In this episode of The ModSquad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male are joined by Tea Kuseva, Community Manager at the Financial Modeling Institute, for a detailed discussion on the state of AI tools in financial modeling. The group continues its hands-on testing of seven tools, including TabAI, Excel Agent, Shortcut, and TrufflePig, evaluating how these platforms perform on real-world financial modeling tasksTea Kuseva is the Community Manager at the Financial Modeling Institute (FMI), the only global accreditation body dedicated to financial modeling. With her deep involvement in the modeling community and her role supporting professionals worldwide, Tea Kuseva brings thoughtful questions and provides structure to the discussion, helping translate technical insights into practical takeaways for finance professionals.Expect to LearnHow leading AI tools perform on real financial modeling tasksCommon issues like unbalanced sheets and flawed formulasKey differences between Excel-based and standalone toolsPractical ways AI can assist with analysis and reportingWhy Excel and modeling expertise still matter in an AI-driven workflowHere are a few quotes from the episode:“Even five years from now, you’ll still need to understand every cell if you're handing in a model.” – Ian Schnoor“Fast, consistent outputs are still better achieved by experienced humans than by today’s AI tools.” – Giles MaleAI tools show promise in assisting with financial modeling, but they are not yet reliable enough to replace human expertise. Strong Excel skills and sound judgment remain essential. Used wisely, AI can enhance productivity, but it should complement, not replace, technical understanding. The future of modeling is human-led, AI-assisted.Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=caFollow Giles Male:LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/Follow Tea:LinkedIn: https://www.linkedin.com/in/tkuseva/In today’s episode:[01:16] - Guest Intro[06:07] - Tools Under the Microscope[07:59] - The Testing Framework[13:43] - Lessons from the Esports Challenges[19:33] - Real Examples from the Tools[25:54] - Practical Use Cases for AI Today[33:56] - Variability in AI Outputs[39:40] - Looking Ahead: The Next Five Years[44:58] - Final Comments[46:13] - Final Thoughts and Key Takeaways

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