The CFO’s Guide to AI-Powered ERP: Adopting AI to Thrive in Uncertain Times

In other words: AI is like a CFO’s financial analyst, risk manager, and strategic advisor—all rolled into one. 

While AI has limitations—such as its inability to engage in ethical reasoning—these limitations are diminishing every day, and AI is letting CFOs do more, faster, while keeping the same staffing level.

CFOs everywhere are taking AI more seriously. A recent study I received from one of our partners cites that 86% of finance leaders have incorporated AI into their operations. 

Whether you’ve started to leverage AI in your company or not, the opportunities presented by AI are growing every day, and it’s becoming easier than ever to get started— especially since ERP solutions like SAP S/4HANA Cloud Public Edition are integrating AI functionalities directly into their core capabilities.

What AI Is Available in ERP Solutions?

At Vision33, we’re frequently asked to explain what types of AI are functional in today’s ERP solutions versus what’s still speculative or a pipe dream. So, let’s look at this.

Here’s a breakdown of four core AI technologies commonly available in ERP today:

1. Machine learning (ML): ML algorithms use historical data to detect patterns and make predictions based on past information. ML is a fundamental driver of predictive analytics, and for finance teams, it lets ERP solutions do things like automate reconciliation, detect anomalies, and monitor compliance.  
2. Predictive analytics: Building on ML, predictive analytics uses statistical models to forecast outcomes. In ERP, financial leaders use predictive analytics for forecasting things like cash flow, demand, and financial performance. 
3. Robotic process automation (RPA): RPA is used to automate tasks that are both repetitive and rule-based. For financial teams, this can include automating data entry, invoice processing, and report generation. RPA enables employees to spend less time on rote tasks and more time on strategic work.
4. Generative AI: This form of AI can summarize large amounts of data and create new content. Regarding ERP solutions, financial teams use generative AI to automate the creation of reports, conduct scenario planning, and interpret data. Generative AI can produce highly accurate content in a fraction of the time a person can.

Joule is SAP S/4HANA Cloud Public Edition’s generative AI co-pilot. Joule agents work together to help users accomplish tasks via natural language queries.

With AI embedded in ERP, finance leaders can move from looking in hindsight to looking with foresight. They can identify new revenue opportunities, build financial resilience, and improve operating cash flow.

In other words, AI in ERP can help financial teams become an engine of profitable growth.

Now, let’s examine in more detail the key benefits AI-powered ERP solutions offer CFOs and other financial decision-makers.

5 Benefits for CFOs of AI-Powered ERPs

Vision33 is an SAP Gold Partner. That means we have deep experience providing SAP solutions to businesses that want to transform. And perhaps the most transformative SAP product on the market right now is SAP S/4HANA Cloud Public Edition.

With SAP Business AI at its core, SAP S/4HANA Cloud Public Edition offers many benefits for CFOs.

It helps CFOs:

1. Improve working capital

• Make predictions about which invoice might be paid late and where collection efforts would be most effective 
• Reduce the time needed to match accounts receivables

2. Accelerate financial close and accounting

• More quickly resolve common errors experienced during the financial close 
• Make it quicker and easier to specify depreciation keys
• Using Joule, simplify the process of US internal tax configuration

3. Optimize compliance

• Detect unusual transactions and prevent fraud earlier than ever

4. Support planning and analytics

• Speed up planning and analytics processes by using natural language to query your data and rapidly generate charts and tables
• Enrich planning and analysis activities with predictive scenarios and historical data and describe data based on trends, outliers, and patterns
• Eliminate hours spent summarizing financial data and transform data into actionable financial business insights

5. Process travel and expenses faster

• Automate expense submission, reduce non-compliant spend, and uncover payment risk at scale

Because of AI’s many benefits, it plays a pivotal role in digital transformation. Acting as a catalyst, AI opens new possibilities for businesses to navigate disruption, unlock efficiencies, and build future-ready enterprises.  

Yet, AI isn’t a standalone tool. 

Instead, it’s part of a broader suite of processes responsible for digital transformation. Let’s look at this idea more closely.

AI’s Role in Digital Transformation

To undertake digital transformation in the age of AI, businesses should still start by adopting cloud ERP. 

Businesses should also continue to focus on integrating various aspects of their operations, such as financial planning, procurement, and supply chain management.

After these steps are taken, the AI embedded within the ERP system can draw on company-specific data and use cloud technology to further fuel digital transformation.

Watch these three brief videos from SAP to see how SAP S/4HANA Cloud Public Edition uses SAP Business AI and your company data to advance digital transformation:

• Transform Your Wholesale Distribution Business With AI
• Transform Your High-Tech Manufacturing Business With AI
• Transform Your Consumer Products Business With AI

Now that we’ve discussed the fundamentals of AI in ERP, let’s turn our attention to implementation. Let’s look at six real-life use cases and discuss building a roadmap, so your financial team can start thriving with AI.

