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As finance teams evolve from number crunchers to strategic partners, artificial intelligence (AI) is quickly becoming a must-have.
But the picture isn’t uniform. AI adoption is widespread in finance, yet maturity levels vary. While some teams are embedding AI across their workflows and seeing measurable returns, others are still testing pilots or struggling to unlock value from early investments.
At the same time, the rise of agentic AI – advanced systems that can initiate and complete tasks with minimal human input – is changing expectations and realities in FP&A functions. Team development strategies and vendor partnerships are proving just as important as the technology itself.
This article brings together the latest research and survey data to highlight 10 statistics that capture where finance teams stand today – from adoption trends and ROI benchmarks to the rise of agentic AI and the skills gaps shaping future hiring. Taken together, they offer a data-backed view of how AI is reshaping finance and what FP&A leaders should prioritize next.
1. 90% of finance teams will deploy at least one AI-enabled solution by 2026
Source: Gartner, 2024
Gartner’s survey of finance functions in the US and Europe shows just how quickly AI adoption is accelerating. In 2023, only 37% of finance professionals reported using AI; by 2024, that number jumped to 58%. Looking ahead, Gartner projects that nine out of ten finance teams will be running at least one AI-enabled technology within the next two years.
What this means: The “early adopter” window is closing. By 2026, AI won’t be a differentiator – it will be table stakes.
2. 75% of finance leaders expect agentic AI to become routine by 2028
Source: Boston Consulting Group (BCG), 2025
Agentic AI represents the next major leap in AI technology – one that will define the next chapter of finance. Most finance leaders see agentic AI shifting from pilot projects to a standard part of operations within the next three years. What feels experimental today will soon become standard practice, much like automation and cloud adoption did before AI came along.
According to BCG, 17% of finance teams are already deploying generative agents, and 75% expect agentic AI to be routine by 2028. This differs from Wolters Kluwer’s estimate of 6% current adoption. Variations like these are common across surveys – driven by differences in phrasing, respondent groups, and measurement methods. But the signal is consistent: agentic AI is advancing rapidly.
What this means: Agentic AI is moving quickly from the margins to the mainstream. Teams that build literacy now won’t just be ready for 2028 – they’ll be ahead of the curve.
3. 70% of CFOs say their teams are moving faster and delivering more with AI
Source: Mostly Metrics, 2025
CFOs report that AI adoption has sped up workflows and boosted output across finance functions. 88% of CFOs did not report headcount reductions with AI adoption. Most of those leaders said AI is actually helping them to redeploy staff into higher-value work rather than eliminating roles altogether. Adoption is especially strong among mid-market firms ($50M–$100M in revenue), where nearly a third report organization-wide AI deployment.
This shift reflects what Gartner calls the capability diffusion model: instead of relying on finance professionals to provide one-on-one decision support, teams embed their expertise directly into tools and platforms. That way, decision makers across the business can self-serve insights while finance teams focus on higher-value work.
What this means: AI is less about replacing jobs and more about redirecting existing talent toward strategic priorities.
4. Two-thirds of senior executives say inadequate data foundations hold AI initiatives back
Source: EY, 2024
Sixty-seven percent of senior executives acknowledge inadequate data infrastructure as a significant barrier to implementing AI in their organizations. Meanwhile, at 83%, an overwhelming majority believe that building more robust data foundations would accelerate their AI initiatives.
When data is confined within individual business units, it limits AI systems to partial insights. This fragmentation leads to restricted deployment, superficial applications, and unrealized potential for organization-wide impact. The cost is real: 68% of organizations with decentralized data – where less than half of their information is unified – report revenue losses from failed or delayed AI projects.
What this means: Data is the real bottleneck in AI adoption. Without clean, centralized data, AI initiatives stall at proof-of-concept. For finance teams, that translates to unreliable forecasts, fragmented reporting, and wasted investments in tools that can’t reach their potential.
5. Only 18% of companies have established comprehensive governance committees for AI oversight
Source: McKinsey, 2024
Despite clear evidence linking governance to financial performance, just 18% of organizations have enterprise-wide councils or boards with the authority to make decisions about responsible AI governance. McKinsey's research shows that companies achieving meaningful EBIT impact from generative AI are nearly twice as likely to embed risk reviews early in development and involve legal teams from the start.
