As AI usage expands across finance functions, many CFOs are looking to direct capital toward data automation as they coordinate more closely with technology and risk leaders on auditability and accountability.
That shift is more often now being shaped by how quickly AI-enabled tools are becoming part of everyday work habits across organizations. Within that, newer finance and operations employees are coming on board with a high degree of comfort using AI, and they’re likely to have higher expectations about speed, access and decision support throughout the finance function.
Those dynamics, when taken into consideration from the CFO perspective, bring added emphasis on data discipline and governance as new technologies like generative AI become embedded at the will of employees across workflows, both in finance and company wide.
Recent survey data from multiple studies show finance leaders funding these efforts with dedicated budgets and IT support as AI use expands across finance’s core processes, and broader research points to uneven enterprise readiness, underscoring the importance of governance and data architecture in shaping how AI is deployed and assessed. All together, the data points to the growing importance of business leaders defining and enforcing AI governance structures in order to preserve the integrity of their core functions.
Governance investment expands alongside AI use
Workiva found that 79% of organizations are prioritizing data automation and governance to address lingering data issues across the business. These initiatives are supported by lots of resources, with 73% reporting dedicated IT team support for transformation efforts and 71% indicating they have secured a dedicated budget.
The availability of this funding is closely tied to how finance leaders are assessing the risks associated with data quality. Respondents most often pointed to another familiar problem for CFOs: bad or delayed operational decisions as a consequence of weak data. These findings were followed by regulatory fines or legal action and the loss of investor or lender credibility.
Almost all respondents (91%) say AI has improved the timeliness and strategic value of financial decisions. Its presence in reporting continues to grow, with 65% using AI in select components of quarterly or annual disclosures. Nearly half (46%) added that they’ve been applying it extensively across the reporting process. As usage expands, oversight mechanisms are keeping pace, with 76% reporting that internal audit teams test AI models.
How these initiatives are executed increasingly depends on coordination across functions. Nearly all respondents (96%) say alignment among the CFO, CIO and CSO is imperative to break down data silos, and the same percentage say improved access to shared data increases the likelihood of achieving optimal business outcomes. These responses point to governance structures that span finance, technology and risk functions and reflect shared ownership of data and controls.
Enterprise readiness shapes how governance investments pay off
Workiva’s survey also shows finance leaders prioritizing data automation and governance and backing those efforts with dedicated budgets, IT support and formal oversight. That emphasis in the data reflects how closely data quality and control are now tied to financial decision-making and reporting processes.
Additional research compiled in late 2025 from Cisco helps quantify why those investments matter. The Cisco AI Readiness Index found that only about 13% of organizations qualify as AI “pacesetters,” meaning they have the infrastructure, data integration and governance maturity required to scale AI effectively. Those organizations are roughly four times more likely to move AI initiatives from pilot into production and about 50% more likely to report measurable business value from AI.
When viewed alongside Workiva’s findings, the Cisco data highlights the operational impact of governance spending. Investments in data architecture, interoperability and controls directly address the readiness gaps Cisco identifies as barriers to AI value. Where data environments remain fragmented, or systems underinvested, organizations face greater difficulty evaluating AI outputs, measuring return on investment and applying AI consistently across functions.
As adoption expands, workforce behavior is also influencing how quickly AI tools are incorporated into daily operations across the business. For CFOs, this is where the risk comes in. Recent findings from tutoring platform Wiingy illustrate a concerning mix of familiarity and dependence that many employees are bringing into organizations.
According to the Wiingy report, 54% of Gen Z respondents said they use AI multiple times a day, while another 15% use it a few times a week. In total, 72% say they cannot go a week without using AI, often outside formal enterprise systems and policies.
Data from Workiva shows that as AI becomes part of a worker’s routine, inconsistent data inputs and unmanaged tools introduce unprecedented amounts of risk.
Wiingy’s research also shows that AI is primarily used as a productivity and learning tool. Three-quarters of respondents (75%) say they feel a personal connection to AI, and 62% say it helps them express ideas more effectively. Only 3% say AI shapes their values, signaling that the ship to develop and educate teams on AI has not yet set sail.
All the data is sounding a warning bell to CFOs and other senior leaders. They still have time to clearly define how AI is used in their organizations, but must do so by establishing governance frameworks that preserve accountability for judgment-based decisions, support efficient use across finance functions and balance data quality goals with risk management and employee upskilling.
Workiva’s 2026 Executive Benchmark Survey was released on Feb. 3, 2026 and includes responses from 1,497 professionals across finance and accounting, sustainability, internal audit, operations and legal functions at global organizations.
Cisco’s AI Readiness Index 2025 was released in October of 2025 and is based on a double-blind survey of more than 8,000 senior business and IT leaders responsible for AI strategy and implementation across multiple industries and regions.
Wiingy’s 2025 Gen Z AI study was released in September of 2025 and includes responses from 1,532 Gen Z participants, primarily ages 18-26, across several countries, and incorporates qualitative analysis of public online discussions related to technology, education and work.
