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Background and Introduction

In an era where AI tools are widely used in software development, the product development model is undergoing profound changes. Development teams now extensively adopt AI coding assistants (such as the popular Cursor or Copilot), AI documentation generation tools, Vibe Coding collaboration platforms, AI testing tools, etc., which have significantly enhanced development efficiency. Statistics show that using AI programming assistants can increase coding speed by approximately 55%; the 2024 Developer Survey Report also indicates that as many as 70% of developers have integrated AI tools into their workflow. With the surge in development efficiency and iteration speed, the responsibilities and workflow of product managers (PMs) are also evolving. PMs need to reevaluate their role positions and fully utilize AI tools to boost productivity. This guide will focus on exploring the changes in PM workflows and new methods for efficient PRD (Product Requirements Document) writing in the context of AI-driven development. Topics include: changes in PM responsibilities, using AI for requirements analysis and prototype design, adjustments to PRD documents in AI-human collaboration, analysis of typical case scenarios, and how to build a “human + AI” collaborative product development mechanism.

I. The Transformation of the Product Manager Role and Workflow

1. Faster iteration pace demands greater PM agility.

With AI-assisted development, the cycle from product conception to prototyping and launch has significantly shortened. Many features that once took engineers weeks to develop can now be completed in a preliminary version within days with the help of AI tools. This means PMs need to adapt to shorter iteration cycles and more frequent release schedules to keep up with market feedback and adjust product plans promptly. In the past, PMs often handed over finalized requirements to the development team and waited for an extended period until development was completed. However, with AI assistance, development can produce a “prototype in seconds.” PMs must be able to quickly validate solutions, identify issues, and propose improvements. This requires PMs to embrace agile iteration thinking: taking small steps, rapidly experimenting, and maintaining the correct product direction amidst rapid changes.

2. Shift in focus from execution to strategic planning.

As AI takes over a large amount of repetitive and routine execution work, the value of PMs becomes more evident in direction control and decision-making. As stated by industry experts, “A product manager is the locomotive, the direction setter, and the coordinator. This means more thinking than action, with thinking accounting for at least 80%, and action just 20%.” Much of the work that PMs used to handle personally (such as competitive research, data analysis, and writing lengthy documents) can now be preliminarily completed by AI. Consequently, PMs can allocate 80% of their time to strategic thinking and architecture design, while devoting only 20% of their efforts to supervising AI outputs and coordinating resources. This does not imply that PMs will have more leisure time. On the contrary, they must take on higher-level responsibilities, such as deeply understanding the essence of user requirements, formulating long-term product roadmaps, and balancing business objectives with technical feasibility. These are tasks that AI currently cannot perform. By freeing up their hands, PMs can invest more energy in these high-value tasks.

3. Learning new skills for mastering AI tools.

With AI integration into workflows, product managers must expand their skill sets to fully harness the efficiency gains brought by AI tools. First and foremost is the skill of prompt engineering, which involves using clear and precise language instructions to drive desired results from AI. For instance, when using generative AI to draft requirements documents, PMs need to learn how to set contexts for AI and ask questions step by step to obtain high-quality responses. Additionally, there is the need to enhance technical understanding. In the AI era, PMs must not only be business-savvy but also understand the basic technical principles and the boundaries of AI capabilities. Andrew Ng has pointed out that for many applications based on large models, product managers can assess technical feasibility by writing prompts or a small amount of code. The emergence of LLMs and low-code platforms enables PMs to independently complete preliminary prototype verifications without complete reliance on engineers. This requires PMs to have a basic foundation in programming and data analysis to engage in reasonable conversations with AI and judge the correctness of AI outputs. In summary, “AI-savvy” product managers will hold a greater advantage in their teams. They can communicate deeply with engineering teams on technical details and expand their capabilities through AI.

