AI & DataCase StudyPublished 8/10/2026 · 116 views29 downloadsDOI 10.66308/air.e2026065

Beyond Code Generation: AI Across the Product Development Lifecycle

Iuliia MineevaSibedge, Moscow, Russia
Received 7/8/2026Accepted 7/30/2026
artificial intelligenceproduct developmentproduct lifecycleproduct managementlean startupgenerative AIsoftware developmentdigital product developmentproductivitytechnology startup
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Cover: Beyond Code Generation: AI Across the Product Development Lifecycle

Abstract

Artificial intelligence (AI) is becoming an important part of modern product development. However, most existing studies focus on software development or individual AI applications rather than the entire product lifecycle. This study explores how AI can support different stages of product development in a lean technology startup. A comparative case study was conducted using two similar product development projects completed by the same company. The projects had similar functionality, target market, technology stack, and development process, but differed in the level of AI adoption. The study combined quantitative analysis of labor effort with qualitative observations of how AI tools were used throughout the product lifecycle. The results show that AI supported activities from market research and hypothesis validation to software development, testing, release preparation, and post-release product improvement. The greatest benefits were observed in research, requirements preparation, documentation, and design, while software development and testing also became more efficient. Overall labor effort was reduced by 35.8% in the AI-assisted project. The findings suggest that AI can support the entire product development lifecycle, helping lean startup teams work more efficiently while leaving key decisions and expert judgment to people.

Keywords: artificial intelligence, product development, product lifecycle, product management, lean startup, generative AI, software development, digital product development, productivity, technology startup

Cite asIuliia Mineeva (2026). Beyond Code Generation: AI Across the Product Development Lifecycle. American Impact Review. https://doi.org/10.66308/air.e2026065Copy

Declarations

Data availability

The data supporting the findings of this study are derived from internal company project management records and are not publicly available due to commercial confidentiality restrictions.

Ethics statement

Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.

Author contributions

Conceptualization, I.M.; methodology, I.M.; formal analysis, I.M.; investigation, I.M.; data curation, I.M.; writing-original draft preparation, I.M.; writing-review and editing, I.M.; visualization, I.M.; project administration, I.M. The author has read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Competing interests

The author declares no conflicts of interest.