Imagine finishing your monthly close with hours to spare. Instead of wrestling with spreadsheets, you’re crafting insightful narratives, exploring new growth scenarios, or advising the executive team on critical decisions.

The friction and tedium of manual data management recede into the background, replaced by streamlined workflows and automated processes that practically run themselves.

This isn’t a far-off fantasy—it’s the reality for finance professionals who embrace Python and Generative AI.

For seasoned finance professionals such as CFOs, directors, and controllers, the core challenge often lies not in the complexity of financial concepts but in the sheer volume of repetitive, mechanical tasks.

While Excel has served as the backbone for decades, it struggles when data becomes massive and dynamic or when nuanced analytics require more than built-in pivot tables can deliver.

Python, enhanced by AI-driven tools, can step in to handle these complexities, empowering you to focus on high-level strategy and forward-looking analysis rather than day-to-day grunt work.

Why Python and AI Are Your Next Power Tools

Finance professionals have traditionally relied on tools like Excel, ERP systems, and BI platforms.

These tools are tried and tested but come with limitations when dealing with increasing complexity, evolving data sources, and the demand for real-time analytics.

Why Now?

• Data Explosion: Finance teams are no longer dealing with just monthly statements. You now have streaming data from ERP systems, CRMs, banks, and external market feeds. Excel can buckle under this load.
• Speed and Accuracy: Python, coupled with AI, allows you to set up scripts that run instantly, ensure data integrity, and reduce human error.
• Strategic Freedoms: Freeing yourself from routine chores means you can invest time in scenario planning, capital allocation strategies, and advising the executive team on big-picture decisions.
• Scalability and Customization: Python isn’t a one-size-fits-all tool—it’s fully customizable. Whether you need to integrate new data sources, run complex sensitivity analyses, or produce sophisticated dashboards, Python can do it all. Combine that with AI-driven code assistance (e.g., ChatGPT, Copilot, or Gemini) to speed up development and reduce friction.

Start small. If you spend two hours a week merging department-level budget files, try writing a Python script to merge them automatically.

Use an AI assistant to help you draft the code. You’ll see immediate ROI and build confidence for bigger projects.

10 Finance Tasks You Can Automate Right Now

To truly appreciate the power of Python and AI, consider how they apply to tasks you tackle every day. Think about something as fundamental as cleaning and consolidating files.

Instead of copying and pasting data into one master workbook, you can write a Python script—assisted by AI prompts—that systematically reads, cleans, and merges all your input files. What once felt like a chore now runs quietly in the background.

Or picture the complexity of forecasting. Rather than tinkering with Excel’s forecasting functions and updating cell references each month, Python can integrate with libraries that apply advanced models like ARIMA or Prophet to your historical data.

One prompt to the AI could yield a script that updates your revenue projections in real time, adjusting instantly when fresh data flows in.

Here are the 10 tasks you can automate:

#1: Forecasting and Time Series Analysis

Use AI tools to generate a baseline forecast script. Then, refine parameters until you get consistently accurate predictions.

Why Python is better?

Python simplifies forecasting by integrating historical data, adjusting for seasonality, and automating updates as new data comes in.

Once the model is set up, Python recalculates forecasts in real time, making it far more flexible and powerful than Excel.

Example
A company looking to forecast future cash flows based on historical sales data can automate the process with Python.

The ability to adjust for seasonal trends or unpredictable changes makes Python especially useful for real-time business decision-making.

#2: Data Cleaning and Standardization

Convert messy, multi-format spreadsheets from multiple regional offices into a clean, standardized dataset ready for analysis.

Define a set of cleaning rules and let Python’s pandas library handle missing values, inconsistent date formats, and stray text fields.

Why Python is better?

Python allows you to automate data cleaning through pre-defined rules. You can standardize data formats, remove inconsistencies, and handle large datasets seamlessly.

This is especially helpful when dealing with transactions across multiple systems or departments.

#3: Complex Visualizations (e.g., Cohort Analysis)

Tip: Use matplotlib, seaborn, or plotly libraries. With AI help, you can generate the code for complex visualizations without deep technical know-how.

Imagine tracking customer revenue retention over time using cohort analysis.

With Python, this involves creating the cohorts once and then generating the visualizations effortlessly, allowing finance teams to focus on interpreting results rather than building charts.

Why Python is better?

Python streamlines the creation of complex graphs by automating both data preparation and visualization steps.

Once the data structure is set, generating these visuals becomes a dynamic and repeatable process.

Unlike Excel, where you need to manually format and update charts, Python handles this automatically.

Example
Imagine tracking customer revenue retention over time using cohort analysis.

With Python, this involves creating the cohorts once, then generating the visualizations effortlessly, allowing finance teams to focus on interpreting results rather than building charts.

#4: Consolidating Multiple Files

Merge monthly P&L files from 15 subsidiaries into a single workbook with one click.

Set up a Python script to open every file in a directory, standardize column names, and output a consolidated file. You’ll never open 15 separate spreadsheets again.

Why Python is better?

Python automates the entire process, enabling you to read multiple files, identify common fields, and merge them into a single dataset with ease.

Python efficiently handles large volumes of data from various formats (Excel, CSV, JSON), freeing you from repetitive work.

#5: Dynamic Cash Flow Projections

Update cash flow projections the moment new sales or expenses are recorded.

Integrate Python scripts with a scheduling tool (like cron or Windows Task Scheduler) to run nightly and email you a refreshed cash flow statement each morning.

Why Python is better?

Python automates cash flow projections by integrating various data inputs (e.g., sales forecasts and payment schedules) and dynamically updating projections as conditions change.

It handles multiple scenarios without the manual reworking that Excel requires.

Example
A company can automatically generate monthly cash flow projections that adjust in real-time as new sales and expense data are received.

With Python, these projections can incorporate different assumptions, such as late payments or seasonal fluctuations, making it much more adaptable than Excel.

#6: Bank Reconciliations

You can cross-check thousands of bank transactions against internal ledgers in seconds.

Use Python’s data-frame merge operations to flag discrepancies automatically and send a report of mismatches to the accounting team.

Why Python is better?

Python automates the reconciliation process by comparing transactions between different datasets, quickly identifying mismatches, and flagging potential issues.

This eliminates the need for manual checks, which are slow and error-prone in Excel.

#7: Automate Scenario & Sensitivity Analysis

You can write a Python script that loops through scenarios, updates your models, and outputs a comparison table, complete with charts and narrative summaries generated by AI.

Why Python is better?

Python enables you to simulate hundreds of scenarios automatically, providing quick insights into how changes in key financial variables impact the company’s bottom line.

This allows for real-time analysis and decision-making.

Example
Imagine assessing how a 2% increase in interest rates would affect your company’s debt servicing costs.

Instead of manually adjusting numbers and recalculating, Python runs the simulations in seconds, producing detailed outputs for multiple interest rate scenarios.

#8: Expense Categorization

You can classify thousands of transactions by expense type using predefined rules or even a simple AI model.

Tip: Start with rule-based logic. Later, teach a machine learning model in Python to categorize expenses based on historical classifications.

Why Python is better?

Python automates categorizing transactions by applying predefined business rules.

It can also reconcile discrepancies faster and more accurately than Excel, reducing manual labor and improving efficiency.

#9: Risk Modeling & Monte Carlo Simulations

Python libraries like numpy and scipy make simulation simple. AI tools can help write initial code and explain each step.

Why Python is better?
Python automates running complex risk models and simulates potential outcomes based on multiple factors. It’s ideal for running large-scale simulations, which would be difficult to manage in Excel due to its limited processing power.

Example
A finance team might use Python to simulate the potential impact of fluctuating market conditions on a portfolio of investments.

This analysis, which could take hours to set up in Excel, can be performed quickly and repeatedly with Python.

#10: Automated Data Fetching

You can pull real-time foreign exchange rates or stock prices via an API and integrate these into your models without manual data entry.

Leverage Python’s requests library to fetch API data. AI assistants can help troubleshoot authentication and formatting issues.

Why Python is better?

Python automates data fetching from various APIs or databases, integrating real-time data directly into financial models.

It eliminates the need for manual updates and ensures your analysis is always based on the latest data.

As you gradually incorporate Python and AI into tasks like expense categorization, multi-file consolidation, and complex reporting, you’ll notice a pattern: each automation adds a layer of reliability and frees you to focus on higher-level thinking.

You’re no longer in the weeds; you’re on higher ground, surveying the landscape.

Your Path to Mastering AI and Python for Finance

Embracing AI and Python doesn’t mean you need to become a full-time software engineer. Instead, you’re layering new capabilities onto your existing expertise.

Stage 1: Curiosity to Action

Start by using ChatGPT or Bard to handle small tasks like summarizing a PDF or generating a first draft of a memo.

Keep track of tasks where you feel stuck, then try: “My job is X, I want to do Y. ChatGPT, can you draft a Python script to achieve this?” Even if 20% works, that’s a big head start.

Stage 2: Structured Prompting

Adopt prompting frameworks like CSI+FBI (Context, Specific, Instruction + Format, Blueprint, Identity) for consistent results.

Example: Instead of “Give me a Python script,” say: “Context: I am a CFO consolidating monthly reports. Specific: I need code to merge CSV files. Instruction: Write a Python script using pandas. Format: Provide code snippet with comments. Blueprint: Base it on merging three CSV files. Identity: You are a finance coding coach.”

Stage 3: Prompt Engineering

Master techniques like “chain-of-thought” prompting, which encourages AI to break down problems step-by-step.

Chunk large tasks into smaller steps: first clean data, then calculate KPIs, then visualize results.

Learn how to ask AI agents to reason through complex problems rather than just giving final answers.

Stage 4: Integrating Python

Install Python and get comfortable with pandas. Use AI to help you fix errors quickly.

Pair simple Python scripts with AI prompting. If something breaks, copy the error into ChatGPT: “Here’s my error, how do I fix it?”

Stage 5: Full Customization and Deployment

Connect Python scripts to confidential company data in secure environments (like Azure or AWS).

Customize AI models for your use cases. For example, use private large language models fine-tuned on your industry’s financial reports.

Create end-to-end workflows: Python pulls data, AI analyzes it, Python outputs a dashboard, AI generates narrative summaries. You’re no longer just automating; you’re innovating.

The Path to Python Proficiency

If you’re new to programming, start with the fundamentals in an environment like Google Colab or Jupyter Notebook.

Load a sample CSV file and see if you can filter certain rows, calculate sums, or group data by category.

If something goes wrong, copy the error message and ask your AI assistant for help.

