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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.

PRONOY CHOPRA Reviewer

badge Review Request Accepted

PRONOY CHOPRA Reviewer

25 Nov 2025 01:36 PM

badge Approved

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

1. Relevance and Originality

The abstract addresses an increasingly important topic as generative AI continues to reshape product development in many industries. The broad examination of how AI influences ideation, prototyping, testing, and team coordination makes the subject highly relevant. To further emphasize originality, the abstract could identify one specific perspective or insight that sets this work apart from existing discussions on AI enabled development practices.

2. Methodology

Although the abstract covers a wide range of concepts, it does not specify how these observations were gathered or analyzed. Readers may struggle to understand whether the work is grounded in empirical study, conceptual synthesis, or industry experience. A brief methodological reference would give the research greater structure and help clarify its academic grounding.

3. Validity and Reliability

The claims about AI supported functions such as requirement generation, test case creation, and workflow automation appear consistent with current advancements, but the reliability of these conclusions cannot be evaluated without information on the evidence base. A short mention of sources, evaluation criteria, or analytical tools would improve confidence in the assertions included in the abstract.

4. Clarity and Structure

The abstract is informative, but it contains dense passages that merge several ideas into single statements. A slightly more refined structure that separates technological capabilities from organizational implications would enhance readability. Simplifying the sequencing of ideas would allow the main themes to emerge more distinctly for the reader.

5. Results and Analysis

The abstract outlines the themes explored within the study but does not indicate any specific outputs or results. Including even a brief reference to insights gained, observed impacts, or notable patterns would make the analytical contribution of the paper clearer. Highlighting one meaningful finding would strengthen this section considerably.

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IJ Publication Publisher

Thank you for completing the review with such clarity and depth. Your analysis offers a balanced perspective that is highly beneficial for our decision making process. The editorial team relies on reviewers like you, whose expertise and fairness help sustain the academic integrity and credibility of the journal. We truly appreciate your contribution.

Publisher

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IJ Publication

Reviewer

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PRONOY CHOPRA

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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