Finance Teams Embrace AI Adoption Amid Shifting Operational Priorities

Gartner's recent survey indicates that 58% of finance teams have adopted artificial intelligence (AI) in their operations as of 2024, marking a significant increase of 21 percentage points from the previous year. This surge in adoption reflects a broader trend in the finance sector, where leaders are increasingly optimistic about AI's potential to enhance efficiency and decision-making. The findings suggest a pivotal shift in the finance landscape, as organizations transition from initial exploration of AI to practical implementation and governance.

The survey, which included responses from 121 finance leaders across various industries, reveals that 66% of these leaders feel more positive about AI compared to the prior year. This growing enthusiasm is noteworthy, especially considering that half of the 42% of finance functions not currently using AI are planning to implement it. However, while adoption rates are rising, the survey lacks specific metrics on return on investment (ROI), cost savings, and revenue impacts. This absence of quantitative data presents a challenge for finance teams looking to justify AI investments and set realistic expectations.

As finance teams grapple with these dynamics, Gartner is positioning itself as a key resource for CFOs, offering tools and benchmarks designed to facilitate AI integration. The firm emphasizes the importance of governance and measurement, providing frameworks that help finance leaders navigate the complexities of AI implementation. Gartner's offerings include AI use-case libraries, efficiency benchmarks, and decision-making tools that align with the operational needs of finance organizations. This strategic focus on governance and control reflects a critical shift in how finance functions will manage AI technologies moving forward.

The implications for finance operations are significant. As AI becomes more prevalent, finance teams will need to adopt standardized definitions and controlled workflows similar to those used for other enterprise systems. This will require a reevaluation of procurement processes, as finance leaders increasingly seek to justify AI expenditures through peer comparisons rather than anecdotal evidence. The emphasis on governance artifacts will also reshape vendor conversations, as procurement and IT governance teams will need to ensure that AI tools align with existing policies on data handling and cost management.

The evolving landscape signals a need for finance teams to establish clear metrics for what constitutes "using AI." This includes defining whether AI is integrated into production workflows, supervised pilot programs, or individual tools. Furthermore, organizations must determine where AI-related costs will be allocated—whether as part of a central IT budget, a finance cost center, or embedded within vendor contracts. Establishing these parameters will be crucial as finance teams prepare for scaling AI initiatives.

Looking ahead, the increasing adoption of AI in finance suggests a shift toward more sophisticated governance frameworks and performance metrics. As finance leaders continue to embrace AI, the focus will likely shift from mere adoption to effective management and measurement of AI's impact on operations. Organizations that proactively develop robust governance models and clear ROI frameworks will not only enhance their operational efficiency but also position themselves competitively in a rapidly evolving market. This strategic foresight will be essential for finance teams aiming to leverage AI as a transformative tool rather than just a trend.