By doing this, you learn not just how to solve a single issue, but how to handle future challenges more confidently.

As you get comfortable, introduce visualizations.

Instead of static charts that require manual updates, Python allows you to generate sophisticated plots that refresh automatically as new data arrives.

With a single script, you can create correlation heatmaps, cohort analyses, and dashboards that pull in daily KPIs.

The more you do, the more you’ll realize Python’s immense capability to transform your reporting process.

Further down this path lies automation at scale.

Perhaps you set up a script that fetches exchange rates from an API every morning, updates your currency conversion tables, and sends you a daily summary.

Or maybe you build a model that forecasts sales based on historical data, clustering algorithms, and external indicators.

As you progress, you’ll incorporate machine learning techniques, experiment with advanced forecasting models, and even integrate your Python scripts with other enterprise systems.

Eventually, Python becomes more than just another tool.

It becomes a medium through which you create value and guide strategic decisions.

By the time you’ve mastered these advanced techniques, you won’t just be adopting the latest technologies—you’ll be shaping your team’s future, setting the standard for what’s possible in finance analytics.

Here is your path to proficiency simplified:

Phase 1: Basics

Start in Google Colab or Jupyter Notebook. Learn to load CSV files, filter rows, and perform aggregate calculations. Use ChatGPT to troubleshoot errors.

Take one of your Excel reports and replicate the pivot table operations in Python. Once comfortable, you’ll never look back.

Phase 2: Visualization

Use the Python libraries: matplotlib, seaborn, plotly. Replace static Excel charts with dynamic Python dashboards.

Update charts automatically whenever data changes. Ask AI to generate code for a correlation heatmap.

Tweak colors and labels for immediate professional-quality visuals.

Phase 3: Automation

Automate month-end close tasks, create Monte Carlo simulations for project valuations, or generate daily KPI dashboards.

Use Python scripts triggered by a scheduler. Let the computer run your tasks at 2 a.m. while you sleep.

Phase 4: Forecasting

Explore ARIMA, Prophet, linear regression, or even machine learning models.

Generate a sales forecast model that updates and emails results to the finance team every week.

Start with a basic regression model, then use AI to experiment with more advanced approaches. Compare accuracies and refine.

Phase 5: Advanced Techniques

Integrate APIs, automate emails and slide deck creation, and incorporate advanced statistical models. If you produce a monthly board meeting deck, Python can compile the updated charts, and AI can draft commentary in PowerPoint. You review and refine, saving hours.

Why Are The Python Libraries Important for Finance?

One of Python’s greatest advantages is its vast ecosystem of libraries that are designed for specialized tasks.

Some of the key libraries that finance professionals can leverage within Excel include:

pandas

The most popular Python library for data manipulation and analysis. With pandas, you can quickly analyze, clean, and structure data into formats that are ideal for financial modeling.

NumPy

Perfect for handling large datasets and performing mathematical operations, including linear algebra and statistical calculations.

matplotlib and seaborn

These libraries allow you to create a wide variety of visualizations, from simple bar charts to more complex financial graphs and charts.

scikit-learn

A machine learning library that can help finance professionals build predictive models to forecast trends or make data-driven decisions.

Faker

Useful for generating synthetic datasets that can be used for testing financial models or training machine learning algorithms.

statsmodels

A library that allows for advanced statistical modeling, perfect for regression analysis and time-series forecasting in finance.

Building a Simple Python App Using AI

Creating full-fledged applications may sound like a developer’s job. But with AI tools, you can transform a Python script into a user-friendly tool your team can use—no coding knowledge required.

Example Scenario: Monthly headcount reporting across multiple countries is inconsistent. You need a single harmonized report.

 Here’s how to do it step-by-step:

#1: Set Context with AI

Prompt your chosen AI model with: “I have multiple country-level Excel files with slightly different columns. I need a Python script that standardizes the columns, cleans the data, and merges them into one Excel workbook with separate tabs per country. Can you help?”

#2: Refine Logic

After the AI provides the initial code, say: “This looks good, but I also need to ensure that if ‘Full Name’ is missing, we combine ‘First Name’ and ‘Last Name.’ If ‘Annual Salary’ is missing, calculate it from ‘Salary’ * 12. Please add these rules.”

#3: Test and Tweak

Run the code in Visual Studio Code. If there’s an error, copy and paste it back into AI: “I got this error, how do I fix it?” You’ll quickly resolve issues.

#4: Add A GUI (Graphical User Interface)

Once the script works, prompt AI: “Create a simple GUI that lets a user select a folder of files and outputs a consolidated Excel file. Include instructions and a success message.”

AI will produce code using libraries like tkinter in Python.

#5: Package into an App

Use Python tools like pyinstaller to convert the script into an executable file. Now, colleagues can run the app on their laptops without having Python installed.

Result: A user-friendly tool that automates hours of monthly data prep. Next time you or your colleagues need that consolidated report, just double-click, select the folder, and let Python and AI do the heavy lifting.

From Manual Work to Strategic Insight with Python and AI

Integrating Python and AI into your financial workflows isn’t about adding unnecessary complexity—it’s about liberating you from low-value, time-consuming tasks.

By automating data cleaning, report generation, forecasting, and more, you reclaim the hours you once lost to manual processes.

In turn, you can redirect that energy toward strategic planning, insightful analysis, and forward-looking decision-making that truly shapes the future of your organization.

Adopting these tools may feel like a significant change, but the payoff is enormous.

With each step, from basic scripting to advanced custom applications, you’re redefining what’s possible in modern finance.

Instead of reacting to problems at month-end, you can proactively guide corporate strategy.

This journey enables you to deliver more timely, accurate insights while fostering a dynamic finance culture that is ready to meet tomorrow’s challenges.

FAQ

Q: Do I need coding experience to start using Python and AI in finance?
A: You don’t need a formal programming background. Modern AI tools can write initial drafts of your code, explain errors, and help you understand the logic. You can learn incrementally, starting with small tasks and letting the AI assist you at every step.

Q: How can I ensure data confidentiality when using AI and Python?
A: Most enterprises run AI models in secure, private environments. You can also anonymize sensitive data or rely on internal servers. Always follow your company’s data protection policies and consider solutions that allow AI-powered coding within a firewall-protected infrastructure.

Q: When will I start seeing results?
A: You can see immediate benefits even from small automation efforts. For example, automating the consolidation of monthly reports saves time right away. Over the following weeks and months, as you integrate more advanced capabilities—like forecasting models or scenario testing tools—the return on your investment grows exponentially.

Q: Which AI tools should I experiment with first?
A: Start with user-friendly and well-known platforms like ChatGPT or Microsoft Copilot. These tools integrate smoothly with common development environments, making it simpler to get started. Test a few options, see which aligns best with your workflows, and invest time in mastering that tool.

Q: How do I handle errors that pop up in the code?
A: Errors are part of the learning process. Copy the error message and ask your AI assistant how to fix it. Often, the solution is simple. Over time, you’ll become more comfortable interpreting errors and resolving them efficiently. The interplay between your growing skills and the AI’s assistance will gradually strengthen your coding expertise.

Imagine you work in the finance department of a supermarket, and your boss asks you for an analysis of which product promotions were successful this year and which ones weren’t.

If you’ve never done this type of analysis before, it might seem overwhelming.

But what if I told you that combining ChatGPT with Excel could make your job much easier and help you become irreplaceable at work?

In this blog,I’ll walk you through how you can use ChatGPT and Excel together to simplify your workflow.

I’ll share practical examples that you can apply right away, along with a trick that lets you leverage ChatGPT without sharing any confidential data.

Ready to boost your productivity? Let’s dive in!

Watch my YouTube Video

In this video, I explain how you can use ChatGPT to 10X your Excel skills as a finance professional.

Click on the GIF to watch it.

Boost your Productivity by Using ChatGPT with Excel

Here are the steps on how you can increase your productivity by 10x:

#1: Identifying KPIs Without Sharing Confidential Data

The key to using AI like ChatGPT without compromising confidentiality is the clever use of data headers.

For example, you have an Excel file that contains product names, revenue from last year and this year, as well as details about promotions during those years.

Instead of uploading the whole dataset to ChatGPT, you simply provide the headers: “Product,” “Revenue Last Year,” “Revenue Current Year,” etc.

This way, ChatGPT can understand the context without accessing sensitive numbers, which helps keep your data secure.

After you provide the headers, you can ask ChatGPT to suggest relevant KPIs for analyzing the promotion performance.

#2: Choosing the Right KPIs for Promotion Analysis

After inputting the headers, ChatGPT suggested several potential KPIs, including:

• Promotion Effectiveness
• Revenue Growth Rate
• Promotion Spending Growth Rate
• Revenue to Promotion Ratio
• Incremental Revenue per Unit of Promotion

Each of these KPIs tells you something different about how the promotions performed, but I found the last one particularly interesting because it helps identify how much additional revenue was generated for every dollar spent on promotions.

I then asked ChatGPT how to calculate this KPI in Excel.

#3: Getting the Right Excel Formula from ChatGPT

ChatGPT provided a detailed explanation and even gave me an Excel formula to calculate the incremental revenue per unit of promotion.

What I found most useful was that ChatGPT also assumed certain columns for the data (e.g., “Revenue Last Year” in Column D).

I double-checked my Excel file to ensure that these columns matched, and they did, so I used the formula directly.

After entering the formula, I formatted the results, and just like that, I had the incremental revenue per unit of promotion for each product.

This KPI allowed me to quickly see which promotions were effective and which weren’t.

#4: Making the Data Speak: Conditional Formatting

But numbers alone can be overwhelming, especially if you have dozens of products to analyze. That’s where conditional formatting in Excel becomes a powerful ally.

I asked ChatGPT how to highlight the best and worst-performing promotions, and it suggested using conditional formatting.

With conditional formatting, I applied a color scale to the KPI column, making it easy to see which products performed well (in blue) and which did poorly (in red).

For instance, it was immediately clear that while walnuts had a high return on promotion, sodas didn’t perform as well.

#5: Visualizing the Results with Graphs

If you’re dealing with a lot of data, it can be challenging to communicate your findings effectively.

To make the data more digestible, I asked ChatGPT which graph would be best to represent the KPI data for all 50 products (SKUs).

It suggested a few options, such as a bar chart, scatter plot, and heat map.

Based on my experience, I decided to go with a scatter plot because it clearly shows the relationship between promotional spending and revenue performance.

ChatGPT even guided me step-by-step on how to create the scatter plot and label each point with the corresponding product name, making it easy to identify which products were overperforming or underperforming.

