insightful. Thank you so much.
Shaun Walker: Absolutely, thanks for having me.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hey, everyone, this is Emily, and I’m a digital transformation consultant at hyperbots. I’m pleased to have John Silverstein on the call with me, who is the VP of FP&A at Extreme Reach. So thank you so much for joining us, John.
John Silverstein: No problem. Thank you for having me.
Emily: So, John, the topic that we’d be discussing today is managing and scaling the chart of accounts in Quickbooks online. The 1st thing that I ask you is, what are some of the key considerations when setting up a chart of accounts in Quickbooks online for a small business?
John Silverstein: Yeah, when setting up the chart of accounts for QuickBooks online, it’s critical to be simple and logical. But you also need to look forward a little bit to the growth of your business. Look at your business model and the industry standards because you want to be aligned with how that’s going to be, and how you can look at your financials compared to either other companies, or if you eventually go for a sale or you acquire another business. It’s good to be in line with that. So assets, liabilities, equity, revenue, expenses. You want to make sure that those are broken out into the proper categories for that industry and that it also meets your regulations. You wanna enable the account number early. This is a big mistake. I see a lot in the smaller businesses that they just name, and their names are all over the place, and they change over time. So it’s hard to trace and understand the data. As it moves. And so it’s if you enable the account number, and it’s easier to integrate into work with other systems. As you and your business grow. So it’s important to implement that as early as possible. It. It creates a little bit of extra work, but it’s not that much.
Emily: Got it. So, John, as a business grows, how can it maintain an effective chart of accounts in QuickBooks online without having to move to a more complex ERP system?
John Silverstein: Yeah, it’s surprising how flexible and how good and what rigor and things you can get into QuickBooks online if you enable the right things. So it’s important to work with your accountants, and if you don’t have one internal, but if you are the accountant and things that you look at and leverage. You know the platform of QuickBooks like classes, locations, and the other dimensions that are there. So you don’t have to overcomplicate. I see a lot of companies that don’t enable classes until it’s too late. And again, it gets really hard. And you’ve overcomplicated your chart of accounts to try to do something that really could have been solved by classes or locations and things like that. So it’s also important to try to understand what those dimensions are. It goes back to the 1st question about industry and things that you do. So segment the data. You have to think about how you’re gonna measure and monitor the business review and monitor and make sure that that chart of accounts is always in line with how you’re gonna do it. If there are any redundant accounts close or inactivate them, make sure they’re properly categorized. Use sub-accounts, use a hierarchy that helps out. And you can go pretty far with QuickBooks online. I’ve been in companies that have made it to that 100 million dollar mark on QuickBooks. So it. It does scale more than what many would expect.
Emily: Got it. Got it. So any common mistake, John, that you’d have seen small businesses make when setting up their chart of accounts in Quickbooks online?
John Silverstein: Yeah, one of the things is there, the biggest thing. And this goes across the board. Any system has too many accounts, and they don’t enable the other dimensions and things. So then you try to use an account for everything, account per vendor, account per customer, and things like that Just remember that there’s reporting and things that can get you there without having to break it out in your chart of accounts that overcomplicate it, and then there are more mistakes, and it causes a lot of confusion as you bring in new people Or you might have to have another accountant or other people look at it, or your management looks at it and things, and it gets more confusing, and it even makes it harder for audit as well, and it creates a lot of clutter.
Emily: Got it, got it. So how can businesses create a flexible and scalable chart of account structure that supports growth?
John Silverstein: Yeah. The best way to do this is to make sure that you have a numbering system that allows for the expansion. And to do, add-ons and things, and to make sure that you’re you, you can change the names without affecting other reporting and things you don’t want to leave gaps between you. You need to leave the gaps in between the account numbers, so you can add accounts and easily use sub-accounts to track more detailed information. So you can have the details when you need to answer certain questions and have it at your fingertips. Make sure that you have that available, and then you plan on growing. So you have those spaces and things, and then this approach will allow you to keep it simple but also have detailed financial analysis and reporting.
Emily: Okay, why, exactly, is it important for different businesses to regularly review and optimize their chart of accounts in QuickBooks online?
John Silverstein: Yeah, it’s critical to ensure that the accounts reflect how your business is today. If you’re selling new things, maybe your models change. Maybe you were initially transactional or your pricing wasn’t, it was more value pricing and things like that. Or, yeah, when you’re smaller you might have more to give. But as you grow and things, it’s critical that you have the information. There, the accounts reflect your current business operations. What does your cost structure look like, are you in? Is everything in the house? Are you doing things yourself? Are you outsourcing those types of things? You must have that at your fingertips. You also don’t want to have to roll up many redundant accounts or account hierarchies even to try to get an answer in your financials, so make sure that you continue to look at it. So you know which accounts to use when you’re answering the questions that you have on your finances, and it also alleviates the errors of things going to too many different places. And then you have to try to figure out how to map it all back together.
Emily A: Got it. Got it. So, John, can you explain the role of AI in maintaining charts of accounts, integrity, and QuickBooks online?
