AI adoption in finance is no longer an early-adopter phenomenon. 79% of respondents are either already running AI in production or actively piloting tools today — yet the actual percentage of finance work automated remains very low. The gap between intention and execution is wide, and it's driven by a consistent set of obstacles explored in Section 4.
79%
of finance leaders are already using or actively evaluating AI tools — only 1 in 5 respondents has no concrete AI plans in place today.
Current AI Adoption Status
Takeaway
The majority are in the evaluation or pilot phase — meaning most organizations are 1–2 cycles away from production decisions. Early movers have an advantage in learning curve.
Finance Functions Using AI in Production
Current vs. Expected Automation — % of Finance Work Automated by AI
The Expectation Gap
67% of finance orgs have less than 5% of work automated today, but 47% expect to reach 11–25% within three years — a dramatic ramp that will depend heavily on vendors delivering on production-grade accuracy and integration.
CFOs are overwhelmingly choosing to buy — not build — and to layer AI on top of existing systems rather than replace them. The dominant philosophy is cautious incrementalism: add AI capabilities through point solutions, prove ROI in narrow use cases, then expand.
How Will You Adopt AI?
Layer AI on existing systems
Replace core systems with AI-native
Build vs. Buy Orientation
Autonomous vs. Assistive AI Preference
Task-dependent — mix of both
Assistive only (human approval)
Not comfortable with autonomous yet
Prefer autonomous for routine work
General LLMs vs. Purpose-Built Finance AI
LLMs for ad-hoc, purpose-built for core
Purpose-built essential — LLMs lack rigor
LLMs can handle most needs
AI Budget: Dedicated Allocation Today
0% — No dedicated AI spend
AI Budget: Net New vs. Reallocation
On Pilot-to-Production Conversion
49% of respondents who run pilots say either none or fewer than 25% of AI pilots have successfully made it into production. This is a significant credibility gap for vendors — and a signal that finance buyers need production references, not proof-of-concepts, to justify budget commitments.
Despite widespread interest, adoption is slowing against a consistent set of barriers. The most common theme across open-ended responses: AI tools look impressive in demos but fall short in production environments where accuracy, auditability, and integration with existing systems are non-negotiable.
Biggest Concerns About Adopting AI (Select all that apply)
Inaccuracy of Model/Agent
Primary Barrier Slowing Adoption
Unclear ROI / Vendor Immaturity
Skills Gap on Finance Team
Data Quality / Availability
Auditability / Explainability
Data Readiness for AI
Fair — siloed, inconsistent quality
Good — mostly clean, some gaps
Poor — significant quality issues
Excellent — clean, centralized
Data Readiness Reality
85% of respondents rate their data quality as only "fair" or below — meaning most organizations will need data remediation work before AI tools can be deployed effectively in production environments.
CFOs have a clear hierarchy when it comes to evaluating AI and finance technology vendors. Accuracy tops everything — by a significant margin. Vendors who lead with flashy features over provable accuracy and seamless integration are consistently losing deals. Price premiums are tolerated, but only when tied directly to measurable value.
Vendor Evaluation Criteria — Average Importance Score (1–10)
| Criterion |
Avg Score |
Relative Weight |
| Accuracy & Reliability of Outputs |
8.9 / 10 |
|
| Total Cost of Ownership |
8.3 / 10 |
|
| Integration Capabilities with Existing Stack |
8.0 / 10 |
|
| Implementation Speed & Complexity |
7.7 / 10 |
|
| Vendor Support Quality & Responsiveness |
7.7 / 10 |
|
| Security Certifications & Compliance (SOC 2, etc.) |
7.3 / 10 |
|
| Reference Customers in Similar Industries |
7.0 / 10 |
|
| Product Roadmap & Innovation Pace |
6.8 / 10 |
|
| AI Maturity of the Vendor |
6.7 / 10 |
|
AI Capabilities That Would Unlock Budget (Select up to 2)
Invoice processing, 99%+ accuracy
Automated month-end reconciliations
Cash flow forecasting w/ variance
Predictive revenue forecasting
Real-time anomaly / fraud detection
Autonomous journal entry creation
Natural language financial reporting
Willingness to Pay Premium for AI-Enabled Finance Software
Key Takeaway
95% of respondents are willing to pay some premium for AI-enabled software. Most are comfortable with 1–15%, but 34% will go 15% or more when ROI is demonstrable.
