ACCA’s report, The Smart Alliance: Accounting expertise meets machine intelligence, is based on a survey and interviews with accounting leaders who are already using AI.
It aims to provide a clear picture of the current state of AI adoption in accounting. We explore the strategic approaches that organisations are taking, the challenges they face, and the outlook for AI in the profession.
The most prevalent types of AI are based in machine learning. Within machine learning, deep learning encompasses: computer vision (image recognition, object detection, and data extraction); natural language processing (text analysis, machine translation, sentiment analysis); and GenAI (text generation, summarisation, image sythesis, etc). Symbolic AI encompasses decision-support systems and diagnostic systems. And machine learning algorithms encompass regression (eg SVM, random trees), clustering (eg K-means), forecasting, anomaly detection.
Three high-level takeaways emerge from the report:
- AI adoption is growing and firms are investing in capabilities: But organic adoption is outpacing strategic adoption which feeds into risk concerns.
- Adopters are taking a strategic approach to AI, starting with data: But finance can champion a more collaborative approach, esp. pertaining to its competencies around data governance.
- A collaborative approach to managing risk is emerging: But adopters are still in the early stages of developing clear policies addressing key risks around AI use or establishing training for employees.
This report serves as a crucial resource for accounting and finance professionals. It highlights the need for a balanced approach to AI adoption, emphasising the importance of human oversight and expertise alongside technological advancements.
As AI continues to evolve, understanding its potential and limitations becomes increasingly vital for decision-making and strategic planning.
- Organisations should understand the limitations associated with different types and uses of AI and implement appropriate safeguards – such as human oversight and validation processes – particularly for tasks requiring high accuracy.
- As data becomes central to organisational success, finance departments are well placed to foster cross-functional collaboration, bridge the gap between organisational strategy and day-to-day operations, and ensure AI initiatives align with business objectives.
- Our report emphasises that as AI adoption grows, there's an increasing need to develop more mature, collaborative approaches to risk and governance that can keep pace with the technology's evolution.
- Our report synthesises insights from various case studies to create a framework for successful AI adoption in the finance function. Key themes include the importance of strategic planning, data management, and human-AI collaboration.Â
