Finance Leaders Embrace AI as Core Driver
At the 2026 AFP FP&A Forum, finance leaders highlighted a strategic shift toward using artificial intelligence as a core driver of efficiency and competitive advantage. The consensus was that AI is now used to augment finance expertise in modeling and planning rather than replace it. Professionals who leverage AI as a co-pilot are expected to outpace those who resist automation.
- The adoption of AI in finance departments has accelerated rapidly, with Gartner projecting that 90% of finance teams will use at least one AI-enabled solution by 2026, a significant increase from 37% in 2023. - Beyond planning, key applications now include fraud detection, where over 70% of financial institutions use AI, and automated regulatory compliance, where "RegTech" solutions scan documents and monitor activities in real-time. - A primary barrier to successful implementation is data quality, with two-thirds of senior executives stating that inadequate data foundations hinder their AI initiatives. Other major challenges include the high costs of implementation, a shortage of qualified talent, and ensuring data security. - The next evolution is "agentic AI," systems that can manage workflows and make decisions with minimal human input. 75% of finance leaders expect this technology to be a routine part of operations by 2028. - While many companies are targeting a return on investment of over 20% for AI projects, the average ROI in finance has been around 10% so far. However, a 2025 survey found midsize companies are already reporting an average ROI of 35%. - The demand for new skills is reshaping hiring, with 85% of finance leaders now prioritizing AI fluency in recruitment. The role is shifting from data compilation to interpreting AI-driven insights and acting as a strategic advisor to the business. - AI is being used to automate time-consuming closing and reporting tasks, with some companies saving an estimated 30% of finance professionals' time on tasks like analyzing budget variances. This allows teams to focus more on strategic analysis and decision-making. - A significant concern is the potential for algorithmic bias, where AI models perpetuate historical biases found in data, which can lead to unfair outcomes in areas like loan approvals and credit scoring.
