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A prescriptive generative AI maturity model for new product development processes

Aron Witkowski & Andrzej Wodecki

Journal of Manufacturing Technology Management2026https://doi.org/10.1108/jmtm-09-2025-0884article
AJG 1ABDC B
Weight
0.50

Abstract

Purpose This study introduces CLIMB2-OLIMP, a dual-maturity model designed to facilitate the structured integration of Generative Artificial intelligence (AI) (GenAI) into New Product Development (NPD) processes. The model aims to provide a comprehensive tool for organizations to integrate GenAI into their NPD processes, ensuring a strong operational foundation. Design/methodology/approach Developed using the Design Science Research approach, CLIMB2-OLIMP first evaluates an organization’s NPD maturity (CLIMB2) before assessing its readiness for GenAI implementation (OLIMP). The OLIMP component uniquely incorporates a prescriptive element, utilizing large language models (LLMs) to generate tailored improvement pathways with a clear cost-benefit perspective. A systematic literature review was conducted, and the model development involved iterative stages and expert validation. Findings Case studies in manufacturing organizations demonstrated the model’s effectiveness, revealing moderate NPD maturity but limited GenAI adoption. The research emphasizes structured AI integration, including workforce upskilling, strategic alignment, and ethical considerations. Practical implications CLIMB2-OLIMP provides diagnostic insights and actionable recommendations, serving as a comprehensive tool for organizations seeking to integrate GenAI into their NPD processes. It guides organizations in advancing their AI maturity with a clear cost-benefit perspective and supports a bold, entrepreneurial strategy for AI adoption. Originality/value CLIMB2-OLIMP is original in its dual-maturity approach, conditioning GenAI maturity assessment on NPD maturity, and its prescriptive component driven by LLMs, which addresses a significant gap in existing descriptive AI maturity models that lack actionable guidance and cost-benefit analysis.

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https://doi.org/https://doi.org/10.1108/jmtm-09-2025-0884

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@article{aron2026,
  title        = {{A prescriptive generative AI maturity model for new product development processes}},
  author       = {Aron Witkowski & Andrzej Wodecki},
  journal      = {Journal of Manufacturing Technology Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jmtm-09-2025-0884},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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