PM's guide to using AI

By Allen Yang

August 13, 2025

About this collection

To all PMs: AI might not replace you, but PMs who use AI might This is a collection of curated resources about how PMs can use AI to help them with various tasks. An overall theme here is that there are many specific tasks where AI can help, and if you zoom out, there's potential (though harder to achieve) to reshape longer workflows around AI's capabilities as well. This is an interactive knowledge base, so try asking it some questions, such as: 1. What are all the tactical ways PMs can incorporate AI into their roles? 2. What are some tips for using AI to do competitive analysis? 3. What are some pros / cons to using AI to help with user research synthesis? 4. This is all intimidating and I don't have much time - what are a couple of first steps I can take to use AI more?

Curated Sources

3 Ways to Use AI For Product Managers (that isn’t ChatGPT)

The article discusses three advanced AI use cases for product managers: competitive monitoring, aggregating and analyzing customer data, and launch planning. It highlights how AI can automate tasks such as monitoring competitors, summarizing customer feedback, and generating launch plans. The author introduces Ignition, a platform that offers AI-powered tools for product management, including competitive intelligence, customer research, and roadmapping. The article also provides examples of companies that have benefited from using Ignition's AI tools, such as saving time, improving product strategy, and enhancing launch efficiency. Additionally, it addresses frequently asked questions about AI in product management, emphasizing that AI is meant to augment human capabilities, not replace them.

Key Takeaways

AI can significantly enhance product management by automating routine tasks, allowing product managers to focus on strategic work.
Tools like Ignition provide comprehensive AI-powered solutions for competitive intelligence, customer research, and launch planning, leading to improved efficiency and decision-making.
The integration of AI in product management is not about replacing human product managers but about augmenting their capabilities to work more effectively and strategically.

The Ultimate Guide to AI Copilots for Product Managers: Revolutionizing PM Workflows | Revo.pm

This comprehensive guide explores how AI copilots are transforming product management by providing unprecedented support and efficiency. AI copilots for product managers are advanced software tools that use artificial intelligence to assist in various aspects of product management, such as data analysis, automated documentation, predictive analytics, task prioritization, resource allocation, stakeholder communication, and market research assistance. The guide discusses the key features, benefits, and challenges of using AI copilots, as well as how to integrate them into product management workflows. It also highlights the future of AI copilots in product management, including more sophisticated natural language interfaces, enhanced predictive capabilities, and greater personalization in insights and recommendations.

Key Takeaways

The integration of AI copilots in product management is revolutionizing workflows by automating routine tasks, providing data-driven insights, and enhancing decision-making.
To maximize the benefits of AI copilots, product managers must assess their specific needs, choose the right tool, and ensure seamless integration with existing workflows.
The future of AI copilots in product management holds significant promise, with advancements in natural language processing, predictive analytics, and personalization expected to further transform the field.
Effective adoption of AI copilots requires careful consideration of challenges such as data quality, over-reliance on AI recommendations, and integration issues.
By embracing AI copilots, product managers can not only streamline their workflows but also drive innovation and stay competitive in the fast-paced world of product development.

Using AI for Product Roadmap Prioritization | Productboard

The document discusses how AI can enhance product roadmap prioritization by integrating diverse data sources, recognizing patterns, and providing predictive analytics. Traditional product prioritization methods are often subjective, biased, and unable to scale with complex products. AI-driven solutions like Productboard Pulse and integrations with Amplitude and Mixpanel can help teams make data-informed decisions. Key challenges include ensuring data quality, gaining stakeholder trust, and maintaining human oversight. The document provides guidance on getting started with AI product roadmaps, including starting with a clear use case, auditing current tools and data, and choosing scalable tools.

Key Takeaways

AI can revolutionize product roadmap prioritization by integrating customer feedback, behavior analytics, and market signals into a unified decision-making engine.
Successful AI implementation requires high-quality data, transparency in decision-making, and human oversight to avoid bias and ensure strategic alignment.
Teams can start with focused use cases like customer feedback analysis and gradually expand AI adoption, choosing tools that integrate with their existing workflow.

(1) How to Orchestrate Planning & Prioritization with AI (AI PM Part 3)

This article is the third part of a series on the 'Orchestrator PM' concept, focusing on using AI to enhance planning and prioritization in product management. It outlines a four-phase approach to creating an 'Evidence Engine' that structures customer needs, connects them to business outcomes, automates scoring, and enables continuous planning. The process involves using AI agents to cluster customer feedback into an 'Opportunity Map', linking these opportunities to a data-driven 'KPI Tree', automating ICE (Impact, Confidence, Ease) scoring, and setting up a 'Continuous Planning Agent' that updates the roadmap based on new information. The goal is to transform product management from a manual, opinion-driven process to a data-driven, strategic function.

