“Making things complex is easy. Simplifying is extremely difficult” - Anonymous

According to the study The GenAI Divide - State of AI in business 2025 from MIT’s Nanda project, 95% of generative AI projects generate no business impact.

We’re not talking about putting initiatives into production; we’re talking about only 5% of initiatives generating real value.

Despite enormous investment, real transformation is minimal. We launch pilots without a fixed direction, and the market no longer values potential; it’s starting to demand measurable impact on business results.

According to the study, the three main causes of failure are:

1. Using generic models to solve niche problems. LLMs lack domain knowledge to be truly useful, leading to irrelevant and vague responses.

2. Projects without a business case. Prototypes are launched without a measurable goal. They show potential but never scale or deliver ROI. As a CIO in the study says: “We’ve seen dozens of demos this year. Maybe one or two are genuinely useful. The rest are wrappers or science projects.”

3. Zero integration with real work. Solutions create friction. If they don’t fit into employee or user workflows, they simply aren’t used. The study breaks the myth that “Generative AI is transforming businesses because its adoption is high,” stating that “transformation is rare. Only 5% of enterprises have AI tools integrated in workflows at scale and 7 of 9 sectors show no real structural change.”

This isn’t new. It’s the same pattern as always: enthusiasm, investment, and frustration. We’ve lived through this before.

Do you remember when no digital product could exist without microservices, without serverless, without blockchain or the metaverso?

Let’s not repeat the same mistakes integrating AI…