John Silverstein: Yeah. So AI, and this is something that’s gonna have a significant impact. Going forward is on the chart of accounts because it can keep that integrity, it can also have the knowledge to recommend and detect errors between how? What’s posting? To which accounts, and as long? It can make sure that the definitions are consistent on what’s going on, and it can recommend even when you should create a new account and break it out. It could also tell you that you have duplicates or missing entries. So AI algorithms, it’ll enforce consistency in accounting. It’ll make it easier to do analysis, it’ll make it easier to be compliant. You’ll have clarity on where things should be booked, and why. It can also have predictive analytics to suggest. Hey, you need a new account. You need a subaccount. This is a hierarchy and to go into the patterns of transaction history to recommend that. You can also have real-time data validation. So your book closes and things will be faster and more accurate. This is critical as you go through to make sure you have consistent financial data.
Emily: Got it so little bit about ERP migration, John. So how can I help a business that is considering migrating from Quickbooks online to, let’s say, a more complex ERP system like NetSuite?
John Silverstein: Yeah. So if AI could help you start getting there, the more you’re aligned with how the bigger Erps work and you have classes already set up. You have the things set up in QuickBooks online that are more aligned with NetSuite. It’s easier to migrate, and your process is if and flows. If they’re proper and doing the same things as some of these other Erps, it may make it better data, integrity, and continuity, and easier to go through the conversion without having to do a lot of data cleanup it also can automate your reconciliations and validations through the migration to make sure that everything’s in sync it’ll save a lot of time. Reduce the errors. Maybe the Erps will get a little bit worse. 3. Letter acronym. It will become a little more doable and foreseeable to go into an ERP that makes more sense for your business without a huge lift in cost and time.
Emily: Understood. And just one last question, John. So what are some best practices for using QuickBooks online as a growing business? And you know, when should a business consider moving to a more robust or more? Do you know the nuance?
John Silverstein: Yeah. So the one thing I would say is that you need to try to get as much structure as possible in QuickBooks with numbering systems. Reviewing the chart of accounts. A lot of this structure and things and controls in QuickBooks tend to be manual in the process where you have to do manual reviews or have AI review it. Now you have that option. But you didn’t in the past be a business should consider moving to a more robust ERP when they’re getting into more complex workflows and complexities like consolidations and things. If you have multi-entity management QuickBooks, don’t really. It’s a separate entity and roll-ups are hard and complex. They’re getting a little bit better, but it doesn’t. It’s not made for that when you get into some of the currency and other things that you might face as a larger enterprise. Quickbooks aren’t made for that, or designed for that. So if you need more sophisticated reporting, you need to have more data too. It’s probably better to move on to a tool like Netsuite.
Emily: Got it. Got it. Thank you so much, John, for being here and talking to us about managing and scaling the chart of accounts in Quickbooks online. It was great having you. And it was a fruitful discussion. So thank you.
John Silverstein: No problem.
Moderated by Emily Digital Transformation Consultant at Hyperbots
Emily: Hey, everyone, this is Emily, and I am a digital transformation consultant at hyperbots today on the call with me. I’m extremely happy to have Anna. Anna Tiomina is the founder of Blend to Balance Llc, with over a decade of experience in senior finance roles. Anna is also the leader of AI innovators in finance and beyond a community dedicated to merging tech innovations with traditional finance.
Emily: Really happy to have you, Anna.
Anna: Thanks for having me.
Emily: Excited to hear your insights on how AI is revolutionizing the detection of fraud and anomalies in vendor payment. So let’s dive right in as a CFO. Anna, how do you see the importance of detecting fraud and anomalies in vendor payments?
Anna: Yeah. So detecting fraud and ensuring compliance is one of the key functions of financial operations. In recent years we see much more fraud in this area. So it is challenging for CFOs to keep up with the technology and to be always ready to react to the new ways. The fraud actors are trying to reach the companies. So it is a really critical and very challenging task nowadays.
Emily: Correct. So, Anna, what are some of the most common types of fraud and anomalies you’ve seen or heard in vendor payments.
Anna: Yeah, so there are simple ones like replacing the bank account number on an invoice with a fraudulent bank account number. It can be as simple as sending the invoice that was never approved for payment, and the service or product was never received. It can also be inflated invoices, either through the pricing or through the quantities of products or services on the invoices or for example, demanding a payment before the product or service was delivered or received. So these are the most common risks.Listed vendors, and sometimes, if the company has some processes in place, the requester. The payment requester splits the invoice to get to a lower approval level. Sometimes it is a fraud, and sometimes it’s just a mistake or an attempt to speed things up so it can really vary. But each of these events represents a risk.The funds that the company is mistakenly or fraudulent to send cannot be revoked in many cases. So I mean, that’s a huge thing for the company to be able to keep things in order in this space.
Emily: And how can these frauds and anomalies be prevented with traditional methods per se?
