In this article, we explore how the Chicago FP&A Board highlights the shift toward AI-driven, system-initiated FP&A operating models and more proactive, insight-led decision-making.

In this article, practical AI use cases show how FP&A teams can improve forecasting, analysis, and business partnering through better prompting and structured thinking.

In this article, we explore how predictive planning, driver-based models, and AI-driven insights are reshaping FP&A into a more agile, decision-oriented function.

In this article, the author explains why agentic AI adoption in FP&A depends less on technology and more on organisational readiness across data, processes, and people.

In this article, FP&A leaders explore how AI is transforming business partnering from data-heavy reporting into real-time, insight-driven decision support.

FP&A understands scenarios — but turning them into something usable takes time, coordination, and alignment across data, models and teams. Too often, that effort comes too late. In this digest issue, we explore why — from the role of foresight in decision-making to the practical limits of scenario planning and the data foundations behind it.

In this article, FP&A leaders explore how finance teams can evolve from transactional reporting to strategic business partnering through predictive analytics, AI, and mindset transformation.

In this article, the New York FP&A Board explores how organizations can operationalize Agile FP&A through driver-based planning, integrated processes, and technology to enable faster, predictive, and scenario-driven decision-making.

In this article, the author explains how FP&A professionals can structure AI prompts using modern reasoning frameworks to generate more accurate insights, analyses, and board-level narratives from large language models.

In this article, finance professionals learn which AI tools matter most for FP&A, how to use them safely, and where they can deliver immediate productivity gains.