Interrelationships and dependencies between GL codes in ERP systems

Find out interesting insights with Dave Sackett, VP of Finance at Percy Moon Technologies

Moderated by Emily, Digital Transformation Consultant at Hyperbots

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

Emily: Hey, everyone, this is Emily and I’m a digital transformation consultant with Hyperbots. And today we have the privilege of speaking with Dave Sackett, who is the VP of Finance at Percy Moon Technologies. So thank you so much for joining us today, Dave.

Dave Sackett: Yeah. Thank you, Emily. A pleasure to be here.

Emily: The topic we’d be discussing today is interrelationships and dependencies between GL codes and different ERP solutions. To start, Dave, can you please explain the importance of understanding the interrelationships and the dependencies between different GL codes in an organization’s financial reporting?

Dave Sackett: Yeah. It all starts with the chart of accounts and the way you structure it. You’ll have interrelationships between them. For example, revenue, COGS, expenses, assets, liabilities, and taxes. These are fundamental to accurate financial reporting and management. Revenue and COGS directly impact gross profit, while assets and liabilities are crucial for maintaining the balance sheet. Mismanagement or misclassification of any of these can lead to inaccurate financial reporting.

Emily: Got it. So, Dave, how do different ERP systems like SAP S/4HANA or Microsoft Dynamics 365 help maintain these interrelationships effectively?

Dave Sackett: ERP systems like SAP and Microsoft Dynamics are designed with a robust financial model that enables these relationships to be maintained accurately. SAP uses a universal journal that consolidates financial and managerial accounts into a single data source, making it easy to track dependencies and dimensions and advanced account structures to categorize and report financial data flexibly, ensuring that all interrelated GL accounts are consistently updated through the workflows and approval process.

Emily: Understood. Could you also please elaborate on these specific features in NetSuite and Sage Intacct that help maintain interdependencies between these GL codes?

Dave Sackett: Certainly. NetSuite provides a segmented chart of accounts and real-time reporting, which allows clear tracking of management interdependencies. Its revenue management module automates revenue recognition and ensures alignment with expenses, which is critical for accurate financial reporting. On the other hand, Sage Intacct is known for its dimensional chart of accounts, allowing for a more granular and flexible approach to managing GL codes. It supports automatic allocations and offers robust audit trails, ensuring that changes in GL codes are documented and those dependencies are maintained accurately by the accounting staff.

Emily: Understood. And, Dave, what about ERP solutions like QuickBooks, which are, you know, often used by small to medium-sized businesses? How do they handle these interrelationships?

Dave Sackett: With QuickBooks, it’s a very simple system, but there are essential tools to manage these interrelationships between GL codes. It uses linked accounts to automatically update related GL codes when transactions are recorded, ensuring the basic dependencies are still maintained. For example, when an invoice is generated, it automatically updates the revenue account and accounts receivable. This simplicity is beneficial for small businesses that don’t have complex financial needs and still require accuracy in their financial reporting.

Emily: Got it. So, talking a little bit about, you know, regulated environments such as companies in the government or contracting sector, how does ERP like Deltek Costpoint help maintain these interrelationships?

Dave Sackett: One of the specialties of Deltek Costpoint is that it’s specifically designed for government contractors and heavily regulated environments. It offers project-based accounting that links revenue, COGS, and expenses to specific projects and contracts, ensuring compliance with strict government regulations. Costpoint’s multi-entity and multi-currency management features help keep accurate interdependencies across different entities and countries, while its automated billing and revenue recognition ensures that revenue and related costs are properly matched and reported. Many government-type jobs are cost-plus, meaning that the government is going to have access to your cost records. So, you need to have it set up logically and structured for that audit and that review as part of your sale to the government.

Emily: Understood. Also, how important are third-party AI tools in maintaining these GL codes and the interrelationships for various ERPs?

