AI is everywhere — or at least it feels that way. In just a few years, artificial intelligence has gone from a buzzword to a boardroom imperative. Finance leaders are exploring ways to apply it across everything from invoice approvals, forecasting, and risk detection. The pressure is on to act fast, with competitors seemingly racing ahead.
But behind the urgency lies a more complex truth: Confidence is growing, but capability is not evenly distributed. Many finance teams are eager to invest in AI, yet still face substantial gaps in skills, infrastructure, and readiness. The result is a growing divide between those experimenting with AI and those scaling it with impact.
So where exactly do today’s finance teams stand? Over 100 CFOs and finance leaders from across the US told us:
Key takeaways
- Nearly half of finance leaders feel “very confident” in evaluating AI solutions, but 1 in 6 admit they’re still in the dark.
- Data fluency, technical skills, and change management are the top barriers to successful AI adoption.
- Real readiness requires more than mindset; it depends on infrastructure, integration, and regulatory preparedness.
- Teams that take a phased, process-driven approach to AI adoption are seeing the best results.
Are finance leaders confident in evaluating AI vendors?
Confidence in AI is high, but it’s not universal. And confidence alone isn’t enough to scale AI in finance.
When asked how confident they feel about evaluating AI solutions for finance, 49% of finance leaders said they feel “very confident,” citing internal AI expertise. Another 34% said they’re “somewhat confident,” relying on external advice. But nearly 1 in 6 (16%) admitted they’re “not very confident,” with 1% saying they don’t know what to look for.
In other words, while nearly half of teams are charging ahead, a significant portion remain uncertain and risk falling behind.
This mixed picture reflects an important nuance. Confidence may signal interest and initiative, but it doesn't always equate to preparedness. Many teams are still navigating an overwhelming landscape of tools and promises, especially as the capabilities of AI shift rapidly.
Emil Fleron, Lead AI Engineer at Rillion, says:
Skills lag behind ambition
Beneath the surface of AI confidence lies a clear skills gap.
When asked which capabilities their teams are lacking most, 48% of respondents pointed to data analysis and interpretation, followed closely by technical skills (41%) and change management/user training (40%). Only 4% of finance leaders said their team is “well prepared.”
Even among confident respondents, weaknesses were widespread. Many recognize that the ability to evaluate tools is not the same as being able to implement or optimize them. For AI to thrive, teams need to go beyond vendor selection and build core competencies in data, process design, and continuous learning.
“Since finance is numbers-heavy, it’s well-suited for custom machine learning models,” notes Emil. “But building and maintaining those models requires both data fluency and technical collaboration — skills that many teams are still developing.”
Barriers beyond skills
Skill gaps aren’t the only blocker: infrastructure, trust, and internal resistance also stand in the way.
Finance leaders identified several barriers likely to delay AI adoption. Their top concerns include:
- Employee resistance to automation (29%)
- Difficulty integrating with existing finance systems (28%)
- Irreversible AI decisions (28%)
- Poor data quality or fragmentation (10%)
These concerns reflect the reality that AI adoption isn’t plug-and-play. It demands robust data systems, cross-functional integration, and thoughtful change management. Some finance teams are hesitant to hand over decision-making authority to systems they can’t easily override, especially when regulatory compliance and financial accuracy are at stake.
“Financial processes are often distributed across multiple independent systems,” says Emil. “That fragmentation makes automation and AI implementation harder, even when the use case is strong.”
Compliance as a readiness factor
Technical readiness isn’t enough. AI solutions must also align with compliance requirements, especially for global finance teams.
53% of finance leaders said compliance support is “extremely important” in choosing AI tools, with another 43% saying it’s “somewhat important.” Only 2% of respondents said they hadn’t considered compliance yet.
From tax regulations to data residency laws, the rules around financial information vary widely by region. AI solutions that can’t adapt to local requirements create risk and complexity. For international finance teams, regulatory adaptability is as important as algorithmic accuracy.
This requirement adds another layer to the readiness gap. It’s not just about getting AI to work. It’s about ensuring that it works legally, securely, and ethically.
Bridging the AI readiness gap
How can finance teams move from confident to capable? Based on the survey data, the most successful organizations are following a set of guiding principles:
- Start small, but with purpose: Target processes that are repetitive and rules-based (invoice coding, payment matching, and audit flagging, for example). These are often the lowest-hanging fruit for AI and automation.
- Build on what’s already working: Processes that are already partially automated but still require human intervention are prime candidates for AI enhancement. Use AI as a parallel output source first, allowing teams to validate results before going fully autonomous.
- Focus on structure before scale: Get your data house in order. Fragmented, unstructured, or siloed data makes AI implementation exponentially harder. Prioritize integrations, data cleanliness, and ownership.
- Embrace the human-AI partnership: Rather than replacing human finance professionals, successful AI tools augment them. Train teams to interpret, question, and validate AI outputs. This not only builds trust but also delivers better outcomes.
- Make compliance a first-class citizen: Ensure your AI tools support region-specific tax, audit, and privacy rules. Don’t treat compliance as an afterthought — it’s a readiness requirement, not an add-on.
Mikael Rask, Chief Product and Technology Officer at Rillion, comments:
Ready to move AI forward?
Check out our AI Readiness Checklist to assess your current capabilities, identify gaps, and build a roadmap for sustainable, impactful AI adoption.
What’s The Finance Lab?
The Finance Lab by Rillion is your go-to source for finance automation insights. Each month, we deliver bite-sized reports designed for CFOs and finance leaders, packed with the latest trends in finance and accounts payable automation.
Our insights are backed by real-world data from Rillion’s platform and anonymous surveys of finance leaders across the US and EMEA.