Key Takeaways

The Orchestrator PM uses AI to create a structured 'Opportunity Map' from unstructured customer feedback, enabling more informed prioritization decisions.
By linking customer needs to a data-driven 'KPI Tree', product managers can prioritize initiatives based on their strategic impact on business outcomes.
Automating ICE scoring with AI reduces bias and provides a data-driven foundation for prioritization, allowing product managers to focus on refining scores rather than generating them.
Implementing a 'Continuous Planning Agent' enables proactive roadmap updates, ensuring that product plans remain aligned with changing customer needs and business objectives.

The Product Manager's New Co-Pilot: A Practical Guide to Using LLMs… - Theodore Altanis

This document provides a comprehensive guide on leveraging Large Language Models (LLMs) across the entire product development lifecycle (PDLC), from ideation to post-launch. It highlights how LLMs can enhance efficiency, deepen insights, and accelerate various stages of product development. The guide covers the application of LLMs in different phases, including ideation, design, development, testing, launch, and post-launch, and discusses the emerging role of AI in product strategy. It emphasizes the importance of human oversight to manage potential biases and hallucinations, and provides a three-step action plan for integrating LLMs into product management workflows.

Key Takeaways

The integration of LLMs into product development can significantly enhance efficiency and accelerate the product development lifecycle, but requires human oversight to manage potential biases.
LLMs can be used to automate manual tasks such as synthesizing qualitative data, generating wireframes, and creating test cases, freeing up human resources for more strategic tasks.
The role of product managers is likely to evolve into that of 'systems architects' as LLMs and multi-agent systems become more prevalent in product development, requiring new skills and operational models like LLMOps.

How We Use AI to Write Requirements - ArgonDigital | Making Technology a Strategic Advantage

ArgonDigital has developed an AI-powered solution to streamline software requirement gathering, reducing manual effort and improving accuracy. The AI tool transcribes and summarizes elicitation meetings, identifies key points and actionable requirements, and organizes information into structured requirement formats. It can also reverse engineer requirements from existing solutions, highlighting gaps and suggesting enhancements. Best practices for leveraging AI include combining AI outputs with human oversight, iterative reviews, stakeholder verification, and training AI models with project-specific data. By adopting AI, development teams can work more efficiently and precisely, leading to faster and more reliable software development.

Key Takeaways

The integration of AI in requirement gathering significantly reduces manual effort and improves accuracy, enabling teams to focus on higher-value tasks.
AI can analyze transcripts from elicitation meetings to identify critical points and stakeholder expectations, ensuring that requirements are comprehensive and aligned with project objectives.
Reverse engineering capabilities allow AI to extract detailed requirements from existing solutions, identify gaps, and suggest potential enhancements based on analyzed patterns.
To maximize AI benefits, it's crucial to combine AI outputs with human oversight and validation to ensure alignment with project goals and client expectations.

The Ultimate Guide for Writing PRDs with AI | Revo.pm

The document introduces Revo, an AI copilot for product teams that revolutionizes the process of writing Product Requirements Documents (PRDs). It outlines the importance of PRDs in product development, detailing their key components and the challenges associated with traditional PRD writing. The document then explains how AI, particularly Revo, can enhance the PRD writing process by improving efficiency, consistency, and data integration. It discusses various PRD templates and writing philosophies, and how Revo's capabilities can be leveraged to create more effective PRDs. The document also provides best practices and tips for using Revo to create comprehensive, data-driven, and collaborative PRDs.

Key Takeaways

Revo's AI capabilities can significantly enhance the PRD writing process by improving efficiency, consistency, and data integration.
Different PRD templates (Basic, Agile, Lean) cater to various product development methodologies and organizational needs.
Revo's unique features, such as long-term memory and knowledge base integration, enable the creation of context-rich and data-driven PRDs.
Adopting a user-centric, data-driven, and iterative approach to PRD writing can lead to more effective product development.
Leveraging Revo's capabilities can help product teams create PRDs that are not only comprehensive but also dynamic, collaborative, and future-focused.

