New report from Rillion reveals the Finance AI Illusion across U.S. finance functions

New research (Rillion’s 2026 AI in Finance Report) finds AI usage is high—68% of finance teams use AI—but only 39% of CFOs are comfortable letting it act independently, highlighting trust and automation gaps. The survey of 250 U.S. finance leaders also points to continued manual work and emerging skills shortages, suggesting adoption is outpacing true operational transformation.

Analysis

The market implication is not that finance is “behind” on AI; it is that the first monetization wave is likely to come from control-heavy workflow software, not autonomous agents. CFOs will pay for auditability, approvals, and exception handling before they allow systems to make decisions, which favors incumbent ERP, close/controls, and back-office platforms over pure AI point solutions. That means the revenue pool shifts toward embedded automation and compliance layers, while vendors selling labor-elimination narratives face slower conversion and higher churn risk.

The second-order effect is on multiples. Names pitching rapid headcount reduction may need to show measurable cycle-time and error-rate improvements before the market gives them durable ARR premium. By contrast, software that can prove faster close, lower rework, and cleaner controls can expand wallet share even if “autonomy” remains limited. For banks and large corporates, the real near-term spend is likely in governance, data lineage, and human-in-the-loop orchestration rather than fully agentic systems.

Contrarian view: consensus may be underestimating how sticky this will be. Finance is the last place where buyers tolerate black-box behavior, so adoption can look broad while economic impact stays incremental for quarters. If upcoming enterprise earnings show AI attach rates rising but autonomous usage still capped, the market should rotate away from pure AI narratives and toward trusted workflow franchises.

Watch the thesis if CFO comfort jumps above ~50% or if vendors start reporting meaningful transaction-volume automation rather than pilot activity; that would argue the current skepticism is too high.

Key Decisions for Investors

• Long SSNC / short PATH for 3-6 months: SS&C should benefit from regulated, control-heavy finance automation, while the market may overprice agentic labor-replacement at PATH. Risk/reward is roughly 2:1 if AI adoption remains human-in-the-loop; stop if PATH shows material booking acceleration or SSNC loses core-retention momentum.
• Accumulate BL on weakness into earnings over the next 1-2 quarters: finance close and controls software is better positioned than generic AI tools because trust is the product. Falsify if close-related upsell decelerates or management indicates AI features are not expanding usage.
• Avoid chasing standalone AI claims in finance software until evidence appears in transaction volume, not survey data. The near-term downside is multiple compression for vendors whose thesis depends on autonomous decision-making without proof of control and compliance adoption.
• Watch INTU and the broader ERP stack (ORCL, SAP) as longer-duration beneficiaries over 6-18 months: embedded workflows and trusted data layers should capture budget before pure AI copilots. Use any post-earnings selloff to add only if the companies can show AI is improving throughput, not just engagement.