News from across industries about AI adoption—including agentic integration, new product features, and massive layoffs—make it seem that change is happening at a breakneck pace. But are the latest developments in AI technology driving real AI adoption? Or are they just hype?
A new research paper, AI Adoption in S&P 500 Firms, offers revealing answers about what’s happening on the ground. Authors Yang Yu, Martin Fleming, Lucy Hampton, Christophe Combemale, and Neil Thompson—researchers with the MIT Initiative on the Digital Economy and MIT FutureTech—built a methodology to measure AI adoption across the S&P 500. Their analysis reveals:
- Just 11% of S&P 500 firms had AI deeply embedded in their business by the end of 2025. While that’s up from near zero a few years ago, 11% is still a small minority.
- Nearly half of all companies (45%) are running AI pilots. However, many of these pilots will not move into full production.
- Two-thirds of deep AI integration is happening at technology companies. For most other industries, AI adoption is still slow.
These results not only reveal the level of AI integration happening today but are also part of a larger body of work by the FutureTech researchers, aimed at understanding how AI adoption could reshape both companies and their workforces.
How Do You Measure Real AI Adoption?
To get their data, the MIT researchers skipped the most common sources for information about AI adoption, namely press releases, interviews and marketing material. Instead, they turned to companies’ annual 10-K filings with the U.S. Securities & Exchange Commission. These documents are legally binding, making them a highly reliable source for data about AI adoption.
Once the researchers had collected the 10-K filings, they had to figure out a way to parse the voluminous information these documents contain. Some 10-K reports are nearly a million words long.
The team then isolated report sections that mention AI-related terms. This both ensured that enough information was included and avoided the risk of diluting the context.
The researchers fed these excerpts into GPT-5-mini, a generative AI model. They then used a five-point AI adoption rubric that forced the model to make just one clear judgment per firm, per year. For each company, five options were possible:
- No current AI adoption
- Exploring or building early AI adoption
- AI is integrated into specific products, but is not yet driving financial results
- AI is used in production and has clear financial goals
- AI is deeply embedded across the business and its strategy
The researchers repeated this across 510 companies over a period of 10 years, producing a decade-long trendline of enterprise AI adoption.
Tech Pulls Ahead
The results show that when it comes to AI adoption, there are clear differences among industries.
As of year-end 2025, the researchers found that 27.6% of the tech firms were piloting AI projects and half (50%) had reached deep AI integration.
At non-tech companies, AI adoption looks very different. The researchers found that while nearly seven in 10 financial services firms (68%) show heavy AI pilot activity, only about 4% report deep integration. Banks alone presented an even starker case: While nearly 85% had AI pilots in place, essentially none had reached deep AI integration.
“Now the question is, What’s the success rate of pilots?” said Fleming. In other words, are pilots leading to more fully developed AI projects? Does launching more pilots lead to higher rates of AI integration? Or is focusing on a smaller number of AI pilots the path to success? These are questions the team hopes will be answered with further review of the data.
Key Findings on AI Adoption and Performance
The financial numbers behind these adoption scores tell a more complicated story. Here are some of the MIT researchers’ key takeaways:
The J-Curve is real
Companies in the early stages of AI adoption often see lower profit margins. Non-tech firms in particular report margins that are two to three percentage points lower than those of non-adopters. The researchers attribute this to upfront costs such as new infrastructure, retraining, and workflow disruption.
The good news is that over time, among tech firms those that achieve deep integration eventually see a 3% margin improvement. Among non-tech firms those that deploy AI in the production of goods or the delivery of services, see a 5% margin improvement.
No change in productivity—yet
In fact, early-stage adopters of AI show slightly lower productivity than others. And firms with deeper AI adoption fail to produce more output per worker than non-adopters. The researchers suggest several possible causes: organizational friction, bottlenecks elsewhere in the business, or a lag effect where gains simply haven’t shown up yet.
AI spending: Service vs. infrastructure
Despite headlines about a massive AI infrastructure race, the surge in capital spending is concentrated in a tiny handful of companies – Amazon, Google, Meta, Microsoft Nvidia, and Tesla. Most firms are buying AI as a service rather than building it themselves. As a result, AI spending often shows up as an operating cost, rather than as a capital investment.
Investors react to AI investment
Investors appear to be rewarding AI adoption by tech firms more than adoption by non-tech companies. The researchers found that Tobin’s Q—a measure of a company’s market value relative to its book value—rises consistently with AI adoption for tech firms. But for non-tech firms, that relationship is far less consistent. This suggests markets may be pricing AI as a sector-wide tech story, rather than carefully rewarding non-tech companies for real integration.
What’s Next for AI Adoption Research
For the researchers, understanding the current AI landscape is just a first step. Next, they plan to dig into how large companies are innovating with AI. Then the team wants to determine whether AI is affecting companies’ hiring patterns.
“We can measure how organizations are transforming by looking at how hiring is changing,” Fleming explains. “What tasks are those hires expected to focus on? And how are these tasks changing?”
Once those questions are explored, the researchers hope to examine the implications of AI for workers and labor markets. Fleming says one key question will be, “Does AI augment skills or automate them?”