AI Agents for Product Managers: Tools That Work for You

The article discusses the role of AI agents in product management, highlighting their ability to automate tasks, improve decision-making, and enhance productivity. It explains the difference between AI tools and AI agents, with AI agents being more autonomous and capable of handling complex tasks. The article provides examples of AI agents used in various product management tasks such as research, planning, project management, and customer feedback analysis. It also offers best practices for implementing AI agents, including starting with clear use cases, keeping human oversight, and measuring impact. The article concludes by discussing emerging trends in AI agents, such as their integration into existing tools, improved reasoning capabilities, and domain-specific AI.

Key Takeaways

AI agents can significantly enhance product management workflows by automating repetitive tasks and providing actionable insights.
Implementing AI agents requires a clear understanding of their capabilities and limitations, as well as ongoing human oversight to ensure accuracy and relevance.
The future of AI in product management is likely to involve more integrated, adaptive, and domain-specific agents that can collaborate and make complex decisions.

Product manager is an unfair role. So work unfairly.

The article discusses the challenges faced by product managers in the current tech industry, referred to as 'the great flattening,' where expectations on individual contributors (ICs) have increased. It highlights the unfairness of the product manager role, where they are expected to be both makers and managers without clear boundaries or support. The author, Lenny, shares insights from Tal Raviv, an experienced product manager who has developed strategies to thrive in this environment. Tal shares seven productivity tactics to help product managers work 'unfairly' and achieve success without sacrificing their well-being. These tactics include getting tasks done during meetings, using '59-second Looms' for asynchronous communication, managing Slack effectively, cultivating a self-reliant team, using 'product scrapbooking' for discovery, leveraging AI for writing tasks, and maintaining brain freshness through disciplined breaks.

Key Takeaways

Product managers must redefine work norms and build personal productivity systems to thrive in the 'great flattening' of tech.
Effective use of AI tools can significantly enhance productivity, particularly in writing tasks such as PRDs and Jira tickets.
Cultivating team autonomy is crucial for product managers to manage their workload and focus on high-impact tasks.

AI for Product Management: Tools & Techniques All PM Must Know | Amoeboids

The document discusses how AI is transforming product management by improving workflows, decision-making, and customer alignment. It highlights key AI tools such as predictive analytics platforms, machine learning software, AI-driven customer insights tools, automation tools, and data visualization solutions. The article also provides best practices for integrating AI into product management, including blending AI with human intuition, maintaining a user-centric approach, and fostering cross-functional collaboration. Additionally, it addresses challenges in adopting AI, such as data quality issues, resistance to change, skill gaps, and integration complexity. Emerging trends like AI-driven product development and hyper-personalization are also explored.

Key Takeaways

AI is revolutionizing product management by enabling data-driven decisions, improving customer alignment, and streamlining workflows.
Effective integration of AI requires blending AI insights with human judgment and maintaining a user-centric approach.
Product managers must address challenges like data quality issues and skill gaps to fully leverage AI's potential.

AI tools for Product Managers [The Ultimate Guide] - HelloPM

This comprehensive guide explores how AI tools can revolutionize product management by enhancing productivity and innovation. It covers the current product management workflow, challenges faced by product managers, and various AI-powered tools that can assist with tasks such as market research, user feedback analysis, stakeholder communication, and analytics. The guide also discusses the future of product management with AI, improving communication with AI, and provides next steps for product managers to effectively integrate AI into their workflow. Key AI tools discussed include ChatGPT, Notebook LM, Atlassian Intelligence, Mixpanel Spark AI, Kraftful, Chatbase, MoEngage, and Text-to-SQL Tools. These tools can help with tasks such as generating insights, automating customer support, and optimizing customer journeys. The guide emphasizes the importance of continuous learning, experimentation, and adapting to the AI-driven future of product management.

Key Takeaways

The integration of AI tools in product management can significantly enhance productivity and innovation by automating operational tasks and providing strategic insights.
Product managers can leverage AI for various tasks, including market research, user persona creation, feature ideation, and customer support, allowing them to focus on high-level strategy and creative problem-solving.
The future of product management with AI involves a shift towards more strategic roles, enhanced creativity through AI-assisted thinking, and more efficient teamwork with smaller, AI-empowered teams.
Effective communication is crucial for product managers, and AI can help refine ideas, improve articulation, and enhance stakeholder management skills.
To successfully integrate AI into their workflow, product managers should regularly audit their AI processes, focus on product discovery, and empower their teams to leverage AI tools.

