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.
Moderated by Niyati Chhaya, Co-Founder at Hyperbots
Niyati: Hi everyone, good morning, good afternoon, and good evening. I’m Niyati, Co-founder and AI Lead at Hyperbots Inc. Today, we have Mike Vaishnav with us, a CFO, consultant, and strategic advisor to many privately owned organizations.
Before we delve into our discussion on how AI complements ERP systems, Mike, could you introduce yourself?
Mike Vaishnav: Thank you, Niyati. I’ve worked in Silicon Valley for almost 30 years across diversified industries in various roles, including controllership, FP&A, treasury, tax, investor relations, and operational roles. In my last two CFO positions, I managed fund, IT, legal, HR, and procurement functions. I’ve covered all aspects of finance and operations in different industries.
Niyati: Wow, that’s a broad range. Today, we’ll address our topic in three broad categories: the efficacy of ERP systems, how AI and ERP work together, and the actual integration of AI into ERP systems.
Niyati: You have been part of several large and medium-sized organizations. What kind of ERPs and business processes have you worked with?
Mike Vaishnav: I’ve used both small ERPs and large ERPs like Oracle and SAP. I’ve been involved in every module for ERP, including procure-to-pay, accounting, sales, and inventory processing. I’ve implemented ERP systems globally over the past 20 years.
Niyati: What gains do you see in companies through effective ERP implementation?
Mike Vaishnav: Key gains include process automation, process improvement, audit trails, and data security. ERPs provide detailed analysis and streamline financial information, moving away from manual processes.
Niyati: What are the challenges despite effective implementations?
Mike Vaishnav: Challenges often arise during data migration and integration with old systems. Proper testing and documentation are crucial to ensure successful ERP implementation. Companies should conduct parallel test runs in a test environment for about two to three months to ensure data accuracy before going live.
Niyati: Let’s now discuss how AI and ERP systems complement each other.
Mike Vaishnav: AI is complementary to ERP. It provides add-on solutions that make data analysis more effective. While ERP systems collect and process data, AI enhances the ability to make timely and informed decisions, especially in mid-size or small ERPs that may lack advanced data analytics capabilities.
Niyati: Can you give an example, like invoice processing?
Mike Vaishnav: Sure. In large ERPs, the entire procure-to-pay process is automated. However, mid-size or small ERPs might lack such automation. AI can automate processes like opening and approving POs, providing real-time answers to specific queries, and creating customized dashboards for different departments. This enhances efficiency and privacy.
Niyati: Why is it better to use AI to complement an existing ERP rather than upgrading to a bigger ERP?
Mike Vaishnav: Upgrading to a bigger ERP is a complex and costly process. AI add-ons can enhance the existing ERP’s capabilities without the need for a complete overhaul. This approach is more efficient and less disruptive.
Niyati: Where will the budget for AI come from?
Mike Vaishnav: Companies need to work smartly, balancing their budgets. AI can help automate high-volume transactions, improving accuracy and timeliness. In the long run, AI provides better return on investment by enhancing process and operational efficiency, ultimately adding to the bottom line.
Niyati: How should a company assess the need for AI in its various use cases?
Mike Vaishnav: It’s case-by-case. AI is customizable, so companies need to evaluate their specific requirements, budget, and departmental needs. SMBs, in particular, can benefit from AI add-ons to enhance their existing ERP systems.
Niyati: Do you see ERP vendors integrating AI modules themselves?
Mike Vaishnav: Some top-tier ERP vendors are incorporating AI solutions, but mid-tier and lower-tier ERPs are slower to adopt these technologies. AI can help enhance these existing systems, especially for SMBs.
Niyati: When does it not make sense for organizations to adopt AI?
Mike Vaishnav: For companies with low transaction volumes or extremely small operations, AI may be unnecessary. In such cases, manual processing by a single person might suffice.
Niyati: To summarize, AI is a good friend to finance professionals, complementing ERP systems. While AI will not replace ERP, it enhances the capabilities of ERP systems, especially for SMBs and mid-tier ERPs.
Mike Vaishnav: Absolutely. AI adds significant value to ERP systems, making processes more efficient and helping companies make timely decisions.
