Study finds 95% of companies see no return on AI investments due to ‘learning gap’

The study also highlighted the slow progression of AI adoption.

By Storyboard18| Sep 29, 2025 11:36 AM

Study finds 95% of companies see no return on AI investments due to ‘learning gap’

The study also highlighted the slow progression of AI adoption.

A new study by the Massachusetts Institute of Technology (MIT) has revealed that 95% of organisations are seeing zero return on their generative AI (GenAI) investments, despite $30–40 billion being poured into enterprise deployments.

Titled The GenAI Divide: State of AI in Business 2025, the report surveyed 300 AI deployments and interviewed approximately 350 employees to understand why such a large share of AI initiatives fail to generate measurable profit.

According to the study, the primary barrier is not infrastructure, regulation, or talent, but a “learning gap.” Most GenAI systems, it notes, “do not retain feedback, adapt to context, or improve over time,” meaning that investments fail to scale effectively. Only 5% of integrated AI pilots were found to extract millions in value, while the vast majority produced no measurable impact on profit and loss.

The research found that while more than 80% of companies had explored or piloted tools such as OpenAI’s ChatGPT and Microsoft’s Copilot, and nearly 40% had deployed them, these tools primarily enhanced individual productivity rather than improving overall financial performance. Failures were often linked to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.

The study also highlighted the slow progression of AI adoption: 60% of organisations evaluated AI tools, only 20% advanced to the pilot stage, and a mere 5% reached full production. Enterprise-grade systems, whether custom-built or vendor-supplied, are increasingly being sidelined.

MIT identified four key patterns defining the “GenAI Divide”:

Limited disruption: Only two of eight major sectors show significant structural change.

Enterprise paradox: Large firms lead in the number of AI pilots but lag in scaling them.

Investment bias: Budgets favour high-visibility, top-line functions over high-ROI back-office operations.

Implementation advantage: Partnerships with external vendors achieve twice the success rate of in-house builds.

The report underscores that simply deploying AI tools is insufficient. For companies to realise meaningful returns, systems must learn, adapt, and integrate seamlessly into operational workflows.