What You Will Learn

• Integrate generative AI into daily corporate finance workflows
• Use CSI and FBI Pro prompting to get precise AI outputs
• Automate data tasks with Python while maintaining data confidentiality
• Leverage AI to enhance Excel, PowerPoint, and finance procedures
• Create long documents using the chunking method

Study Guide

Introduction

Welcome to the comprehensive guide on using generative AI in corporate finance. This course is designed to transform finance professionals into AI champions by integrating AI into their daily workflows. By the end of this course, you'll understand how to leverage AI tools like ChatGPT to enhance productivity, streamline finance procedures, and conduct sophisticated financial analyses, all while maintaining data confidentiality. This knowledge isn't just valuable—it's essential for staying competitive and efficient in the modern finance landscape.

Understanding the Gap: AI Awareness vs. Daily Use

Many finance professionals are aware of AI technologies but haven't integrated them into their daily tasks. This section explores why this gap exists and how to bridge it.

Concept: The gap between knowing about AI and using it daily often stems from a lack of understanding of practical applications.
Example: A finance manager might know about AI's capabilities but not realize it can automate repetitive tasks like data entry.

Practical Application: Identifying specific tasks where AI can be applied, such as drafting routine emails or generating reports, can encourage daily use.
Example: Using AI to draft meeting minutes can save time and ensure accuracy.

Tip: Start with small, manageable tasks to build confidence and gradually expand AI usage.

Four Key Categories of AI Application in Finance

This section delves into the primary areas where generative AI can significantly enhance corporate finance functions.

Productivity Enhancement

Concept: AI can handle word-based tasks, improving productivity by automating routine communications and simplifying complex language.
Example: AI can draft and translate emails, making communication more efficient.

Practical Application: Use AI to create presentations and translate documents.
Example: An AI-generated presentation can be quickly customized for different audiences.

Tip: Regularly update AI tools with relevant business terminology to improve output quality.

Streamlining Finance Procedures

Concept: Generative AI can draft a significant portion of finance procedures, saving time and effort.
Example: AI can create the first draft of a financial policy document, covering 80% of the content.

Practical Application: Use AI to develop internal control documents required by auditors.
Example: AI can generate a compliance report outline, which can then be refined by a human expert.

Tip: Use the "chunking method" to manage large documents efficiently.

Enhancing Existing Tools

Concept: AI acts as a "super assistant" for tools like Excel, PowerPoint, and QuickBooks, providing instant support and tutorials.
Example: AI can generate Excel formulas or troubleshoot errors.

Practical Application: Use AI to automate data analysis in Excel or create dynamic presentations in PowerPoint.
Example: AI can automate data consolidation from multiple sheets in Excel.

Tip: Integrate AI tools with existing software to maximize their potential.

Financial Analysis

Concept: AI aids in performing preliminary analyses, uncovering insights, and generating visualizations.
Example: AI can analyze sales data to identify trends and anomalies.

Practical Application: Use AI to generate financial forecasts or visualize complex datasets.
Example: AI can create a heatmap of sales performance across different regions.

Tip: Regularly validate AI-generated insights with human expertise to ensure accuracy.

Effective Prompting Techniques: CSI and FBI Pro

Learn how to communicate effectively with AI to get the best results.

CSI Technique

Concept: The CSI technique involves providing Context, Specificity, and Instruction to the AI for better outputs.
Example: Instead of asking for a "financial report," specify the type, format, and key elements.

Practical Application: Use CSI to draft a detailed budget report.
Example: Provide context about the fiscal year, specify the departments involved, and instruct the AI to include financial projections.

Tip: Be as detailed as possible in your prompts to enhance AI understanding.

FBI Pro Framework

Concept: The FBI Pro framework refines prompting by focusing on Format, Blueprint, and Identity.
Example: Specify that the AI should draft a script instead of a formal letter.

Practical Application: Use FBI Pro to create a demand letter.
Example: Instruct the AI to act as a lawyer and include specific legal actions.

Tip: Assign roles to the AI to tailor its responses to specific professional contexts.

Leveraging AI for Data Analysis with Confidentiality

Explore how to use AI for data analysis while maintaining data security.

Concept: AI can analyze financial data securely by using Python code in local environments.
Example: AI can generate code for a headcount analysis, which can be run in Google Colab.

Practical Application: Use AI to automate complex analyses like cohort analysis.
Example: Generate a heatmap of customer segments without uploading sensitive data to the AI platform.

Tip: Always audit AI-generated code before execution to ensure data integrity.

Automation of Repetitive Tasks

Learn how AI can automate mundane tasks, freeing up time for strategic activities.

Concept: AI-generated Python code can automate tasks like combining data files.
Example: AI can merge monthly sales CSV files into a single dataset.

Practical Application: Use AI to automate data cleaning processes.
Example: AI can generate scripts to remove duplicates and standardize formats in large datasets.