Anna: Well, so what companies do they implement internal controls? They implement processes. They implement things like freeway mention. Right? So you have a PO that has to be approved. Then you have an invoice that has to be made to the Po, and then the invoice has to be approved on a different approval. Flow? Some companies don’t have POS. They have things like for ice principle, for example, like no payment, gets released until at least 2 people take a look at that. So you need 2 approvers on any payment going out. Also, companies try to make sure that the processes and these rules are followed. So there are things like internal audits. Kind of like processes that make sure that the forest principle is followed. For example, right, or the person that is sending the fund does the verification of the vendor before the funds are being sent.Still, all of this is very time consuming. This is prone to human error. This involves a lot of people in the company, and it doesn’t guarantee that the fraud or mistake doesn’t happen.
Emily: Got it. Got it So, Anna, how does AI enhance the detection of these frauds and anomalies compared to the traditional methods.
Anna: Oh, yeah. So the way I look at that is that AI is another pair of hands or another pair of eyes on your team that doesn’t get biased. That doesn’t get tired. That doesn’t cheat right? So this is another level of control that really helps to correct the bias that your team might have or spot the patterns that can be missed by humans. So AI, for example, can detect subtle differences in the invoices or flag, the duplicated invoices or spot the difference between the original purchase order and the invoice or compare the current, invoice with the historical data, and make sure that the mistakes that happened in the past don’t go forward into the future. So, having an AI complement, your team is a really really helpful tool.
Emily: Got it understood. And can you please help me understand, you know how AI can help prevent duplicate payments and overcharging and vendor invoices?
Anna: Yeah. So that’s a great question, because duplicate payments are a little bit hard to catch, because the invoice looks correct, right? And unless you have really really good controls in place. You might miss that. This is the duplicate invoice, and paid twice, either by mistake or as a result of fraud. So AI can compare the invoices and identify these duplicated invoices better than the humans can do. Also, AI is really great at comparing the invoice against the PO. If the company has a PO or the contract, or the sow, making sure that the vendor hasn’t overcharged the company, and that the agreed terms in the original documents are followed. This can be a very time consuming task for people to find the correct document to find the right line on this document, etc. So AI really shines here, and it saves a lot of time and effort for the human team.
Emily: That’s pretty incredible. So, Anna, what role does AI play in ensuring that payments are made only after you know the goods or services are received?
Anna: Well in a classic case, right, You would have a 3 way match process, and the Requester would have to push some button in your software that you’re using to confirm that the goods for services have been received sometimes. This process doesn’t work. Sometimes the requester wants to really push the payment forward to enhance their relationship with the vendor, or like for other reasons. So AI can really track the history of relationship with the vendor and also make sure that I mean, if there was a certain amount of time where the service was expected to be delivered, that these all timelines are followed. So again, this is another level of control, another pair of eyes that can be really helpful.
Emily: Got it and how does AI help in detecting payments made to unlisted or fraudulent vendors per se?
Anna: Well, yeah, that’s an excellent question, because like, usually, the companies have some controls in place to make sure that the payment doesn’t go to an unlisted vendor, and you will have to add each new vendor manually. But what sometimes happens is that you have, for example, an improved vendor. But then the invoice kind of duplicates the name, but that has other payment details. And this is how you send the funds to the wrong account. Number right, so AI can help verify that the original payment details are corresponding to what you received from your vendor when the vendor was listed and approved. Also there are public databases of fraudulent vendors. So AI is great to, you know, to be tasked with monitoring these databases and flagging.You know the fact that you might be paid to someone who is not on your approved vendor database. So again, because there is such a rise of fraud nowadays it is very difficult to keep track of everything that’s going on. So having technology as a compliment, your team is really really helpful and increases the team efficiency, too.
Emily: Got it and just to summarize everything, Anna, you know, looking ahead, how do you see the role of AI evolving in the area of vendor payment management?
Anna: Yeah. So I think that now we see just the beginning of AI complementing the Ap teams, I think that it’s gonna be used more and more, because this is just so efficient and handy, and also honestly, because the fraud actors are using a lot of AI. It is impossible to really like, offset this effort without having technology on your side too. So it’s like a race of technologies in a way. And I think that at some point it’s gonna be like a must have for the companies to have some level of AI in internal fraud. Detection process.Not immediately. But we are getting there.
Emily: Definitely. Thank you so much, Anna, for joining us today and talking to us about, you know such an important topic. Thank you for the valuable insight. It’s clear that AI is transforming the way we manage and protect our financial operations, and how, especially in the realm of vendor payments. So thank you once again.
Anna: Thank you for having me.
Moderated by Niharika Sharma, Head of Marketing at Hyperbots.
Niharika: Hi everyone, this is Niharika and I take care of marketing at Hyprbots. Today we have with us Anna Tiomina, who’s a CFO with Blend2Balance currently and has been operating in the finance domain for over two decades. Hi Anna, how are you doing?
Anna: Good morning, thanks for having me. Doing great.
