News

MIT Finds Only 1 in 20 AI Investments Translate into ROI

• By David Ramel
• August 25, 2025

A new study by MIT Media Lab's Project NANDA highlights a harsh reality for enterprise AI: despite pouring billions into generative AI technologies, 95 percent of businesses have yet to see any measurable return on investment.

Titled "The GenAI Divide: State of AI in Business 2025," the report describes a stark split between winners and stragglers in the enterprise AI race. Indeed, "Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact," the authors write. This divide spans organizations of all sizes, from startups to global enterprises, signaling a fundamental issue in how AI is deployed and operationalized.

The divide -- which spans organizations of all sizes, from startups to global enterprises -- is defined by high adoption but low transformation. The report says only two industries show clear signs of structural disruption, while seven others show "widespread experimentation without transformation.

It backs this with an AI Market Disruption Index and includes an interview quote from a mid-market manufacturing COO: "The hype on LinkedIn says everything has changed, but in our operations, nothing fundamental has shifted. We're processing some contracts faster, but that's all that has changed."

Pilot-to-Production: Where Most Efforts Stall
The sharpest evidence of the divide is deployment: "only 5% of custom enterprise AI tools reach production." The report characterizes this as a 95 percent failure rate for enterprise AI solutions and attributes it to brittle workflows, weak contextual learning and misalignment with day-to-day operations. It also records user skepticism about vendor offerings: "We've seen dozens of demos this year. Maybe one or two are genuinely useful. The rest are wrappers or science projects."

Enterprises run the most pilots but convert the fewest; mid-market organizations move faster from pilot to full implementation (about 90 days) than large enterprises (nine months or longer).

General-purpose tools are widely explored, but impact is limited: "Over 80% of organizations have explored or piloted [ChatGPT/Copilot], and nearly 40% report deployment," yet these mainly improve individual productivity, not P&L performance. Meanwhile, 60 percent of organizations evaluated enterprise-grade systems, "but only 20% reached pilot stage and just 5% reached production."

While official programs lag, a "shadow AI economy" has emerged: "only 40% of companies say they purchased an official LLM subscription," yet workers from over 90 percent of the companies reported regular use of personal AI tools for work. This pattern shows individuals can cross the divide with flexible tools even when enterprise initiatives stall.

The Root Cause: The Learning Gap
The report's central explanation is that the core barrier is learning rather than infrastructure, regulation, or talent: "Most GenAI systems do not retain feedback, adapt to context, or improve over time."

Users often prefer consumer LLM interfaces for drafts, but reject them for mission-critical work due to lack of memory and persistence. One interviewee explains: "It's excellent for brainstorming and first drafts, but it doesn't retain knowledge of client preferences or learn from previous edits. It repeats the same mistakes and requires extensive context input for each session. For high-stakes work, I need a system that accumulates knowledge and improves over time."

The report summarizes this gap succinctly: "ChatGPT's very limitations reveal the core issue behind the GenAI Divide: it forgets context, doesn't learn, and can't evolve." For complex, longer-running tasks, humans remain the strong preference.