Enterprises adopting generative AI lack systematic frameworks to classify use cases by complexity, assess associated risks, and sequence implementation according to organizational readiness. We synthesize five perspectives from academic and industry literature—application context, value creation, strategic alignment, technical autonomy, and data governance—to develop a multi-dimensional taxonomy for generative AI deployment. Our taxonomy classifies use cases into four ascending complexity levels: (A) work assistants, (B) automated code generation, (C) system-integrated text generation, and (D) tool use. Each level builds upon prior capabilities while introducing distinct technical, organizational, and risk management requirements. We map these patterns across two application contexts: internal operational efficiency and external customer experience enhancement, showing how risk profiles differ between them. By cross-referencing our taxonomy with the five analytical perspectives, we demonstrate how enterprises can assess current maturity, identify strategically aligned use cases, and construct phased implementation roadmaps that balance innovation velocity with risk governance. This framework bridges technical feasibility assessments with business value realization, enabling evidence-based generative AI adoption across industries.
Harald Stein (Sun,) studied this question.