Niharika: Lovely to have you, Anna. We are so excited to have you here. Before we begin with the topic, which is ROI on AI-led automation initiatives, it would be great if you could take us through your journey, your roles, and past experiences as a CFO, and how you’ve seen the finance domain evolve over the years.
Anna: Okay, so I’ve been in finance throughout all of my career, which is almost two decades by now, which is unbelievable. I’ve progressed from controller to senior controller to CFO. I’ve served as a CFO for more than 10 years, and I’ve worked in different companies and smaller companies in big multinational companies. So I think I have a pretty good understanding of the financial environment and the challenges that companies face in this sphere. My biggest goal is always to ensure the financial health of the organization but also to drive all the finance-related processes because finance is responsible for a lot of important things, you can’t drop any of them. In a way, a person who guards their organization from getting into trouble.
Niharika: Thank you so much for explaining that, Anna. I’m sure this conversation is going to be very valuable for all the listeners and viewers that we have. Your experience in the finance domain is going to enlighten us with what kind of decisions one should make when considering introducing AI in their finance processes. To begin with, I would love to understand from your vantage point, how you perceive the current landscape of AI-led automation in the finance industry.
Anna: Yeah, I might say this is a very exciting process right now. A lot of new things are happening in the area. It looks like AI automation would help finance professionals streamline processes, reduce errors, provide insight, and help us avoid manual tasks, which I think is the most unpleasant part of what we people in finance do. From the perspective of a finance executive, I see that this topic is on the agenda for many CFOs and organizations. It’s important to stay on top of this process for everyone who wants to progress in their careers and lead their organizations to success.
Niharika: Lovely. I’m sure this is going to be very insightful for all of us. From your understanding, what sort of investment do you anticipate when someone is thinking of implementing AI?
Anna: The first thing to mention is I’ve read research very recently, and it shows that finance is not among the top areas for AI adoption and implementation in industries. Companies usually start in areas such as customer relations, marketing, sales, software development, and R&D. Finance is somewhere at the end of this list. My understanding is that because finance is a very risk-averse area, this is not the first place where you would put innovation. Many companies are not ready for AI adoption because the data is not integrated. There is no single source of truth in many data points, and the processes are not smooth enough to implement automation. Implementing AI in finance is a substantial investment. We finance people always think about ROI, so when starting any effort, we think about what we will get out of that. When we talk about investment in implementing AI, there are three main areas: financial investment, time, and effort.
Niharika: Absolutely.
Anna: The most obvious is the financial investment: the cost of software, the cost of maintaining the software going forward. The second is time because there is not much experience in this area, and sometimes it’s hard to understand if it would take a lot of time or not to implement and adopt any technology. Let’s not forget about effort. I’ve always worked with very lean organizations where every additional process stretches the current team. I’m always very mindful of what I put on the shoulders of my team. So the three main areas of investment time, effort, and money are what it gets to if we talk about investment.
Niharika: Absolutely. When it comes to transactional data, value associated with money, and ROI, it’s good to be very mindful of where you’re investing concerning AI. Of course, AI is created by humans and it was not 100% accurate when it was introduced. After having it tested and tried in various domains, the application of AI in finance sounds like a good decision. At the same time, it’s very opportunistic in terms of what it has to offer shortly.
Anna: Yeah, I mean, it sounds exciting, right, in the beginning. But with any new technology, you need to assess some kind of ROI when making this decision. If you’re presenting this as an offer to a board or management team, you also need to put some kind of calculation behind it. When we think about returns that companies can expect out of this whole process, it turns out it’s not that easy to quantify at this point. We can think about measurable KPIs like productivity increase, cycle time reduction, fewer errors, being able to do more with less, reducing manual input, and customer and employee satisfaction. But to quantify those, you really need some kind of experience and data based on previous implementations. We don’t have that now, so we can run some kind of very high-level calculation. At this point, I think it’s wiser to focus on long-term goals rather than pure mathematical ROI calculation.
Niharika: Absolutely. Agree with you on this, Anna. Let’s say an organization has already introduced AI in its finance section. What sort of insights derived from data-driven by AI can impact strategic decision-making within the finance function, as per you? What are your thoughts about it?
Anna: Well, in my career, I was always developing my time around data preparation and then data analysis and getting the insights from the data. It would be great to get rid of this first part and get straight to data analysis. It is amazing to see sometimes what you get out of working with data. Sometimes, you have one perception of what’s going on, but then you run the analysis and you see a different picture. So, of course, having the ability to process more data, having AI do all the operational work for you, and not spending too much time organizing tables or preparing the data, is amazing. I think it’s an amazing transformation going on in the industry. A lot of valuable insights can happen out of data-driven processes.
Niharika: Absolutely. As we evolve in introducing AI to different organizations, it will surely give us some impactful outcomes sooner or later. As we are almost approaching the end of this conversation, do you have any advice for CFOs or finance leaders considering AI-led automation initiatives to enhance their ROI?