#6: Interpreting the Graph for Insights

Once the scatter plot was ready, I wanted to understand what it meant for each product. I used ChatGPT again, asking how to interpret the graph.

The AI provided a helpful explanation that showed me which products had high promotional spending but low returns and vice versa.

For example, I noticed that walnuts were performing exceptionally well—we didn’t spend much on promoting them, but they generated a lot of additional revenue.

On the other hand, chicken promotions didn’t fare as well. I even filtered the data to focus on just the meat category, allowing me to compare beef and chicken directly.

Practical Tips for Your Next Analysis

• Use Headers Only: Always provide headers instead of confidential data when using AI tools.
• Conditional Formatting: Make large datasets easier to understand by color-coding key metrics.
• Visualize with Scatter Plots: Use scatter plots for clear visual representation, especially when analyzing multiple products.
• Ask ChatGPT for Explanations: Don’t hesitate to ask ChatGPT how to explain complex metrics to your boss or team.

Watch my YouTube video on how you can use ChatGPT to 10X your Excel skills as a finance professional by clicking here.

Wrapping Up

In just a few minutes, we used ChatGPT to identify the right KPIs, calculate them, visualize the results, and even interpret the data.

By combining ChatGPT with Excel, you can not only make data analysis easier but also become more valuable to your organization.

With this method, you will quickly deliver insights that drive better decision-making.

If you want to continue improving your skills and stay ahead in corporate finance, I have something for you.

I’ve created a free 5-day email course covering how to get the most out of AI tools like ChatGPT. Click here to enroll!

FAQ

Q: How can I use ChatGPT with Excel without compromising data security?
A: You can use headers instead of actual data, which allows ChatGPT to understand the context without accessing confidential information.

Q: What are the best KPIs to analyze promotion performance in Excel?
A: Some effective KPIs include Promotion Effectiveness, Revenue Growth Rate, and Incremental Revenue per Unit of Promotion.

Q: How can I visualize KPI data effectively in Excel?
A: Scatter plots, bar charts, and heat maps are great for visualizing relationships between data points, especially for promotion analysis.

Q: How can I highlight the best and worst promotions in Excel?
A: Use conditional formatting to color-code KPIs, making it easier to see which promotions performed well and which didn’t.

Q: How does ChatGPT help explain complex metrics?
A: ChatGPT can provide clear explanations and even suggest how to present metrics to your boss or team in an understandable way.

AI is advancing at a staggering pace. Constantly, its capabilities seem to double, and it can already do things that were unimaginable just a few years ago: write and speak like us, generate images, and even write code.

Soon, AI will integrate deeply with our productive environments through APIs, handling mundane tasks on an unprecedented scale.

With these changes, it’s natural to wonder: how can you protect your job against AI?

The answer lies not in fearing AI but in harnessing its power and evolving alongside it.

Here are practical tips to safeguard your career and thrive in this AI-driven era.

Why Is It Important to Adapt to AI?

AI is here to stay, and it’s reshaping industries at lightning speed.

For finance professionals, AI has already started transforming how we analyze data, make decisions, and automate routine tasks.

If we don’t adapt, we risk falling behind. But the good news is that AI also brings opportunities – if you know how to leverage it.

By learning, evolving, and using AI as a tool, you can enhance your role, contribute more value, and secure your position in this new landscape.

Long-Term Strategies to Future-Proof Your Career

Below, you’ll find 10 long-term strategies to ensure that you don’t just keep up with AI, but thrive alongside it.

#1: Embrace Continuous Learning

How to do it

Set up Google Alerts for terms like “AI in finance” or “Fintech innovations” to get daily updates. Follow industry influencers on LinkedIn or Twitter for insights. Dedicate time each week to read articles, attend webinars, or take courses that provide updates on AI developments in finance.

Why it is important

AI evolves quickly, and staying informed is crucial to anticipate changes and adapt effectively. Continuous learning will help you stay ahead of industry trends and position yourself as an informed finance professional who can leverage AI for better decision-making.

#2: Develop Technical Skills

How to do it

Spend 1-2 hours each week learning about tools like data analytics, machine learning, or automation. Practice using Python, Power BI, or similar tools to automate routine financial tasks.

Why it is important

AI is a technical field, and having practical skills will make a significant difference in your ability to leverage AI for automation and data analysis. By understanding these tools, you’ll be better equipped to use AI to optimize financial processes and make informed decisions.

#3: Enhance Your Soft Skills

How to do it

Join a Toastmasters club to improve your public speaking skills. Practice leading meetings, presenting data-driven insights to your team, or taking on more leadership roles in your projects. Seek feedback from colleagues to continually refine your communication and leadership abilities.

Why it is important

AI can’t replace human qualities like empathy, leadership, and communication. These skills are critical for leading teams, conveying complex financial insights, and adapting to change—all of which are essential for adding value beyond what AI can offer.

#4: Specialize in Niche Areas

How to do it

Identify niche areas within finance that require human judgment, such as ESG analysis or financial compliance. Take courses or get certifications in these areas, and seek out projects that allow you to apply your knowledge in a specialized context.

Why it is important

AI excels at data processing, but human judgment is crucial in areas that involve strategic decision-making, ethics, or nuanced regulations. By specializing in a niche, you position yourself as an indispensable expert who brings unique value that AI cannot replace.

#5: Leverage AI Tools to Boost Productivity

How to do it

Familiarize yourself with platforms like Microsoft’s AI Builder or UiPath. Use these tools to automate repetitive tasks such as data entry, financial report generation, or invoice processing. Experiment with AI tools to find ways to save time and improve efficiency.

Why it is important

AI tools can help you work smarter, not harder. By automating mundane financial tasks, you free up time to focus on strategic initiatives that require human insight, thereby increasing your value to the organization.

6. Focus on Data Interpretation

How to do it

Take a course on Power BI or Tableau to learn how to create dashboards that present data in an insightful way. Practice interpreting financial data and presenting it to stakeholders in a manner that drives business decisions.

Why It Is Important

While AI can analyze data, it still requires humans to interpret the results in a meaningful way. Your ability to present data-driven insights will make you a valuable asset to your team, helping them make informed strategic decisions.

7. Strengthen Problem-Solving Abilities

How to do it

Practice solving complex business problems by applying newly learned cost-control techniques or optimizing financial processes. Participate in problem-solving workshops or case studies to develop a structured approach to tackling business challenges.

Why It Is Important

Problem-solving is a uniquely human skill that is highly valued in finance. AI may provide data and analysis, but the creative application of these insights to solve business problems is something only humans can do effectively.

8. Adapt to New Technologies

How to do it

Attend industry expos, webinars, and virtual conferences to stay informed about the latest tools and technologies in finance. Experiment with new tools in a sandbox environment to understand their potential applications in your work.

Why it is important

Finance technology is evolving rapidly, and adapting to these changes is key to staying relevant. The more familiar you are with new technologies, the better you can leverage them to enhance efficiency and effectiveness in your role.

9. Network and Collaborate

How to do it

Join LinkedIn groups, finance-focused Slack communities, or professional organizations. Attend industry events, both in-person and virtual, to meet other professionals. Actively engage in discussions and contribute insights to build meaningful connections.

Why it is important

Human connections are irreplaceable, and a strong network can open up opportunities for career advancement, learning, and collaboration. Networking ensures you remain visible in the industry and helps you stay informed about new trends and opportunities.

10. Focus on Ethical AI Use

How to do it

Take courses on AI ethics from institutions like Stanford or MIT to understand the ethical implications of AI in finance. Apply ethical guidelines in your work to ensure compliance and fairness when using AI tools.

Why it is important

AI brings significant ethical challenges, especially in finance. Understanding and applying ethical AI practices will make you invaluable to your organization as companies strive to ensure responsible and fair use of AI technologies.

Take Control of Your Future

The AI revolution isn’t a wave to run from – it’s an opportunity to ride. By continuously learning, adapting, and using AI as a tool, you can protect your job against AI while also becoming more effective and valuable in your role. The future belongs to those who are proactive, curious, and ready to evolve.

Are you ready to take the first step? Start today by signing up for my 5-day course on AI for Finance, and let’s make sure you stay ahead in this rapidly changing world.

We all know the drill of finance work: endless spreadsheets, repetitive data entries, and the constant pressure to meet deadlines.

As a finance professional, you’re no stranger to the headaches that come with managing complex data and tackling routine tasks that seem to never end.

It can feel like there’s no time left for the work that matters, like providing strategic insights and making a real impact.

But what if those draining and time-consuming tasks could be approached and done differently?

What if you could focus on the bigger picture and finally have the time to drive meaningful change?

Learn how you can automate your finance tasks using Python and Generative AI.

FAQ

Q: What makes Python better than Excel for automating finance tasks?

A: Python offers better scalability and flexibility compared to Excel, especially when handling large datasets or complex analyses. It automates repetitive processes, reduces errors, and updates in real-time. Python also integrates seamlessly with various data sources, allowing finance teams to focus on strategy instead of manual data manipulation.

Q: How does Python help with forecasting and time series analysis?

A: Python allows for advanced forecasting by integrating historical data, adjusting for seasonality, and automating updates as new data comes in. Unlike Excel, where manual updates are often required, Python can handle dynamic recalculations in real-time, making it ideal for accurate financial forecasting and decision-making.

3. Can Python handle data cleaning tasks more efficiently than Excel?

A: Yes, Python can automate the data-cleaning process using predefined rules. It can standardize formats, remove duplicates, and handle missing values efficiently, which is especially useful when dealing with large datasets from multiple sources. In Excel, this process is often manual and time-consuming.

4. How does Python improve financial visualizations like cohort analysis?

A: Python simplifies complex visualizations by automating both data preparation and visualization. Once the data structure is set, Python can generate dynamic and repeatable visuals like cohort analysis, waterfall charts, and more, whereas Excel requires more manual formatting and updates for such tasks.

5. What are the benefits of automating bank reconciliations with Python?

A: Python can automatically compare transactions between different datasets, identify mismatches, and flag potential issues. This process is much faster and more accurate than manually checking transactions in Excel, significantly reducing the time and effort required for bank reconciliations.

Incorporating AI into SAP isn’t just about adding another tech tool to the mix; it’s about reshaping how businesses operate from the inside out.

AI in SAP takes everyday tasks – like filtering data or closing financial periods – and turns them into efficient, streamlined processes that save time and reduce human error.

Whether you’re a finance professional trying to close the books faster or a project manager trying to staff a team, AI is quietly making your job easier behind the scenes.

In this post, we’ll dive into 10 practical ways AI is used in SAP to make a real difference in day-to-day business operations.