Dave Sackett: The more complex your system, the more important it is for your ERP system to maintain GL code interrelationships. While ERPs like SAP, NetSuite, and Microsoft Dynamics come with built-in automation and analytic tools, third-party AI tools offer advanced capabilities, such as predictive analytics, anomaly detection, intelligent automation, and flux analysis. For example, AI tools can automatically identify patterns and anomalies in transactions that could affect multiple GL accounts, such as unusual spikes or unexpected expense increases, and suggest corrective actions. So that’s your flux analysis where it’s looking and saying, “Hey, this data doesn’t belong here. It’s not in the normal spec.” That’ll help users of that AI zero in and figure out why it’s doing that. Then, once they find that anomaly, they can train the bot to process it normally or still keep it as an exception, depending on the nature of the anomaly.

Emily: That’s amazing. So, Dave, can you provide an example of how an ERP system might handle a complex transaction that affects multiple GL codes?

Dave Sackett: Yeah, certainly. Let’s take an example of a sales transaction involving inventory in SAP. While the inventory is sold, the system automatically decreases the inventory asset account, increases the COGS expense, increases the revenue, and at the same time, calculates and records tax liability based on the tax rules that are set up in the system. All of these entries are done automatically, ensuring that every GL code involved in this transaction is accurate, and it’s being updated in real-time. Not only does it maintain interdependencies, but it also ensures compliance with accounting standards as it’s programmed specifically that way.

Emily: Got it. So finally, to wind things up, one last question, Dave. How can CFOs leverage these ERP systems to ensure continuous improvement in financial management and reporting?

Dave Sackett: CFOs can leverage ERP systems by utilizing advanced features like real-time reporting, automated workflows, and data analytics to continually monitor and improve financial performance. Regularly reviewing configurations, such as account structures, financial dimensions, and chart of accounts, ensures alignment with the company’s evolving needs. Additionally, investing in training and leveraging ERP vendor support can help maximize the use of these systems. By doing so, CFOs can ensure that financial management processes remain efficient, accurate, and compliant with regulations. Your ERP system is an investment, and you want to constantly check it to make sure it’s meeting your needs and your future needs. So, it’s not a one-time investment. It’s something that you want to have to grow with the company and continue to invest in that technology to give you better and better reporting.

Emily: Got it. Thank you so much, Dave, for sharing your insights on this complex yet essential topic. It was great having you, and this discussion was truly fruitful. So, thank you so much.

Dave Sackett: Yeah, thank you, Emily.

Mastering the digital age: a comprehensive learning plan for CFOs on AI, automation, data security, and generative AI

In the rapidly evolving landscape of finance and accounting, the integration of cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), automation, data security, generative AI, and large language models (LLMs) has very high potential to transform the operational processes. Chief Financial Officers (CFOs) need to be at the forefront of adopting these technologies to drive efficiency and innovation. 

This detailed 6-month proposed learning plan is designed to equip CFOs with the necessary skills and knowledge to navigate these changes successfully, with a clear outline of the benefits associated with each section.

Month 1 & 2: Foundations in data science, AI, and data security

1. Skills to Acquire: Basics of data science, statistical analysis, Python programming, introduction to AI and ML concepts, and fundamental data security principles.

2. Courses: 

3. Benefits: Acquiring these foundational skills enables CFOs to understand and leverage data more effectively, make informed decisions based on statistical analysis, and implement basic cybersecurity measures to protect sensitive financial information.

Month 3 & 4: Advanced AI/ML, Automation, and introduction to generative AI

1. Skills to Acquire: Advanced ML techniques, AI applications in finance, robotic process automation (RPA), and an introduction to generative AI and LLMs.

2. Courses:

3. Benefits: Learning these skills helps CFOs to automate routine financial tasks, freeing up valuable time for strategic activities. Additionally, an understanding of generative AI and LLMs can unlock new possibilities for data analysis, report generation, and predictive modeling, enhancing the financial decision-making process.

Month 5 & 6: Strategic implementation, ethical considerations, and advanced data security

1. Skills to Acquire: Strategic implementation of AI/ML and automation technologies, ethical considerations in AI, leading AI-driven transformation projects, and advanced data security strategies, with a focus on generative AI and LLMs.

