The deployment of generative software tools across enterprise environments is creating an unexpectedly high correction burden. This is the central finding of a report titled 'Redesigning Work: AI's Performance Review', published by software provider BambooHR on September 1, 2026. The study surveyed more than 1,600 full-time salaried office workers in the United States, including 520 human resources leaders. The gathered data reveals a stark imbalance between the daily hours dedicated to software interaction and the finished output generated.
On average, surveyed employees spend 87 minutes per day interacting with artificial intelligence systems, which equals approximately 47 full working days annually. However, 42 percent of this time is consumed by troubleshooting, correcting flawed text, and iterating prompts. This remediation effort adds up to roughly 20 full working days lost every year solely to fixing software mistakes. In contrast, only 35 percent of user time, representing roughly 16 working days per year, produces directly usable work results.
A notable disconnect exists between this measured operational friction and subjective worker sentiment. Despite losing nearly half their tool time to troubleshooting, 65 percent of respondents report feeling enthusiastic about using these systems at work. Furthermore, 58 percent cite perceived time savings as their primary motivation. In reality, imperfect prompts and factual inaccuracies generate persistent loops of manual verification and line-by-line editing.
The findings also highlight a clear divide along organizational hierarchies. Executives at the vice-president and C-suite levels spend nearly twice as much time using generative tools as individual contributors without management responsibilities. While senior leadership relies on software for high-level summaries and draft strategies, non-managerial staff frequently shoulder the burden of auditing detailed outputs, transforming traditional routine workflows into intensive verification tasks.
Despite the lack of documented efficiency gains, employers continue to expand their financial commitments to the technology. According to the report, 63 percent of surveyed organizations have increased their budgets for enterprise AI tools. Yet, empirical evidence demonstrating that these software expenditures translate into measurable net productivity remains absent in most companies. Management faces growing pressure to institute structured training that curbs the time lost to operational troubleshooting.