Moderated by Niharika Marketing Manager at Hyperbots.
Niharika: Good morning, good afternoon, good evening, everyone, depending on wherever you are. I am Niharika, and I take care of marketing at Hyperbots. Today, we have with us Mr. Ayo Fashina, the CFO of Kobo 360, Africa’s leading integrated logistics solution provider. Ayo brings a wealth of experience and insights, having operated in the domain for a little more than 20 years now. It’s a pleasure to have you on board, Ayo.
Ayo Fashina: Thank you. Happy to be here.
Niharika: Today, we will be discussing a very interesting topic: how AI can not only improve but also revolutionize cash outflow management. To set the stage, can you help us understand what cash flow is and why businesses must manage it effectively?
Ayo Fashina: Thank you, Niharika. Let me start by defining cash outflow. Cash outflow refers to the movement of money out of a business for various needs like expenses, investments, debt repayment, or salary payments. Managing it effectively ensures that a business can meet its obligations while pursuing growth opportunities. Managing both cash outflows and inflows is essential. The timing of money inflows and outflows determines a business’s solvency. If a business does not manage its cash flow properly, it may become insolvent, and unable to meet its obligations, leading to potential closure. Effective cash flow management is crucial for businesses, including banks, which also face challenges in managing cash flows.
Niharika: How does AI fit into this picture, especially for those new to this concept?
Ayo Fashina: AI enhances decision-making and operational efficiency. In finance, AI can process vast amounts of data to forecast and manage cash flows. It can identify savings opportunities and automate transactions, making cash flow management more efficient. AI can also connect with APIs to consolidate all your bank information, eliminating the need for manual data entry and enabling seamless financial analysis from a single interface tools generate detailed reports and perform in-depth financial analyses, providing the insights needed to track financial performance and identify improvement opportunities.
Niharika: How can AI improve the accuracy of forecasting and budgeting compared to traditional methods?
Ayo Fashina: AI algorithms can analyze historical data, and market conditions, and predict future cash flow needs more accurately than humans. AI can synthesize a vast amount of data quickly, creating more realistic budgets, financial plans, and forecasts. It provides real-time comprehensive forecasting, offering complete visibility into cash flows and enabling better-informed financial decisions.
AI also aids in scenario analysis and planning by simulating various what-if scenarios, helping businesses understand potential future changes and their impacts. This capability allows for more accurate financial forecasting and decision-making.
Niharika: Can you explain how AI enhances operational efficiency and expense management?
Ayo Fashina: AI tools streamline expense management by identifying patterns and anomalies in spending, helping businesses cut unnecessary costs and negotiate better terms with suppliers. AI optimizes cash flow by monitoring payment terms, taking advantage of discounts, and delaying payments when appropriate can also increase visibility into procurement data, ensuring that purchase orders and invoices are properly matched. This enhances cash flow forecasting accuracy and enables efficient payment scheduling. Overall, AI significantly reduces the time required for financial tasks, improving operational efficiency.
Niharika: How does AI contribute to risk management and fraud detection?
Ayo Fashina: AI is adept at identifying irregularities and spotting slight changes that humans might overlook. In fraud detection, AI can monitor transactions and flag unusual activity, such as sudden large transactions on a credit card, potentially preventing fraud. By checking trends and identifying irregular transactions, AI enhances risk management and protects company finances.
Niharika: With AI playing such a big role, how do companies ensure compliance and ethical use?
Ayo Fashina: AI providers must adhere to international standards and regulatory requirements, ensuring ethical data handling and management. Compliance involves following regulations around personally identifiable information and confidential data. AI tools should have access rights and data classification to maintain trust and reliability. Ensuring compliance with these standards is crucial for the ethical use of AI in financial management.
Niharika: Absolutely. Thank you for answering that, Ayo. I think we’ve covered fraud detection and risk management well. But are there other examples where AI has successfully optimized cash flows?
Ayo: Certainly. At our organization, we are an e-logistics platform matching transporters with goods owners. We manage payments between transporters and goods owners. Initially, managing these cash flows was manual and prone to errors. To optimize this, we adopted an AI solution. By connecting our systems to banks via APIs, we automated payments, eliminating duplication and ensuring timely payments. AI also optimized our cash outflow reporting, providing automated and accurate financial reports. On the accounts receivable side, AI generates and tracks invoices, sending automated reminders to customers about due payments. This has significantly reduced our cash-to-cash cycle the time between money going out and coming back in. For example, we reduced our cash-to-cash cycle from 45 days to about 10 days. Some customers even make partial advance payments, further improving our cash flow. These improvements allow us to conduct more business with the same amount of cash, demonstrating AI’s impact on financial efficiency.
