Finance Functions Have Largely Closed their AI Adoption Gap with Other Corporate Functions
Gartner surveyed 121 finance leaders in June 2024 to understand their use of AI technologies for the finance function. Gartner experts are discussing key issues around finance AI at the Gartner CFO & Finance Executive Conference 2024 taking place in London today.
“AI adoption in the finance function is advancing quickly,” said Marco Steecker, senior director, research in the Gartner finance practice. “It’s also encouraging to note that two-thirds of finance leaders feel more optimistic about AI’s impact than they did a year ago, particularly among those who have already made progress leveraging AI solutions.”
While 42% of finance functions are not currently using AI, half of these are planning implementation (see Figure 1).
Figure 1: Current Levels of AI Use in Finance, 2023 vs 2024
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Source: Gartner (September 2024)
- Intelligent process automation (used by 44% of finance functions) — Automation that leverages the AI capabilities of existing automation tools (such as RPA) to enhance information processing.
- Anomaly and error detection (used by 39% of finance functions) — AI-enabled identification and reporting of errors and outliers in large datasets (e.g., internal claims, expenses, and invoices).
- Analytics (used by 28% of finance functions) — The creation of better financial forecasts and results analysis that can lead to improved decision making.
- Operational assistance and augmentation (used by 27% of finance functions) — Emulation of human-judgment-based decisions in operations through AI (often generative AI).
Marco Steecker, senior director, research at Gartner is discussing implementing AI in enterprises with attendees at the Gartner CFO & Finance Executive Conference in London today.
Data and Talent Shortages Present Challenges
To do this CFOs will need to address three primary challenges that hinder finance AI talent plans: a limited understanding of the necessary roles and skills involved in AI implementation; a difficulty attracting and retaining AI talent; and slow progress developing AI skills within existing employees.
In terms of data quality, Gartner experts recommend considering leaving behind a “single version of the truth” data management philosophy because it is almost impossible to attain this kind of perfection given the volume and volatility of data in modern companies. The alternative is a “sufficient versions of the truth” approach that balances data quality with ensuring it is useful in decision making.
