Accountants and bookkeepers are experiencing a shift in their work, with automation handling routine tasks and allowing them to focus on more analytical aspects of their jobs, according to automation tool developers. This trend, observed as early as 2024, isn’t necessarily eliminating positions but fundamentally altering job descriptions.
Jack Franklyn, a builder of automation tools for accounting professionals, noted a clear division emerging. “Before our tools: a bookkeeper spends 80% of their time on data entry and transaction categorisation, 20% on actually thinking about the numbers. After: those ratios flip,” he said. The change, however, creates a disruption for those whose skills were centered on the mechanical aspects of the work. Those proficient in data entry and memorizing category codes locate their competitive advantage diminished, while those skilled in analytical thinking become more valuable.
This reallocation of tasks isn’t limited to accounting. Reports indicate a similar pattern across various white-collar professions, including sales and legal work. Individuals engaging with AI tools often find that while the initial task may become easier, it reveals a multitude of smaller tasks that ultimately increase their overall workload. One observer noted that productivity gains from AI often lead to a “flywheel of increasing work.”
The impact on entry-level positions is a growing concern. While some companies, including those within the FAANG group, are seeing an increase in junior-level openings after a period of cost-cutting measures that included reducing these roles, the narrative that AI is automating these positions is contested. Some analysts believe the initial reduction in junior roles was a strategic financial decision, not a precursor to full automation. However, the industry is too undergoing a correction following a period of rapid growth fueled by readily available capital.
The shift isn’t simply about job displacement, but also about where the remaining human judgment is sourced. AI reduces the need for extensive domain expertise, making it easier to offshore tasks that require analytical thinking. Training time is reduced as the focus shifts to exception handling within an AI-driven system and quality control becomes more manageable through automated review processes.
Concerns are also being raised about the potential for AI to intensify work rather than reduce it. The question of whether leveraging AI tools translates to increased earnings or reduced working hours remains unanswered. If neither of these outcomes is achieved, some argue that AI may not be improving the lives of working professionals.
Despite these concerns, some remain optimistic about the future of jobs in the age of AI. There is a belief that AI will not replace software engineers, and that the profession will adapt to incorporate these new technologies. However, the potential for a “Moloch problem” – where using AI makes your life worse, but not using it makes it even worse – is also acknowledged, particularly in the context of a lack of worker protections like unionization.
Recent data suggests that job losses may be more directly attributable to outsourcing than to AI itself, with the United States offshoring approximately 300,000 jobs annually.
