Despite heightened leadership expectations fueled by AI advancements, FP&A professionals worldwide spend the vast majority of their time on low-value tasks due to fragmented data and outdated processes, according to experts from SquareTrade, Neo4j, and Planful.
- FP&A teams spend under one-fifth of time on high-value analysis
- Data fragmentation and process issues limit AI effectiveness
- Skill gaps and resource constraints hinder AI adoption
What happened
A recent FP&A Circle webinar featuring leaders from SquareTrade, Neo4j, and Planful examined the ongoing challenges finance planning teams face in adopting AI tools. Survey data revealed that FP&A professionals allocate only 14% of their time to driving action and 18% generating insight, with the majority consumed by tedious, non-value-added activities.
Panelists underscored that although leadership is increasingly expecting faster, data-driven answers due to AI, the underlying data environment remains fragmented. Disparate systems, inconsistent definitions, and disconnected ERP platforms force teams to spend significant effort validating AI outputs, which often hallucinate or work with incomplete information.
Why it matters
The gap between AI potential and current FP&A realities illustrates a broader issue: technology alone cannot fix foundational data and process problems. As teams grapple with siloed workflows and evolving role expectations, many report feeling undervalued and stretched thin, which can block effective data democratization and innovation.
Moreover, firms are investing much of their AI budgets outside finance, in operations, marketing, and call centers, leaving FP&A groups under-resourced for the AI skills and capabilities needed. Without addressing these core issues, organizations risk exposing themselves to greater operational risk and missed opportunities for improved business agility.
What to watch next
Key indicators to monitor include how FP&A teams advance beyond reactive control and agility stages toward integrated orchestration that synchronizes workflows, data context, and causal relationships. Progress in implementing single sources of truth and AI explainability will be critical for success.
Additionally, investments in upskilling finance professionals, improving data quality, and aligning AI initiatives with leadership support will determine whether companies can transform FP&A from a cost center to a strategic value driver. Automation of repetitive tasks remains the highest priority for easing team workloads and unlocking time for higher-value insights.