4. Evolution of team collaboration methods.

As AI deeply integrates into the development process, team collaboration models are also changing. With AI empowering roles such as development and testing to achieve higher efficiency, PMs need to synchronize adjustments to collaboration methods to adapt to these changes. On the one hand, PMs must participate in the design and development processes earlier and more frequently. For example, when development teams leverage AI to rapidly produce prototype code, PMs can promptly participate in reviews to ensure compliance with requirements. If there are deviations, PMs do not need to wait for the next iteration to propose requirement changes but can immediately adjust the description to allow developers to correct it through AI, achieving real-time interaction between requirements and development. On the other hand, AI itself becomes a member of the team, and PMs need to learn how to collaborate with this “AI colleague.” For instance, on a platform like Vibe Coding, PMs may directly describe product requirements in natural language to enable AI to generate code in real time. Vibe Coding advocates an extreme AI development model: 100% code generation by AI, with no direct human code modification, and all adjustments made through prompts. In this scenario of human-machine co-creation, the role of a product manager resembles that of a “director.” They need to break down requirements into instructions understandable by AI and communicate repeatedly with AI programmers to refine the implementation. This high level of collaboration demands stronger abstract expression and communication skills from PMs to ensure effective synergy between humans and AI. In general, with AI integrated into the team, PMs must more closely embed themselves into the entire development process, proactively coordinate between humans and AI, and leverage a combined human-machine effort to achieve greater than additive efficiency.

II. Applications of AI in Requirements, Design, and Project Management

1. Requirements Collection and User Insights

Requirement research is the starting point of product work. In the past, PMs had to sift through大量 user feedback and market reports to extract valuable requirement information. Now, with the powerful information processing capabilities of AI, requirement collection has become unprecedentedly efficient and in-depth:

• Massive Data Analysis: AI can conduct in-depth mining of large amounts of data to help product managers accurately grasp market trends and user preferences. For instance, AI can rapidly organize user reviews from social media, app store comments, customer service chat records, and other unstructured data to identify user pain points and potential demands. This large-scale data analysis, which was previously difficult to accomplish in a short time, is something AI excels at. PMs can have AI generate user feedback clusters and sentiment analysis reports to quickly understand what users are most concerned about. For example, in the e-commerce field, inputting user comment data from the past year into an AI model might reveal that many users have complained about “imprecise search results,” enabling PMs to identify the need for search optimization.
• Automatic Organization and Summarization: For qualitative materials such as interview records and survey results, PMs can also utilize AI for summary extraction. Through large language models, complex research texts can be condensed into key points. For example, after conducting interviews with 10 seed users, tools like ChatGPT can distill common demand points and frequently mentioned opinions from the records, generating research summaries for PMs to reference. This AI-driven organization ensures that important information is not overlooked while significantly reducing the time spent on manual note-taking.
• Predictive Insights: More advanced AI analysis can also provide decision-making references. For example, using AI2SQL tools, product managers can directly query databases in natural language to quickly validate the potential value of different functional solutions, such as the possible impact of a new feature on user growth or conversion rates. Additionally, by automatically analyzing the functional highlights and user feedback of competitors’ products through AI tools, PMs can swiftly identify market opportunities for their own products. Unlike relying solely on subjective user insights in the past, product managers can now achieve more precise and objective requirement analysis through “data + AI.” Of course, when using insights provided by AI, PMs still need to make rational judgments and decisions based on specific business contexts.

By leveraging these methods, AI enables more comprehensive and efficient requirement collection. PMs collaborate with AI in the following manner: PMs are responsible for posing the right questions, selecting data sources, and interpreting the business implications of AI analysis results, while AI handles the rapid processing of information and provision of insights. This partnership ensures that product requirements are built on a solid data foundation rather than on assumptions or fragmented impressions.

