Foxit study reveals AI's limited impact on productivity despite high expectations

Foxit's recent report challenges prevailing assumptions about AI's productivity benefits, revealing significant time spent on reviewing AI outputs.

A study by Foxit examines the gap between expectations and realities of artificial intelligence (AI) in improving productivity within document workflows. While many anticipate AI as a productivity enhancer, the research presents a more nuanced picture.

Foxit's findings indicate that although 89% of executives believe AI improves productivity, the net gains are limited once the time spent verifying AI outputs is included. Executives effectively achieve only 16 minutes of productivity improvement per week, while end users experience an actual loss of 14 minutes weekly.

This discrepancy arises primarily from the “verification burden”—the need for employees to review and validate AI-generated outputs carefully. Senior executives spend around 4 hours and 20 minutes each week checking AI results, offsetting the 4.6 hours of time initially saved by AI. End users save 3.6 hours but spend 3 hours and 50 minutes correcting outputs.

Trust also remains a key factor affecting AI adoption. Concerns about data privacy, security, and output accuracy influence integration across roles. Only a third of end users report high confidence in AI accuracy, compared with more positive views from executives.

The report notes that AI adoption is prompting structural changes within organisations. Around 68% of executives report that AI has influenced hiring or restructuring decisions, often focusing on retraining and upskilling employees. However, only 12% of end users express concerns about job security, indicating a gap between executive perspectives and employee awareness.

Despite increasing AI use, both executives and end users emphasise the importance of maintaining human problem-solving skills, recognising that human judgement remains critical to decision-making.

Executives anticipate further integration of AI into document workflows in the coming years. Future success will rely on reducing verification requirements, improving output reliability, and enhancing tool interoperability. Measures such as Return on Employee (ROE), which capture improvements in employee capability and satisfaction, are emerging as additional benchmarks for AI effectiveness beyond traditional ROI.

Overall, the research highlights the importance of balancing technological capabilities with trust, accuracy, and accountability when implementing AI in document workflows.

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