In this Planning Aces episode, Jack Sweeney and co-host Brett Knowles spotlight three CFOs who are advancing their organizations’ FP&A capabilities through thoughtful AI adoption. CFO Andrea Hecht of CSAA Insurance discusses aligning generative AI with enterprise strategy and efficiency. CFO Matthias Steinberg of MindBridge explores combining machine learning and LLMs in finance workflows. CFO Brian Hogeland of Packer Fastener highlights how AI training and grassroots adoption can foster a tech-forward culture. Together, these leaders offer a cross-industry view of how CFOs are balancing risk, innovation, and ROI while helping their organizations navigate today’s fast-evolving planning landscape.
Brett Knowles’ Key Takeaways
Brett Knowles emphasizes three recurring themes: the importance of framing AI narratives carefully to avoid workforce fear, the rising expectation among employees for AI-enabled tools, and the need to align AI efforts with real business value. He also highlights the necessity of risk awareness and the evolving role of FP&A as a driver of organizational agility. Across the board, Brett sees finance leaders striving to balance innovation with caution in a way that positions their teams for scalable growth.
Insights from our Planing Aces
Andrea Hecht outlines CSAA’s strategic approach to generative AI by focusing on productivity tools and targeted business transformations, particularly in claims processing. She stresses the importance of aligning AI investments with clear value propositions, avoiding overreach. Andrea also reflects on the evolving “build vs. buy” debate, advocating for close collaboration between business and IT when selecting tools. Her overarching message is clear: innovation must be focused, measurable, and in step with enterprise-wide strategy.
Brian Hogeland shares how Packer Fastener jumpstarted AI adoption with “AI 101,” a companywide session designed to foster curiosity and spark cross-functional dialogue. He explores the early use of agentic AI tools to automate repeatable tasks, aiming to scale operations without expanding headcount. Hogeland sees cultural readiness as critical and views AI as a collaborative tool, not a replacement. His remarks reveal how even traditional industries can lead in digital enablement through thoughtful planning and education.
Matthias Steinberg describes how MindBridge’s finance function uses its own machine learning platform while layering in LLMs for usability. He underscores the critical distinction between deterministic ML (suited for finance) and the unpredictability of LLMs, arguing for their hybrid use. Steinberg stresses the importance of internal adoption, value alignment, and controlling data inputs. His perspective offers a cautionary but forward-looking approach—balancing innovation with the rigor and precision demanded by financial analysis.