2. Function Breakdown and Solution Design

After obtaining requirements, product managers need to break them down into specific executable functions and solutions. AI plays a powerful supporting role in this step, helping PMs to design functions and plan tasks more quickly and effectively:

• General Solution Reference: For certain common requirements, AI can provide references to industry-standard solutions. For example, when designing a coupon system for an e-commerce platform, a PM can ask ChatGPT, “What are the typical processes and rules for coupon functions on e-commerce platforms?” AI can quickly list key points such as collection, usage, expiration, and stacking rules. These serve as design inspirations for PMs to adapt to their own business needs. Similarly, when designing a permission management module, asking AI to summarize the common RBAC model role division can provide quick access to industry best practices. This knowledge-based support allows PMs to stand on the shoulders of giants when designing solutions, rather than starting from scratch.
• Function List and User Story Generation: AI excels at decomposing large tasks into smaller ones. After clarifying a major product goal, PMs can ask AI to generate a list of functions or user stories. For instance, for the requirement of “supporting multi-language interfaces,” inputting a prompt asking AI to list all sub-functions that need modification may yield outputs such as: “1) Interface language switching settings; 2) Multi-language content management backend; 3) Automatic language switching based on user region…” These decomposition items serve as checklists for PMs to prevent the omission of key modules. Many AI tools (such as PMAI) already feature a “one-click PRD outline generation” function. Inputting the requirement name can produce a module structure and key point list. This outline often covers standard functional components, helping PMs quickly build a framework for requirement design.
• Brainstorming and Creative Divergence: During the solution conception phase, AI serves as an excellent brainstorming partner. When multiple solution ideas are needed, PMs can ask AI to propose suggestions from different perspectives. For example, “How to enhance user积极性 in posting on a community platform?” AI may present ideas from three directions: gamification incentives, content recommendation, and social interaction. Alternatively, using conversational AI for brainstorming, PMs can pose questions to AI, which in turn asks follow-up questions and extends ideas. The creative sparks generated by such human-machine dialogue can sometimes inspire PMs to come up with entirely new solutions.
• Structured Documentation and Process Diagrams: AI can assist in drafting functional structures and processes, making solutions more intuitive. Using specialized conversational mind mapping tools (such as ChatMind), PMs can input functional key points, and AI will automatically generate corresponding functional architecture diagrams or process diagrams. For instance, if PMs inform ChatMind that their app consists of modules such as user registration and login, content browsing, content publishing, and notification messages, AI will produce a brain map that clearly displays the subordinate functions and relationships of each module. This is particularly useful for organizing complex systems. Additionally, some AI tools can directly generate interaction flowcharts, state diagrams, and other models based on textual descriptions, helping PMs verify the completeness and correctness of logic. Through graphical representation, team members can gain a more intuitive understanding of the solution.

3. Prototype Design and User Experience

After finalizing the functional solution, product managers typically need to produce prototypes or wireframes to showcase interface layouts and interaction flows. AI’s generative capabilities are also highly useful in the field of prototype design:

• Rapid Prototype Generation: Today, numerous AI-powered prototype design tools (such as Uizard, Instant Design AI, and Mockplus AI) can automatically create interface prototypes based on textual descriptions. Actual case studies have shown that by inputting requirement descriptions into the text box of an AI design tool and clicking the generate button, as few as half a minute can yield four high-fidelity prototype designs. This significantly lowers the threshold for prototype creation, enabling PMs with “zero design background” to produce visually plausible interface sketches. For example, if a PM wants to design a product detail page prototype for an e-commerce app, they can simply describe in a few sentences the elements needed, such as product images, price, purchase buttons, and recommended products. AI will then generate multiple layout options for selection. By continuously refining the text description, PMs can quickly iterate to a satisfying page prototype. Compared to manual component dragging, AI-driven prototyping is far more efficient.
• Multi-Solution Creation and Inspiration Exploration: A key advantage of AI-generated prototypes is the ability to produce batches of diverse designs. Given the same requirements, AI tools often provide various stylistic design drafts. PMs can simultaneously reference multiple options and select the best elements from them. This is akin to having an unlimited team of designers brainstorming. Moreover, when PMs encounter design bottlenecks, AI can serve as an “inspiration catalyst.” For instance, by asking AI to “attempt an innovative navigation menu layout,” it may generate unconventional yet creative interface proposals that expand the PM’s thinking. This is highly beneficial for creating a positive user experience: human-machine collaboration often generates richer design possibilities than human brainstorming alone.
• User Experience Optimization: AI can not only generate interfaces but also assess and enhance user experience to a certain extent. Some advanced prototype tools are equipped with UX optimization suggestion features that can automatically identify usability issues based on design drafts. For example, they may highlight whether button colors are prominent enough, whether layout hierarchies are clear, or whether interaction flows align with intuition. Additionally, some AI tools can simulate user gaze focus areas to help PMs evaluate the prioritization of information on a page. These auxiliary functions are equivalent to having AI conduct a preliminary usability review, allowing PMs to identify and improve experience issues early in the prototyping phase. Furthermore, AI can leverage extensive user feedback data to predict potential pain points in the current prototype, such as high form abandonment rates due to overly lengthy forms, providing PMs with improvement references. With AI assistance, prototyping becomes not just a design process but an intelligent one that involves simultaneous design and evaluation, enabling more efficient iteration toward user-satisfying solutions.
• Prototype Annotation and Interaction Description: Annotations and interaction explanations for generated prototypes are necessary to ensure development teams understand them. AI can also be of service in this regard. PMs can ask AI to automatically generate interaction descriptions based on the prototype, including details such as the logic behind each button/link’s navigation and state changes. For instance, inputting “Generate interaction descriptions for the above prototype” may yield explanations like, “Clicking the shopping cart icon navigates to the shopping cart page; pull-down to refresh the product list; the submit button remains grayed out and inactive until the input field is filled…” PMs can then make adjustments based on actual business rules. By leveraging AI to quickly produce comprehensive annotations, the risk of omitting critical interaction details in manual documentation is minimized, ensuring accurate understanding by developers.