Niharika: Thank you for that insight, Ayo. It’s wonderful to hear how AI has been implemented successfully. However, I’m sure integrating AI comes with challenges. Could you share your experience with that?
Ayo: The primary challenge with adopting any technology, including AI, is people. There’s natural resistance to change. Convincing staff and even senior management can be tough. The second challenge is ensuring the quality of data and outputs from the AI. It’s crucial to monitor and clean the data used by AI systems to ensure accuracy. Being a startup, our resistance to change wasn’t as pronounced as it might be in larger, more established organizations. In such companies, where processes have been done a certain way for a long time, resistance can be stronger. Building a culture that embraces change is essential for successful AI integration.
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 Moderated by Niyati Chhaya, Co-founder at Hyperbots
Niyati Chhaya: Hi everyone, good morning, good evening, and good afternoon. This is Niyati Chhaya. I am a co-founder and I lead AI at Hyperbots. I am thrilled to have Mike with us today. Mike Vaishnav is a CFO, Consultant, and Strategic Advisor to many privately owned organizations. We’re going to pick his brains on AI and compliance. But before that, Mike, why don’t you introduce yourself?
Mike Vaishnav: Thank you, Niyati, it’s a pleasure to be here. I’ve been working in Silicon Valley for about close to 30 years in diverse industries. I’ve had the opportunity to touch each and every aspect of finance, from the controllership role to FP&A, treasury, tax, investor relations, and more. In my last two roles, I also managed operations departments like HR, IT, facilities, legal, and procurement. So, I bring a broad range of experience in finance and operations.
Niyati Chhaya: Wow, I think you are the right person to talk about finance, compliance, and how AI will help in compliance. How do you think AI will assist in compliance?
Mike Vaishnav: AI can significantly enhance compliance. With the right algorithms, AI can flag issues related to government and regulatory requirements. By identifying violations early, companies can address problems before they escalate. AI helps ensure that businesses are operating within the bounds of policies and regulations.
Niyati Chhaya: Got it, and I assume AI can help with fraud detection as well?
Mike Vaishnav: Absolutely, fraud detection is a critical area where AI can be very effective. By monitoring data and raising flags at appropriate levels, AI can help mitigate fraud and ensure compliance with regulatory requirements.
Niyati Chhaya: Do you think AI can play a role in the audit process?
Mike Vaishnav: Definitely. AI can analyze vast amounts of data and identify specific patterns and exceptions. For example, AI can track journal entry approvals, identifying who processed and approved entries. This can streamline the audit process, allowing auditors to focus on more value-added functions rather than spending time on sample tests and data checks.
Niyati Chhaya: Do you think AI can ensure the accuracy of financial reporting?
Mike Vaishnav: Yes, AI can enhance the accuracy of financial reporting. While ERP systems are robust, there are instances where information might be missed. AI can identify these gaps early by using predefined rules and algorithms. For example, when generating financial statements, AI can ensure that all relevant chart accounts are included, reducing the risk of errors. Additionally, AI can assist in compiling and analyzing data for SEC filings, providing insights that ERP systems might not offer.
Niyati Chhaya: Thanks for those insights. My takeaway here is that building reliable AI systems can greatly benefit compliance processes.
Mike Vaishnav: Absolutely. Compliance is crucial for all finance professionals. With AI ensuring compliance, finance teams can rest easy, knowing that they have a reliable system monitoring their processes. AI provides detailed analytics, helping to maintain and improve compliance.
Niyati Chhaya: Got it. Thank you so much, Mike, for sharing your expertise and insights on AI and compliance.
Mike Vaishnav: My pleasure.
Moderated by Moderated by Emily , Digital Transformation Consultant at Hyperbots
Emily: Hi everyone! In this segment, we’ll be discussing strategies for alignment and optimization. Mike, thank you so much for walking us through the approval workflow and organizational structures in our last section. To delve deeper into this segment, what strategies do you recommend for designing approval workflows that align with an organization’s hierarchical structures?