Supercharge Your Productivity with AI

Harnessing AI to boost efficiency has never been easier, and SAP is leading the charge with features that make daily tasks more intuitive and streamlined.

Easy Filtering with AI

In SAP Fiori apps, AI enables customers to filter elements using natural language. This upgrade significantly improves the current filtering experience, making it more intuitive, especially for new users. No more manual sorting through endless filters—AI makes finding what you need quick and easy.

Joule: The AI-Powered Copilot

Joule is SAP’s AI-powered assistant that enables users to interact with their business systems through natural language. Whether you need help navigating through tasks, retrieving information, or even completing transactions, Joule has you covered, saving you time and effort in your daily operations.

Smart Summarization for Quick Proposals

By leveraging generative AI, SAP users can create business summaries seamlessly. The AI not only generates context-aware proposals but also supports multiple languages, facilitating faster collaboration across borders. Communication and report generation have never been easier.

Collaborative ERP with AI

Generative AI enhances collaboration within SAP’s ERP systems by offering context-aware summaries and multiple language support. Whether you’re working with international teams or just need quick insights, AI makes communication smoother and decision-making faster.

Speed Up Business with AI

AI isn’t just about productivity; it’s also about making smarter, faster decisions. SAP’s intelligent systems are designed to do just that.

Intelligent Recommendations for Decision-Making

AI doesn’t just speed up workflows; it helps you make better decisions. With AI-driven recommendations, users can make faster, more accurate decisions by relying on data-driven insights, significantly reducing time spent on manual processes.

Match Employees to Projects with AI

Project staffing becomes easier with AI innovations in SAP. By matching employees with the right skills to specific projects, AI reduces the time and effort required for staffing, ensuring that projects move forward without unnecessary delays.

AI-Powered Communication Intelligence

AI in SAP can now classify, summarize, and even respond to customer emails automatically. By extracting key information and routing it to the right departments, AI reduces response times and improves the efficiency of customer service and issue resolution.

AI for Finance Excellence

SAP’s AI-driven tools don’t just optimize workflows—they also bring significant advantages to finance departments, helping businesses manage their financial operations with greater precision.

Financial Business Insights with AI

AI provides real-time financial insights in SAP, allowing teams to make better data-driven decisions. Whether it’s optimizing profitability or reviewing cost centers, AI helps simplify complex financial data and provides tailored recommendations to improve outcomes.

Guided Error Resolution in Financial Closing

Errors during the financial close process can slow down operations, but SAP’s AI-generated insights help identify and resolve these issues quickly. AI speeds up the financial close process, reduces manual errors, and helps lower IT costs by automating error resolution.

Assisted Configuration for Complex Processes

AI simplifies complex financial configurations such as depreciation and tax codes in SAP. By providing user-friendly language explanations and automated configuration assistance, AI ensures that even the most intricate setups, like US tax codes, are handled with ease. This reduces errors and ensures compliance with financial regulations, saving businesses time and reducing costly mistakes.

Note:

This blog post is based on SAP’s article “Unlock Tomorrow’s Potential with SAP S/4HANA Cloud Public Edition”.

Final Words

AI in SAP is less about futuristic ideas and more about solving real problems today.

From reducing the time spent on manual data entry to helping finance teams close their books faster, AI is steadily becoming a critical part of running a business efficiently.

The best part?

You don’t need to be a tech expert to take advantage of these tools. They’re designed to work in the background, making your workday smoother without the extra complexity.

By embracing AI now, businesses can stay competitive, focus on what truly matters, and let the technology handle the repetitive, time-consuming tasks.

FAQ

Q: How does AI enhance filtering in SAP?
A: AI allows users to filter data using natural language in SAP Fiori apps, making the process more intuitive and user-friendly, especially for new users.

Q: What role does Joule play in SAP?
A: Joule is an AI-powered assistant that helps users interact with their business systems through natural language, providing assistance with navigation, information retrieval, and transactions.

Q: How does AI improve SAP’s financial operations?
A: AI provides real-time financial insights, assists with optimizing profitability, and helps resolve errors during the financial close process, making financial management more efficient.

Q: Can AI help with project staffing?
A: Yes, AI in SAP efficiently matches employees to projects based on their skills, reducing the time and effort required for project staffing and improving resource allocation.

Q: How does AI improve customer service in SAP?
A: AI can classify, summarize, and automatically respond to customer emails, helping businesses reduce response times and improve issue resolution.

As a finance leader, your role extends beyond overseeing financial operations, and Order-to-Cash and AR functions are critical to your company’s cash flow.

You’re also responsible for driving efficiency and enhancing value, and AI offers a powerful way to revolutionize your Order-to-Cash (O2C) and accounts receivable (AR) processes.

With AI, you can optimize operations, minimize manual errors, and boost cash flow.

In this blog, you will discover the roadmap to save time, cut costs, and deliver greater value to your business.

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How can AI help the Order-to-Cash and AR processes?

Use case 1: Automating Data Entry in Order Management with AI

Traditional Process: Data entry in order management has typically been a manual task, where orders received via email, PDF, or even phone calls are entered into the ERP or CRM system by employees. This manual process is prone to errors, time-consuming, and leads to delays in order fulfillment.

AI-Driven Automation of Data Entry

Best-in-Class Process: The use of AI, particularly tools like Optical Character Recognition (OCR) and Intelligent Document Processing (IDP), can automate the entry of orders from multiple formats (email, PDF, scanned documents). AI-based systems recognize patterns and extract key data such as product codes, quantities, and customer information, automatically populating ERP or CRM systems with minimal human intervention.

How AI Improves Data Entry:

1. OCR and IDP: Tools like ABBYY FlexiCapture or Kofax use AI to scan emails or documents, extract relevant data (e.g., order details, customer information), and directly input it into the system. This eliminates human input errors and speeds up order processing.
2. Real-Time Validation: AI can validate the extracted data against existing records, ensuring accuracy before orders are finalized.
3. Seamless Integration: AI-driven data entry systems integrate with your ERP (e.g., SAP, Oracle, NetSuite) to ensure real-time updates and immediate access to order information across departments.

Steps to Implement:

1. Evaluate Order Sources: Identify all formats (e.g., email, PDFs) from which orders are received.
2. Select an AI Tool: Implement OCR and IDP tools like ABBYY to capture data from these formats automatically.
3. Integrate with ERP: Ensure that the AI tool is integrated with your ERP for real-time order updates and validation.

Use case 2: AI-Driven Automation in Credit and Risk Management

Best-in-Class Process: AI enhances credit management by automating the credit assessment process, continuously monitoring customer behavior, and predicting potential risks in real-time. This allows finance teams to assess risks dynamically and make faster, more informed decisions about credit approvals and payment terms.

How AI Improves Credit and Risk Management:

1. Dynamic Credit Scoring:
   • AI models analyze internal payment histories and external financial data (e.g., credit ratings, market trends) to dynamically assess a customer’s credit risk. Unlike static checks, AI models continuously update credit scores in real-time.
   • Tools like Azure AI and Google Cloud AI allow you to set up predictive models that assess credit risks based on multiple variables, including customer payment behavior, order volume, and external data sources like credit bureaus.
2. Real-Time Risk Alerts:
   • AI systems monitor customer activity and send real-time alerts if there are sudden changes in risk levels. This could include a customer making late payments, external credit score changes, or even macroeconomic factors affecting their industry.
   • With AI, companies can automate credit holds or payment term adjustments if a customer’s risk profile deteriorates, helping to protect against bad debts before they escalate.
3. Proactive Credit Policies:
   • AI helps optimize credit policies by suggesting tailored terms based on a customer’s payment history and risk profile. For example, high-risk customers may automatically be shifted to shorter payment terms or asked for prepayments.
   • AI tools can simulate different credit scenarios to evaluate the impact of changes in credit terms on cash flow, helping finance leaders make data-driven decisions.

Technology to Use and How to Get There:

• Credit Risk Management Tools: Platforms like HighRadius or BlackLine provide AI-powered credit risk management modules that automate credit scoring and monitoring.
• ERP Integration: Ensure your credit and risk tools integrate with your ERP (e.g., SAP, Oracle, NetSuite) to provide seamless updates on credit scores, customer payment behaviors, and real-time alerts.
• Continuous Data Flow: AI solutions require a constant data flow from both internal (payment records, sales history) and external sources (credit ratings, market data). Set up integrations with services like Dun & Bradstreet or Experian for real-time credit data.

Use case 3: Invoice Generation and Delivery: Automating with AI

Traditional Process: In the past, invoices were manually created by pulling data from multiple sources, such as ERP systems, sales orders, and contracts. This manual data entry often resulted in errors, delayed invoice delivery, and disputes due to inconsistencies in pricing, taxes, or terms.

Best-in-Class Process: AI transforms the entire invoice generation process by automatically extracting and verifying data from various sources, ensuring accuracy, compliance, and timely delivery. This eliminates the need for human intervention in most cases, speeding up the process and reducing errors.

How AI Improves Invoice Generation and Delivery:

1. Automated Data Extraction:
   • Natural Language Processing (NLP): AI-powered NLP algorithms can read and extract structured data from unstructured sources, such as contracts or sales orders. These algorithms identify essential details like customer names, quantities, and terms, automatically populating invoice templates.
   • Optical Character Recognition (OCR): AI-based OCR tools, such as ABBYY and Kofax, digitize documents (e.g., PDFs, scanned contracts), extracting key invoice details and inputting them into the ERP system with high accuracy. This eliminates manual data entry errors and speeds up invoice creation.
2. Accurate Invoice Verification:
   • Machine Learning (ML): AI-powered ML algorithms compare current invoice data with historical patterns, ensuring that details such as pricing, tax calculations, and discount terms are correct. AI can flag discrepancies or automatically adjust based on previous invoices or contract specifics, reducing disputes.
   • Rule-Based AI for Compliance: AI systems apply pre-set rules that ensure tax rates, discounts, and other terms comply with local regulations or contract requirements. These rules ensure that invoices are compliant, minimizing the risk of errors that lead to delays or legal issues.
3. Intelligent Invoice Delivery and Tracking:
   • Automated Delivery: AI can automate the delivery of invoices via multiple channels—email, customer portals, or APIs—while tracking when the invoice is viewed, opened, or processed. If no response is detected, AI can trigger follow-ups, ensuring customers receive and act on invoices promptly.
   • Real-Time Insights: AI tools provide real-time tracking and alerts, allowing finance teams to monitor invoice status and customer engagement. This helps identify when action is needed to avoid delays in payment.