4. Team Collaboration and Progress Management

Once the product design is finalized and enters the development phase, AI can enhance the efficiency of team collaboration and project management, making the entire R&D process smoother and more efficient:

• Task Management and Communication: Modern project management tools are increasingly integrating AI functionalities to assist PMs in better allocating and tracking tasks. For example, some tools can automatically generate task cards based on requirement descriptions, preliminarily filling in development steps and estimated timeframes for PMs to adjust as needed. Additionally, chat collaboration platforms with built-in AI assistants can answer team members’ questions about PRDs 24/7 (based on the content of previously input requirement documents) or automatically summarize meeting highlights to generate minutes. These AI assistants alleviate PMs’ communication and coordination burdens. Common questions from team members can be directly answered by AI or through PRD references, ensuring transparent and consistent information. PMs can then focus on more complex communication and decision-making tasks. For remote or cross-time zone teams, AI robots ensure information synchronization and timely responses, preventing project delays due to human factors.
• Collaborative Editing and Version Control: With AI support, PRD documents can become “living documents” for collaboration. Multi-user simultaneous editing on cloud documents is no longer a challenge, and AI can monitor document modifications in real time, alerting users to inconsistencies and even automatically merging conflicting changes based on context. For instance, if developers supplement technical implementation details in the PRD, AI can help normalize the wording; if testers mark an overlooked business scenario in the PRD, AI assistants can remind PMs to complete corresponding requirement descriptions. In this human-machine collaborative editing model, PRD documents remain highly accurate and up-to-date, serving as a single source of truth for the team. When product requirements change, after PMs update the document, AI can generate a change summary to notify all relevant members, ensuring everyone is promptly informed of updates.
• Progress Forecasting and Risk Warning: Project management AI can predict project progress and warn of risks based on historical data and current task allocation. For example, by analyzing the completion time of similar projects in the past and combining it with the complexity of current tasks, AI can predict the probability of module delivery delays. If progress deviates from the plan, AI will timely alert PMs to pay attention. Additionally, by analyzing task statuses in tools like Jira, AI can automatically identify technical risk points, such as a developer spending excessive time on bug fixes, and notify PMs. Data-driven management allows PMs to identify risks and reallocate resources earlier, preventing minor issues from escalating into project delays. Previously, these types of aggregated analyses required manual effort from PMs, but AI’s insights now make project management more proactive.
• Quality Assurance and Testing: AI testing tools are transforming traditional software testing processes. Previously, testers had to manually design a large number of test cases based on requirements. Now, AI can generate various test cases and boundary conditions based on PRD descriptions. For example, given a payment process requirement, AI can list numerous scenarios such as normal payment, insufficient balance, and network interruption as test cases. Furthermore, using model predictions, AI can directly highlight weak points in processes (e.g., form validation, concurrent operations) that require focused testing. In addition to test design, AI can automatically execute tests, including UI automation and API interface testing, tirelessly running tests repeatedly. More advanced AI testing tools can adaptively adjust test cases based on previous results to discover new anomalies. These capabilities significantly improve test coverage and efficiency. In continuous integration environments, AI testing robots can quickly provide regression test results after each code submission, enabling developers to immediately fix issues and facilitate rapid product releases. For PMs, the introduction of AI testing means they can confidently shorten iteration cycles, boldly experiment with new features while ensuring quality stability. This high-speed, high-quality testing assurance makes rapid iteration feasible: PMs can propose new requirements that can be quickly verified and launched, as AI assistance ensures consistent quality.
• DevOps and Release: In the deployment and release phase, AI also offers convenience. For instance, infrastructure-as-code configurations can be generated and verified by AI; release notes and launch announcements can be rewritten by AI based on PRD content to ensure consistency with requirement descriptions. AI can even intelligently schedule the proportion and timing of phased rollouts based on user segmentation strategies to optimize the launch of new features. When monitoring detects abnormal metrics post-release, AI promptly notifies PMs and relevant engineers to locate issues and roll back as needed. These automated measures make the launch process more controllable and efficient. PMs gain real-time visibility into the entire release process, enabling more precise control over product release timelines.

