Larger finance departments view their return on artificial intelligence as even less impactful than smaller ones do.
Limited Measurable Financial Impact from AI Despite enthusiastic investment in artificial intelligence, a Zuora survey of 321 U.S. finance and accounting decision-makers reveals that only 28% of finance teams using AI tools are currently experiencing a measurable financial impact. This is particularly notable given that 92% of the polled teams are already utilizing AI solutions in some capacity. Larger Companies See Even Less Impact The disparity between AI investment and tangible outcomes is more pronounced in larger companies. While 100% of companies with at least 1,000 workers have invested in AI, a significant 72% of them have not seen a measurable financial impact, compared to 48% of smaller firms. Furthermore, larger companies are less optimistic, with 35% expecting AI to deliver measurable results in a year or more, versus 15% of smaller companies. Key Reasons for AI's Performance Gaps A substantial 87% of survey participants acknowledged a gap between AI’s promise and its practical reality in finance. The primary contributing factors cited include difficulties in integrating AI outputs into existing finance workflows (41%), challenges in embedding AI into processes requiring cross-functional collaboration (39%), and discrepancies when AI insights do not align with data in core finance systems (35%). Zuora emphasizes that if AI fails to meet standards of precision and traceability, it introduces risk rather than mitigating it. Concerns and Preference for Trustworthy AI Ninety-one percent of respondents expressed concerns about using AI for critical financial processes. The top three concerns are cybersecurity risks and data privacy (45%), insufficient human oversight (38%), and issues related to data quality, integrity, and reliability (36%). When asked about 'trustworthy AI,' over half (53%) of participants stated they would trust AI embedded directly within existing systems the most, highlighting a preference for integrated solutions over standalone or internally developed AI tools.
Limited Measurable Financial Impact from AI
Despite enthusiastic investment in artificial intelligence, a Zuora survey of 321 U.S. finance and accounting decision-makers reveals that only 28% of finance teams using AI tools are currently experiencing a measurable financial impact. This is particularly notable given that 92% of the polled teams are already utilizing AI solutions in some capacity.
Larger Companies See Even Less Impact
The disparity between AI investment and tangible outcomes is more pronounced in larger companies. While 100% of companies with at least 1,000 workers have invested in AI, a significant 72% of them have not seen a measurable financial impact, compared to 48% of smaller firms. Furthermore, larger companies are less optimistic, with 35% expecting AI to deliver measurable results in a year or more, versus 15% of smaller companies.
Key Reasons for AI's Performance Gaps
A substantial 87% of survey participants acknowledged a gap between AI’s promise and its practical reality in finance. The primary contributing factors cited include difficulties in integrating AI outputs into existing finance workflows (41%), challenges in embedding AI into processes requiring cross-functional collaboration (39%), and discrepancies when AI insights do not align with data in core finance systems (35%). Zuora emphasizes that if AI fails to meet standards of precision and traceability, it introduces risk rather than mitigating it.
Concerns and Preference for Trustworthy AI
Ninety-one percent of respondents expressed concerns about using AI for critical financial processes. The top three concerns are cybersecurity risks and data privacy (45%), insufficient human oversight (38%), and issues related to data quality, integrity, and reliability (36%). When asked about 'trustworthy AI,' over half (53%) of participants stated they would trust AI embedded directly within existing systems the most, highlighting a preference for integrated solutions over standalone or internally developed AI tools.