Technology to Use:

• NLP and OCR: Solutions like ABBYY FlexiCapture and Kofax help automate data extraction from contracts, sales orders, and documents.
• ERP Systems with AI Modules: Platforms like SAP, Oracle, and NetSuite can automatically generate and verify invoices, ensuring compliance and accuracy.
• Invoicing and Delivery Platforms: Tools like Billtrust or Tesorio streamline invoice generation, tracking, and delivery, integrating with ERP systems to ensure seamless data flow.

Use case 4: Payment Collection: Automating with AI

Traditional Process: Traditionally, payment collection involved manual tracking of overdue invoices, sending reminders, and escalating late payments. Finance teams had to rely on fixed timelines (e.g., 30, 60, 90 days overdue) to trigger follow-ups, with little insight into which accounts were truly at risk of default. This reactive approach led to longer collection cycles and higher Days Sales Outstanding (DSO).

AI-Powered Automation in Payment Collection

Best-in-Class Process: AI automates and optimizes payment collection by predicting customer payment behaviors, personalizing follow-up strategies, and improving overall collections efficiency. It turns a reactive process into a proactive one, helping finance teams prioritize high-risk accounts and reducing the effort needed for routine follow-ups.

How AI Improves Payment Collection:

1. Predictive Analytics for Payment Behavior:
   • Machine Learning (ML) models analyze past payment histories, customer financial health, and external data (e.g., industry trends) to predict when customers are likely to pay. This allows finance teams to prioritize accounts based on risk, focusing collections efforts on customers showing early signs of late payment.
   • AI-driven tools like HighRadius or Tesorio can flag customers at risk of default or delay, enabling proactive engagement to secure payments before invoices go past due.
2. Automated and Personalized Payment Reminders:
   • AI tools can send automated, personalized reminders to customers based on their payment behaviors and preferences. For example, a customer who consistently pays late might receive more frequent reminders, while another customer might prefer receiving them closer to the due date.
   • AI tailors the message tone and frequency, using historical data to decide the most effective approach (e.g., a polite reminder or a firm notice). Tools like Upflow use AI to optimize communication to improve response rates.
3. AI-Powered Dispute Resolution:
   • When customers raise disputes regarding invoices, AI helps speed up resolution by automatically categorizing issues (e.g., pricing discrepancies, product returns). AI can suggest solutions based on similar past disputes, reducing the time spent resolving issues and getting payments back on track.
   • AI tools integrated into customer portals allow customers to raise and track disputes automatically, streamlining the process for both sides.
4. Automated Escalation and Reporting:
   • If an account goes beyond a certain threshold without payment, AI systems automatically escalate the case by notifying collections specialists or transferring the case to a dedicated collections team. AI can generate real-time reports highlighting problematic accounts, providing managers with actionable insights.
   • This process is often integrated into ERP and CRM systems to ensure seamless tracking of the entire collection process.

Technology to Use and How to Get There:

• AI-Powered Collections Tools: Platforms like HighRadius, Upflow, and Tesorio use machine learning and automation to predict payment behaviors, send personalized reminders, and manage escalations.
• CRM Integration: AI-driven collections tools should integrate with your CRM (e.g., Salesforce, HubSpot) and ERP systems to track all customer interactions and financial details, enabling real-time visibility and automated workflows.
• Payment Gateways: Integrated payment gateways (e.g., Stripe, PayPal) help automate payment receipt and matching. AI tools connected to these systems can track payment status and automatically update records when payments are received.

Steps to Implement:

1. Evaluate Current Collections Process: Identify manual bottlenecks in your current collections process, such as when and how follow-ups are triggered or how disputes are handled.
2. Select an AI-Driven Platform: Choose AI tools like HighRadius or Upflow that fit your company’s size and customer base. These platforms automate everything from predictive analytics to customer communication.
3. Integrate AI with CRM and ERP: Ensure your AI-driven collections platform is integrated with your ERP and CRM systems for seamless data flow, ensuring accurate customer records and real-time tracking.

Practical Steps and roadmap to Implement AI in Order-to-Cash and AR

Here’s the roadmap to ensure a smooth transition, from assessing your current processes to scaling AI and measuring its impact.

#1: Assess Your Current Processes

Before implementing AI, you need to map out and evaluate your existing O2C and AR workflows.

This step is crucial for identifying bottlenecks, inefficiencies, and opportunities for automation.

Key Areas to Assess:

Invoice Generation

How are invoices currently generated and sent to customers? Are there any delays or manual steps that could be automated?

Collections and Payment Follow-up

What’s your process for following up on overdue invoices? Are reminders and escalations handled manually?

Cash Application

How are payments currently reconciled against invoices? Is manual intervention required for every payment, or is there some level of automation?

Data Visibility and Forecasting

Do you have real-time visibility into outstanding receivables? How accurate is your cash flow forecasting? Are there any gaps in your data?

Outcome

The goal of this step is to pinpoint where manual tasks, delays, or errors occur and to identify areas where AI can help. For example, if your team spends excessive time on invoice reconciliation, AI can automate matching payments to invoices.

#2: Define Your AI Objectives

Once you’ve assessed your current processes, it’s time to define what you want AI to achieve.

Setting clear, measurable goals will guide your AI implementation and ensure you’re focusing on areas with the highest impact.

Common AI Objectives:

Reduce Days Sales Outstanding (DSO)

This is one of the most common metrics in AR. AI can help by automating follow-up reminders and predicting when customers will pay, enabling you to act proactively.

Improve Cash Flow Forecasting

AI-driven predictive models can provide more accurate cash flow projections, allowing you to make better-informed financial decisions.

Automate Manual Tasks

Identify specific tasks you want to automate, such as sending invoices, reconciling payments, or following up on overdue accounts.

Increase Collections Efficiency

AI can prioritize collection efforts by identifying at-risk accounts earlier, ensuring your team spends time where it matters most.

Enhance Data Visibility

AI can provide real-time insights into AR performance, helping you make faster, more informed decisions.

Outcome

Establishing objectives will help you focus on the right AI solutions and measure their effectiveness. It’s important to align these goals with broader business priorities like cash flow optimization or reducing operational costs.

#3: Choose the Right AI Solution

Based on your goals and the assessment of your current processes, you can now choose the AI solution that best fits your needs.

Consider whether you want an out-of-the-box solution, a customizable platform, or a hybrid approach.

Factors to Consider:

Business Size

Smaller companies might benefit from out-of-the-box solutions like Tesorio, which offer fast deployment and minimal customization.

Medium companies can invest in larger solutions like Highradius.

Larger enterprises with complex processes may prefer customizable platforms like Azure AI or AWS AI.

Integration with Existing Systems

Ensure that the AI solution integrates smoothly with your ERP system (SAP, Oracle, NetSuite, etc.). Integration is key to automating workflows and avoiding data silos.

Scalability

Choose a solution that can grow with your business. As your transaction volume increases, the system should be able to handle larger datasets and more complex workflows without slowing down.

Customization Needs

If your AR processes are unique or require advanced machine learning models, customizable platforms like Google Cloud AI or Microsoft Azure AI offer more flexibility.

#4: Run a Pilot Program

Before rolling out AI across your entire finance operation, it’s wise to start with a pilot program.

This will allow you to test the AI solution on a smaller scale, measure its effectiveness, and address any challenges before full deployment.

Pilot Program Steps:

Select a Subset of Customers or Invoices

Choose a specific segment of your customer base or a sample of invoices for the pilot. This could be a group of customers with high DSO, for example.

Test Key Workflows

Focus on one or two specific workflows, such as invoicing and collections follow-ups, to see how well the AI system automates these tasks.

Track Metrics

Monitor key metrics like DSO, collections efficiency, and error rates during the pilot to evaluate AI’s impact. Compare the results to your baseline data from before the pilot.

Collect Feedback

Get input from your finance team to understand how the system is working in practice. Are there any challenges or limitations that need to be addressed?

#5: Scale AI Across Finance Operations

After a successful pilot, you’re ready to scale AI across your entire O2C and AR processes.

This involves expanding AI automation to more customers, invoices, and workflows while ensuring that the system continues to deliver value as it scales.

Scaling Steps:

Expand Automation

Roll out automated invoicing, collections, and payment reconciliation to all customers. Ensure that the AI system is handling larger volumes of data effectively.

Refine AI Models

As more data is fed into your AI system, refine your machine learning models to improve accuracy. This is particularly important for cash flow forecasting and payment behavior predictions.

Integrate with Other Financial Processes

Consider integrating AI into adjacent financial processes, such as credit risk management or financial close, to further streamline your finance operations.

Continuous Monitoring

Use real-time dashboards and reports to monitor the performance of the AI system. Track key metrics such as DSO, cash flow accuracy, and collections performance to ensure ongoing value.

#6: Continuous Optimization and Measurement

AI isn’t a one-time implementation—it requires ongoing monitoring and optimization to deliver maximum value.

Once AI is integrated into your O2C and AR processes, you’ll need to continuously fine-tune the system and track its performance against your objectives.

Steps for Ongoing Optimization:

Monitor Key Metrics

Track key performance indicators (KPIs) like DSO, percentage of automated invoices, collections success rate, and cash flow accuracy. Regularly compare these metrics to your baseline to assess improvement.

Refine AI Models

As more data flows through the system, your AI models should become more accurate. Regularly retrain and adjust your machine learning models to account for new patterns or trends in customer behavior.

Adjust Workflows

As your business grows or changes, you may need to adjust your AI workflows. For example, if your customer base expands to a new region, you might need to fine-tune your collections strategy for that market.

Gather Team Feedback

Continue to collect feedback from your finance team to identify any areas where AI could be improved or further integrated into the workflow.

Outcome

Ongoing optimization ensures that your AI system evolves with your business and drives value, improving operational efficiency and financial performance over time.

Final Words

Have you ever wondered how much smoother your finance work could be if you just knew the right questions to ask?

Imagine being able to ask ChatGPT exactly what you need and getting precise, immediate insights.

Learning to craft these prompts isn’t hard; you just require the right guidance.

That’s where prompt engineering comes into play.

Think of it as financial modeling, a must-have skill for staying ahead of your peers.

Today, I’ll walk you through the techniques of prompt engineering and show you how you can master them in just 1 hour.

Advanced Prompt Engineering Techniques You Need to Know

Here are top 10 advanced prompt engineering techniques you need to start using:

#1: Chain of Thoughts

Breaking down a complex problem into a simple step-by-step process and or steps.

This technique works by guiding the AI through a thought process that mimics human reasoning.

