MIT’s Analysis Says that 95% of Enterprise AI Projects Drive No Revenue Growth
- 95% of enterprise AI projects fail to drive revenue growth, according to MIT’s “The GenAI Divide: State of AI in Business 2025” report.
- Only about 5% of AI pilot programs achieve rapid revenue acceleration, highlighting the challenges of implementing AI in corporate settings.
- MIT found that back-office automation, eliminating process outsourcing, and streamlining operations yield the biggest ROI from generative AI investments.
- Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds are less successful, with only one-third as many successes.
- Successful organizations prioritize empowering line managers to drive adoption, selecting tools that can integrate deeply and adapt over time, and partnering smartly with companies using their tools.
IBL News | New York
A new report published by MIT’s NANDA initiative, titled “The GenAI Divide: State of AI in Business 2025,” reveals that 95% of initiatives trying to drive rapid revenue growth at corporations fail, delivering little to no measurable impact.
Only about 5% of AI pilot programs are achieving rapid revenue acceleration.
The research is based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments.
MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows.
Aditya Challapally, the lead author of the report, explained that “successful organizations pick one pain point, execute well, and partner smartly with companies who use their tools.”
More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI is in back-office automation, when eliminating process outsourcing, cutting external agency costs, and streamlining operations.
Advanced organizations are already experimenting with agentic systems that can learn, remember, and act independently within set boundaries.
MIT’s report states that purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often. The analysis suggests companies see far more failures when going solo.
This finding is particularly relevant in financial services and other highly regulated sectors, where many firms are building their own proprietary generative AI systems in 2025.
Other key factors for success include empowering line managers—not just central AI labs—to drive adoption, and selecting tools that can integrate deeply and adapt over time.
Workforce disruption, although no mass layoffs, is underway in customer support and administrative roles.