Niyati: Thank you, Mike, for sharing your insights on how AI complements ERP systems.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, or good afternoon depending on where you are. I am very pleased to have you back. The topic that we will be discussing today is large language models and their applications in accounting. But before we dive into it, Ayo, would you mind telling us a little more about yourself?
Ayo Fashina: Hi Emily, it’s good to see you again. My name is Ayo Fashina, I’m the CFO of Kobo360. It’s an e-logistics company startup, about five going on six years old now. We match goods owners to transporters and operate in seven African countries.
Emily: Got it. Thank you so much, Ayo, for the introduction. Today we’ll be talking about large language models (LLMs). It’s a vast topic in itself, but let’s start with the basics. Ayo, can you explain what large language models are and how they are relevant to the field of accounting?
Ayo Fashina: Thank you, Emily. In short, a large language model (LLM) is a type of artificial intelligence program that can recognize and generate text, among other tasks. LLMs are trained on huge datasets, hence the name large. They are built on machine learning, specifically a type of neural network called a transformer model. They analyze large datasets to learn what to look for when queried and use that knowledge to improve their performance. This makes them highly relevant to accounting, which involves data analysis and data collection.
Emily: Got it. Traditional methods of data analysis in accounting are human-based. Accounting staff or finance analysts do all the data entry and analysis, often using tools like Excel. How do LLMs compare to these traditional methods?
Ayo Fashina: One of the most significant benefits of LLMs is that they substantially increase the efficiency of accounting tasks. These models can process large volumes of data at an unprecedented speed, drastically reducing the time accountants spend on routine tasks such as data entry, transaction categorization, and report generation. Essentially, LLMs make the life of an accountant easier.
Emily: Understood. Can you dive into some specific applications of LLMs in accounting that you’ve come across or implemented?
Ayo Fashina: One application I’ve come across involves processing accounts payable. LLMs are trained on data from invoices, purchase orders, and delivery notes. They can match these documents to process accounts payable on behalf of accountants and even handle general ledger entries. Impressively, these models can read both typed and handwritten documents, which is a significant advancement.
Emily: How do LLMs contribute to improving efficiency, accuracy, and decision-making in accounting tasks?
Ayo Fashina: LLMs can process a lot of data very quickly and with high accuracy, thereby minimizing human errors. They ensure consistency in handling transactions and applying rules uniformly, which is crucial in accounting. By automating routine tasks, LLMs allow firms to allocate human resources to more strategic tasks, enhancing overall productivity. They also provide valuable insights for financial forecasting and decision-making by identifying trends and patterns that might be overlooked by human analysts.
Emily: What challenges do finance professionals face when implementing LLMs in accounting processes, and how can these challenges be addressed?
Ayo Fashina: Despite their advanced capabilities, LLMs lack human judgment and the ability to interpret complex and ambiguous financial situations. They operate based on the data provided to them without understanding the nuances of certain accounting decisions. Therefore, LLMs should be applied to tasks where human judgment is not a high priority. It’s also crucial to have robust data validation processes to ensure the quality of data used to train LLMs, as inaccurate data can lead to erroneous outputs.
Emily: What opportunities do LLMs present for innovation and advancement in accounting?
Ayo Fashina: LLMs hold great promise in revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. They can be used for risk assessment, fraud detection, and even forensic accounting. For example, LLMs can analyze data to uncover hidden patterns and trends, help identify risks, and flag anomalies in transactions, which aids in fraud prevention.
Emily: With the increasing use of LLMs in accounting, how do you ensure the security and privacy of sensitive financial data?
Ayo Fashina: Ensuring data confidentiality and security is paramount. Data hygiene is essential, meaning the data used to train LLMs should be sanitized of any personal identifiable information. Organizations should implement stringent data cleaning and sanitation procedures to remove sensitive information and identify potential biases and errors in the data.
Emily: How important is it for LLM-driven accounting solutions to integrate seamlessly with existing financial systems?
Ayo Fashina: It’s very important. If an LLM solution cannot integrate with existing systems, it defeats the purpose of having the LLM in the first place. Full integration ensures that the efficiency gains from LLMs are realized. Without it, the benefits are eroded by manual data transfer, which reintroduces human error.