Part Two: Thriving With AI-Powered ERP

6 Use Cases for AI in Financial Management

It’s good to understand AI conceptually. But how does it play out in the real world?

Here’s a breakdown of six use cases for AI in financial management. They show the tangible benefits through possible real-life scenarios.

Use Case #1: Smarter Financial Forecasting in Volatile Markets

⚠️Challenge: A manufacturing company is facing volatility due to fluctuating material costs and unpredictable consumer demand. Their traditional forecasting model is failing to predict cash flow accurately, which is leading to financial challenges and missed opportunities.

💡AI solution: The company has recently implemented SAP S/4HANA Cloud Public Edition and sees an opportunity to take advantage of its integrated predictive analytics. The AI solution analyzes its historical and real-time market data and adjusts cash flow forecasts, giving the CFO a significantly more accurate picture.

✅Outcome: The CFO now receives real-time cash flow forecasts, enabling proactive adjustments to budget planning. This lets the company make data-driven business decisions and better anticipate economic shifts, improving its financial resilience in volatile market conditions.

Use Case #2: Enhanced Compliance Tracking & Fraud Detection

⚠️Challenge: A retail company is struggling to keep up with manual compliance tracking. The sheer volume of financial transactions it processes in a day means compliance tracking is a significant undertaking. Without an automated system, the CFO is worried about human error and how that might affect compliance.

💡AI solution: Having recently implemented SAP S/4HANA Cloud Public Edition, the CFO recognizes that it’s time to take advantage of the solution’s built-in ML-powered risk and compliance features. Once configured, the AI solution can detect anomalies and fraud in financial transactions with incredible speed and accuracy—far surpassing human abilities.

✅Outcome: With AI on its side, the company can now address anomalies quickly, measurably reducing fraud. This minimizes compliance risks and allows the CFO to operate with a greater sense of certainty and confidence. It also frees up the finance team’s time, allowing them to focus on strategic activities. 

Use Case #3: Accelerated Financial Close

⚠️Challenge: A high-tech company is struggling to produce timely business insights due to its painfully long financial close cycles. Each month, employees must do manual reconciliations and troubleshoot data discrepancies. This delay is hindering the CFO’s ability to make timely decisions and is negatively affecting business agility. It’s also a slow and frustrating process for the already-stretched-thin finance team.

💡AI solution: The company is almost done implementing SAP S/4HANA Cloud Public Edition and is planning how to use its AI-powered reconciliation capabilities to speed up the financial close. When the implementation is complete, the company immediately rolls out an AI solution to automate financial transaction matching, resolve financial discrepancies in real-time, and speed up the financial close process.

✅Outcome: Almost overnight, the company reduces its financial close cycle—from days to hours. This gives the CFO faster access to financial information, enabling agility and better decision-making. It also allows the CFO to improve operational efficiency and empowers the finance team to focus on strategic planning activities rather than manual work.

Use Case #4: Procurement and Spend Optimization

⚠️Challenge: A food and beverage company is struggling with inefficient procurement processes and overspending on supplier contracts. Its bottom line is flailing. The finance team is overscheduled and simply doesn’t have the resources to manually analyze supplier contracts.

💡AI solution: The company implemented SAP S/4HANA Cloud Public Edition last year but hasn’t explored its AI capabilities. The CFO decides it’s time and wants to begin by leveraging the software’s AI-driven spend analysis capabilities. They use this AI solution to analyze their vast amount of procurement data quickly and identify opportunities to right-size supplier contracts.

✅Outcome: The company negotiates more favorable supplier contracts, enhances efficiencies, lowers overall spend, and improves the bottom line. The CFO can drive change within the organization and implements processes to avoid these issues in the future.

Use Case #5: Integrated Data & Real-Time Financial Reporting

⚠️Challenge: A distribution company is struggling with siloed financial data across its ERP solution, supply chain, and standalone financial systems. This lack of integration is halting the flow of data and leading to unreliable, inaccurate, and delayed reporting. The CFO is struggling to make real-time decisions, and the business is at risk.

💡AI solution: With SAP S/4HANA Cloud Public Edition, the company decides it’s time to leverage the ERP solution’s embedded AI capabilities to eliminate the problem. After cleaning and structuring its data, the company uses the AI solution to consolidate data across its multiple sources. It then leverages AI-powered analytics and SAP S/4HANA Cloud Public Edition’s co-pilot Joule to gain a holistic view of its financial performance and identify quick wins to turn things around.

✅Outcome: With data connected across financial systems, the CFO is empowered to make informed business decisions. Data is flowing in real-time, reporting is accurate and trustworthy, and the team can focus on strategic tasks. 

Use Case #6: Improved Strategic Decision-Making

⚠️Challenge: A consumer product company is at a crossroads. The CFO needs to make important strategic decisions regarding profitability, cost optimization, and investment planning. However, they’re relying on disparate reports and manual analysis, which is both time-consuming and error-prone. The CFO is concerned this is putting the company at financial risk.