Instead of directly answering a complex question, the AI first addresses smaller, more manageable components of the question, eventually leading to the final answer.

This method is effective, especially in complex domains like finance, because it allows for a more nuanced and detailed exploration of the problem.

How to Use It:

1. Identify the Core Question:
Start by understanding the main question you want to answer. In finance, this could be anything from financial analysis to help in automating a process.

2. Break It Down:
Decompose the main question into smaller, more straightforward questions.

3. Sequential Queries:
Frame your prompt by including these smaller questions in a logical sequence. Ask the AI to consider each part of the problem step by step.

4. Guide the Reasoning:
In your prompt, guide the AI through the reasoning process.

5. Synthesize the Conclusion:
After the AI has addressed each step, the final part of your prompt should guide it in synthesizing these individual insights into a coherent conclusion or answer to your main question.

6. Review and Refine:
After receiving the response, review each step of the AI’s reasoning. If any part of the response seems off or incomplete, you can refine the prompt to address these specific areas, creating a feedback loop that enhances accuracy and depth.

For example, you can use it to learn how to automate your bank reconciliation.

Example

1. Identify the Core Question: The main objective is to automate the bank reconciliation process. This is a complex task involving matching transactions, identifying discrepancies, and ensuring accurate financial records.

2. Break It Down: Decompose this task into smaller, manageable steps. For instance:

• Identifying common transaction types and patterns.
• Defining rules for matching transactions.
• Developing methods to flag and investigate discrepancies.
• Integrating these processes into an automated system.
  

3. Sequential Queries: Frame your prompt in a logical sequence addressing each part:

First Prompt: ”You are going to help me automate my bank reconciliation process.
First, I will give you examples of typical transactions and their equivalent in bank statements.
Start by analyzing and categorizing them.”

Second Prompt: “Now, determine rules that can automatically match these transactions against entries in the accounting software.”

Third Prompt: “Outline a process for flagging transactions that don’t match and require manual review.”

Fourth Prompt: “Suggest a way to integrate these steps into an automated reconciliation system using for example Excel.”

4. Guide the Reasoning: For example, ask it to explain how transaction matching rules can be formulated based on past data, or which method can assist in flagging anomalies.

5. Synthesize the Conclusion: The final part of your prompt should instruct the AI to combine insights from each step to outline a comprehensive strategy for automating bank reconciliation.

6. Review and Refine

Learn how to use the technique in the free lesson from my course that I’ve unlocked for you:

#2: Chunking

“Chunking” is a technique where complex information or problems are broken down into smaller, more manageable ‘chunks’.

You can use it both for input (for example, copying several parts of a document using several prompts) or output (asking ChatGPT to give the output in several answers).

The main reason for using chunking is the limited number of characters a model can have.

When this document was produced, ChatGPT 3.5 had a limit of around 4,000 characters, the equivalent of 800 words (like a long email or a mini-article).

Example:

Financial Analysis: Breaking Down Revenue Streams

• • Prompt: “Divide our company’s revenue streams into manageable categories and suggest a brief analysis approach for each.“
  • Expected Outcome: The AI categorizes the revenue streams (e.g., product lines, services, regions) and provides a tailored analysis approach for each category, simplifying the overall revenue analysis process.

#3: Explicit Reasoning

A technique where the AI details its process or reasoning in a clear, step-by-step manner.

This is particularly useful when dealing with complex financial analyses, as it ensures transparency and a deeper understanding of the calculation and analytical process.

This is a way to force the model to act like a spreadsheet, reducing the probability of mistakes in the reasoning and calculations.

Prompt example:

“Using the following data, calculate the three most important liquidity KPIs for our company. Please provide a step-by-step explanation of each calculation. Assume our current assets are $500,000, inventory is $150,000, current liabilities are $250,000, and cash & cash equivalents are $200,000.“

Expected Outcome for the Current Ratio Calculation

• Step 1: Define the Current Ratio: “Current Ratio is calculated by dividing current assets by current liabilities.”
• Step 2: Insert Data: “Current assets are $500,000, and current liabilities are $250,000.”
• Step 3: Calculate: “Current Ratio = Current Assets / Current Liabilities = $500,000 / $250,000.”
• Step 4: Result: “The Current Ratio is 2.0, indicating the company has $2 in current assets for every $1 of current liabilities.”

#4: Agent Prompting

Framing prompts as if they are tasks or queries for an ‘agent’ within the AI’s framework.

“Agent Prompting” involves framing prompts as if they are tasks or queries for an ‘agent’ within the AI’s framework. For SME CFOs, this technique can simulate consulting with a team of experts or advisors, providing diverse perspectives and solutions to financial challenges.

How do you create your Agent?

Define the following traits (you can customize them at your convenience and see the different results)

• NAME
• DEFINITION
• KNOWLEDGE
• TRAITS
• ANALYSIS
• OUTPUT
• FORMAT
• ENGLISH
• START

Example: Excel Expert

NAME: You are an Excel and Financial Expert

DEFINITION: You are an experienced Financial Analyst with the following knowledge and traits.

KNOWLEDGE: analyst a top-tier management consulting firm, strategic consultant, financial consultant, management consultant, business analyst, data analyst

TRAITS: high business acumen, Excel advanced skills, complex problem-solving skills, adaptability, creativity, financial analysis, financial modeling, meta-analysis.

ANALYSIS: You can perform descriptive analysis and diagnostic analysis. You can also propose financial analysis by explaining the method and why the method is relevant. You can also calculate relevant KPIs if needed.

OUTPUT: First propose the methods you want to use and ask user for confirmation. Also ask in which cell in Excel does their table start.

Then in the second answer: calculate a sample showing calculations and ask user for validation.

Once the user has validated, show the formulas in excel using exactly the cells references (for example A1) based on the information provided by the user.

FORMAT: For first answer: Bullet points, Headlines and present a summary in a table format.

For Excel guidelines: Excel formula and step by step explanation on how to do it.

ENGLISH: Simple english, short sentences with figures.

START: Do you understand? If yes, then ask the user for the data.

#5: Team Prompting

Different hypothetical ‘agents,’ each with distinct roles, are defined and assigned specific tasks.

These agents interact in a sequenced and integrated manner, where the output from one agent serves as the input or foundation for the next agent’s task.

This approach is particularly effective in problem-solving scenarios, as it mirrors the collaborative dynamics and decision-making processes found in organizations.

Example: Cash Action Plan for a SaaS Company 

1. Define the Agents and Their Roles

FP&A Expert: Analyzes financial data to identify cash flow trends and areas for improvement.

Marketing Manager: Develops strategies to increase revenue through customer acquisition and retention.

Web Developer: Implements technical solutions to optimize the SaaS platform for better customer engagement and sales.

2. Assign Tasks and Sequence

The FP&A Expert starts by analyzing financials and identifying key areas.

The Marketing Manager then uses this analysis to devise revenue-boosting strategies.

Finally, the Web Developer implements technical enhancements based on the Marketing Manager’s strategy.

3. Facilitate Collaborative Interaction

The prompt should guide the AI through each agent’s contribution, ensuring a cohesive and sequential development of the cash action plan.

Prompt:

“First, have the FP&A Expert analyze our SaaS company’s financials, focusing on cash flow and subscription metrics. Next, let the Marketing Manager devise strategies based on the FP&A’s analysis to increase subscriptions and customer retention. Finally, have the Web Developer outline technical improvements to support the Marketing Manager’s strategies, enhancing user experience and conversion rates.”

Expected Outcome

FP&A Expert Phase: The AI, as the FP&A Expert, reviews cash flow and subscription data, identifying trends such as high churn rates or periods of low cash flow.

Marketing Manager Phase: Building on the FP&A’s insights, the AI, now as a Marketing Manager, proposes targeted campaigns to reduce churn and attract new subscribers, possibly suggesting promotional offers or referral programs.

Web Developer Phase: The AI, acting as a Web Developer, outlines technical improvements like optimizing the signup process, enhancing user interface, or implementing new features to boost engagement and conversions, aligning with the marketing strategies.

#6: Meta-Cognition

“Meta-Cognition” in AI prompting involves encouraging the AI to reflect on its own thought processes, biases, and decision-making strategies. This technique is valuable for SME CFOs as it can help in understanding the limitations and strengths of AI in financial decision-making and strategy development.

How to Use Meta-Cognition

1. Prompt AI for Self-Reflection:
   Start by asking the AI to describe its reasoning or the data it uses for a particular task.
2. Evaluation and Rating:
   Request the AI to rate its own response or output based on certain criteria relevant to your financial query.
3. Request for Adjustment:
   Following the rating, prompt the AI to modify its response or strategy to aim for a ‘perfect’ score, thereby enhancing the quality of output.
4. Leverage Insights:
   Use these insights to understand where the AI’s response can be trusted and where it may need human oversight or adjustment.

#7: Socratic Prompting

“Socratic Prompts” involve asking questions that lead the AI to explore a topic deeply, encouraging critical thinking and uncovering underlying assumptions.

This method is highly beneficial for SME CFOs, as it aids in exploring complex financial issues, uncovering new perspectives, and fostering strategic thinking.

How to Use Socratic Prompts

1. Ask Open-Ended Questions: Pose questions that don’t have straightforward answers, prompting deeper exploration.
2. Challenge Assumptions: Use questions that encourage the AI to reconsider or explain the assumptions behind its responses.
3. Seek Clarifications: Prompt the AI to clarify and expand on its answers, leading to a more nuanced understanding.

#8: Prompt Optimization & Expansion

“Prompt Optimization & Expansion” is a technique where you start with a basic prompt and ask the AI to improve it based on your desired results.

The advantage of this technique is to learn what are the right words to use and sentence formulations to get to your desired results.

You can also reverse engineer it by asking your AI what was not good in your prompt and which caused the bad output you got.

#9: Fact-Checking

“Fact Checking” involves using prompts to verify the accuracy and credibility of information. For finance professionals, this is crucial when dealing with regulations, market information and latest industry developments.

How to Use Fact-Checking:

1. Question the Source: Ask the AI about the sources of its information or the basis of its claims.
2. Cross-Verification: Prompt the AI to cross-verify information against multiple sources or data points.
3. Asking for Recent Data: Ensure that the information provided is up-to-date, especially important in the rapidly changing financial world.
4. Ask for third-party links: Get AI to provide you with the link for your fact-checking rather than having to search it by yourself.
   

Examples of how and when to use it are available in the video lesson in my course.

#10: Iterative Inquiry & Sequential Questioning

Iterative Inquiry

This term emphasizes the ongoing process of asking questions to gradually refine and improve the understanding or output of a task.

