MIT Harnesses AI to Accelerate Startup Ambitions
At a January boot camp, the Martin Trust Center for MIT Entrepreneurship introduced students, including Hotchkis, to software that effectively automates starting a business by using artificial intelligence to do market research and analysis. 'Somebody can sit down for an afternoon and get a very thoughtful output,' says Paul Cheek, the Trust Center's executive director and a senior lecturer at MIT's Sloan School of Management. After six days in the center's Entrepreneurship Development program, where participants attend about eight hours of lectures each day, followed by several hours working with teammates and the JetPacks, as MIT calls the software, 'people come out with a compelling business plan,' Cheek says.
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MIT report: 95% of generative AI pilots at companies are failing
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 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 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 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 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 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 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 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 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 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 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 This story was originally featured on Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
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The ‘shadow AI economy' is booming: Workers at 90% of companies say they use chatbots, but most of them are hiding it from IT
The mainstream AI economy is struggling, but the 'shadow AI economy' is booming. That's one of the key takeaways from a sweeping new MIT study on generative AI in the workplace. The study finds that workers at more than 90% of companies are using personal chatbot accounts for daily tasks, often without approval from IT, while only 40% of companies actually have official LLM subscriptions. A sweeping new report from MIT's Project NANDA, State of AI in Business 2025, has uncovered a dramatic split in the landscape of enterprise artificial intelligence: While official AI adoption in companies stalls, a robust 'shadow AI economy' is flourishing under the radar, powered by employees using personal AI tools for day-to-day work. The main thrust of the study is the 'GenAI divide': the finding by MIT that despite $30 billion to $40 billion invested in gen-AI initiatives, only 5% of organizations are seeing transformative returns. 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Yahoo
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The ‘shadow AI economy' is booming: Workers at 90% of companies say they use chatbots, but most of them are hiding it from IT
The mainstream AI economy is struggling, but the 'shadow AI economy' is booming. That's one of the key takeaways from a sweeping new MIT study on generative AI in the workplace. The study finds that workers at more than 90% of companies are using personal chatbot accounts for daily tasks, often without approval from IT, while only 40% of companies actually have official LLM subscriptions. A sweeping new report from MIT's Project NANDA, 'State of AI in Business 2025,' has uncovered a dramatic split in the landscape of enterprise artificial intelligence: while official AI adoption in companies stalls, a robust 'shadow AI economy' is flourishing under the radar, powered by employees using personal AI tools for day-to-day work. The main thrust of the study is the 'GenAI divide': the finding by MIT that despite $30 billion-$40 billion invested in GenAI initiatives, only 5% of organizations are seeing transformative returns. The vast majority—95%—report zero impact on profit and loss statements from formal AI investments. Lurking under the surface, though, MIT also finds huge engagement with LLM tools on the part of workers, a shadow economy of seemingly widespread AI adoption. Rather than waiting for official enterprise GenAI projects to overcome technical and organizational hurdles, employees are routinely leveraging personal ChatGPT accounts, Claude subscriptions, and other consumer-grade AI tools to automate tasks. This activity is often invisible to IT departments and C-suites. 'Employees are already crossing the GenAI Divide through personal AI tools. This 'shadow AI' often delivers better ROI than formal initiatives and reveals what actually works for bridging the divide,' the report states. The study was based on a review of over 300 publicly disclosed AI initiatives, interviews with representatives from 52 organizations, and survey responses from 153 senior leaders. It reveals that while only 40% of companies have purchased official LLM subscriptions, employees in over 90% of companies regularly use personal AI tools for work. In fact, nearly every respondent reported using LLMs in some form as part of their regular workflow. Many shadow users describe interacting with LLMs multiple times a day, every workday—with adoption often far outpacing their companies' sanctioned AI initiatives, which remain stuck in pilot stages. Project NANDA's analysis highlights key reasons for this divide: Flexibility and immediate utility: Tools like ChatGPT and Copilot are praised for their ease of use, adaptability, and instantly visible value—qualities missing from many custom-built enterprise solutions. Workflow fit: Employees customize consumer tools to their specific needs, bypassing enterprise approval cycles and integration challenges. Low barriers: Shadow AI's accessibility accelerates adoption, as users can iterate and experiment freely. As the report notes, 'The organizations that recognize this pattern and build on it represent the future of enterprise AI adoption.' These advantages contrast sharply with official GenAI deployments, where complex integrations, inflexible interfaces, and lack of persistent memory often stall progress. This helps explain a 'chasm' in between pilots and production. The 'war for simple work' According to the report, shadow AI usage creates a feedback loop: as employees become more familiar with personal AI tools that suit their needs, they become less tolerant of static enterprise tools. 'The dividing line isn't intelligence,' the authors write, explaining that the problems with enterprise AI have to do with memory, adaptability, and learning capability. As a result, 90% of users said they prefer humans to do 'mission-critical work,' while AI has 'won the war for simple work,' with 70% preferring AI for drafting emails and 65% for basic analysis. Meanwhile, the study engages in some myth-busting, puncturing five commonly held beliefs about enterprise AI. Contrary to the hype, it finds: Few jobs have been replaced by AI. Beyond the limited impact on jobs, generative AI also isn't transforming the way business is done. Most companies have already invested heavily in GenAI pilots. Problems stem less from regulations or model performance, and more from tools that fail to learn or adapt. Internal AI development 'build' projects fail twice as often as externally sourced 'buy' solutions. That being said, the tech sector layoffs of the last several years have become entrenched in the economy, whether they are related to AI adoption or not. And research on the declining wage premium of the college degree suggests that a fundamental shift is occurring in the labor market. But the AI sector may be hitting a plateau, with the underwhelming launch of OpenAI's ChatGPT5 leading some prominent writers to wonder: what if this is as good as AI gets? In fact, the Federal Reserve commissioned several staff economists to consider the question, and their base case is that it will significantly boost productivity. But they also said it could end up having an import more like an invention that literally banished shadows when it appeared over 100 years ago: the light bulb. For this story, Fortune used generative AI to help with an initial draft. An editor verified the accuracy of the information before publishing. This story was originally featured on