💡AI solution: The CFO decides it’s time to take advantage of Joule, SAP S/4HANA Cloud Public Edition’s integrated AI co-pilot. Joule allows the financial team to query company data in real-time and receive instant AI-generated insights on KPIs and financial risk. Using natural language prompts, Joule summarizes the information and generates a document with comprehensive, up-to-date information. 

✅Outcome: With the report in hand, the CFO can make critical strategic decisions with confidence and clarity. Every time the CFO needs updated information, Joule creates a new report with the click of a button. This restores the company’s agility and adaptive resilience and puts it on the right path for improved profitability and sustainable growth.

Building a Roadmap to Get Started With AI

The seamless integration of AI in ERP systems is transforming financial strategy and operational efficiency and future-proofing businesses.

But how can you get started with AI in ERP?

Here are three key actions to consider when building your roadmap:

1. Identify Quick Wins for AI Integration

• Select key use cases. With so many opportunities available, knowing where to start with AI implementation can be overwhelming. Choose a few priorities to begin with and use those to create momentum, achieve buy-in from above and below, and garner quick wins. We suggest our clients start with high-impact, low-risk use cases. This could be invoice matching, anomaly detection, or expense process automation.
  
  
• Use AI that’s readily available. AI is already embedded and ready to be enabled in SAP S/4HANA Cloud Public Edition. Start with something intuitive, such as using Joule to perform queries of your data. Joule can quickly demonstrate AI’s value, even for AI-resistant employees.
  
  
• Start with pilot projects. To test and evaluate AI capabilities, begin with small-scale pilot projects. This allows for controlled deployment and will help identify potential challenges early on.

• Ensure your data is accurate, clean, and structured. Clean, structured data is critical for CFOs to effectively leverage AI-driven insights. While AI can enhance financial forecasting, risk assessment, fraud detection, and more, it can only do so if the underlying data is clean and structured. Before you implement AI, look at your data and see if enhancements are needed. Poor data leads to flawed insights, unreliable predictions, biased outcomes, and regulatory risk. As they say: Garbage in, garbage out.
  
  
• Strengthen data governance and integration across financial systems. Poorly governed data will also cause problems regarding AI use. Since financial data is often scattered across disparate systems, ensuring appropriate integration before deploying AI will allow AI models to access a comprehensive view of your organization's financial picture. Integration and consistency are vital for AI to identify meaningful patterns and trends.
  
  
• Review your system’s security controls and role-based user access restrictions. Role-based access control (RBAC) is a foundational security feature in ERP systems like SAP S/4HANA Cloud Public Edition. These restrictions extend to how users can access data using AI and Joule. Only authorized personnel will have access to sensitive financial data, such as profit margins, revenue, and expenses. Before deploying AI, check that your RBAC is set appropriately and that AI tools will respect these security restrictions.
  
  
• Account for an AI learning period. When you deploy AI in your ERP solution, it will take some time for the AI to ‘learn’ from your data. Learning periods are shortening all the time and cloud-based ERP solutions, like SAP S/4HANA Cloud Public Edition, often leverage pre-trained AI models, which can significantly reduce the initial learning time. You can also help speed things up by ensuring your data is clean and well-structured before you deploy the AI.
  
  
• Provide employee training. Before you roll out the AI, ensure your employees are trained on how to use its features effectively. This could include offering training on performing natural language queries, interpreting AI-generated insights, and using automation tools.

• Key business questions include:

• What specific financial management problems are we trying to solve with AI?

• Where can AI provide the most significant value in our ERP solution?

• Where can AI reduce complexity in financial processes?

• How does AI align with our digital transformation roadmap?

• What KPIs should we track to measure AI’s impact?

• Key data quality questions include:

• Is our data clean, structured, reliable, and accessible for AI?

• Do we have an adequate data governance strategy?

• Is our data appropriately integrated from disparate systems?

• Do we have enough historical data to train AI effectively?

• Key risk management questions include:

• Do we have the expertise to implement and manage AI? 

• How will we train employees to use and adopt AI effectively? 

• Will our security features, such as RBAC, extend to AI co-pilots?

• What ethical guidelines will we establish for AI use? 

• How will we monitor the AI system to ensure it’s working effectively?

At Vision33, we can help you answer these questions and devise an implementation plan that will ensure a seamless deployment and mitigate risk.

Conclusion

In today’s rapidly changing landscape, AI is becoming essential, rather than optional, for businesses to both survive and thrive. 

AI and ERP integration are transforming financial strategy and operational efficiency and future-proofing businesses, and we’re seeing that CFOs who reimagine possibilities—not those who just manage risks—are the ones driving real impact. 

By embracing AI-powered ERP solutions, leaders can turn uncertainty into a competitive advantage and fuel sustainable growth. 

Interested in learning how Vision33 can help transform your business with AI-powered SAP S/4HANA Cloud Public Edition? Connect with our sales team today.