Open Access

Lukas Weiss, Michael Möhring, Keshav Dahal

• Small- and medium-sized manufacturing enterprises face significant challenges in adopting Artificial Intelligence due to resource scarcity and a lack of tailored guidance. Existing artificial intelligence maturity models are frequently too abstract and biased. This research-in-progress addresses this gap by proposing the Artificial Intelligence Adoption Navigator, a conceptual framework specifically designed to evaluate AI adoption readiness small- and medium-sized manufacturing enterprises. Following the Design Science Research approach and utilizing the Technology-OrganizationEnvironment (TOE) framework, the proposed artifact deconstructs artificial intelligence adoption into granular, manageable micro-steps. A key innovation is the use of deterministic binary logic to replace subjective scales, mitigating self-assessment bias and providing actionable feedback. Preliminary validation through expert walkthroughs confirms the model's pragmatic alignment with the needs of small- and medium-sized manufacturing enterprises. This work provides a theoretical foundation of artificial intelligence for resource-constrained manufacturing entities.