III. Adjustments to PRD Documentation in AI Human-Machine Co-Creation Environments

1. More Frequent Document Iteration

With AI accelerating development节奏, PRD documents must keep pace with rapid updates. PRD should no longer be regarded as a fixed blueprint but as a “living document” that evolves with each iteration. To this end, PMs need to establish clear version management and change record mechanisms. Every adjustment in requirements or AI implementation details should be promptly documented in the PRD’s revision history and communicated to relevant team members. An “Iteration Log” or “Change Summary” section can be added to the PRD to list the new and modified requirements of each version and the reasons behind them. With AI tools, maintaining consistency between documents and code has become easier: AI can automatically map code repository annotations or changes back to corresponding sections of the PRD, prompting PMs to update wording. This high-frequency, incremental approach to document updates ensures that the PRD remains synchronized with product implementation, avoiding the risk of guiding development with outdated information.

2. Modularization and Lightweight Design

In the face of rapid iteration, the structure of PRD should become more modular to facilitate localized updates and team collaboration. Instead of lengthy, monolithic documents, it is now more advantageous to divide PRDs into relatively independent sections based on functional modules or user stories. For example, product requirements can be segmented into separate User Story or Feature documents, each focusing on a distinct function. This way, when a specific functional requirement changes, only the corresponding sub-document needs to be updated, without disrupting the overall structure of the PRD. Each module should follow a unified template, including elements such as background, objectives, prototypes, interactions, and acceptance criteria, ensuring consistency in format and completeness of content. AI can assist in generating these template skeletons in bulk, allowing PMs to fill in the specifics. Additionally, the content should be concise and clear, avoiding redundancy. In the past, PRDs often became overly verbose in an attempt to cover all aspects, but in the AI era, such lengthy descriptions may increase maintenance burdens and hinder AI understanding. Now, there is a greater emphasis on concise expression: using tables, checklists, and prototype diagrams to replace lengthy textual descriptions, condensing key information for presentation. For instance, a functional checklist table can outline the requirements and acceptance criteria of each sub-function, rather than elaborating over several pages. This structured, lightweight documentation is better suited to rapid modifications and reviews.