Emily: What strategies do you recommend for ensuring smooth integration and compatibility with other accounting and enterprise systems?
Ayo Fashina: Testing compatibility ahead of full implementation is key. The provider of the LLM-driven solution should ensure seamless connection to existing systems and workflows. A sandbox test run before full implementation can help identify and resolve any integration issues.
Emily: What steps should organizations take to ensure compliance with relevant regulations while leveraging LLMs in accounting operations?
Ayo Fashina: Regulatory compliance is dynamic and always changing, which poses a challenge to LLMs. Ongoing monitoring and adjustments are required to keep LLMs compliant, which can be resource-intensive but necessary. Developers should ensure that LLMs are regularly updated to reflect changes in regulations.
Emily: Are there any emerging trends or advancements in LLMs that you believe will shape the future of accounting and finance?
Ayo Fashina: LLMs have the potential to revolutionize the finance sector in numerous ways. They can be used for risk assessment, fraud detection, and forensic accounting. LLMs can also simplify audit processes by organizing data for easier transaction tracing. As these technologies evolve, they will continue to provide valuable insights and efficiencies.
Emily: Based on your experience and insights, do you have any additional advice or recommendations for financial professionals looking to harness the power of LLMs?
Ayo Fashina: Financial professionals should consider learning Python, a versatile programming language that many LLMs are based on. Understanding Python fundamentals can help professionals leverage LLMs for code generation and task automation. As LLMs become more prevalent, the demand for Python skills will grow, making it a valuable skill for finance and accounting professionals.
Emily: Thank you so much, Ayo, for sharing your expertise on the applications of large language models in accounting. Is there anything else you would like to add before we conclude our discussion?
Ayo Fashina: Just to summarize, LLMs hold great promise for revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. However, it’s crucial to be aware of their limitations and the need for human judgment. Data security and regulatory compliance are also important considerations. Thank you, Emily, for having me. It’s been a pleasure.
Emily: Thank you, Ayo. It was great having you, and this discussion was truly insightful.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, or good afternoon depending on where you are. I am very pleased to have you back. The topic that we will be discussing today is large language models and their applications in accounting. But before we dive into it, Ayo, would you mind telling us a little more about yourself?
Ayo Fashina: Hi Emily, it’s good to see you again. My name is Ayo Fashina, I’m the CFO of Kobo360. It’s an e-logistics company startup, about five going on six years old now. We match goods owners to transporters and operate in seven African countries.
Emily: Got it. Thank you so much, Ayo, for the introduction. Today we’ll be talking about large language models (LLMs). It’s a vast topic in itself, but let’s start with the basics. Ayo, can you explain what large language models are and how they are relevant to the field of accounting?
Ayo Fashina: Thank you, Emily. In short, a large language model (LLM) is a type of artificial intelligence program that can recognize and generate text, among other tasks. LLMs are trained on huge datasets, hence the name large. They are built on machine learning, specifically a type of neural network called a transformer model. They analyze large datasets to learn what to look for when queried and use that knowledge to improve their performance. This makes them highly relevant to accounting, which involves data analysis and data collection.
Emily: Got it. Traditional methods of data analysis in accounting are human-based. Accounting staff or finance analysts do all the data entry and analysis, often using tools like Excel. How do LLMs compare to these traditional methods?
Ayo Fashina: One of the most significant benefits of LLMs is that they substantially increase the efficiency of accounting tasks. These models can process large volumes of data at an unprecedented speed, drastically reducing the time accountants spend on routine tasks such as data entry, transaction categorization, and report generation. Essentially, LLMs make the life of an accountant easier.
Emily: Understood. Can you dive into some specific applications of LLMs in accounting that you’ve come across or implemented?
Ayo Fashina: One application I’ve come across involves processing accounts payable. LLMs are trained on data from invoices, purchase orders, and delivery notes. They can match these documents to process accounts payable on behalf of accountants and even handle general ledger entries. Impressively, these models can read both typed and handwritten documents, which is a significant advancement.
