Paper Number

1942

Paper Type

CRP

Abstract

As AI startups increasingly enter various domains and markets, they promise high economies of scale and scope. However, the different technical foundation of AI startups compared to conventional digital startups challenges design flexibility and eased design replicability, e.g., due to unbalanced datasets, customized model development, resource-intensive procedures and constant monitoring. We adopt an action design research approach, drawing on several AI startup batches from an AI accelerator and the existing body of knowledge, to provide an empirically grounded answer on how AI startups can orchestrate their resource bundles to benefit from flexibility and replicability of designs. Our findings suggest an actionable process that helps to reflect on activities and resource bundles in an AI product pipeline, to orchestrate bundles for facilitated design flexibility and design replicability. By providing design principles tailored to the resource orchestration for scaling AI startups, we advance the discourse towards a nascent design theory.

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Jun 14th, 12:00 AM

RESOURCE CONFIGURATION FOR SCALING AI STARTUPS - AN ACTION DESIGN RESEARCH APPROACH

As AI startups increasingly enter various domains and markets, they promise high economies of scale and scope. However, the different technical foundation of AI startups compared to conventional digital startups challenges design flexibility and eased design replicability, e.g., due to unbalanced datasets, customized model development, resource-intensive procedures and constant monitoring. We adopt an action design research approach, drawing on several AI startup batches from an AI accelerator and the existing body of knowledge, to provide an empirically grounded answer on how AI startups can orchestrate their resource bundles to benefit from flexibility and replicability of designs. Our findings suggest an actionable process that helps to reflect on activities and resource bundles in an AI product pipeline, to orchestrate bundles for facilitated design flexibility and design replicability. By providing design principles tailored to the resource orchestration for scaling AI startups, we advance the discourse towards a nascent design theory.

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