In this article, the author introduces a governed AI decision architecture that helps FP&A teams generate options, test them against policy, and produce auditable evidence packs for executive decision-making.

Join this FP&A Trends webinar on June 10th to explore how driver-based planning in the age of AI enables finance teams to turn data into accurate forecasts and actionable insights.

In this article, the author challenges the criticism of dashboards, arguing that they play a critical role in building the foundations of data-driven FP&A despite not directly driving decisions.

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.