Mike: Thank you, Emily. Before developing the workflow, we need to consider the organization hierarchy. One key thing is to think about the best way to structure it. We need to establish a robust organizational hierarchy that includes the reporting lines, levels of authority, and other training and documentation. This impacts the decision-making process and ensures efficient collaboration within the department. Here are some strategies for developing a workflow:
Proper Reporting Lines: Clearly define who reports to whom. This is crucial because the workflow needs to move smoothly from one step to another.
Levels of Authority: Determine the authority level for each position. Not every decision needs to go through the same hierarchy. Define limits and responsibilities accordingly.
Clarity of Roles and Responsibilities: Specify who approves what and why they are involved in the workflow. Not everyone needs to be involved in every step.
Approval Levels: Establish clear approval levels and maintain an audit trail to keep track of the approvals.
Standardization: Standardize the approval process whether the organization is centralized or decentralized. This ensures consistency.
Utilization of Tools: Use appropriate tools like workflow management systems or AI to facilitate the workflow.
Training and Support: Ensure proper training for those involved in the approval process to prevent inefficiencies.
Collaboration and Coordination: Foster cross-functional collaboration and ensure accountability in the approval process.
Alignment with Organizational Objectives: Ensure that the workflow aligns with the overall organizational objectives, not just departmental ones.
Emily: Got it. So, Mike, how do you strike a balance between centralizing control for oversight and decentralizing decision-making authority in approval workflows?
Mike: Striking a balance between centralizing control for oversight and decentralizing decision-making authority is essential for optimizing efficiency, maintaining accountability, and fostering innovation. Here are some strategies to achieve this balance:
Clear Hierarchy: Establish a clear reporting structure that defines who is responsible for approvals, whether centralized or decentralized.
Key Decision Points: Identify critical decision points to determine the required authority level for approvals. For example, the marketing department may approve budgets, but cross-functional collaboration might be necessary for other decisions.
Delegate Authority Appropriately: Delegate routine tasks to lower levels and critical tasks to higher levels, empowering employees with the necessary training and tools.
Escalation Protocols: Establish clear protocols for escalating decisions that exceed certain authority levels.
Technology and Transparency: Implement technology solutions like workflow management systems to provide transparency and ensure all stakeholders are informed.
Cross-Functional Collaboration: Foster collaboration across departments since workflows often span multiple areas.
Training and Documentation: Provide thorough training and maintain documentation to ensure everyone understands their roles and responsibilities.
Emily: How can organizations ensure that approval workflows are flexible enough to accommodate changes in the organizational hierarchy?
Mike: Flexibility in workflows is crucial for accommodating changes in the organizational hierarchy. Here are a few strategies to ensure flexibility:
Modular Design: Design workflows in a modular fashion so changes can be made easily without disrupting the entire process.
Role-Based Approvals: Implement role-based approvals instead of individual-based ones. This allows for smooth transitions when people change jobs.
Dynamic Routing: Use dynamic routing to handle situations where someone is unavailable, enabling delegation and preventing bottlenecks.
Centralized Policy Management: Maintain centralized policies to ensure consistency and compliance across the organization.
Regular Review and Monitoring: Continuously review and monitor workflows to identify areas for improvement and ensure they remain adaptable to changes.
Emily: Gosh, so Mike, how can organizations leverage technology to automate routine approval tasks and streamline workflows? Can you share a few examples as well?
Mike: Before leveraging any technology, organizations need to understand their current processes and identify areas where technology can be beneficial. Here are a few examples of how technology can be used to streamline workflows:
Workflow Management Systems: These systems automate routing, track progress in real-time, enforce processes, and provide notifications and reporting capabilities.
Electronic Document Systems: These systems store data in one place, provide version control, and reduce manual intervention.
Electronic Signatures: Legal electronic signatures can replace physical signatures, streamlining the approval process.
ERP Integration: Integrating workflows with ERP systems ensures data consistency and seamless operation.
AI and Machine Learning: Implementing AI solutions can enhance workflow efficiency by automating routine tasks and providing insights for process improvements. These technologies help automate routine tasks, reduce manual errors, and ensure that workflows are efficient and adaptable to organizational needs.
