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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.

Neelam Gupta Reviewer

badge Review Request Accepted

Neelam Gupta Reviewer

25 Nov 2025 01:33 PM

badge Approved

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

1. Relevance and Originality

The abstract explores a topic that is highly significant in today’s technology landscape, where generative AI is becoming central to product development practices. The emphasis on collaborative human and AI participation is timely and meaningful. The work appears to offer an integrated perspective on how various stages of product creation are shifting, although the abstract would benefit from a clearer indication of what original insight or framework this study specifically introduces.

2. Methodology

The description provides a broad overview of the themes addressed, but the methodological approach is not mentioned. Readers are left uncertain about whether the arguments come from systematic research, practical case observations, or theoretical examination. Adding a short statement about the research strategy or evidence base would make the foundation of the study more transparent.

3. Validity and Reliability

The abstract outlines multiple claims regarding AI capabilities, such as automated requirement generation and real time monitoring. These points seem aligned with current advancements, yet their reliability cannot be fully assessed without knowing the type of validation or supporting data used. A small reference to sources of insight or evaluation mechanisms would help establish credibility.

4. Clarity and Structure

The writing is clear in its intention, but it carries a dense amount of information in a tightly packed format. The overall flow could be more effective if the ideas were grouped into concise segments that distinguish technological functions, team interactions, and organizational challenges. This would make the abstract easier to follow and help highlight the logical progression of the ideas presented.

5. Results and Analysis

The abstract outlines the themes explored but does not mention any specific outcomes or examples that emerge from the research. Including a brief reference to observations, patterns, or findings would make the work’s contributions more concrete. Even one strong insight or demonstrated improvement would significantly enhance the analytical strength conveyed here.

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IJ Publication Publisher

We acknowledge and appreciate the comprehensive review you submitted. The level of detail in your comments provides valuable insight into the strengths and limitations of the manuscript. Your thoughtful evaluation plays an essential role in guiding our editorial decisions, and we are grateful for your continued dedication to our peer review standards.

Publisher

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IJ Publication

Reviewer

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Neelam Gupta

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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