New research shows that access to AI is not the same as knowing where to use it.
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A firm is only as fast as the slowest step in its chain of work. In manufacturing, it might be one particular machine on the line. In software, one overloaded intake service. Many business leaders are accidentally recreating this scenario with artificial intelligence. They provision AI tools to employees and hear about localized productivity spikes, but the company’s overall performance barely moves. This tension lies at the heart of the new working paper “Mapping AI into Production: A Field Experiment on Firm Performance,” from co-authors at INSEAD and the Digital Data Design Institute at Harvard (D^3). By tracking hundreds of organizations, the researchers have uncovered friction points that hold firms back from realizing the true economic promise of generative AI.
Key Insight: A Global Search for AI’s Real Value
“Discovering where and how AI creates value is fundamentally a search problem.” [1]
To test how companies can overcome the barrier of firm-level AI performance, the authors conducted a massive field experiment involving 515 high-growth startups spanning the globe. All participating firms received API credits, access to frontier AI models, and technical training. A randomly selected treatment group of firms also received specialized case studies highlighting how AI-native companies reorganize their production workflows, teams, and business models around the technology. Control firms attended workshops on general entrepreneurship practices. The design let the researchers hold access and technical skill constant while varying which firms gained perspective across a much wider set of organizational functions, thereby expanding their search space for AI opportunities.
Key Insight: A Small Nudge, Outsized Results
“Treated ventures achieve faster growth without proportional increases in labor or capital, consistent with a reduction in the costs of experimentation and scaling seen in earlier technological waves.” [2]
The performance effects were substantial. The treatment startups discovered 44% more AI use cases, particularly in high-leverage areas like strategy and product development. They completed 12% more tasks, became 18% more likely to land paying customers, and generated an astounding 1.9 times higher revenue compared to the control group. What makes these numbers even more fascinating is that these companies did not spend their way to growth. In fact, their demand for external capital investments actually fell by 39.5%, proving that AI enables firms to scale outputs without scaling inputs proportionally. The researchers found that these gains were heavily concentrated in the upper tail, suggesting that AI lifts the ceiling of what top ventures can achieve rather than just making struggling businesses slightly better. One startup built an end-to-end AI pipeline covering classification, compliance checking, and bid pricing without hiring any technical staff, growing from zero to $40,000 in revenue with four paying customers during the ten-week program.
Key Insight: A Cognitive Bottleneck
“Two firms with identical tools, training, and budgets can realize very different returns if one searches more broadly across its production process for where the technology creates value.” [3]
The researchers conclude that the ultimate blocker for AI gains is not the cost of technology or a lack of skills, but what they call the mapping problem: “discovering where and how AI creates value within a firm’s production process.” [4] Most leaders default to localized, obvious AI solutions like launching a customer service chatbot or drafting email responses. The untapped potential comes from discovering how to rethink interconnected, complementary tasks across the entire enterprise. For example, a field services startup in the study rebuilt its entire operations chain of dispatcher, bookkeeper, scheduler, and collections staff into a sequence of AI modules that self-improve, fundamentally changing the firm’s cost structure. Solving the mapping problem is about overcoming cognitive constraints to see AI as a way to redraw your company’s production landscape, rather than simply slapping digital band-aids on legacy processes.
Why This Matters
For business leaders and executives, this research shows that the organizations most likely to realize substantial AI-driven results are those that invest not just in technology, but in the wide-ranging process of exploring where it fits. That is a strategy and execution problem, and leaders will need to ask which parts of their organizations need redesign rather than optimization. If you don’t actively push the boundaries of how AI rewrites your firm, you risk using a map that never leads you to your destination.
Bonus
What happens when the bottleneck lies in the surrounding market, rather than within your business? For example, committing too early to a single AI provider, before the technology has stabilized, risks being locked into a platform that may not be the right fit 6 months or a year from now. For a look at whether the competitive landscape will reward flexibility, check out Is GenAI Heading for a Tech Monopoly?
References
[1] Kim, Hyunjin, Dahyeon Kim, and Rembrand Koning, “Mapping AI into Production: A Field Experiment on Firm Performance,” INSEAD Working Paper No. 2026/20/STR (March 2026), 2. https://dx.doi.org/10.2139/ssrn.6513481.
[2] Kim et al., “Mapping AI into Production,” 4.
[3] Kim et al., “Mapping AI into Production,” 6.
[4] Kim et al., “Mapping AI into Production,” 2.
Meet the Authors
Hyunjin Kim is Assistant Professor of Strategy at INSEAD.
Dahyeon Kim is a PhD student in strategy at INSEAD.
Rembrand Koning is Mary V. and Mark A. Stevens Associate Professor of Business Administration at Harvard Business School, and the co-director and co-founder of the Tech for All lab at D^3.
Watch a video version of the Insight Article here.