(1) The AI Skill That Will Define Your PM Career in 2025 | Aman Khan (Arize)

The article discusses the importance of AI evaluations for product managers (PMs) in 2025, as emphasized by Chief Product Officers of OpenAI and Anthropic. Aman Khan, Director of Product at Arize AI, shares insights on building critical skills for AI PMs, including understanding AI fundamentals, customer obsession, curiosity, learning from great AI experiences, and understanding evaluations and observability. The conversation highlights the significance of writing effective AI evaluations, breaking down complex AI systems into smaller components for evaluation, and iterating quickly in AI product development. It also touches on the role of human judgment in evaluating subjective aspects like tone and sentiment, and the tools and resources available for getting started with AI evaluations, such as Arize's Definitive Guide on AI Evals and open-source tools like Phoenix.

Key Takeaways

The ability to write effective AI evaluations will be a crucial skill for PMs in 2025, enabling them to measure and improve AI product performance.
To become a successful AI PM, one needs to develop skills such as understanding AI fundamentals, customer obsession, curiosity, and the ability to learn from great AI experiences.
The development cycle for AI products needs to be much shorter and more iterative, with a focus on constant improvement and experimentation, rendering traditional waterfall processes less effective.

(1) How the Best Product Managers Use AI (And What Everyone Else Gets Wrong)

The article discusses how top product managers leverage AI to enhance their workflow, decision-making, and overall impact. It highlights the difference between superficial AI use and deeply integrating AI into product management practices. Key insights include training AI on company context, using AI as a 'second brain,' and leveraging AI to navigate organizational complexity. The conversation between Tom Leung and Mustafa Kapatiya, a former Google executive and AI consultant, reveals that elite product managers use AI to produce better work, minimize low-leverage tasks, and make data-driven decisions. The article outlines three core shifts for best-in-class AI execution: writing better and reusable prompts, training AI on specific contexts, and using AI for organizational navigation. It also touches on the future of product teams, suggesting a shift towards smaller, highly effective teams with AI as a co-pilot.

Key Takeaways

Top product managers integrate AI deeply into their workflow, using it to produce better work and minimize low-leverage tasks.
Effective AI use involves training it on company context and using structured inputs to get actionable insights.
The future of product management may involve smaller teams of highly effective PMs working with AI as a co-pilot, changing the traditional team structure and skill requirements.

AI and PM: How to find a path through the noise | Atlassian

The article discusses the integration of AI in product management, highlighting the need for a balanced approach that leverages AI's analytical capabilities while maintaining human judgment and creativity. It emphasizes the importance of understanding AI's strengths and limitations, adopting an 'AI-first mindset,' and developing sustainable AI habits to enhance productivity and innovation. The authors suggest that successful AI adoption involves specialized AI tools integrated with organizational knowledge bases and workflows, and that the future of product management lies in effective human-AI collaboration.

Key Takeaways

The key to successful AI adoption in product management is developing an 'AI-first mindset' that considers how AI can enhance tasks without replacing human judgment and creativity.
Specialized AI tools that are integrated with organizational knowledge bases and workflows will provide significant competitive advantages by offering insights directly relevant to specific organizational challenges.
Effective human-AI collaboration can lead to increased productivity, with AI collaborators achieving 2x ROI on their efforts and saving 105 minutes daily.
The future of AI in product management involves not just adopting AI tools, but understanding how to orchestrate multiple AI capabilities and focusing on interface design and user experience as key differentiators.

The playbook for AI-enhanced product management | Pendo.io

This document outlines how AI is transforming product management across various stages of the product lifecycle, from discovery to iteration. It highlights the role of AI in enhancing product managers' capabilities, improving decision-making, and driving business outcomes. The guide covers the impact of AI on product discovery, validation, building, launching, evaluating, and iterating on products. It emphasizes that AI will augment the product manager's role, making them more efficient and effective in achieving business goals. The document also showcases Pendo's suite of tools designed to support product managers in leveraging AI for better product experiences.

Key Takeaways

AI will revolutionize product management by enhancing data analysis, decision-making, and product development processes.
The integration of AI tools will enable product managers to focus on strategic thinking and creativity, driving business success.
Pendo's platform offers a comprehensive suite of AI-powered tools to support product managers throughout the product lifecycle.

How I’m using AI to 10x my work as a product manager

The article discusses how a product manager at Coda utilizes AI to streamline their workflow and improve decision-making. The author highlights six key areas where AI is applied: prioritizing what to build, defining project scope, setting goals, crafting a Product Requirements Document (PRD), kicking off projects, and keeping stakeholders informed. AI helps the author identify relevant company OKRs, benchmark past projects, and generate ideas for tracking project success. By automating busywork and providing valuable insights, AI enables the product manager to be more efficient and effective.