Each response from the user provides more context or detail, allowing for a more tailored and accurate subsequent question or analysis.

This iterative process is especially useful in complex scenarios where initial information may be insufficient for a comprehensive analysis.

Sequential Questioning

This term highlights the structured approach of asking questions in a sequence, where each question builds upon the previous responses.

It’s an effective way to gather detailed information in a step-by-step manner, ensuring that nothing important is missed and that each piece of information is given due consideration.

How to use them?

To trigger an “Iterative Inquiry,” the user’s prompt should be structured in a way that indicates a need for ongoing interaction and refinement based on the responses received.

Here’s an example of how a user might frame their prompt to initiate this:

“I need help with [task/problem]. Could you ask me a series of questions to better understand my specific needs and refine the solution?”

For “Sequential Questioning,” the user’s prompt should suggest a step-by-step approach where each question builds upon the previous response.

An example prompt for this might be:

“I’m working on [task/project] and need detailed guidance. Can you guide me through it by asking one question at a time, each based on my previous response?”

All of the advanced techniques covered today are part of my extensive Prompt Engineering for Finance Video Course.

Secure your lifetime access now and become part of the top 1% of ChatGPT users!

Last Thoughts

Mastering these advanced prompting techniques can significantly elevate how you leverage AI in finance.

By breaking down complex problems, enhancing reasoning transparency, and optimizing prompts, you can make AI work more effectively for you.

Whether you’re an SME CFO looking to improve decision-making or a finance professional aiming to automate tasks, these methods will empower you to get the most out of AI tools.

Start integrating these strategies today to enhance your financial analysis, strategic planning, and overall productivity.

FAQ

The finance world is full of AI tools, but not all of them are worth your time.

To save you the hassle of sorting through endless options, I’ve put together a list of the Top 100 AI Finance Tools.

These are the tools that can actually help you get your work done faster and more efficiently.

I’ve picked out the ones that stand out for their practical use in real-world finance tasks.

Top 100 AI Finance Tools

The Top 100 AI Finance Tools list is designed to help you become more productive, save valuable time, and execute your finance work better.

This list took me weeks of research to compile as there are thousands of tools available right now.

I’ve chosen the top 100 for you that are the most valuable.

Here are the categories of tools covered with this list:

Accounting Tools

Emagia – Account Receivable
Kapittx – Account Receivable
Paymefy – Account Receivable
Simplifai – Account Receivable
Collect AI – Account Receivable
Highradius – Account Receivable
AccountIQ – Accounting Automation
Booke – Accounting Automation
Bookeeping AI – Accounting Automation
Docyt – Accounting Automation
Dokka – Accounting Automation
FloQast – Accounting Automation
Gridlex – Accounting Automation
Integra Balance – Accounting Automation
klarity – Accounting Automation
Numeric – Accounting Automation
Numra – Accounting Automation
Puzzle – Accounting Automation
Record Me – Accounting Automation
Truewind – Accounting Automation
Zapliance – Accounting Automation
Zeni – Accounting Automation
Appzen – Accounts Payable
Glean – Accounts Payable
Nanonets Flow – Accounts Payable
Vic AI – Accounts Payable
Receiptor AI – Invoice Processing
Smacc – Invoice Processing
Sparkreceipt – Invoice Processing
Wellybox – Invoice Processing
Trullion – Revenue recognition

Consulting & Training

AI Finance Club
Nicolas Boucher Online

Investments

Avanz – Fund Management
Axyon – Fund Management
Boosted AI – Fund Management
Sibli – Fund Management
Capitalise – Investing
Finq AI – Investing
Charli – Investment Research
Finalle – Investment Research
FinChat – Investment Research
Hudson Labs – Investment Research
Sigtech – Investment Research
Stocknews AI – Investment Research

Legal and Compliance

Greenlight AI – Compliance Assistance
Harvey – Compliance Assistance
Contractpodai – Contract Management
Evisort – Contract Management
IronClad – Contract Management
Sirion – Contract Management

Planning and Analysis

Alteryx – Analysis and Insights
Ginimachine – Analysis and Insights
MindBridge – Analysis and Insights
Upmetrics – Analysis and Insights
Watsonx – Analysis and Insights
Zebra AI – Analysis and Insights
Akkio – Financial Planning
Arkifi – Financial Planning
Clockwork – Financial Planning
Datarails – Financial Planning
Precanto – Financial Planning
Qflow AI – Financial Planning
Farseer – Financial Planning
Runway Financial – Financial Planning
Spindle – Financial Planning

Productivity

ChatGPT – Chatbot
Claude – Chatbot
Copilot – Chatbot
Gemini – Chatbot
Alphamoon – Document Processing
Azure AI Form Recognizer – Document Processing
Ocrolus – Document Processing
Rossum – Document Processing
Beautiful – Presentation
Decktopus – Presentation
Gamma – Presentation
PlusDocs – Presentation
Slides AI – Presentation
Tome – Presentation
Consensus – Research
Perplexity – Research
Ai Excel Bot – Spreadsheets
Ajelix – Spreadsheets
Arcwise – Spreadsheets
Formula Generator – Spreadsheets
Formulas HQ – Spreadsheets
Promptloop – Spreadsheets
Taskade – Task Management
Trevor AI – Task Management

Startup Space

Prometai – Business Plan and Fundraising
Sturppy – Business Plan and Fundraising
Angeldoc AI – Startup Investment
Venture Insight –  Startup Investment

Treasury and Taxation

AI Tax – Taxation
Exactera – Taxation
Flyfin – Taxation
Xon AI – Taxation
Grain finance – Treasury
Statement – Treasury

Get the list of the Top 100 AI Finance Tools now!

Review of Some of The AI Finance Tools

I had the chance to meet the founders of some of these tools and see what their solutions can do.

Based on my review, here is my selection of the tools to consider:

#1: Numra – David Kearney

Provides you with a virtual AI accountant called Mary which will automate your AR and AP processes.

On top, you can make Mary do data-cleaning jobs!

I really like the fact that the team is in Europe & ready for SMEs companies.

#2: Trullion – Isaac Heller

Reads your contracts and automates your revenue recognition and lease accounting thanks to AI. Their tool also helps audit companies.

Isaac and his solution impressed me because they are one of the only AI-native tools ready for big companies. Walmart and Siemens already trust them.

#3: Puzzle – Sasha Orloff

Sasha knows exactly what the future of finance should be like and is already making it available to start-ups.

Puzzle’s API integrations + AI functionalities allow that 99% of the bookings are automated and create insights on your key SaaS KPIs in a record time.

#4: Zapliance – Alexander Ruehle

Covers both accounting & audit capabilities and focuses first on clients using SAP (which is what Europe needs!).

It also offers a solution for identifying duplicate invoices and helps you recover VAT that you have missed.

#5: Truewind – Alex Lee

Truewind was built to help companies automate & accelerate their accounting workflows.

From recognizing an invoice, categorizing it & approving it, AI facilitates it & makes it an innovative solution to consider if you are an SME or a fractional CFO.

#6: ZebraAI (sister of Zebra BI) – Andrej Lapajne

ZebraBI is already a successful story in meaningful reporting using IBCS standards.

But now, ZebraAI is producing it and creating your commentaries without any human intervention!

#7: Runway – Siqi Chen

When Notion meets Financial Modelling and Forecasting.

Siqi has built a solution that is easy to use for non-finance people & has a great UX!

#8: Spindle AI (spindle.ai) – Ryan Atallah

It creates scenarios and insights using natural language. This tool has great potential for companies wanting better AI FP&A capabilities.

#9: Glean.ai – Howard Katzenberg

When you want to understand your costs, your general ledger is not enough.

You need to peruse through your invoices, compare them and make ad-hoc analysis.

Howard automated all of this by scanning everything which is on your invoices and creating insights from it.

#10: AppZen – Anant Kale

Leverages AI for expense review. They were already using AI before it was a trend!

5 Stages to Master AI for Finance

Here are the 5 stages you need to complete:

#1: Beginner

You know AI exists, but you don’t know where to start, and you are afraid to start because of confidentiality issues.

My advice to go to the next level:

Start using ChatGPT or Bard (it doesn’t matter which one) and do this:

Today, take notes of all the mini-tasks you do at work.

Tomorrow, try to perform each of the tasks with ChatGPT by just asking: “My job is X and I want to do Y, can you draft it for me?”

It might only work 20% of the time… but that’s already many use cases in one day!

Most importantly, don’t give any confidential information about your company, clients, or colleagues.

#2: Basic

Now that you have discovered some ways where it works and somewhere it doesn’t, you need to be more methodologic.

To go to the next level, you need to use my framework for prompting.

It will bring consistent results that provide you with value.

Here is the framework: CSI for Context / Specific / Instruction.

Then, add the FBI for Format / Blueprint / Identity.

CSI+FBI is the secret framework I teach in all my courses and corporate workshops.

#3: Intermediate

You get consistent output, but you are stuck when complex problems arise.

This is where you need to learn prompt engineering.

Here are the 3 most important you need to master:

• Chain of thought: to solve problems
• Chunking: to create procedures
• Agent prompting: to make AI do financial analysis for you

#4: Advanced

Now you are a master at doing everything inside ChatGPT, but you cannot do it on confidential data, and you cannot scale (which is a pity as AI is by design made for scaling!)

What is the magic way to go to the next step?

The response scares a lot of people…

Because they think it’s not for them or because they cannot learn it.

The response is Python.

Why?

This is the language that can compute figures, create graphics, change and combine Excel files, process mega data sets, and all of these in your own secured environment.

Finance needs to use this language to unleash automation of financial analysis and forecasting abilities.

But the good news is you don’t need to learn it anymore.

You can have AI code it for you.

#5: Master

This is the path that I want to pursue for myself and some of my colleagues who are experts in the field.

This is where you learn how to parameterize an AI model for finance use cases.

For this, you need to learn JSON & Python but also have access to environments like Azure.

Start by getting access to a low-code platform like PowerPlatform, and then set up your first mini-use case using AI Builder from Microsoft, such as an OCR or translation module.

Final Words

The Top 100 AI Finance Tools list gives you a clear path to finding the AI tools that can make a real difference in your finance work.

I’ve done the research so you can focus on what matters—choosing the right tools to improve your productivity and results.

Explore the list and see which tools can best support your goals.

Get the Top 100 AI Finance Tools now!

FAQ

Q: What are AI Finance Tools?
A: AI Finance Tools use artificial intelligence to automate, streamline, and enhance various financial tasks, from accounting and investment management to compliance and analysis.

