AI Labor Unrecorded in Accounts: Companies Struggle to Measure Value

As artificial intelligence (AI) technologies rapidly proliferate within enterprises, a significant portion of the labor and value generated by these advanced systems is not being reflected in companies' financial statements. This phenomenon, dubbed "AI labor orphaning" by market experts, prevents companies from accurately representing their true operational costs and value creation mechanisms.

According to Lanai's 2026 AI Labor Report, released on June 9, 92% of technology executives track the financial impact of AI-generated work, yet only 2% report that more than half of this work is formally recorded as a business outcome. This substantial gap arises because companies are scaling AI much faster than they are building corresponding systems to account for it. While AI systems perform real work, these contributions do not formally enter financial systems, performance records, or systems of record. This leads to profit and loss statements, unit costs, and even organizational charts drifting away from how the business actually operates.

This accounting challenge emerges despite the significant benefits AI offers in areas such as automation, data analysis, and fraud detection within financial reporting. With roughly 97% of leaders in the financial reporting space planning to make greater use of generative AI in the next three years, the integration of this technology continues apace. However, AI-first accounting systems also come with hidden costs, including invisible errors, compliance gaps, poor audit trails, and over-dependence on tools. Notably, smaller companies and those with fewer financial reporting resources are more likely to use AI in their reporting.

These developments are creating uncertainty in markets regarding the true value measurement of AI investments. With Gartner projecting worldwide AI spending to hit $2.52 trillion in 2026, much of this expenditure is landing in areas that finance teams did not forecast. Furthermore, 72% of companies report breaking even or losing money on their AI investments. The fact that 90% of employees use AI tools for work, but only 40% of companies provide enterprise access, exacerbates "shadow AI" usage and its associated security risks.

To overcome these issues, companies need to treat AI execution costs as a labor line item rather than a generic IT expense and establish attribution methodologies early. Experts recommend that finance leaders model a three-year total cost of ownership rather than just a launch budget. Additionally, creating new roles like a "Chief Intelligence Officer" and defining clear policies for AI usage are critical for managing "shadow AI" and data security risks. The indispensable role of human judgment and oversight in AI-assisted financial reporting processes is also emphasized.