As the landscape of artificial intelligence adoption in accounting evolves, it is noted that there is no single approach being taken.
According to the Association of Chartered Certified Accountants, as organisations are at various stages of AI integration, there is no single application that the majority of sectors are pursuing.
A survey by the accounting body reveals that the pace and extent of implementation varies significantly across different organisations, thereby enumerating three current states of AI adoption in the financial profession.
Implementation across functions
At the forefront of AI adoption, ACCA says data analysis and reporting emerges as the clear leader.
One third (33%) of the surveyed organisations have already implemented AI solutions in this domain – signalling a strong focus on enhancing insight through advanced analytics. In the audit space, AI offers enhanced capabilities for data analysis – enabling auditors to process information and identify outliers or anomalies more efficiently.
ACCA says this incorporates use for analytical procedures and replacing sampling-based techniques with the potential for full coverage and more detailed trend analysis, relevant for both external and internal audit functions.
Providers like Inflo, MindBridge or Thomson Reuters, as well as custom-built platforms, are part of a growing ecosystem of AI solutions for audit professionals that support the demand for greater insight from data.
Data analysis and reporting is closely followed by applications in financial planning and analysis, invoice processing, office productivity, and accounts payable or accounts receivable. There is a clear recognition that AI and ML are transforming traditional financial planning and analysis roles – with a shift towards more advanced data analysis and predictive modelling. This evolution necessitates a change in skill sets, with finance professionals needing to become more adept at ML and AI technologies.
The most popular areas for AI adoption include:
- Data analysis and reporting (33%)
- Financial planning and analysis (31%)
- Invoice and payment processing (28%)
- Office productivity (28%)
- Accounts payable / receivable (24%)
Applications vary by organisation size and type
ACCA says the AI adoption story is not uniform across the accounting landscape, as a notable divide exists between larger firms and smaller practices.
While over 40% of large corporate firms have embraced AI for data analysis and reporting, – less than 30% of sole practitioners and small or medium-sized practices (SMPs) have done so. This disparity reflects the broader challenges of resource allocation and technological capacity that smaller firms often face in keeping pace with rapid technological changes.
GenAI has also lowered the bar to entry for some – offering opportunities to simplify correspondence, draft and review content quickly, and even support Excel or programming activities. Consequently, it is not totally surprising to see that office productivity is a primary objective for many organisations that have relatively few established uses.
Focusing on tangible outcomes
According to ACCA, organisations are pursuing several key tangible outcomes through their AI initiatives, as illustrated by our case studies and survey data. These objectives include improving the quality of products and services, boosting efficiency of existing processes, upskilling employees, expanding organisational capabilities, enhancing decision-making, driving competitive advantage, and reducing operational costs.
Many organisations are leveraging AI to enhance core accounting and finance processes, allowing for greater accuracy and speed in tasks such as balance sheet reviews, payment reconciliations, and cost estimations.
By automating routine tasks, staff can focus on more strategic work. Enhanced analytics and insights from AI are also supporting better business decisions across various areas, from store location planning to customer credit assessments. This improved decision-making capability is seen as crucial for maintaining a competitive edge. Finally, cost reduction remains a significant driver for AI adoption. Organisations are leveraging AI to handle increased workloads without proportional increases in headcount, and to eliminate costly manual processes.