Tip: Regularly update automation scripts to accommodate changes in data structure.

Creating Long Documents Using the "Chunking Method"

Understand how to overcome AI's context window limitations when drafting extensive documents.

Concept: The "chunking method" involves breaking down large documents into smaller sections.
Example: Draft a 40-page finance procedure by creating individual sections.

Practical Application: Use chunking to draft a comprehensive annual report.
Example: Generate an outline, then prompt the AI to write each chapter separately.

Tip: Ensure consistency across sections by providing detailed style and content guidelines for each chunk.

Immediate Action and Accessibility

Discover how to start using AI tools without complex setups or enterprise-level packages.

Concept: Finance professionals can begin using AI tools immediately, without waiting for large-scale transformations.
Example: Start by integrating AI into daily tasks like email drafting.

Practical Application: Use AI to enhance personal productivity tools.
Example: Implement AI-generated reminders and task lists.

Tip: Always avoid inputting confidential data into AI platforms without proper agreements.

Becoming "AI Champions"

Learn how finance teams can lead AI adoption within their organizations.

Concept: Empower finance teams to become leaders in AI usage.
Example: Train team members to use AI tools effectively, fostering a culture of innovation.

Practical Application: Develop an AI integration roadmap for your finance department.
Example: Create workshops and training sessions to upskill team members.

Tip: Encourage continuous learning and experimentation with AI tools to keep up with advancements.

Conclusion

Congratulations on completing this comprehensive guide to using generative AI in corporate finance. You've learned how to leverage AI tools to enhance productivity, streamline processes, and conduct sophisticated analyses while maintaining data confidentiality. By applying these skills thoughtfully, you can transform your finance team into AI champions, leading your organization into a future where AI is an integral part of daily operations. Remember, the journey doesn't stop here—continue exploring and integrating AI to unlock even greater potential in your finance functions.

Podcast

There'll soon be a podcast available for this course.

Frequently Asked Questions

Welcome to the FAQ section for the 'Video Course: How to Use Generative AI in Corporate Finance Full Course.' This resource is designed to help you navigate the integration of generative AI into your corporate finance tasks. Whether you're a beginner or an advanced user, these FAQs will address common questions and provide practical insights to enhance your understanding and application of AI in finance.

Why is there a gap between knowing about AI and using it daily for finance work?

Many finance professionals are aware of generative AI like ChatGPT but haven't fully integrated it into their daily workflows. This is often because they haven't yet identified concrete, practical applications relevant to their specific tasks. The course aims to bridge this gap by providing actionable techniques and showcasing specific use cases that finance professionals can implement immediately.

What are the primary areas within corporate finance where generative AI can offer significant benefits?

Four key categories are identified:

• Productivity: AI can assist with word-based tasks like writing emails, minutes, presentations, translating text, and simplifying complex language for better communication.
• Finance Procedures: Generative AI can rapidly draft the majority (around 80%) of finance procedures, saving considerable time and effort when internal controls or auditors require documentation.
• Utilising Existing Tools: AI can act as a "super assistant" for tools like Excel, PowerPoint, SAP, Oracle, and QuickBooks, providing instant tutorials and solutions to problems, reducing time spent searching for answers.
• Financial Analysis: AI can aid in performing preliminary analysis, uncovering insights, and generating visualisations that might not be immediately obvious.

What is the CSI technique, and how does it improve prompting with generative AI?

The CSI technique stands for Context, Specificity, and Instruction. Instead of using generative AI like a search engine with simple keywords, the CSI method involves providing sufficient context about the task, being specific about the desired outcome, and giving clear instructions on what the AI should do. This structured approach leads to more relevant and useful outputs compared to basic keyword prompting.

How does the FBI Pro framework build upon the CSI technique for more advanced AI prompting?

The FBI Pro framework enhances the CSI technique by focusing on refining the output. It stands for:

• Format: Specifying the desired output format (e.g., a script instead of a formal letter).
• Blueprint: Describing the essential elements and content that should be included in the output (e.g., mentioning legal actions in a demand letter).
• Identity: Instructing the AI to adopt a specific persona or role (e.g., "act as a lawyer").

By incorporating the FBI Pro elements after establishing context, specificity, and instructions, users can achieve more tailored and impactful results from generative AI.

Can generative AI be used for financial analysis involving numerical data, and how can this be done securely with confidential information?

Yes, generative AI can be used for financial analysis. The course demonstrates how to upload and analyse headcount and customer data to generate visualisations and insights. To handle confidential data securely, the recommended approach is to ask the AI for the Python code required for the analysis. This code can then be executed in a secure, local environment like Google Colab, Azure, or a personal laptop, allowing users to work with sensitive data without directly inputting it into the AI platform. This also enables auditing of the code and repeated analysis on multiple files.

How can generative AI assist with the often time-consuming task of combining multiple data files for analysis?

