Ashok Manthena: FP&A at Google and GAP to AI leadership
Aug 15, 2023
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Experienced FP&A professional, Ashok Manthena, discusses the importance of automating manual finance processes at Google. He shares insights on the future of automation and AI in FP&A departments. The podcast also covers topics like the power of meetups, the prevalence of Excel in finance teams, and the evolution of the finance tech stack. Additionally, they explore the benefits of face-to-face interactions and the significance of having sufficient data for predictive analytics.
Predictive analytics can automate forecasting processes in finance, leading to more accurate and data-driven decision-making.
Generative AI, like chat GPT, can revolutionize finance departments by serving as a digital assistant and streamlining workflows.
Deep dives
Using predictive analytics in finance
Predictive analytics can be used in finance to automate forecasting processes. By feeding historical data and future assumptions into machine learning models, accurate predictions can be generated for various financial metrics such as revenue. This approach allows finance teams to have more accurate and data-driven forecasts, leading to improved decision-making. As more data is collected and saved over time, the accuracy and effectiveness of predictive analytics in finance will continue to grow.
The potential of generative AI in finance
Generative AI, such as chat GPT, has the potential to revolutionize finance departments. For example, it can serve as a digital assistant, allowing finance professionals to interact with their ERP systems or perform tasks using natural language. It can automate processes like report generation, data retrieval, and answering finance-related queries. The use of generative AI as a conversational interface not only enhances user experience but also streamlines workflows, making finance operations more efficient and less dependent on IT specialists.
Addressing challenges in AI implementation
Implementing AI in finance comes with its own set of challenges. Two key considerations are data bias and accuracy. Finance professionals should be cautious about potential biases in the data used to train AI models and take steps to mitigate them. Additionally, while generative AI can provide helpful responses, it is important to review and validate the output to ensure accuracy, especially in sensitive financial scenarios. Building trust and using iterative processes can help address these challenges and improve the reliability of AI applications in finance.
Advice for aspiring AI learners in FP&A
For finance professionals looking to learn more about AI and utilize available tools, it is recommended to develop a foundational understanding of data science concepts and coding. Learning the basics of data science and acquiring some Python programming skills can be beneficial in automating tasks and analyzing financial data. While there are currently few finance-specific AI courses available, resources like free online data science courses on platforms like Coursera can serve as a starting point. Connecting with AI and finance professionals on platforms like LinkedIn can provide further insights and networking opportunities.
Ashok Manthena has supported FP&A teams at some of the biggest and most well-known companies on the planet including Google, GAP, and Ingersoll Rand. This included a period at Google which was striking because of “the amount of resources available for finance” particularly when it comes to killing manual finance processes. He says there is an in-built DNA to “automate manual processes”. He says: “This bubbles up naturally when they find there is an, there is a manual process and everyone comes together and thinks about how to automate that process.” In a second career stage Ashok has been a leader in AI finance carrying out practical research, giving keynote speeches and providing practical advice on transforming businesses through the use of AI.
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The playbook for smaller finance teams and businesses to thrive in the AI age
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