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, and a research contributor to project NANDA at MIT.
“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally said. Startups led by 19
But for 95% of companies in the dataset, generative AI implementation is falling short. “The 95% failure rate for enterprise AI solutions represents the clearest manifestation of the GenAI Divide,” the report states. 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.
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
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
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.
“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.”
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