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Tipalti
The report examines how AI is transforming the finance function. It highlights a significant shift as organisations are adopting to various degrees. There are still barriers to adoption and a lack of trust in AI. There are two driving forces: one sees 97% of respondents as either somewhat or extremely optimistic about the benefits that AI can offer.
That trust gap centres on data privacy and security (31%), data quality & data provenance (28%), and integration with legacy systems (28%). However, those organisations that have overcome these reservations already see the value. And see it becoming part of the way of working in the future.
The report looks at how organisations already use AI for reporting, fraud detection, and spend analytics, with other areas planned for.
Manish Vrishaketu, Chief Customer and Operating Officer at Tipalti, commented, “Finance teams are embracing AI for real, measurable impact, but only when they can trust how it operates.
“The results are clear: trust is now the gating factor between incremental automation and true, strategic transformation. The future belongs to finance teams who can see what AI is doing, audit it, and scale it with confidence. Those who operationalize trust will unlock AI’s full potential, not just for efficiency, but for decision-making, risk mitigation, and business growth.”
What is in the report
After an executive summary, the 20-page report is divided into four parts. And concludes with a conclusion that draws out seven key findings from the survey and subsequent analysis. Each section comprises a breakdown of one or more survey questions. The section provides a data visualisation of the survey responses and an analysis that dives slightly deeper into the subject area.
The report presents some qualitative responses from the survey, though there does not appear to have been a separate qualitative survey conducted. One question that broke the format asked what organisations would have done earlier or better to make AI a success. Five themes emerged, though no data points associated with the response were given.
- Start with data quality and integration groundwork
- Define owners and success metrics up front
- Pilot narrowly, measure, then scale
- Establish review-before-action and audit trails, and ensure human oversight
- Provide role-specific training and clear communications around the expected use of AI
Each section looks at a different aspect of the research. Their research findings are broken down in some instances by country, organisation size, and the function in which people work. Though some of the latter have small sample sizes.
However, the regional variances do not account for cultural differences; for example, while 47% of North Americans say AI is extremely important, only 37% of UK respondents do so. It would have been useful to identify the percentage of those who thought it somewhat useful, partly because UK answers might be more conservative.
Some highlights
Some of the survey’s highlights include the reasons organisations are adopting AI: productivity (64%), quality and accuracy (62%), and enhanced decision-making (58%). The results for those using AI seem to back this up: 62% say it saves time, and 59% say it improves work quality.
Perhaps unusually, only 53% said to save money. However, Tipalti did not ask how key this was to the business case. While trust is a barrier to adoption, it is not the only one. Besides those highlighted above, access to talent was cited by 24%.
Despite this AI adoption, at least in the early stages, it is high. With over 90% of respondents either experimenting with or using AI across all use cases. Financial analysis/benchmarking (53.5%) and generating reports and insights (62%) are the most frequently used use cases. It will be interesting to see how this changes if Tipalti repeat the report next year.
The report also looks at how organisations are addressing the talent gap, with 58% of respondents offering AI Training and skills development through internal resources, and 53% looking externally.
The authors do not shy away from the headcount question: 20% say they have already reduced headcount, and 28% intend to reduce it. It is one of the most honest analyses Enterprise Times has seen around this subject. Looking forward, respondents expect to see more from AI, though they also highlight that they want changes.
- 52% want stronger governance frameworks (ethics, transparency)
- 47% want clearer accountability for AI decisions
- 45% want improved data lineage and quality controls
If these and other criteria are met, AI can change how finance works. Looking 3-5 years ahead, respondents see:
- Broader automation and process integration (59%)
- Smarter fraud prevention and compliance (48%)
- More predictive forecasting and cash flow insights (44%)
Enterprise Times: What does this mean
AI is here to stay, and there are huge benefits after successful adoption. However, concerns remain, leading to a lack of trust in AI. Vendors and subsequently organisations need to ensure this trust gap is closed.
One of the gaps concerns data accuracy. Data has never been that accurate in most organisations, but if we are to rely on AI, then organisations must increase trust in their data. That means improving data quality. In doing so, AI will give better answers, and trust will increase.
This is a solid report that is easy to read and includes useful data points, insights, and analysis. There is, however, no clear takeaway for the reader on what to do next. It could have been strengthened by the authors providing reflective questions throughout and adding next steps for finance leaders to follow.
Tipalti notes that, “To gain a competitive edge in 2026, finance teams must standardize AI oversight, establish consistent methods for measuring and improving real-world impact, and ensure that human expertise remains at the center of the finance function.”
The post Tipalti Explores the AI Trust Gap appeared first on Enterprise Times.
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