Emily: How do LLMs contribute to improving efficiency, accuracy, and decision-making in accounting tasks?
Ayo Fashina: LLMs can process a lot of data very quickly and with high accuracy, thereby minimizing human errors. They ensure consistency in handling transactions and applying rules uniformly, which is crucial in accounting. By automating routine tasks, LLMs allow firms to allocate human resources to more strategic tasks, enhancing overall productivity. They also provide valuable insights for financial forecasting and decision-making by identifying trends and patterns that might be overlooked by human analysts.
Emily: What challenges do finance professionals face when implementing LLMs in accounting processes, and how can these challenges be addressed?
Ayo Fashina: Despite their advanced capabilities, LLMs lack human judgment and the ability to interpret complex and ambiguous financial situations. They operate based on the data provided to them without understanding the nuances of certain accounting decisions. Therefore, LLMs should be applied to tasks where human judgment is not a high priority. It’s also crucial to have robust data validation processes to ensure the quality of data used to train LLMs, as inaccurate data can lead to erroneous outputs.
Emily: What opportunities do LLMs present for innovation and advancement in accounting?
Ayo Fashina: LLMs hold great promise in revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. They can be used for risk assessment, fraud detection, and even forensic accounting. For example, LLMs can analyze data to uncover hidden patterns and trends, help identify risks, and flag anomalies in transactions, which aids in fraud prevention.
Emily: With the increasing use of LLMs in accounting, how do you ensure the security and privacy of sensitive financial data?
Ayo Fashina: Ensuring data confidentiality and security is paramount. Data hygiene is essential, meaning the data used to train LLMs should be sanitized of any personal identifiable information. Organizations should implement stringent data cleaning and sanitation procedures to remove sensitive information and identify potential biases and errors in the data.
Emily: How important is it for LLM-driven accounting solutions to integrate seamlessly with existing financial systems?
Ayo Fashina: It’s very important. If an LLM solution cannot integrate with existing systems, it defeats the purpose of having the LLM in the first place. Full integration ensures that the efficiency gains from LLMs are realized. Without it, the benefits are eroded by manual data transfer, which reintroduces human error.
Emily: What strategies do you recommend for ensuring smooth integration and compatibility with other accounting and enterprise systems?
Ayo Fashina: Testing compatibility ahead of full implementation is key. The provider of the LLM-driven solution should ensure seamless connection to existing systems and workflows. A sandbox test run before full implementation can help identify and resolve any integration issues.
Emily: What steps should organizations take to ensure compliance with relevant regulations while leveraging LLMs in accounting operations?
Ayo Fashina: Regulatory compliance is dynamic and always changing, which poses a challenge to LLMs. Ongoing monitoring and adjustments are required to keep LLMs compliant, which can be resource-intensive but necessary. Developers should ensure that LLMs are regularly updated to reflect changes in regulations.
Emily: Are there any emerging trends or advancements in LLMs that you believe will shape the future of accounting and finance?
Ayo Fashina: LLMs have the potential to revolutionize the finance sector in numerous ways. They can be used for risk assessment, fraud detection, and forensic accounting. LLMs can also simplify audit processes by organizing data for easier transaction tracing. As these technologies evolve, they will continue to provide valuable insights and efficiencies.
Emily: Based on your experience and insights, do you have any additional advice or recommendations for financial professionals looking to harness the power of LLMs?
Ayo Fashina: Financial professionals should consider learning Python, a versatile programming language that many LLMs are based on. Understanding Python fundamentals can help professionals leverage LLMs for code generation and task automation. As LLMs become more prevalent, the demand for Python skills will grow, making it a valuable skill for finance and accounting professionals.
Emily: Thank you so much, Ayo, for sharing your expertise on the applications of large language models in accounting. Is there anything else you would like to add before we conclude our discussion?
Ayo Fashina: Just to summarize, LLMs hold great promise for revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. However, it’s crucial to be aware of their limitations and the need for human judgment. Data security and regulatory compliance are also important considerations. Thank you, Emily, for having me. It’s been a pleasure.
Emily: Thank you, Ayo. It was great having you, and this discussion was truly insightful.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
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.