Without these governance structures, organizations struggle with inaccuracy, compliance issues, and missed opportunities to scale AI responsibly.
What this means: Governance isn't bureaucracy – it’s the foundation that makes AI reliable. Finance teams should document all integration points early, involve IT and business process teams from the start, and allocate sufficient time for testing integrated workflows.
6. 85% of finance leaders now prioritize AI skills when hiring
Source: Wolters Kluwer, 2025
AI fluency is quickly becoming table stakes in the finance function. A strong majority (85%) of finance leaders now view AI skills as important in recruitment, with 11% calling them “essential.” Leaders also pointed to data readiness (44%), AI training (23%), and adoption of AI-powered platforms (25%) as key drivers of success. Together, these findings underline that talent strategy and technical capabilities must advance in lockstep if finance teams are to unlock ROI from AI.
What this means: AI maturity depends as much on people as on platforms. Hiring for new skills and building them internally is now a top priority for finance leaders.
7. 1 in 5 finance teams already see ROI from AI initiatives above 20% – and many more expect breakthroughs ahead
Source: Boston Consulting Group (BCG), 2025
While the median ROI from AI initiatives is just 10%, leading finance teams are already achieving returns above 20% according to BCG. At the same time, only 45% of executives can even quantify the ROI of their efforts, and a third report limited or no gains to date. Yet optimism runs high: 30% of executives expect transformative value by the end of 2025, and half anticipate breakthrough results within three years.
What this means: The AI payoff is real but uneven. Finance teams that embed AI into broader transformation programs, sequence use cases thoughtfully, and work closely with IT are far more likely to join the ranks of high performers.
8. Leading teams run 10 to 11 AI use cases at once
Source: Boston Consulting Group (BCG), 2025
The typical finance function has six AI use cases in proof-of-concept and five already in production. Leaders who embed AI and GenAI initiatives into their broader finance transformation agenda increase their probability of success by 7 percentage points, compared to those who treat AI initiatives as standalone experiments.
What this means: Successful AI initiatives involve a broad range of use cases. Connecting those use cases compounds impact and drives transformation enterprise-wide.
9. 87% of reinvention‑ready companies excel at process mining and benchmarking
Source: Accenture, 2024
Accenture measures AI readiness on a maturity curve that runs from foundational to reinvention-ready. At the top of that curve are reinvention-ready companies – organizations that have already transformed their operations from end to end. Instead of piecemeal automation, these teams have standardized processes across functions and applied rule-based and advanced methods consistently.
One marker of this maturity is process mining and benchmarking: 87% of reinvention-ready companies use these tools to map workflows, identify bottlenecks, and compare performance against best-in-class peers. This disciplined foundation is what enables them to scale AI with confidence – because they understand their business processes deeply before asking AI to optimize them.
What this means: AI maturity starts with process maturity. You need to understand how your business runs before AI can make it run better.
10. Partnering with vendors raises AI success rates by 5%
Source: Boston Consulting Group (BCG), 2025
Finance teams don’t have to go it alone. Partnering with vendors to tap their expertise raises transformation success rates by 5% according to BCG. This aligns with Forrester's prediction that 75% of organizations trying to build AI agents in-house will fail, citing the complexity of architectures that require multiple models, advanced RAG stacks, and specialized expertise. Successful teams lean on vendor capabilities for scalable, repeatable use cases while reserving custom builds for cases with clear differentiation.
What this means: The right vendor partnerships accelerate results. Tapping external expertise helps teams reach value faster and scale with confidence.
See how Pigment customers are putting this into practice →
Looking ahead
Together, these AI in finance statistics reveal a finance function in transition. Adoption is widespread and optimism is high, but the path to ROI remains uneven.
For leaders, the takeaway is clear: winning with AI requires more than experimentation. It requires sequencing initiatives thoughtfully, reskilling teams, and embedding AI into the broader transformation of the finance function. Those who succeed will define not just their organization’s next quarter, but its long-term competitive edge.
