International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072 © 2025, IRJET | Impact Factor value: 8.315 | ISO 9001:2008 Certified Journal | Page 673 Generative AI in Product Management and Lifecycle Optimization Rohan Paliwal 1 , Akshit Kurani 2 1 Rohan Paliwal, Manager, Product Management, Western Union 2 Akshit Kurani, Technical Product Owner, GRUBBRR ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The rapid evolution of Generative Artificial Intelligence (GenAI) is transforming the role of Product Managers (PMs) by enhancing productivity, cross-functional collaboration, and decision-making across the product lifecycle. This paper explores the ecosystem of GenAI tools— ranging from large language models like ChatGPT and Claude to workflow-specific platforms like Notion AI, Writer, and Atlassian Intelligence—and their practical applications in ideation, planning, design, development, and product launches. This paper uses industry case studies from Atlassian, Amazon, and Stripe to show how GenAI enhances human creativity and strategic alignment in addition to automating repetitive jobs. Additionally, it outlines a readiness framework for organizations to adopt GenAI responsibly. The study concludes that with thoughtful integration, GenAI has the potential to significantly empower PMs and reshape how products are built, launched, and scaled. Key Words: Generative AI, Product Management, LLMs, AI Tools, Atlassian Intelligence, Product Lifecycle, AI Readiness, Cross-functional Collaboration 1. INTRODUCTION In recent years, the term AI has become a household name, and it comes as no surprise since AI has a vast majority of uses in real-world applications [1] . Generative AI (GenAI) specifically is quickly transforming the way products are developed. Today, 71% of organizations report using GenAI in at least one function [5] , and product teams are experimenting with it to tackle urgent challenges: keeping requirements aligned across teams, handling growing complexity, cutting time-to-market, and extracting real- time customer insights. Early adopters report dramatic efficiency gains. In practice, GenAI tools are helping product managers (PMs) streamline workflows, spark ideas, and improve cross-team coordination. With a focus on new tools beyond ChatGPT (such as Claude, Notion AI, and Writer), tangible industry examples, collaborative impacts, ready frameworks, and best practices for responsibly growing AI, this paper examines the current status of GenAI for product management. 2. Generative AI Tools for Product Managers Apart from famous chatbots like ChatGPT, a growing community of AI tools is becoming common for PMs to use: Large Language Models (LLMs). OpenAI’s ChatGPT and Google’s Gemini remain versatile assistants for brainstorming features, drafting user stories, and summarizing research. Anthropic’s Claude offers a similar AI writing assistant with an emphasis on safer, more steerable outputs (e.g., Claude 3 has a long context window suitable for lengthy product docs). Enterprise- focused AI writing platforms like Writer offer robust, secure AI agents designed for business needs; for instance, Writer’s agents enhance workflows across marketing and product development while prioritizing enterprise-grade security and compliance Knowledge & Research AIs: Search-enhanced AI like Perplexity or Google Bard/Gemini can ingest large amounts of market and technical data to surface insights quickly. Notion AI, integrated into the Notion workspace, can automatically organize meeting notes, suggest action items, and condense long documents (For example, Notion’s AI-powered summarization tool is useful for turning long documents into clear, concise summaries.). New tools like ClickUp AI and Coda AI help with project planning and user-story creation. AI-powered image and UI generators-such as DALL·E, Midjourney, and Stable Diffusion-enable teams to rapidly create prototypes for mockups and marketing visuals. Modern prototyping tools with AI capabilities can transform basic sketches or text descriptions into interactive wireframes, accelerating the early stages of design and iteration. Development Assistants: Code-focused assistants (e.g., GitHub Copilot, OpenAI’s Code Interpreter, or specialized plugins) help developers by auto-generating code snippets or writing test plans from requirements. Atlassian’s new AI features (Atlassian Intelligence) can even “define test plans for product updates in Jira” and generate documentation from Confluence. Collaboration and Productivity: Tools like tl;dv automatically transcribe and summarize meetings (it “captures meeting content, generating instant transcripts and summaries” ), allowing PMs to focus on discussion rather than note-taking. Unified inboxes with AI (e.g., Missive) and team assistants (e.g., Slack’s AI, Microsoft’s Copilot for Teams) help distill discussions and surface decisions. New AI tools are constantly broadening the options available to product managers. For example, a team might use ChatGPT to draft marketing pitches, turn to Gemini or Bard for researching industry data, and leverage Notion AI to summarize meeting notes into actionable user-story checklists. On the enterprise side, platforms like Writer, Anthropic’s Claude for enterprise, and IBM Watson are prioritizing security and governance, making it easier for organizations to implement AI solutions at scale.