Anna: Yes, I have some advice to share. First of all, this is very unusual advice from my side, but don’t focus on ROI in the first steps of implementation. Think more strategically and focus on long-term perspective rather than short-term. Second, invest in proper training for your team. Experiment a little bit with some smaller processes. Every finance organization has some ugly data processing tasks they hate, so try to start with these smaller parts and then move to bigger processes. Establish a robust data management framework because as soon as you move to AI and start to utilize more AI-driven functions, the quality of data becomes paramount. Make sure the data is structured, consistent, and has a single source of truth.
Niharika: Absolutely. You rightly mentioned that each one of us who interacts with AI in the finance domain should be very mindful of the decisions we are making. Thank you for that. I think these were some great insights that will surely help future CFOs. It was great having you on board and having this conversation. I had a lovely experience, and it was very insightful for me as well. I’m looking forward to having more sessions with you. Thank you so much.
Anna: Thank you for having me.
Finance and accounting (F&A) are critical to the operational efficiency and strategic decision-making of any business. The advent of artificial intelligence (AI) presents a transformative opportunity for these functions. This article analyzes manual, analytical, and strategic activities within these functions and determines the most optimal AI adoption roadmap.
The following table estimates the volume of manual, analytical, and strategic activities in these functions as high, medium, or low:
| Functions | Manual | Analytical | Strategic |
| Procure to Pay | High | Medium | Low |
| Order to Cash | High | Medium | Medium |
| Expense Management | High | Medium | Low |
| Tax and Compliance | Medium | High | High |
| Treasury | Medium | High | High |
| Financial Planning & Analysis | Low | High | High |
| Mergers & Acquisitions | Low | High | High |
The next-generation AI technologies are mature and can be applied well in Finance and Accounting with a significant financial impact. We recommend prioritizing the Procure-to-Pay, Order-to-Cash, and Expense Management functions for AI adoption.
AI techniques for interpreting unstructured data have advanced significantly in recent years. These techniques now permit what was previously considered human-level intelligence tasks.
Transformer-based frameworks allow for unstructured content understanding, language generation as well as predictive tasks. Large Language Models (LLMs) accelerate the ability of AI systems in language understanding, information retrieval, summarization, text generation, and conversational AI. Data-driven econometrics models for forecasting and trend analysis enable numeric and financial data analysis.
In finance automation, this is how these AI techniques can radically transform each of these tasks:
The structured and orderly nature of finance processes, underpinned by a robust ERP knowledge base, provides a solid foundation to leverage sophisticated machine learning and AI methodologies. Now is an opportune moment to invest in the adoption of AI-native strategies for a substantial positive business impact.
The P2P function involves numerous repetitive and manual activities where AI can significantly increase efficiency and reduce errors.
| AI Capabilities | Readiness |
| Uses machine learning and LLMs to achieve straight-through processing for 80% of invoices. This includes automated invoice extraction, understanding, validation, matching, GL coding, and posting | Short-term |
| Uses forecasting systems to automate accruals | Short-term |
| Uses predictive and prescriptive models for optimal vendor payment timings | Short-term |
| Uses advanced ML techniques to detect fraudulent and duplicate invoices | Short-term |
| Uses classification techniques to classify expenses for capitalization | Short-term |
| Uses AI models and tax dictionaries to verify the sales and other types of applicable taxes | Short-term |
| Builds company and F&A-specific conversational AI models to provide chatGPT-like analytics | Medium-term |
| Optimizes vendor selection using predictive analytics | Medium-term |
The O2C function is also highly manual and prone to AI automation.
| AI Capabilities | Readiness |
| Uses machine learning and text processing techniques to extract and validate purchase order information from customers PO documents and contracts and auto-uploads information into the ERP system, resulting in 95% plus automation | Short-term |
| Uses advanced AI techniques to generate customer invoices based on purchase orders, customer master, inventory, and shipment information to generate 100% First Time Right (FTR) invoice. | Short-term |
| Uses recommender systems to create a daily/weekly priority list of customers for collections. | Short-term |
| Uses dynamic models to enhance customer credit scoring | Short-term |
| Uses advanced data science techniques for cash management including discovery of discrepancies, over and under-payments | Short-term |
| Uses generative AI to automatically communicate with customers on invoices and payments, including follow-ups | Short-term |
| Uses generative AI for conversational analytics on O2C data | Medium-term |
Employee expense management continues to be tedious for employees and the finance teams. This process can make use of AI to achieve a very high degree of automation.
| AI Capabilities | Readiness |
| Uses image and text processing techniques to automatically extract information from receipts and bills, and validate and auto-create expense reports for employees. | Short-term |
| Uses machine learning techniques to verify expense reports against policies and proofs. | Short-term |
| Uses advanced ML techniques to detect fraudulent and duplicate expenses. | Short-term |
| Uses classification techniques to identify the correct GL code for each expense | Short-term |
| Uses generative AI to communicate and answer employee queries | Medium-term |
AI has a high potential to optimize and streamline the tax and compliance function.