Q: Why should I use AI tools in finance?
A: AI tools can save you time, reduce errors, and provide insights that are hard to get manually. They help you work more efficiently and make better decisions.

Q: Are these tools suitable for small businesses?
A: Absolutely. While some tools are designed for large enterprises, many are tailored to meet the needs of small and medium-sized businesses, offering solutions that scale with your growth.

Q: Do I need technical skills to use these tools?
A: Most tools are user-friendly and designed to be accessible, even if you don’t have a technical background. For more advanced tools, a bit of learning might be required, but the benefits are well worth it.

Q: Is my data safe with AI tools?
A: Data security is a top priority for these tools. They come with robust security measures to ensure that your information is protected. Always check the security features of each tool to be sure.

Everyone talks about AI, but no one shows you how to use it for finance. Finance pros using ChatGPT, Copilot, Gemini, and Python are already ahead.

They’re more productive, their work looks professional, and they bring valuable insights to their teams.

If you really want to stand out, learning Python and using AI can set you apart.

Today, I will show you the stages of learning AI for Finance and the path to mastering Python.

5 Stages to Master AI for Finance

Here are the 5 stages you need to complete:

#1: Beginner

You know AI exists, but you don’t know where to start, and you are afraid to start because of confidentiality issues.

My advice to go to the next level:

Start using ChatGPT or Bard (it doesn’t matter which one) and do this:

Today, take notes of all the mini-tasks you do at work.

Tomorrow, try to perform each of the tasks with ChatGPT by just asking: “My job is X and I want to do Y, can you draft it for me?”

It might only work 20% of the time… but that’s already many use cases in one day!

Most importantly, don’t give any confidential information about your company, clients, or colleagues.

#2: Basic

Now that you have discovered some ways where it works and somewhere it doesn’t, you need to be more methodologic.

To go to the next level, you need to use my framework for prompting.

It will bring consistent results that provide you with value.

Here is the framework: CSI for Context / Specific / Instruction.

Then, add the FBI for Format / Blueprint / Identity.

CSI+FBI is the secret framework I teach in all my courses and corporate workshops.

#3: Intermediate

You get consistent output, but you are stuck when complex problems arise.

This is where you need to learn prompt engineering.

Here are the 3 most important you need to master:

• Chain of thought: to solve problems
• Chunking: to create procedures
• Agent prompting: to make AI do financial analysis for you

#4: Advanced

Now you are a master at doing everything inside ChatGPT, but you cannot do it on confidential data, and you cannot scale (which is a pity as AI is by design made for scaling!)

What is the magic way to go to the next step?

The response scares a lot of people…

Because they think it’s not for them or because they cannot learn it.

The response is Python.

Why?

This is the language that can compute figures, create graphics, change and combine Excel files, process mega data sets, and all of these in your own secured environment.

Finance needs to use this language to unleash automation of financial analysis and forecasting abilities.

But the good news is you don’t need to learn it anymore.

You can have AI code it for you.

#5: Master

This is the path that I want to pursue for myself and some of my colleagues who are experts in the field.

This is where you learn how to parameterize an AI model for finance use cases.

For this, you need to learn JSON & Python but also have access to environments like Azure.

Start by getting access to a low-code platform like PowerPlatform, and then set up your first mini-use case using AI Builder from Microsoft, such as an OCR or translation module.

The Path to Master Python

Python is becoming the number one differentiator in software skills for finance professionals, and knowing Excel is not enough.

Here are the phases that you need to go through to master Python:

Phase 1: Basics

• Learn about Google Colab
• Add your data to the Python environment
• Use Python as a Pivot Table
• Handle first Python error with ChatGPT
• Use ChatGPT to generate code
• Learn about Python libraries for Finance

Phase 2: Visualization

• Simple bar chart in Python
• Box plot for statistical analysis
• Correlation analysis with Heatmap
• Learn about Seaborn Library
• Customize any data visualization
• Create your first dashboard using Plotly

Phase 3: Automation

• Clean up data using Python
• Automate any simple finance task
• Merge 3 files together
• Automate Excel report generation
• Create a Monte Carlo Simulation
• Calculate Net Present Value
• Do statistical analysis with Python
• Retrieve data from a stock using the YF library

Phase 4: Forecasting

• Learn about Machine Learning
• Create a linear regression
• Do a clustering algorithm
• Use ARIMA for forecasting
• Use Prophet for forecasting
• Build a predictive model using sk learn

Phase 5: Advanced

• Automate Email generation
• Automate slides generation
• Automate a Discounted Cash Flow Model
• Learn about Jupyter notebooks & Anaconda

Final Thoughts

Mastering AI and Python in finance isn’t just about keeping up with the latest trends – it’s about transforming the way you work and making a tangible impact in your field.

As you progress through the five stages, you’ll unlock new levels of productivity, enhance the quality of your work, and become a valuable asset to your team.

Imagine automating routine tasks, analyzing large datasets with ease, and creating professional reports that impress your colleagues and managers.

With the right skills, you can leverage AI tools like ChatGPT, Copilot, and Gemini and harness the power of Python to achieve all this and more.

Don’t let the fear of new technology hold you back.

Embrace the learning journey, and you’ll find that the rewards far outweigh the challenges.

By investing in your skills today, you’re not just securing your career for tomorrow—you’re positioning yourself as a leader in finance.

FAQ

Q: What if I don’t have any experience with AI or coding?

A: You don’t need prior experience. Start with basic AI tools like ChatGPT for simple tasks. Use AI to simplify Python coding, making it accessible even if you have no coding background.

Q: How can I use AI without compromising confidential information?

A: Start by using AI for non-confidential tasks. Avoid sharing sensitive information. As you progress, learn to use Python in a secure environment to handle confidential data.

Q: What are the benefits of using Python in finance?

A: Python can handle large datasets, perform complex computations, automate tasks, and create advanced visualizations. It enhances productivity, accuracy, and efficiency in financial analysis and reporting.

Q: How can AI improve my day-to-day tasks in finance?

A: AI can automate routine tasks, generate reports, analyze large datasets, and even draft emails or documents. This saves time, reduces errors, and allows you to focus on more strategic activities.

Q: What is prompt engineering, and why is it important?

A: Prompt engineering involves crafting specific queries to get the best results from AI models. It’s crucial for solving complex problems, creating procedures, and making AI perform financial analysis effectively.

Many of my clients have started using Copilot for Excel, PowerPoint, Teams, Word, and Office.

If you are one of the lucky ones who got access to it but don’t know where to start yet, this one is for you.

I am convinced people only touched the surface of how to use Copilot effectively.

AI is here to stay and I am here to help you go through that!

Copilot’s Early Insights

Many of you wondered about Copilot’s impact.

Here are the early insights into Copilot’s performance:

• 70% of the Copilot users said they were more productive
• 68% of them said it improved the quality of their work
• Overall, users were 29% faster in a series of tasks (searching, writing, and summarizing)

Also, users could get caught up on a missed meeting nearly 4x faster.

With Copilot, people save time on key tasks.
Quantitative findings show Copilot increasing speed on tasks like writing, summarizing a meeting, and searching for information.

The early insights additionally showed that:

• Copilot saved 6 minutes for writing the first draft
• Copilot saved 6 minutes in searching for information
• Copilot saved 32 minutes in summarizing a missed meeting

Use Cases of Copilot for Finance

Here are the use cases:

#1: Excel

Examples of use cases:

• Create a formula to calculate the Net Present Value (NPV) of a series of cash flows
• Explain how to use a Pivot Table to summarize your data
• Generate a graph showing data insights

Prompt examples:

Generate formulas

Prompt: “Add a column that calculates the margin”

Identify insights

Prompt: “Show the total sales of January 2024”

#2: PowerPoint

Examples of use cases:

• Helps to create a presentation from scratch
• Add a relevant stock photo picture to make your slide more enjoyable
• Provide tips on how to create an engaging presentation

Prompt examples:

Create presentation

Prompt: “Create a presentation for an investment business case”

Summarize presentations

Prompt: “Create an outline from this presentation”

#3: Word

Examples of use cases:

• Help draft a professional procedure
• Explain how to use Word’s referencing features to manage your document sources
• Provide a template or an example of a business proposal

Prompt examples:

Document Drafting

Prompt: “Draft a procedure for a bank reconciliation”

Ask a question about the document

Prompt: “Is there a definition of revenue recognition in this document”

#4: Outlook

Examples of use cases:

• Draft a reply to an email for you
• Guide on how to set up an automatic reply for when you’re out of the office
• Summarize an email for you

Prompt examples:

Summarize email thread

Prompt: “Summarize the key points from this discussion”

Draft your emails

Prompt: “Draft an answer to this email”

Final Words

The early insights into Copilot’s performance reveal its significant impact on productivity and work quality for finance professionals.

Users report substantial improvements, with 70% experiencing increased productivity and 68% noting enhanced work quality.

Copilot accelerates tasks like writing, summarizing, and searching, making users 29% faster overall.

Remarkably, it also allows users to catch up on missed meetings nearly four times faster.

By saving time on key tasks, Copilot proves to be an invaluable tool, streamlining workflows and increasing efficiency across various applications like Excel, PowerPoint, Word, and Outlook.

FAQ

Q: How does Copilot improve productivity for finance professionals?

A: Copilot enhances productivity by streamlining tasks such as writing, summarizing, and searching for information. It saves significant time, making users 29% faster overall, and helps them catch up on missed meetings nearly four times quicker.

Q: In which applications does Copilot provide the most benefit?

A: Copilot proves highly beneficial in Excel, PowerPoint, Word, and Outlook. It assists with tasks like generating formulas, creating presentations, drafting documents, and summarizing emails, thus improving efficiency in various financial workflows.

Q: What are some specific use cases of Copilot in Excel?

A: In Excel, Copilot can create formulas to calculate Net Present Value (NPV), explain how to use Pivot Tables, and generate graphs to show data insights. For example, you can prompt Copilot to “Add a column that calculates the margin” or “Show the total sales of January 2024.”

Q: How does Copilot assist in creating presentations in PowerPoint?

A: Copilot helps create presentations from scratch, add relevant stock photos, and provide tips on making engaging presentations. For instance, you can ask it to “Create a presentation for an investment business case” or “Create an outline from this presentation.”

Q: Can Copilot help with drafting and managing documents in Word?

A: Yes, Copilot can draft professional procedures, explain how to use Word’s referencing features, and provide templates or examples of business proposals. You can prompt it to “Draft a procedure for a bank reconciliation” or ask, “Is there a definition of revenue recognition in this document?”