2. Courses:

3. Benefits: This final phase empowers CFOs to confidently lead digital transformation initiatives, ensuring they are ethically sound and compliant with data protection laws. Advanced cybersecurity knowledge is crucial for protecting against increasingly sophisticated cyber threats, safeguarding the organization’s financial data, and maintaining stakeholder trust.

Conclusion

This learning plan provides CFOs with a robust framework to master AI, ML, automation, data security, generative AI, and LLMs. By embarking on this learning journey, CFOs will gain a competitive edge in the digital transformation of finance, driving operational efficiencies, fostering innovation, and ensuring the highest standards of data security. The skills and knowledge acquired will not only enhance the strategic decision-making process but also position CFOs as visionary leaders in the digital age, ready to tackle the challenges and opportunities that lie ahead in the evolving landscape of finance.

Conversational user experience (UX) for AI in finance

Introduction

Conversational UX is gaining traction in tandem with rapid advancement in AI tech. It seems intuitive that humans would want to communicate with AI agents or bots as naturally as possible. Nothing about conversational UX is new, of course. We just happen to be at a tipping point where various AI technology trends are pushing it into prominence. Substantial research and successful application of that research for real-world scenarios over the past decade have made conversational UX ubiquitous and ready for primetime where the best is yet to come.

Start of an AI-volution

The new age of sophisticated applications using generative AI demands that designers dig deep into the art of conversation. This is an exciting time to explore possibilities to make a dialogue between humans and bots, natural, meaningful, fun, and engaging. On the B2B SaaS front, we are just scratching the surface.

With AI infused in all kinds of finance process automation, there are a ton of possibilities to make conversational UX a key part of such applications. It begs a radical question. What if there is no traditional UI layer in finance applications? Can business outcomes be achieved through good old easy-going conversations between humans and AI solely through a chat window with no regular app interface to speak of? How can designers design and orchestrate the creation of these environments? Designers must investigate, for instance, what’s the equivalent of a casual business meeting in a cafe versus a mission-critical exchange in a conference room about budgeting between a CFO and their AI assistant.

At Hyperbots, designers are exploring ways to create a humane, relatable avatar for the powerful AI capabilities of our platform addressing the automation needs of finance processes like Accounts Payable and Expense Processing for the CFO’s office – processes that are still woefully manual.

The core work that the Hyperbots AI Assistants do are:

Accountant queries can be as practical as asking the AI assistant about invoices that can be safely bulk-approved or the ones that need their manual review.

The AI assistant reponds with real-time actionable data about pending invoices that need manual review.

Analytics critical to business decision making can be easily pulled up with a simple query.

These are sneak peeks into the early work that’s emerging as part of the conversational UX design charter for the design team at Hyperbots. Within broad chatbot categories that exist today, here’s where we might fit in.

Begin here

Designers at Hyperbots know that if they want to create distinct AI Assistant identities, they need to focus beyond the visual elements of an avatar or the UI layer of a dialogue box. They must ask the question what makes a dialogue meaningful? Especially between a machine and a human. They must dive deep into the science of Human-Computer Interaction and the art of conversation. So far our secondary design research has pointed to some seminal work already in the public domain like the recent ethnographic study by NNGroup into usage patterns of ChatGPT, Bing, and Bard users suggesting there could be 6 different types of conversations with generative AI.

  1. Search queries
  2. Funneling conversations
  3. Exploring conversations
  4. Chiseling conversations
  5. Expanding conversations
  6. Pinpointing conversations

Conclusion

These provide a great basis for brushing up on fundamentals and taking the right first step. What should follow is arriving at a solid hypothesis of what specific approaches might work for ur CFOs and their teams and then testing these hypotheses with rigor. 

We are early in our exploration of conversational UX at Hyperbots. We are more than convinced this space cannot remain untapped if we are to create a groundbreaking experience for our customers grappling with legacy applications to conduct their finance operations central to the customer experience we want to build for our CFOs and their teams. As they say, watch this space!