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Posted on 08/21/2025 9:31:54 PM PDT by SeekAndFind
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, 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- 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.
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.
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At least for what we do in engineering, it’s amazing how much it can assist when used in the right context. That said, it’s a “use but review” approach, although that is no different than if an engineer didn’t have the tool. Everything must be reviewed by people. There’s so much busy work, especially when it comes to writing requirements and test cases - where the process of writing by AI is far beyond any human, in terms of speed.
We’re also using it to translate documentation from engineers where their English is sub-par (2nd language), basically rewrite so we have a consistent reading level.
I suppose my argument is, yes it’ll mess up - but when do humans not? What is the error rate in any job done by a human? For us, it’s not replacing people, it’s making them more productive. I still need dozens more people. Although there’s definitely clerical positions that might be at risk.
What a stupid article. “Rapid revenue acceleration” is not the only measure of success. There is immense productivity growth in the vast majority of cases using these co-pilot tools.
There will be an “AI Crash” in the markets.
It will be an epic disaster.
Yep, I am basically thinking of moving to all Cash in my 401k to ride it out. Tech bubble is going to pop, big time.
No doubt, ask Eastman Kodak. Which is fine, America is built for competition, experimentation etc - so you will have losers even if the loss is caused by idiot management.
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