OpenAI Enterprise Report: Heavy users send six times more messages than regular employees, with the gap in data analysis scenarios widening to 16 times

Who is Closing the Gap

• Message Volume Disparity: The top 5% of users have a message volume 6 times that of the median employee; the disparity in data analysis scenarios reaches 16 times.
• Overall Growth Rate: Enterprise message volume has increased 8 times year-on-year; the usage of reasoning model tokens has grown 320 times; custom GPT usage has increased 19 times.
• Organizational Differences: The per capita message volume of high adoption companies is 2 times that of low adoption companies; technology companies grew 11 times, healthcare grew 8 times.

Data Sources

• De-identified usage data from over 1 million enterprise customers.
• Research samples from 100 companies and 9,000 employees.

Key Metrics

What These Numbers Mean

• Efficiency Gains are Real: Heavy users save 40 to 60 minutes daily, allowing them to accomplish tasks they couldn’t before (like coding).
• Non-engineers are Coding Too: Code output is 36% higher than the baseline.
• Organizational Capability is More Important than Model Capability: Data integration, governance, and workflow design have become the main reasons for closing the gap.
• Keeping Up with Product Iteration is Difficult: OpenAI releases new features approximately every 3 days; continuously absorbing these updates requires supporting infrastructure and processes.

The model dividend has entered a phase of amplifying organizational capability. Companies that can align product pace with internal processes will widen their competitive advantage.

What Companies Should Do

• Establish a flexible data and permissions governance foundation, prioritizing the embedding of reasoning models in high-frequency workflows.
• Use custom GPT as task templates to narrow the gap between ordinary users and heavy users.
• Measure message volume, token usage, quality, and time spent to identify scalable efficiency points.
• Note: High-value scenarios like data analysis show the most severe disparity (16 times); if lagging teams do not quickly catch up on tools and processes, the gap will continue to widen.

Impact Assessment

• Importance: High
• Category: Industry Trends, Market Impact, AI Research

Conclusion: The speed at which the gap widens is faster than the speed at which the median enterprise can catch up. Organizational capability has surpassed model capability, becoming the dominant factor in marginal returns.

For enterprises with existing data and workflow foundations, we are still in the early dividend phase. Observers without implementation capabilities may find that the entry timing has become too late. In the long term, they should bet on teams that can convert model dividends into process assets.