Corporate treasury departments are expanding their focus on AI and automation initiatives while continuing to manage longstanding liquidity responsibilities, according to the 2026 AFP Treasury Benchmarking Survey Report, released by the Association for Financial Professionals and sponsored by PNC Bank.

Key takeaways 

• AI enters top priorities alongside major challenges:AI/automation ranked among the top five treasury priorities (30%), putting it alongside core areas such as cash management and liquidity planning. At the same time, managing AI opportunities and risks (38%) and using AI to automate manual processes (35%) rank among treasury's most significant challenges.
• Cash forecasting remains the top priority and pain point: Despite years of technology investments, cash and liquidity forecasting remains the most challenging treasury activity (49%) and the top priority overall (63%).
• Soft skills remain widely used alongside technical skills: In addition to bank relationship management (86%) and cash forecasting (77%), treasury professionals rely heavily on communication (78%), collaboration (75%) and critical or strategic thinking (75%).
• Largest leadership capability gap centers on future planning: While 90% of respondents consider vision and future planning essential for treasury leaders, only 61% rate senior treasury professionals as effective in this area, creating the survey's largest leadership capability gap at 29 percentage points.

Treasury departments are managing an expanding mandate. Alongside longstanding responsibilities such as liquidity management and cash forecasting, teams are increasingly expected to support AI initiatives and provide strategic guidance. The survey findings suggest that technology investments alone are not enough; organizations also need the governance, leadership development and workforce capabilities to support these evolving responsibilities.

AI priorities rise alongside governance and capability challenges
AI and automation have become strategic priorities for treasury departments, particularly among larger organizations, but many organizations report gaps in policy effectiveness and AI-related knowledge that could affect broader adoption.

• Governance effectiveness gap: Traditional policy areas such as cash management (4.4 out of 5) and bank relationship management (4.3 out of 5) received high effectiveness scores, while AI and emerging technology policies received a score of 2.9 out of 5, the lowest among all policy areas measured.
• AI knowledge gap: Only 34% of respondents view themselves as effective in AI knowledge, compared with 50% who consider it important.