🦾 Startups that learned to use AI correctly generated 1.9 times higher revenue

• Startups that learned how other companies had reorganized their operations around AI found 44 percent more use cases for the technology.
• The startups that received this information had 1.9 times higher revenue and were 18 percent more likely to acquire paying customers.
• Despite faster growth, the need for external capital fell by 39.5 percent — with no change in staffing needs.

The experiment

A field experiment with 515 high-growth startups from around the world shows that giving companies access to AI tools is not enough. What matters is whether they understand where in their operations AI creates value.

The study was conducted within a three-month startup program at the business school INSEAD. Participants were randomly divided into two groups. Both received the same tools, technical training, and API credits worth approximately $25,000 per company. Access to mentors, investor contacts, and weekly peer learning groups was also identical.

The only difference was the workshop content starting in week three. The treatment group received case studies showing how AI-native companies had reorganized their workflows, teams, business models, and financing around AI. The control group instead received case studies on general entrepreneurship — how to build a customer profile, test ideas, and similar topics.

More use cases and more completed tasks

The treatment group identified an average of 8.8 cumulative AI use cases during the program. The control group identified 6.1. That is an increase of 44 percent, and the gap grew each week from the start of treatment — a sign that this reflects continuous learning rather than a one-time effect.

Treatment firms used AI across more parts of their operations: on average 0.84 more functional categories than the control group. The largest differences were in product development, product and strategy design, and business operations — the areas where AI requires actually rethinking how work is organized, not just layering a tool on top of an existing process.

Treatment firms also completed more concrete tasks: 20.9 on average compared to 18.5 for the control group. The difference was driven almost entirely by internal tasks — product development, prototyping, and similar work — which is consistent with both groups having equal access to investors and customers.

Strong business results

On business metrics, the differences were clear. Treatment firms were 18 percent more likely to have acquired paying customers. They generated 1.9 times higher total revenue, and among firms that already had revenue, the multiple was 2.2 times.

Revenue effects were largest in the upper tail of the distribution — at the 90th and 95th percentiles — suggesting that AI primarily raises the ceiling for what the most promising companies can achieve, rather than uniformly improving outcomes across the board.

Lower capital needs without reduced headcount

One of the more striking findings concerns capital demand. Treatment firms reported planning to seek approximately $220,000 less in external capital ahead of the program's final demo days — a reduction of 39.5 percent compared to the control group. Staffing needs were unchanged.

This is interpreted as AI enabling firms to produce more with the same resources. The companies believed they needed less outside money to reach their goals.

Broad effects — regardless of background

The effects were evenly distributed. There was no statistically significant difference between technical and non-technical founders, and no clear differences based on a company's starting position. This suggests the barrier is not technical skill or general entrepreneurial ability, but the capacity to search broadly for where AI can create value within one's own production process.

Instrumental variable estimates confirm the relationship

The researchers used treatment assignment as an instrument to estimate the return per AI use case. Each additional use case led to 0.85 more completed tasks, a 3.9 percentage point higher probability of having customers, and approximately 26 percent higher revenue. The first stage of the instrumental variable estimation had an F-statistic above 33, confirming that the treatment genuinely affected AI adoption and not just other factors.

The problem is not access — it is search

The researchers call the central barrier the "mapping problem": the difficulty of discovering where and how AI creates value within a firm's production process. Control firms had exactly the same tools and training as treatment firms — yet realized substantially less value from AI. What treatment firms received was information on how to search more broadly and rethink how their entire operation can be organized around the technology.

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