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Transparent Peer Review By Scholar9

Generative AI for Product Development: From Ideation to Design and Testing

Abstract

The integration of generative artificial intelligence into product development processes represents a fundamental paradigm shift from traditional human-driven methodologies to collaborative human-AI frameworks that transform every stage of the development lifecycle. Contemporary organizations are leveraging AI technologies across ideation, strategic planning, design prototyping, testing validation, and organizational workflow integration to achieve unprecedented levels of efficiency and innovation capability. AI-powered systems demonstrate remarkable proficiency in automated requirement generation, multi-modal data synthesis for strategic intelligence, rapid sketch-to-prototype translation, comprehensive test case creation, and agile workflow optimization. The evolution encompasses sophisticated natural language processing for documentation automation, computer vision algorithms for design interpretation, machine learning models for user behavior prediction, and intelligent decision support systems for feature prioritization. Cross-functional team dynamics have been fundamentally altered as AI systems facilitate seamless information translation between specialized domains while enabling continuous validation through real-time monitoring and automated feedback mechanisms. Professional skill requirements continue evolving as organizations adapt to human-machine collaboration models that emphasize higher-order cognitive capabilities, including critical thinking, creative problem-solving, and strategic planning. Implementation challenges span technical infrastructure requirements, change management complexities, and cultural adaptation barriers that demand comprehensive strategic approaches for successful AI adoption across product development environments.

Ramesh Krishna Mahimalur Reviewer

badge Review Request Accepted

Ramesh Krishna Mahimalur Reviewer

25 Nov 2025 01:34 PM

badge Approved

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

1. Relevance and Originality

The abstract focuses on the growing influence of generative AI in modern product development, a subject that is both important and widely discussed in contemporary research. Its comprehensive view of how AI reshapes ideation, planning, and organizational roles gives it strong thematic relevance. To enhance its originality, the abstract could briefly indicate how this study differs from other discussions on human and AI collaboration in product design.

2. Methodology

While the abstract outlines several areas where AI contributes to development processes, it does not clarify the method used to derive these insights. Without knowing whether the study relies on conceptual reasoning, industry evaluation, comparative analysis, or another strategy, readers may find it difficult to judge the depth of the work. A short indication of the underlying research approach would improve the clarity of the study’s foundation.

3. Validity and Reliability

The capabilities described, such as automated documentation or user behavior prediction, are consistent with current advances in AI systems. However, the absence of any reference to data sources or validation steps makes it challenging to assess the strength of the conclusions. Noting whether the claims stem from practical implementations, interviews, or literature assessment would help establish reliability.

4. Clarity and Structure

The abstract is rich in content, but several sentences contain many ideas in quick succession. The flow would be smoother if the transitions between technological aspects, teamwork changes, and organizational requirements were slightly more defined. By presenting these components in a more sequenced manner, the key messages would stand out more clearly for the reader.

5. Results and Analysis

Although the abstract identifies important themes such as workflow optimization and skill evolution, it does not mention any specific findings or illustrative outcomes. Including a short indication of what the study discovered or demonstrated would make the contribution more tangible. Even a single summarized insight would help showcase the practical or theoretical value of the research.

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IJ Publication Publisher

We wish to express our appreciation for the thoughtful and detailed review you have provided. Your comments demonstrate a strong understanding of the subject matter and offer constructive direction that will assist the authors in strengthening their work. The editorial board values your expertise and the time you invest in maintaining the quality of our publication.

Publisher

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IJ Publication

Reviewer

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Ramesh Krishna Mahimalur

More Detail

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Paper Category

Artificial Intelligence

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Journal Name

TIJER - Technix International Journal for Engineering Research

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p-ISSN

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e-ISSN

2349-9249

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