Moderated by Niyati Chhaya, Co-Founder at Hyperbots
Niyati: Hi everyone, good morning, good afternoon, and good evening. I’m Niyati, Co-founder and AI Lead at Hyperbots Inc. Today, we have Mike Vaishnav with us, a CFO, consultant, and strategic advisor to many privately owned organizations.
Before we delve into our discussion on how AI complements ERP systems, Mike, could you introduce yourself?
Mike Vaishnav: Thank you, Niyati. I’ve worked in Silicon Valley for almost 30 years across diversified industries in various roles, including controllership, FP&A, treasury, tax, investor relations, and operational roles. In my last two CFO positions, I managed fund, IT, legal, HR, and procurement functions. I’ve covered all aspects of finance and operations in different industries.
Niyati: Wow, that’s a broad range. Today, we’ll address our topic in three broad categories: the efficacy of ERP systems, how AI and ERP work together, and the actual integration of AI into ERP systems.
Niyati: You have been part of several large and medium-sized organizations. What kind of ERPs and business processes have you worked with?
Mike Vaishnav: I’ve used both small ERPs and large ERPs like Oracle and SAP. I’ve been involved in every module for ERP, including procure-to-pay, accounting, sales, and inventory processing. I’ve implemented ERP systems globally over the past 20 years.
Niyati: What gains do you see in companies through effective ERP implementation?
Mike Vaishnav: Key gains include process automation, process improvement, audit trails, and data security. ERPs provide detailed analysis and streamline financial information, moving away from manual processes.
Niyati: What are the challenges despite effective implementations?
Mike Vaishnav: Challenges often arise during data migration and integration with old systems. Proper testing and documentation are crucial to ensure successful ERP implementation. Companies should conduct parallel test runs in a test environment for about two to three months to ensure data accuracy before going live.
Niyati: Let’s now discuss how AI and ERP systems complement each other.
Mike Vaishnav: AI is complementary to ERP. It provides add-on solutions that make data analysis more effective. While ERP systems collect and process data, AI enhances the ability to make timely and informed decisions, especially in mid-size or small ERPs that may lack advanced data analytics capabilities.
Niyati: Can you give an example, like invoice processing?
Mike Vaishnav: Sure. In large ERPs, the entire procure-to-pay process is automated. However, mid-size or small ERPs might lack such automation. AI can automate processes like opening and approving POs, providing real-time answers to specific queries, and creating customized dashboards for different departments. This enhances efficiency and privacy.
Niyati: Why is it better to use AI to complement an existing ERP rather than upgrading to a bigger ERP?
Mike Vaishnav: Upgrading to a bigger ERP is a complex and costly process. AI add-ons can enhance the existing ERP’s capabilities without the need for a complete overhaul. This approach is more efficient and less disruptive.
Niyati: Where will the budget for AI come from?
Mike Vaishnav: Companies need to work smartly, balancing their budgets. AI can help automate high-volume transactions, improving accuracy and timeliness. In the long run, AI provides better return on investment by enhancing process and operational efficiency, ultimately adding to the bottom line.
Niyati: How should a company assess the need for AI in its various use cases?
Mike Vaishnav: It’s case-by-case. AI is customizable, so companies need to evaluate their specific requirements, budget, and departmental needs. SMBs, in particular, can benefit from AI add-ons to enhance their existing ERP systems.
Niyati: Do you see ERP vendors integrating AI modules themselves?
Mike Vaishnav: Some top-tier ERP vendors are incorporating AI solutions, but mid-tier and lower-tier ERPs are slower to adopt these technologies. AI can help enhance these existing systems, especially for SMBs.
Niyati: When does it not make sense for organizations to adopt AI?
Mike Vaishnav: For companies with low transaction volumes or extremely small operations, AI may be unnecessary. In such cases, manual processing by a single person might suffice.
Niyati: To summarize, AI is a good friend to finance professionals, complementing ERP systems. While AI will not replace ERP, it enhances the capabilities of ERP systems, especially for SMBs and mid-tier ERPs.
Mike Vaishnav: Absolutely. AI adds significant value to ERP systems, making processes more efficient and helping companies make timely decisions.
Niyati: Thank you, Mike, for sharing your insights on how AI complements ERP systems.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, good afternoon based on where you are. I’m Emily, a digital transformation consultant at Hyperbots, and today I have Mike Vashnum with me. We are going to discuss a practical toolkit to bring efficiency in accruals. But before we get into details, Mike, would you like to introduce yourself?