| AI Capabilities | Readiness |
| Automates collection and validation of data required to file tax returns, ensuring higher accuracy and reduced human effort | Medium-term |
| Applies the correct withholding rates based on payer and recipient jurisdiction, reducing errors | Medium-term |
| Helps organize documentation related to taxation for audit purposes | Long-term |
| Tracks applicable sales and use taxes across jurisdictions, ensuring accurate application to transactions | Long-term |
| Uses generative AI to map financial statements to the latest reporting standards. Facilitates SOX compliance | Long-term |
AI can provide substantial value by automating routine activities and improving decision-making in treasury management.
| AI Capabilities | Readiness |
| Machine learning models analyze historical cash flow patterns to predict future cash needs. | Medium- term |
| Monitors cash balances across accounts and recommends the most efficient pooling techniques. | Medium- term |
| Compares fee structures across banks, helping to negotiate better terms. | Long-term |
| It uses advanced predictive models to assess market risks and helps optimize long-term portfolio allocation. It also recommends low-risk, high-return, short-term investment opportunities. | Long-term |
| Predicts currency fluctuations to help develop effective hedging strategies. Identifies forex arbitrage opportunities. | Long-term |
| Builds models to identify market and operational risks using various internal and external debts. | Long-term |
FP&A involves many analytical and strategic activities. AI can help improve decision-making for these activities.
| Automates the data extraction from structured and unstructured sources like documents and ERPs | Long-term |
| Analyzes the historical data to build predictive budgets and rolling forecasts | Long-term |
| Simulates scenarios and recommends outcomes | Long-term |
| Helps in variance analysis between planned and actual budgets | Long-term |
| Analyzes capital allocations and predicts ROI using historical data | Long-term |
| Predicts future cashflows based on historical trends | Long-term |
AI can play a significant role in M&A, improving efficiency and strategic decision-making.
| AI Capabilities | Readiness |
| Analyzes financial reports and news articles to assess potential targets. | Long-term |
| Builds sophisticated financial models using machine learning to provide a more accurate valuation of the target companies. | Long-term |
| Analyzes contracts and other financial statements for risks and liabilities. | Long-term |
| Identifies and predicts potential risks. | Long-term |
| Provides data-backed insights into potential negotiation points. | Long-term |
Now that we have analyzed the specific AI-based automation of the above finance functions, we can estimate the financial impact it can create.
Having evaluated the financial impact on all F&A functions, we can recommend the AI adoption roadmap.
The next-generation AI technologies are mature and can be applied well in Finance and Accounting with a significant financial impact. We recommend prioritizing the Procure-to-Pay, Order-to-Cash, and Expense Management functions for AI adoption.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hello, everyone. This is Emily, a digital transformation consultant at Hyperbot Systems, and on the call, I’m really glad to have Anna with me. Anna is the CFO at Blend2Balance. In today’s discussion, we’ll be talking about a CFO’s preparatory toolkit for the adoption of AI. But before we dive in, Anna, would you share a brief overview of your background and perhaps set the stage for our discussion?
Anna: Sure. I’ve dedicated my entire career to finance, and I’ve been a CFO for more than 10 years. I’ve worked in various companies and industries. I started in steel manufacturing, spent around five years in pharmaceuticals, and joined an IT services company about four years ago. So I have a very versatile background in terms of industries. I also provide strategic consulting for early-stage startups. Since 2022, there’s been a huge emphasis on AI in all areas, including finance. Many organizations struggle to find the right approach to this transformative technology. It’s a pleasure to be here and shed some light on this crucial topic.
Emily: That’s really amazing. Great to have you as well, Anna. Let’s start with the first question. What would you recommend as the initial action for CFOs venturing into AI adoption?
Anna: I don’t recommend jumping into AI implementation initially. It’s worth running an audit in three main areas: data infrastructure, team skills, and the status of existing processes. For data infrastructure, it’s important to evaluate sources, ensure a single source of truth, address discrepancies, and prepare the data before implementing AI tools. Team readiness is paramount. Some teams are flexible with new technology, while others need more preparation to understand how it works. Lastly, the state of existing processes is vital. Are they unified and documented? Automating chaos leads to automated chaos, which is not what we want.
Emily: Completely agree. Those are insightful points, Anna. Moving forward, what key objectives would you recommend CFOs include in their AI strategic roadmap for the finance department?
Anna: When preparing the strategic roadmap for AI implementation, CFOs should focus on quantifiable objectives such as improving accuracy in financial forecasting, reducing processing times, and enhancing compliance and fraud detection. Setting a goal to automate 30% of manual data entry tasks within a year could significantly boost efficiency and accuracy. As a CFO, I’m always looking at the return on investment. AI implementation in finance operations should also consider potential savings and scalability if the organization plans to grow. Additionally, the cost of mistakes in finance operations is significant. AI can minimize errors, prevent fraud, and save the organization money in the long run.
Emily: True and valuable insights indeed. Considering your experience, what challenges should CFOs anticipate when aligning AI initiatives with their overall business strategy?