Generative AI can provide Python code that automates the process of combining multiple data files (e.g., monthly sales files). By specifying the location of the files, users can execute this code in their secure environment, and the AI will efficiently merge the data into a single file, saving significant time and effort compared to manual methods or traditional tools like VBA.

What is the "chunking method," and why is it important for creating long-form content like finance procedures using generative AI?

The "chunking method" is a strategy for creating lengthy documents by breaking down the task into smaller, manageable parts. Generative AI models often have limitations on the input and output size (context window). For documents like 40-60 page procedures, instead of trying to generate the entire document at once, the user first defines an outline and then prompts the AI to write each section or chapter individually, ensuring a consistent style and detailed content within the AI's output limits.

What is the key takeaway message for finance professionals looking to adopt generative AI?

The main message is that finance professionals can start using generative AI tools like ChatGPT immediately to enhance their productivity, improve their workflows, and gain deeper insights from data, even without enterprise-level agreements or extensive technical expertise. By focusing on practical applications, using effective prompting techniques (CSI and FBI Pro), and employing secure methods for handling confidential data (like using AI-generated code in local environments), finance teams can become AI champions within their organisations. The course also provides a user guide to help finance professionals begin their journey with ChatGPT.

According to Nica Bush, what is a common trend she observes when asking finance professionals about their AI usage at work?

Nica Bush notes that while many finance professionals have experimented with AI personally, significantly fewer use it regularly for their daily work tasks. Her goal is to increase the daily adoption of AI within finance teams.

Describe how Python can be used in conjunction with generative AI for financial analysis involving numerical data.

Python can be used as an intermediary for generative AI to analyse numerical data. ChatGPT can instruct Python to read data files, perform calculations, generate visualisations like graphs, and provide preliminary analysis that it cannot directly do itself. This approach leverages Python's robust data handling capabilities to complement the AI's language processing strengths.

Why might a finance professional choose to extract Python code from ChatGPT and run it in their own environment like Google Colab?

A finance professional might choose to use Python code in their own environment to handle confidential data securely, to have the ability to audit the code, and to overcome limitations ChatGPT might have with large datasets or the inability to run code directly. This ensures data privacy and allows for more complex data manipulation.

Explain the benefit of using Python to automate the task of combining multiple data files for financial analysis.

Using Python to automate combining multiple data files saves significant time compared to manual methods or even using tools like VBA. It allows for the efficient aggregation of data from numerous files, enabling faster and more comprehensive analysis. This automation enhances accuracy and reduces human error in data handling.

According to the course, what is a crucial consideration to keep in mind when using generative AI tools with work-related data?

A crucial consideration is to avoid inputting any confidential data into generative AI tools if the user or their company does not have a specific agreement or enterprise package that ensures data privacy and security. Data protection should always be a priority when using AI tools.

What is the stated goal of Nica Bush regarding the use of AI within finance teams?

Nica Bush's goal is to empower finance teams to become the AI champions within their businesses by demonstrating the practical applications and benefits of using generative AI in their daily tasks. She aims to facilitate a seamless transition to AI-enhanced workflows.

Discuss the potential impact of generative AI on the productivity of finance professionals.

Generative AI has the potential to significantly boost productivity by automating routine tasks, such as drafting emails, creating reports, and conducting preliminary data analysis. Tools like ChatGPT can streamline these processes, allowing finance professionals to focus on more strategic activities. Real-world examples include automating the generation of financial reports and simplifying complex data interpretations.

Critically evaluate the security considerations and potential risks associated with using generative AI for financial analysis.

Security considerations are paramount when using generative AI for financial analysis, especially with sensitive data. Potential risks include data breaches and the misuse of confidential information. Techniques such as extracting Python code for local execution and using secure environments like Google Colab can mitigate these risks. Ensuring robust data governance policies is essential when integrating AI tools.

Analyse the evolving role of finance professionals in the age of generative AI.

As generative AI becomes more integrated into finance workflows, the role of finance professionals is evolving from traditional number-crunching to strategic decision-making. Skills in AI tool management, data interpretation, and strategic insight generation are becoming increasingly important. Professionals need to adapt by developing a blend of technical and analytical skills to leverage AI effectively.

Compare and contrast the traditional methods of creating financial procedures and conducting financial analysis with the approaches enabled by generative AI.

Traditional methods often involve manual data entry and analysis, which can be time-consuming and prone to errors. Generative AI, on the other hand, offers automated solutions that enhance accuracy and efficiency. However, AI approaches require an understanding of how to effectively prompt and interpret AI outputs. The advantages of AI include speed and scalability, while potential disadvantages include the need for technical proficiency.

Outline a strategy for a finance department to successfully adopt and integrate generative AI tools into their daily operations.

A successful strategy involves several key steps: assessing the current workflow to identify AI opportunities, training staff on AI tools, implementing effective prompting techniques like CSI and FBI Pro, and establishing data privacy protocols. Continuous learning and adaptation are crucial to becoming "AI champions" within the business.