Loading...
Scholar9 logo True scholar network
  • Article ▼
    • Article List
    • Deposit Article
  • Mentorship ▼
    • Overview
    • Sessions
  • Questions
  • Scholars
  • Institutions
  • Journals
  • Login/Sign up
Back to Top

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.

Niranjan Reddy Rachamala Reviewer

badge Review Request Accepted

Niranjan Reddy Rachamala Reviewer

25 Nov 2025 01:37 PM

badge Approved

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

1. Relevance and Originality

The abstract presents a subject that fits well within current technological discussions, particularly the growing use of generative AI in product development settings. Its attention to workflow transformation, cross functional collaboration, and evolving skill demands reflects ongoing industry changes. However, the abstract could more clearly highlight what specific new contribution this study brings to the broader conversation, which would strengthen its sense of originality.

2. Methodology

The abstract outlines many conceptual elements, but it does not communicate how the authors structured their investigation. Without knowing whether the study uses theoretical analysis, observational evidence, or practical case insights, the reader cannot assess the methodological strength. Including a brief indication of the research approach would make the study’s foundation easier to understand.

3. Validity and Reliability

The described capabilities of generative AI, such as documentation automation and continuous feedback mechanisms, appear well aligned with current advancements. Even so, the reliability of the claims is difficult to judge because the abstract does not reference any supporting data or evaluation procedures. A small mention of how the authors validated their conclusions would increase the perceived soundness of the work.

4. Clarity and Structure

The abstract communicates its ideas clearly, but the density of information may challenge some readers. The flow would be improved by presenting the technological aspects, team dynamics, and organizational challenges in more clearly separated segments. This would help the main arguments stand out and guide the reader through the abstract more smoothly.

5. Results and Analysis

While the abstract presents a wide thematic range, it does not share any concrete findings or analytical outcomes from the study. Providing even a brief summary of insights gained or patterns observed would greatly enhance this section. A short reference to what the research ultimately reveals would add meaningful depth to the description.

avatar

IJ Publication Publisher

Your review has been received, and we extend our sincere thanks for the effort you put into the evaluation. Your feedback is not only clear and constructive but also offers practical suggestions that will support the authors in refining their submission. The journal deeply values your commitment to delivering high quality assessments.

Publisher

User Profile

IJ Publication

Reviewer

User Profile

Niranjan Reddy Rachamala

More Detail

User Profile

Paper Category

Artificial Intelligence

User Profile

Journal Name

TIJER - Technix International Journal for Engineering Research

User Profile

p-ISSN

User Profile

e-ISSN

2349-9249

Subscribe us to get updated

logo logo

Scholar9 is aiming to empower the research community around the world with the help of technology & innovation. Scholar9 provides the required platform to Scholar for visibility & credibility.

QUICKLINKS

  • What is Scholar9?
  • About Us
  • Mission Vision
  • Contact Us
  • Privacy Policy
  • Terms of Use
  • Blogs
  • FAQ

CONTACT US

  • logo +91 82003 85143
  • logo hello@scholar9.com
  • logo www.scholar9.com

© 2026 Sequence Research & Development Pvt Ltd. All Rights Reserved.

whatsapp