Benchmarks and breakthroughs: How finance teams use AI today

From the 2025 Technology Finance Symposium - East

This session, featuring Eric Sleeth, Senior Manager at Sage, focused on the accelerating adoption of AI within finance, particularly among SaaS companies and the practical steps organizations are taking to unlock its benefits. The session highlighted survey findings from nearly 400 organizations across the U.S., revealing that AI adoption is no longer experimental: 81% of SaaS finance leaders have implemented AI in the past year and 95% are already seeing measurable productivity gains. These gains are primarily realized in data analysis, forecasting accuracy and process efficiency, allowing finance teams to operate with fewer resources while maintaining – or even improving – the quality of insights.

The discussion framed AI adoption within the broader pressures facing modern finance organizations, including an obsession with metrics, efficiency mandates and increasing operational complexity. Finance leaders are expected not only to report historical performance but to forecast future growth with precision and provide actionable insights across the business. AI helps address these pressures by automating repetitive tasks in accounting and financial planning while enabling teams to focus on analysis and strategic decision-making. However, the session emphasized that AI cannot fix poor-quality data; clean and accurate data is a prerequisite for unlocking meaningful insights.

Practical guidance was offered for organizations hesitant to adopt AI, focusing on four common barriers: lack of understanding, insufficient expertise, quality concerns and data privacy. The recommended approach is iterative and exploratory: identify pain points, engage peers and vendors, run small, measurable pilots and gradually scale usage. Importantly, AI is framed as a collaborator rather than a replacement, maintaining a “human in the loop” approach to ensure accuracy and continuous learning.

The session also highlighted best practices from high-performing finance teams, including prioritizing smart insights over historical reporting, implementing confidence scoring to minimize errors and embedding AI within secure, system-integrated environments to reduce risk. While headcount reductions are possible, most organizations expect their teams to maintain or grow as AI enables higher-value work. Real-world outcomes shared included improved monthly close processes, continuous accounting and enhanced forecasting accuracy, with some users leveraging AI to manage over $81 billion in transactions.