Modernizing FP&A: How CFOs can transform finance into a strategic powerhouse

Finance has been a forward-looking function responsible for planning, budgeting, forecasting and overall financial management. Today's accelerated business pace demands faster decision-making capabilities. Hence, the financial planning and analysis (FP&A) function is becoming strategy-focused, helping leadership make timely, informed decisions. When properly configured with the right mix of people, organizational interfaces and technology solutions, FP&A becomes a strategic growth engine, enabling intelligent and quick decision-making.

A recent webinar discussion with Baker Tilly’s Christopher Annand, Principal; Dave DuVarney, Principal; Colt Meraw, Director and Sunil Kumar, Senior Manager revealed insights about the evolution of FP&A from a traditional planning and reporting function to a strategic business engine. The conversation highlighted the challenges organizations face with disconnected systems, the importance of data readiness and the transformative potential of artificial intelligence (AI) integration in financial processes. Below are the key takeaways:

Key challenges in modern FP&A facing CFOs

Disconnected systems and processes: Organizations continue to struggle with fragmented data infrastructure, where systems don't integrate effectively. This creates unreliable real-time information, leading some businesses to view FP&A as a "luxury function" rather than a strategic necessity. Many organizations still rely heavily on Excel for planning processes, even when they have implemented FP&A tools.

Talent gap: As technology advances and systems become more integrated, there's an emerging shortage of finance talent with analytical and technological literacy. Traditional finance roles are evolving beyond report building toward more strategic, analytical functions that require different skill sets. Finance professionals spending time on report creation should adapt to becoming more analytical and leveraging dashboards, predictive analytics, machine learning (ML) and AI.