How Can a New AI Product Differentiate? (Select all that apply)
Higher automation accuracy rates
Fewer / simpler workflows
Looking ahead, CFOs anticipate meaningful budget expansion and workforce shifts tied to AI adoption. Board and executive pressure is accelerating timelines, and the majority believe AI could eventually operate as a system of record — a view with significant implications for the software platforms that finance orgs rely on today.
72%
Expect Finance Tech Budget to Grow
57%
Face Board or Executive AI Pressure
59%
Expect to Reduce Finance Headcount
68%
Believe AI Can Become a System of Record
When Do You Expect AI in Production?
What Is Driving AI Pressure?
Investor / Board Expectations
Operational Efficiency Goals
93%
of respondents said they would shift some labor budget to AI tools if those tools deliver on their promises. The question is no longer whether to invest — it's which vendors can prove production-grade results to justify the reallocation.
The following quotes represent a cross-section of open-ended responses from survey participants. They have been lightly edited for grammar and anonymized by design. They reflect no single industry or company size.
All responses anonymized. Quotes represent individual respondent perspectives, not aggregate findings.
On Manual Pain Points in Finance Today
“Monthly bank and account reconciliations, along with creating forecasts and updating financial reports, consume the most time.”
VP Finance — Mid-Market
“Cash management and treasury functions are our most manual processes today. Significant opportunities exist for automation.”
CFO — Healthcare
“Managing the month-end close task list and tracking task completion and challenges are the most time-intensive activities.”
Director of Finance — Manufacturing
“Collections and cash applications are highly manual because the interface with the bank and credit card merchant is poor, leading to numerous errors requiring manual intervention.”
CFO — Consumer Services
On AI Proof Points Needed Before Committing
“Reference customers are usually my most helpful conversations. If you can prove the concept elsewhere, I believe we can do the same.”
CFO — B2B SaaS
“Hard ROI metrics including cost savings and increased top-line, total cost of ownership, and reference customers would be key considerations.”
VP Finance — Professional Services
“Price, implementation cost and timeline, and know-how from the implementation team as assessed by both finance and IT would be important.”
CFO — Healthcare
“Evaluation of edge case handling, ROI in time and money, and positive customer feedback would be the key proof points.”
CFO — Manufacturing
On AI Disappointments
“Accuracy is not high enough for production use. We've evaluated several tools and none clear the bar we need for financial reporting.”
CFO — Financial Services
“Poor integration with existing systems. The demo looks great until you try to connect it to what we actually use.”
VP Finance — B2B SaaS
“Overpromised, underdelivered on ROI. We had high expectations and the productivity gains didn't materialize the way we projected.”
CFO — Professional Services
“Can't explain or audit the outputs. Finance requires auditability — a black box that produces numbers I can't defend to auditors is a non-starter.”
CFO — Healthcare
On What Is Driving AI Pressure
“Mainly cost reductions and the need to run an efficient company. At companies our size, resources are limited and every efficiency matters.”
CFO — Mid-Market
“We realize other organizations are ahead in implementing AI. Key leaders want us to move forward and not fall further behind.”
CFO — Healthcare System
“What industry leaders and competitors are doing, along with looking for cost reductions, is what drives our interest.”
VP Finance — Manufacturing
“It is mostly market hype paired with real cost pressure. AI adoption is encouraged with little understanding of the internal controls and data accuracy needs of accounting.”
CFO — Professional Services