Good morning. Companies are betting on AI—yet nearly all enterprise pilots are stuck at the starting line.
The GenAI Divide: State of AI in Business 2025, a new report published by MIT’s NANDA initiative, reveals that while generative AI holds promise for enterprises, most initiatives to drive rapid revenue growth are falling flat.
Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.
To unpack these findings, I spoke with Aditya Challapally, the lead author of the report, who heads the Connected AI group at the MIT Media Lab.
“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally said. Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he said. “It’s because they pick one pain point, execute well, and partner smartly with companies who use their tools,” he added.
But for 95% of companies in the dataset, generative AI implementation is falling short. The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, 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, Challapally explained.
The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.
Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.
To unpack these findings, I spoke with Aditya Challapally, the lead author of the report, who heads the Connected AI group at the MIT Media Lab.
“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally said. Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he said. “It’s because they pick one pain point, execute well, and partner smartly with companies who use their tools,” he added.
But for 95% of companies in the dataset, generative AI implementation is falling short. The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, 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, Challapally explained.
The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.
What’s behind successful AI deployments?
How companies adopt AI is crucial. Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often.
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. Yet, MIT’s research suggests companies see far more failures when going solo.
Companies surveyed were often hesitant to share failure rates, Challapally noted. “Almost everywhere we went, enterprises were trying to build their own tool,” he said, but the data showed purchased solutions delivered more reliable results.
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 is already underway, especially in customer support and administrative roles. Rather than mass layoffs, companies are increasingly not backfilling positions as they become vacant. Most changes are concentrated in jobs previously outsourced due to their perceived low value.
The report also highlights the widespread use of “shadow AI”—unsanctioned tools like ChatGPT—and the ongoing challenge of measuring AI’s impact on productivity and profit.
Looking ahead, the most advanced organizations are already experimenting with agentic AI systems that can learn, remember, and act independently within set boundaries—offering a glimpse at how the next phase of enterprise AI might unfold.
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. Yet, MIT’s research suggests companies see far more failures when going solo.
Companies surveyed were often hesitant to share failure rates, Challapally noted. “Almost everywhere we went, enterprises were trying to build their own tool,” he said, but the data showed purchased solutions delivered more reliable results.
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 is already underway, especially in customer support and administrative roles. Rather than mass layoffs, companies are increasingly not backfilling positions as they become vacant. Most changes are concentrated in jobs previously outsourced due to their perceived low value.
The report also highlights the widespread use of “shadow AI”—unsanctioned tools like ChatGPT—and the ongoing challenge of measuring AI’s impact on productivity and profit.
Looking ahead, the most advanced organizations are already experimenting with agentic AI systems that can learn, remember, and act independently within set boundaries—offering a glimpse at how the next phase of enterprise AI might unfold.
Sheryl Estrada
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[email protected]
Leaderboard
Michael A. Discenza was appointed VP and CFO of The Timken Company (NYSE: TKR), effective immediately. Discenza has 25 years of experience at Timken in roles of increasing responsibility, including the last 10 as VP of finance, and group controller.
John Cole was appointed CFO of ELB Learning, a provider of immersive learning solutions. He brings more than 25 years of experience leading finance and operations for Fortune 100 and 500 companies, according to ELB. Cole aims to strengthen the financial infrastructure to support the company’s next phase of growth.
Big Deal
Modern manufacturing relies heavily on connected devices and industrial control systems, which are prime targets for cyberattacks. For protection, manufacturers are increasingly turning to AI to help manage these risks, according to the State of Smart Manufacturing Report by Rockwell Automation, Inc.
The report’s findings are based on a survey of more than 1,500 manufacturing leaders across 17 major manufacturing countries. Cybersecurity now ranks among the top external risks, second only to inflation and economic growth. One-third of respondents hold responsibilities spanning both information technology (IT) and operational technology (OT) cybersecurity.
Nearly half (48%) of cybersecurity professionals identified securing converged architectures as key to positive outcomes over the next five years, compared to just 37% of all respondents.
However, a shortage of skilled talent, training challenges, and rising labor costs remain major hurdles. As manufacturers recruit the next generation, cybersecurity and analytical skills are becoming hiring priorities—reinforcing the need to align technical innovation with human development, according to the report.
Going deeper
In a new Fortune opinion piece, "Future CEOs, erased: the economic cost of losing Black women in the workforce," Katica Roy, the CEO and founder of the Denver-based Pipeline, a SaaS company, explains the implications of almost 300,000 Black women exited the labor force so far this year—thinning a pipeline that was already too narrow.
"This isn’t a seasonal fluctuation or statistical footnote. It’s a strategic failure with long-term consequences," Roy writes. "Black women have long been a cornerstone of America’s economic engine—driving participation, powering key industries, and anchoring family incomes. Now, that foundation is fracturing. And the fallout is more than short-term—it’s a direct threat to corporate succession planning, innovation, and growth. The U.S. economy has always depended on Black women’s labor. In fact, no group of women in America has historically had higher labor force participation than Black women."
Overheard
“Every single Monday was called 'AI Monday.' You couldn’t have customer calls, you couldn’t work on budgets, you had to only work on AI projects.”