Key Takeaways

AI can significantly enhance a product manager's productivity by automating routine tasks and providing data-driven insights.
The strategic application of AI in product management can lead to better decision-making and more effective project prioritization.
As AI technology continues to evolve, product managers can expect to leverage it for increasingly complex tasks and strategic planning.

The Transformative PM’s Guide to AI-Driven User Research | by Gedi | Jun, 2025 | Medium

The document discusses the transformative impact of AI on user research in product management. It highlights the shift from manual, anecdote-driven research to automated, AI-assisted systems that enable continuous discovery and faster insight generation. The author presents a five-phase framework for AI-enabled user research: strategic planning, participant recruitment, data collection, synthesis, and insight activation. The document also emphasizes the importance of integrating AI tools with human judgment and ethical considerations. The 'insight flywheel' concept is introduced, representing a continuous cycle of framing, collecting, synthesizing, activating, and adapting insights. The author concludes that AI amplifies product managers' judgment, accelerates feedback cycles, and enables higher-quality decisions.

Key Takeaways

The integration of AI in user research enables product managers to generate insights faster and make data-driven decisions, creating a competitive advantage.
The 'insight flywheel' represents a continuous and self-improving research process that is critical for elite product organizations.
While AI enhances user research, it is crucial to balance automation with human review to avoid bias and ensure qualitative nuance is considered.

(1) A guide to AI prototyping for product managers

This article provides a detailed guide to AI prototyping for product managers, covering various AI development tools, techniques for building prototypes, and strategies for overcoming common challenges. It discusses chatbots, cloud development environments, and local developer assistants, highlighting their strengths and use cases. The article also offers practical advice on converting designs to functional prototypes, building prototypes from scratch, and debugging issues. Additionally, it touches on the limitations of current AI prototyping tools and the importance of reflection, batching, and specificity in achieving successful outcomes.

Key Takeaways

AI prototyping tools are revolutionizing product development by enabling rapid creation of functional prototypes without extensive coding knowledge.
Cloud development environments like v0, Bolt, Replit, and Lovable offer different strengths for building complex prototypes, from beautiful designs to internal tools and production-ready applications.
Effective strategies for AI prototyping include reflection, batching, and being specific with prompts to achieve desired outcomes and minimize errors.
Product managers can leverage AI prototyping to speed up the discovery process, gather customer feedback early, and explore multiple ideas quickly.

(1) The AI PM's Playbook: How Top Product Managers Are 10x-ing Their Impact in 2025

The article discusses how Product Managers (PMs) can leverage AI to enhance their productivity and impact. It categorizes PMs into three types: AI-powered PMs, AI PMs, and AI feature PMs. The author provides a guide on using AI effectively, including three rules: developing prompt skills, doing initial and final work manually, and revising AI outputs. The top 5 AI use cases for PMs are: creating Product Requirements Documents (PRDs), strategy documents, competitive research, meeting updates, and scaling prototyping. The article also highlights common mistakes to avoid, such as letting AI replace human thinking and not updating AI outputs with current knowledge.

Key Takeaways

To maximize AI's potential, PMs must develop strong prompting skills and understand when to apply human judgment.
The top 5 AI use cases for PMs can significantly enhance productivity in tasks such as PRD creation, strategy documentation, and competitive analysis.
PMs should be cautious of common pitfalls, including over-reliance on AI for critical thinking and failure to update AI-generated content with the latest information.

Frequently Asked Questions

How are successful PMs implementing the 20-60-20 rule across different AI tools like Claude, Productboard Pulse, and specialized prototyping platforms?
What specific AI evaluation frameworks are product teams using to measure LLM performance in customer support, content generation, and decision-making scenarios?
How do AI-powered 'orchestrator PMs' balance automation of routine tasks with maintaining the human judgment needed for stakeholder management and strategic decisions?
What are the key differences in AI tool selection between B2B and B2C product teams, particularly for tools like Replit, v0, Bolt, and specialized research platforms?
How are product teams using AI prototyping tools (Cursor, Replit Agent, v0) to transform the traditional PRD-to-prototype workflow timeline?
What patterns emerge when comparing AI copilot implementations across different product management phases (discovery, validation, build, launch, evaluation)?
How do successful AI-powered PMs handle the 'context problem'—ensuring AI tools understand company-specific constraints, past decisions, and stakeholder dynamics?
What specific prompt engineering techniques are most effective for product management use cases versus general AI applications?
How are product teams measuring ROI and productivity gains from AI tool adoption, particularly the claimed 2x ROI and 105 minutes daily savings?
What integration patterns work best for connecting AI tools with existing PM tech stacks (Jira, Confluence, Productboard, analytics platforms)?