Anna: From what I’ve seen, security is a top concern among CFOs. Not understanding the technology can make it scary to let it make crucial decisions. Addressing security is crucial to reducing friction and gaining agreement from the rest of the team. I also recommend not rushing implementation. Let stakeholders adjust, understand the technology, and recognize its benefits to avoid big mistakes. In the long run, AI is a great technology. However, there’s pressure from leadership to implement it quickly to stay competitive. Finding the right balance between preparation and implementation and getting a leadership agreement is key.
Emily: Got it. Completely agree. Thank you so much, Anna, for sharing your insights and expertise on these critical aspects of adopting AI in finance. Any final thoughts or key takeaways you’d like to leave with our audience?
Anna: For CFOs feeling a bit lost in this process, I encourage them to do some reading or attend webinars. There’s a lot of information available, and it doesn’t take long to understand how the technology works and its benefits. Don’t be scared. It’s exciting to see changes in this market since finance automation tools haven’t seen a revolution since the 1970s.
Emily: That’s some great advice. Thank you so much, Anna, for being here and speaking on a topic that’s buzzing everywhere. It was truly amazing having you here today.
Anna: My pleasure.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Today’s discussion is special as I have John with me. John is the CEO at LiveData LLC, and today we will talk about the journey of automation in the past versus now, especially in the finance and accounting segment. So, before we begin, John, do you mind telling us a little more about yourself?
John: Yeah, no problem. Thank you for having me. It’s a pleasure, and it’s a topic that I love to talk about how to automate. I’ve been involved in this field my entire career. It started with Excel sheets and figuring out how to make them work as simply as possible. As technology evolved, I was able to utilize it more. Now, running LiveData LLC, we help companies with finance automation and process improvements throughout their business.
Emily: Thank you for the introduction, John. You’ve said that you’re a firm believer in the power of automation, and over the last decade, you have spearheaded many automation projects in various organizations. Can you summarize some of the automation projects you’ve implemented?
John: Sure. The biggest projects have focused on enabling finance personnel to work on tasks that add real value instead of just pulling together reports. Initially, I built Excel models using Hyperion, analytics, and financial reporting to simplify data processing. Then, we moved to more automated processes using bots and tools like UiPath for repetitive tasks, such as invoice processing, to eliminate manual data entry. With the advent of OCR technologies, we further reduced manual intervention. Now, I’m excited to be working with HyperBots on AI automation, which is the next generation of finance automation.
Emily: That’s amazing. John, we see a lot of AI buzz today with claims of AI transforming business operations. What is your take on that?
John: The buzz around AI is justified because we can now utilize it in ways we never could before. It’s not just about making machines more intelligent but about processing all the available data both web-based and internal to provide higher-quality answers and insights. Tasks that used to take weeks can now be completed in minutes.
Emily: I agree. What have you been hearing in your peer group about the possibility of AI transforming finance and accounting processes?
John: It’s a critical time for finance and accounting to adopt technology. Historically, finance professionals have relied heavily on manual processes, but now, with the shortage of accountants and the complexity of tax laws and transaction volumes, it’s essential to adopt the latest technologies. Those who don’t adopt will likely fall behind their competitors.
Emily: From a business perspective, what impact does modern automation have compared to traditional methods?
John: Modern automation tools can go beyond just pulling numbers they can provide natural language feedback, synopsis, hypotheses, and suggest areas for further investigation. In the past, tasks like data processing could freeze your computer or take a day to complete. Now, we can get real-time, insightful feedback.
Emily: Can you give some examples of intelligent tasks that weren’t automatable before but are now possible with AI?
John: One example is the variance analysis. In the past, tools like Hyperion automated reporting but couldn’t provide insights about the data. Now, AI can analyze variances and suggest reasons, such as changes in volume or price. It can even correct data inaccuracies and highlight potential issues.
Emily: Let’s talk about invoice processing. Can you elaborate on how AI improves this task?
John: Previously, invoice automation struggled with inconsistencies and required manual data correction. Now, AI can understand invoice details even if they aren’t perfectly formatted, extracting information like amounts, tax details, and vendor names, and suggesting appropriate accounting actions. This reduces the need for human data entry and improves accuracy, allowing accounting staff to focus on review rather than data correction.
Emily: We’ve certainly come a long way. Why wasn’t this possible before, and what has changed in the technology landscape?
John: The biggest change has been the increase in computing power, enabling us to process vast amounts of data in seconds. Previously, tasks like reserve calculations could take 18 hours and weren’t feasible to run frequently. Now, we can run these calculations daily and get real-time insights.
Emily: Are there tasks in accounting that will always require human intelligence?
John: Absolutely. While AI can make us more accurate and efficient, it can also produce errors or hallucinations that need human oversight. Strategic tasks, especially those involving future planning with no existing data, will always require human intelligence and creativity.
Emily: What are the differences between traditional automation and AI-led automation?
John: Traditional automation required exact processes and rigid programming. AI-led automation is more flexible, can learn from other data, and suggest new ways to improve processes without needing explicit programming. However, we must be cautious of information overload and potential errors from AI.
