Adam Shilton, Founder, Tech for Finance, and Sloane Kolt, Head of Datarails Labs, discuss the practical use of AI in finance and FP&A. Topics include generative AI, ethical concerns, common use cases, differences between big and small companies, privacy and security risks, effective prompting in finance, lessons from building a new finance AI tool, favorite non ChatGPT AI tools, and advice for getting started with AI in finance.
AI can automate low-value tasks, provide decision support, and analyze data in finance and accounting.
Finance leaders should empower their teams to explore AI tools and foster a culture of openness towards AI.
Writing effective prompts and ensuring data integrity are crucial for accurate and valuable AI-generated responses in finance.
Deep dives
AI in Finance and How Leaders Should Prepare
AI is being used in finance and accounting to automate low-value tasks, provide decision support, and analyze data. It can help individuals with tasks like document creation, decision-making, and data analysis. Organizations can leverage AI for tasks like process documentation, forecasting, analysis, and visualization. Finance leaders should think about how AI can offload low-value work and empower their teams to focus on high-value tasks. They should encourage their teams to explore AI tools and foster a culture of openness towards AI. Additionally, leaders should seek out AI tools that address specific use cases and provide clear instructions in prompts to get the best results.
The Importance of Effective Prompting in AI
Writing effective prompts is crucial to getting accurate and valuable responses from AI systems. Vague or poorly worded prompts will yield subpar results, while specific and clear prompts can guide AI to produce desired outputs. Providing relevant examples, context, and instructions while prompting helps AI models understand the intended task and deliver accurate results. It is similar to having a conversation with a human, where clarity and follow-up questions are key. Continuously refining prompts and seeking clarification when necessary can improve the quality of AI-generated responses.
Experience in Developing Fp&A Genius
Creating Fp&A Genius, an AI tool for finance, involved a team of skilled developers and product managers. One of the key considerations was ensuring the quality and reliability of the data used by the tool. While AI models are adept at language and context, they may not excel in mathematical accuracy. Therefore, it was crucial to validate and verify data integrity to avoid generating inaccurate financial information. The development process emphasized the importance of data privacy and maintaining the trustworthiness of the tool's outputs.
Importance of Human Input in Predictive Analytics
Predictive analytics, while useful, lacks the ability to understand the nuances and context of a business. It provides trends and possibilities based on data, but it requires human interpretation to assess whether the predictions are valid. Therefore, it is crucial to have a team comfortable with predictive analytics to ensure its effective use as a tool for decision-making.
Addressing Privacy and Security Concerns with AI
Privacy and security concerns exist when using AI, especially with multi-tenant infrastructure where data is shared among users of AI tools. To maintain data integrity and protect sensitive information, steps are recommended, such as anonymizing data, moving generated code and results to private environments, and considering running AI on your own server. Additionally, toggling off data usage for training in AI models can enhance privacy, but it may limit access to advanced tools. It is crucial to prioritize data security and compliance when using AI.
This special edition of FP&A Today saw 1,524 finance pros sign up for this LinkedIn Live session. In this episode Paul and a special panel answer all your burning questions on how Finance teams are practically using AI to advance their careers (and bring instant productivity to their businesses).
Joining Paul is Adam Shilton, Founder, Tech for Finance and a world expert on “Helping finance pros turn systems into superpowers with AI”. In addition he is joined by Sloane Kolt, Head of Datarails Labs, which recently launched FP&A Genius, an AI powered solution transforming FP&A.
Some of your big audience questions answered:
What was the last thing you used Generative AI for?
Ethical questions in AI and Finance (eg. Do we own the model that AI built?)
What are the most common use cases for AI in FP&A, and accounting post ChatGPT?
What are the differences between AI in big vs small companies?
How should finance leadership use AI and adapt it in their team ethos and processes?
Privacy, security, AI and Finance - what are the risks?
What are the secrets of effective Prompting in finance?
Clarifying tasks with your AI Chat
Lessons from building a new finance AI tool
Favorite non ChatGPT AI tools you have seen
Best advice for getting started with AI in finance
Rapid fire questions: favorite Excel function and person I would most like to meet.
FP&A Today is brought to you by Datarails,the AI-powered financial planning and analysis platform. Keep your own Excel financial models and spreadsheets and benefit from AI for data consolidation, reporting and planning.