AI in Finance for Professionals Dealing with Errors, Overhype, and Constant Learning Pressure

In this episode of The Mod Squad, Paul Barnhurst, Ian Schnoor, and Giles Male share their real experiences using AI in financial modelling. They cut through the hype and discuss what AI actually does well, where it still struggles, and how professionals should be thinking about using it today. From building models to handling workflows, the conversation highlights both the value and the limitations of AI tools in real work. Expect to LearnWhere AI actually helps in finance and modelling workWhy most “one-click solution” claims are unrealisticThe importance of checking and guiding AI outputsHow instructions and structure improve AI resultsHere are a few quotes from the episode:“It’s not a one-click solution. You still have to check everything.” – Giles Male“You will have to understand every line to guide AI properly.” – Ian SchnoorAI is powerful, but it’s not a shortcut to good work. It still needs guidance, structure, and strong fundamentals. The people who benefit most will be the ones who understand both the tools and the work behind them. For now, the best approach is simple: use it, test it, and don’t trust it blindly. Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/Follow Giles:LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/In today’s episode: [02:06] – Current thoughts on AI after recent progress[03:45] – Daily use of AI and time savings[05:00] – Mental fatigue and keeping up with AI changes[08:03] – Calling out AI hype and unrealistic claims[12:30] – AI training challenges and business demand[17:57] – “Eye of the storm” phase of AI development[24:13] – Testing AI-built financial models[30:52] – Why fixing AI models can take longer than building from scratch[35:15] – Responsibility to challenge misleading AI claims[38:41] – Using instructions to improve AI output[42:59] – Final thoughts on AI, stress, and the future