Mike: Sure. Thank you, Emily. I’ve been working in Silicon Valley for about 30 years and have had the opportunity to work in a diverse range of industries. I’ve been fortunate enough to touch every aspect of finance, including controllership, FP&A, treasury, tax, and investor relations. In my last two roles as CFO, I also managed non-finance and operations departments like legal, HR, IT, facilities, and procurement. So, I bring a very diversified and wide-ranging experience in finance and operations.
Emily: That’s great, Mike. I have divided this session into two parts. In the first part, we will discuss the current accrual landscape, and in the second part, we’ll dive into AI and accruals. So, for the first part of this discussion, Mike, I’d like to ask you to highlight the importance of expense accruals for companies and how they generally impact financial reports.
Mike: Absolutely. Not just expense accruals, but any accruals are essential for a company. If you look at basic accounting principles, you have to match the revenue with your costs. If you’ve booked the revenue and incurred the cost but haven’t received the invoices or bills, your profitability will be inconsistent. One month you might show higher profitability because of lower expenses that weren’t booked, and the next month you might have all those expenses catching up, but no matching revenue. This mismatch doesn’t present an accurate picture of profitability. By accruing expenses for which you haven’t received invoices, you match your revenue with the associated costs, giving a more accurate representation of profitability. This is crucial for investors and analysts to ensure the company’s profitability for a specific period is correct.
Emily: Understood. Based on your experience, Mike, what is the current process used in companies for accruals?
Mike: Different companies use different processes. Most accrual processes are manual because you have to collect information from respective departments. I would categorize accruals into inventory accrual and expense accrual. Inventory accrual can be automated in sophisticated ERP systems, where goods received notes allow for booking received but not invoiced items as accounts payable. Expense accruals, however, are very manual and time-consuming. Accountants typically send emails to department heads to check for pending invoices and services rendered. They also look at open POs to follow up on whether services or expenses have been incurred. Despite some ERP systems offering recurring accruals, accurate accruals still require manual processes and a lot of back-and-forth communication.
Emily: Can you also share the categories of accruals and how does the reversal happen?
Mike: Sure. As I mentioned, there are inventory and expense accruals. Expenses can vary depending on the company’s size and nature. The reversal process is necessary to avoid duplication of expenses. When you book an accrual at the month-end and then receive the invoice in the following month, you don’t want to double-book the expense. So, the best practice is to book the accrual, reverse it, and rebook it at the next month-end.
Emily: Can you highlight the pain points in the accrual process?
Mike: The main pain point is the manual process. Collecting data, ensuring the accuracy of accruals, and coordinating with various departments are time-consuming tasks. Ensuring all necessary accruals are booked correctly is critical because auditors will not accept general accruals they need specific purposes and processes.
Emily: Thank you, Mike, for explaining accruals and the current landscape. In the next section, we will cover AI and its role in accruals.
Mike: Sure, thank you.
Emily: Welcome back, Mike. Thank you for taking us through the current landscape of accruals. Now, let’s discuss the role of AI in accruals. What role does AI play in accruals, and can you provide some examples to help us understand it better?
Mike: Absolutely. AI can tremendously speed up the accrual process. While ERPs can handle many tasks, AI enhances them, especially in communication. For example, AI can look at open POs and automatically send messages to the respective departments asking if services have been rendered. This eliminates the need for human intervention. By setting AI to check for open POs on specific days, it can collect and follow up on all necessary information automatically. Let’s take the legal department as an example. AI can send automated messages to attorneys asking if services have been rendered, which replaces the manual process where accountants send emails and wait for responses. AI can also help with inventory by identifying received items that haven’t been invoiced and processing those accruals. Essentially, AI can handle the communication aspects of accruals, ensuring accurate and timely data collection without human intervention. This streamlines the month-end close process, allowing accountants to focus on analysis rather than data gathering.
Emily: That gives me a clear vision of how AI helps in accruals. Thank you so much, Mike.
Mike: You’re welcome. I’m looking forward to seeing AI develop further to make the accrual process faster and simpler, ultimately allowing more time for analysis and less on data gathering.
Emily: Thank you, Mike, for being a part of this discussion on AI and accruals. It was great having you here.
Mike: Thank you. Glad to be here.
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.