Emily: What challenges do you foresee for CFOs in adopting AI-led automation?
John: There will be challenges, just like with any new technology. One major issue is ensuring data security and accuracy. AI can sometimes produce incorrect results, and if trusted too much, this could lead to significant errors in financial statements. It’s crucial to balance adoption with oversight.
Emily: How do you see AI-led automation impacting finance and accounting over the next two years?
John: Adoption will be rapid due to the shortage of accountants and the need for efficiency. We’ll see tools becoming smarter and more integrated into daily operations. Companies that adopt AI will likely experience fewer errors and greater efficiency, while those that don’t may struggle to keep up.
Emily: Any advice for CFOs who are unsure about exploring AI for their operations?
John: Start by talking to your current vendors and exploring how they are integrating AI into their platforms. Stay current by reading about the latest developments in AI. Consider bringing in consultants or experts to demonstrate how AI can benefit your specific needs. It’s essential to understand and embrace AI to remain competitive.
Emily: Thank you so much for sharing your insights, John. This discussion on the evolution of automation has been enlightening.
John: Thanks for having me.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Welcome to the latest installment of our interview series, where we delve into the intersection of finance and technology. Today, we are privileged to host Bimal Shah, an esteemed finance professional with extensive experience in the pharmaceutical industry, including serving as a CFO. Our focus for this session is on understanding the return on investment (ROI) of AI-led automation initiatives in finance. Let’s dive in!
Emily: Hello everyone, and welcome! I’m Emily, a digital transformation consultant at Hyperbots, and I’m thrilled to have Bimal joining us today. Bimal, before we jump into the details, could you please share a bit about your background?
Bimal Shah: Certainly, Emily. Thank you for having me. I’ve spent over a decade in senior financial roles within the life sciences industry, ranging from privately held firms to publicly traded companies. My expertise lies in navigating the complexities of finance in the pharmaceutical sector.
Emily: Thank you, Bimal, for that introduction. Let’s structure our discussion today into three key areas: understanding ROI methods, AI adoption in finance, and challenges and recommendations. Starting with ROI methods, Bimal, as a seasoned CFO, what frameworks have you employed to evaluate ROI?
Bimal: ROI, or return on investment, is paramount in financial decision-making. It can be measured through metrics such as internal rate of return, payback period, or simply as a ratio of investment returns. Assessing ROI involves considering factors like technology costs, implementation expenses, and potential cost savings or efficiency gains.
Emily: Fascinating insights, Bimal. Moving on to AI adoption in finance, which processes do you see as ripe for AI integration?
Bimal: Invoice processing, accounts payable, and accounts receivable management are prime candidates for AI adoption. These areas involve repetitive tasks that can benefit from automation, leading to cost savings and improved accuracy.
Emily: That’s insightful. And how would you prioritize AI adoption within the finance function?
Bimal: I would start with areas like accounts payable and receivable, where the tasks are relatively straightforward but labor-intensive. Demonstrating the benefits of AI in these areas can pave the way for adoption in more complex functions like financial planning and analysis.
Emily: Excellent advice, Bimal. Now, let’s delve into the nitty-gritty of calculating ROI. Could you elaborate on the quantitative and qualitative gains of AI-led automation?
Bimal: Quantitative gains include cost savings from reduced headcount and improved payment processing efficiency. On the qualitative side, benefits such as enhanced decision-making and employee satisfaction are harder to measure but equally valuable.
Emily: That’s a comprehensive overview. Bimal, how would you recommend measuring ROI for automation initiatives, considering both direct and indirect costs?
Bimal: Direct costs, such as technology investments and labor expenses, are relatively straightforward to quantify. However, capturing indirect costs and intangible benefits requires a more holistic approach. It’s essential to focus on measurable metrics while acknowledging qualitative gains.
Emily: Thank you for clarifying that, Bimal. As we near the end of our discussion, how would you suggest CFOs and controllers approach ROI measurement and publication for automation initiatives?
Bimal: I advocate for a balanced approach, emphasizing quantifiable benefits while acknowledging qualitative gains. Attempting to overly quantify intangible benefits may dilute the credibility of ROI calculations. Transparency and clarity are key when communicating the value of automation initiatives.
Emily: Wise counsel, Bimal. Finally, in terms of risk assessment, how do you recommend quantifying potential risks associated with AI implementation?
Bimal: While risks such as damaged relationships or employee concerns are challenging to quantify, they must be acknowledged and managed. Mitigating risks requires proactive communication, stakeholder engagement, and a focus on seamless implementation.
Emily: Thank you, Bimal, for your invaluable insights into maximizing ROI on AI-led automation initiatives in finance. It’s been a pleasure discussing these critical topics with you.
Bimal: Likewise, Emily. Thank you for hosting me, and I look forward to future conversations on the evolving landscape of finance and technology and there you have it, folks! A deep dive into the ROI of AI-led automation initiatives in finance, featuring insights from Bimal Shah, a seasoned CFO. Stay tuned for more enriching discussions on the intersection of finance and technology.
