In 2024, KPMG International surveyed 1,800 companies across 10 major economies. The results were clear: nearly three-quarters were already using AI in their financial reporting processes, and almost all expected to do so within the next three years. While followup research would typically wait a year or more, AI is rapidly evolving. Less than a year later, we launched a second global study surveying 2,900 finance leaders including 100 in the UAE to understand how quickly the landscape is shifting.
Decision-makers are now concentrating on highimpact, targeted AI solutions that span the full scope of the finance function including accounting, financial planning and analysis, treasury, tax and risk. By aligning AI’s potential with clearly defined use cases, companies are investing with more clarity and confidence. Skills gaps, data quality and risk management are still key concerns. The rewards however are increasing, especially with the rise of AI agents and more mature generative models. Finance is evolving and CFOs are becoming the key drivers of change.
In the UAE, many organizations are just beginning to explore AI adoption in finance compared to global peers. With limited in-house expertise, unclear governance and insufficient resourcing, capabilities remain inconsistent. Some teams are unequipped to lead large-scale AI transformations or integrate AI into day-to-day decision making.
As regional leaders aim to position finance as a strategic partner, AI provides a defined path to automate, forecast and guide performance. Supported by the government’s national vision and ongoing investment, the UAE has an opportunity to make significant progress. Adoption also remains fragmented despite the growing interest across major UAE organizations, from early pilots to initial generative AI use cases. Few companies have a clear roadmap and fewer have the structure, talent and tools needed to scale effectively.
At KPMG, we enable secure environments that combine traditional and generative AI across services. These technologies help accelerate data processing and analysis while supporting the transformation of finance functions, especially when tailored to specific business needs. Moreover, KPMG Advisory has developed specialized accelerators and assets to support clients throughout their AI transformation journey, adapting to their unique strategic and operational priorities.
For organizations to prepare for the future, they should address early on whether their ROI is meeting expectations and if their AI implementation is consistent across all sectors and departments. Our latest study offers practical solutions, drawing on insight from 2,900 finance leaders around the world with a dedicated UAE perspective. The report highlights proven use cases, lessons from early adopters and recommendations to help elevate your finance function.
Key recommendations
Integrating AI across finance operations and processes is a journey that requires commitment, stamina and planning. As KPMG’s research indicates, the potential rewards are high. Below are key recommendations that may help you focus your efforts and make agile, tangible progress:
Companies should implement a wide range of use cases including data entry and administrative processes, as well as higher-order tasks for conducting research, risk management, cybersecurity, fraud detection and predictive analysis.
This includes actively testing and refining use cases that leverage the power of GenAI, such as composing financial reports and summaries.
Companies should also stay mindful of GenAI’s limitations around data security, sovereignty, accuracy, and copyright and intellectual property.
While AI is currently most commonly used in accounting and financial reporting, its use is spreading across finance. Most AI leaders are already using the technology to optimize financial planning, treasury management, tax operations, and risk management, as well as to drive ROI across their departments.
To fully embed AI into their financial activities, management teams should go beyond seeking AI support from other departments. That means teaming up with AI specialists within finance and providing training on the use of AI to the financial staff. Using AI to improve the productivity, engagement and retention of their people should be top of mind for organization.
A lack of AI skills, inconsistent data, high costs, and data security and privacy concerns can often hold companies back from fully leveraging AI in finance.
To overcome these barriers, financial teams should act early to establish AI guidelines and governance mechanisms, create digital processes to meet regulatory requirements, and shift to modern IT platforms that facilitate AI. They should also pilot AI initiatives to validate ROI and ensure effectiveness before scaling these solutions across the department.
Transparency can be a common blind spot due to the complexity of AI algorithms and the black box nature of AI solutions. If left unattended, this could lead to a loss of trust and accountability. Sustainability is another area often overlooked, leading to increased carbon footprints due to higher AI-driven data consumption.
