AIORG-W002 Working papers AI × management and organizations

Mapping AI into Production

Hyunjin Kim · Dahyeon Kim · Rembrand Koning

Also recorded as: INSEAD Working Paper 2026/20/STR · INSEAD Working Paper No. 2026/20/STR · Mapping AI into Production: A Field Experiment on Firm Performance · SSRN 6513481 · doi:10.2139/ssrn.6513481 · ssrn:6513481

working paper abstract · 2026-04-03

Recorded claims
3
coded from the inspected source
Evidence classes
2
scholarly-metadata, ssrn-abstract-page
Recorded limitations
6
stated, never hidden

From the hash-bound evidence releases

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

Providing startups with concrete examples of how peer firms reorganized production around AI increased reported AI use cases by 44%, AI-supported tasks by 12%, the probability of acquiring a paying customer by 18%, and revenue by a reported factor of 1.9 during the study window.

confidence: medium-high scholarly-metadata apw-r0-seed

Scope: 515 high-growth accelerator startups over a short post-treatment window; not mature-firm or long-run evidence.

The intervention reduced reported capital demand by 39.5% while leaving labor demand statistically unchanged, which is inconsistent with treating near-term AI production reconfiguration as mechanically equivalent to headcount substitution in this setting.

confidence: medium-high scholarly-metadata apw-r0-seed

Scope: Startup accelerator participants; demand intentions/near-term behavior, not economy-wide labor effects.

In a field experiment covering 515 high-growth startups, firms shown how peers had reorganized production around AI found 44% more AI use cases, completed 12% more tasks, were 18% more likely to obtain paying customers, and reported 1.9 times the revenue; external-capital demand fell 39.5% relative to control while labor demand was unchanged.

confidence: high ssrn-abstract-page apw-e1-ssrn-backfill

Scope: Universe and denominator: 515 high-growth startups overall; unit: firm; period: not stated; comparison: treated versus control firms, with arm counts and outcome-specific denominators not displayed.

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Cites (0)

No in-corpus outgoing citation is available in the current enrichment coverage.

Cited by (0)

No in-corpus incoming citation is available in the current enrichment coverage.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • 515 high-growth startups in one accelerator context
  • Abstract-level evidence only; full text was not inspected.
  • Approximately three-month horizon
  • Revenue distribution is skewed and durability is untested
  • Treatment-arm counts, outcome-specific denominators, uncertainty estimates, and the study period are not displayed.
  • Working paper

Source independence

Academic field experiment in a startup accelerator; intervention and outcomes are researcher-reported.