27. Return on AI Absorption in Corporate: A New MIT Report
Nvidia, the AI-driven chips group that became the world’s first $4 trillion company, fell 3.5% yesterday. Palantir, a software group, dropped 9.4%, while Arm, a chip designer, shed 5%. The Nasdaq Composite fell 1.4%, its most significant one-day drop since August 1.
The US traders have attributed this decline to an MIT report on AI Investment and its return. A critical report, authored by an MIT, was released on Monday. The report shows that 95% of organizations are not getting any positive return on their investments in generative AI. This technology has recently driven US stocks to their highest heights.
The report shows that only 5% of integrated AI pilots have successfully given a substantial value to their return, while most organisations remain stuck without any measurable impact on their profits or losses.
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, only about 5% of AI pilot programs achieve rapid revenue acceleration. Most of these programs stall, delivering little to no measurable impact on profit and loss statements. This research distinguishes between successful projects and stalled initiatives based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public agencies ' deployments.
Aditya Challapally, the report's lead author and a research contributor to the project NANDA at MIT, said to Fortune, highlighted the success of large companies’ pilots and younger startups in leveraging generative AI. For instance, startups led by individuals aged 19 or 20 have experienced remarkable revenue growth, with some jumping from zero to $20 million in a single year. Challapally attributed this success to their ability to identify specific pain points, execute effectively, and collaborate strategically with companies that utilise their tools. However, generative AI implementation falls short for most companies in the dataset.
The primary challenge lies not in the quality of the AI models but rather the “learning gap” between tools and organisations. While executives often attribute this issue to regulation or model performance, MIT’s research suggests that flawed enterprise integration is a significant factor. Generic tools like ChatGPT excel in individual use due to their flexibility, but they struggle in enterprise settings because they cannot learn from or adapt to workflows.
The data also reveals a misalignment in resource allocation. Surprisingly, over half of generative AI budgets are allocated to sales and marketing tools. In contrast, MIT’s research indicates that the highest return on investment (ROI) is achieved through back-office automation, eliminating business process outsourcing, reducing external agency costs, and streamlining operations.
According to FT data, tech has driven the market’s recent run higher. The S&P 500 information and technology sub-index has risen 14 per cent since mid-May, led by AI-linked companies such as Oracle and AMD.