3. Increased Use of Examples and Data-Driven Explanations

In AI-driven development environments, example-driven requirement descriptions have become particularly important. Compared to abstract textual descriptions, concrete examples and data samples are easier for both humans and AI to comprehend. Andrew Ng has pointed out that traditional lengthy PRDs are being replaced by specific, vivid examples, with “data becoming the PRD of the AI era.” In other words, instead of using ambiguous language, it is better to provide clear input-output examples to define requirements. For example, in describing the requirements of a chatbot, instead of simply stating “the bot can answer user questions,” specific dialogue samples can be provided: when a user asks about the weather, the bot responds correctly; when the user’s wording is unclear, the bot clarifies the question. Similarly, for image recognition features, rather than vaguely specifying recognition accuracy requirements, a set of labeled images can be provided to visually demonstrate what the bot should recognize and the expected output. These examples can serve as references for developers and as test cases for AI coders. For general software functions, boundaries should also be illustrated with examples. For instance, form validation rules can be explained by listing several valid and invalid input cases instead of merely describing the rules. Likewise, for data computation requirements, input data and expected output samples can be attached to facilitate self-testing by developers and AI validation. Data-driven documentation can also help train AI assistants themselves: by providing the AI with historical requirements and corresponding implementations, it will generate solutions more aligned with team conventions in the future. Therefore, new-style PRDs should incorporate examples and data prototypes as much as possible to make requirement descriptions more actionable and clear.

4. Emphasis on Acceptance Criteria and Test Cases

To adapt to rapid iteration, updated PRDs often integrate testing considerations by explicitly defining acceptance criteria and success/failure conditions for each feature. This is similar to the acceptance criteria or BDD scenarios in Agile methodologies, but becomes even more critical in the AI era—since development may be AI-driven, strict criteria must be established to guide AI implementation. PMs should outline quantifiable acceptance indicators in PRDs, such as “response time < 200ms” and “success rate above 90%,” as well as key scenario test cases. For example, “When a user clicks on registration without entering an email address, the system should display the message ‘Please enter your email’ and prevent submission” — describing business rules in a clear Given-When-Then format helps AI testing tools automatically generate test cases or enables AI coders to write code according to the scenarios. Another example is in order processing, where the requirement “If inventory is insufficient, submitting an order should return an error and notify the user of insufficient stock” becomes the basis for post-development acceptance. Clear acceptance criteria not only enhance clarity but also serve as constraints for AI, reducing the risk of AI implementation deviations. In highly automated development pipelines, PRD acceptance criteria can even be directly integrated into continuous integration, with automated testing verifying compliance, thereby achieving a closed loop from requirements to deployment.

5. Support for Multi-User Collaboration and AI Readability

High collaboration demands that PRDs be easily understood and utilized by both humans and AI. To this end, documents should adopt unified standards and formats to minimize ambiguity. For instance, all terminology should be consistent throughout the document, with an AI-assisted glossary; key concepts should be defined upon first mention to prevent misunderstandings among readers. The language of the document should be as concise and straightforward as possible, avoiding overly colloquial or vague expressions to facilitate AI parsing. For complex business logic, enumerated lists and step-by-step breakdowns can be used to create clear hierarchical structures, which also aid AI language models in understanding context. When collaborating on PRD editing as a team, AI-augmented collaborative platforms can be employed. AI can function as a real-time proofreader and formatting manager: for example, identifying potentially ambiguous sections and suggesting rewrites, or automatically formatting content according to established templates. For feedback from cross-functional team members (developers, testers, operations, etc.), AI can preliminarily integrate diverse viewpoints and generate compromise proposals for PMs to consider. In short, the new-style PRD aims to be “easy for humans to read and easy for AI to use.” Since PRDs serve both the team and AI (as AI may reference them during code generation or testing), it is essential to ensure the documents are clear and rigorous to a higher standard.

IV. Building a “Human + AI” Collaborative Product Development Mechanism

1. Clearly Define Roles and Leverage Strengths

First, it is essential to identify which tasks in the product development process are suitable for AI and which must be undertaken by humans, creating a human-AI task division list. In principle, repetitive, data-driven, and format-standardized tasks should be delegated to AI whenever possible, such as data collection and organization, data analysis, initial drafting of documents, UI prototype design, and test case generation, which AI can efficiently complete. Conversely, tasks involving creativity, judgment, and strategic planning should be handled by humans, such as requirement insights, product vision planning, and solution evaluation and decision-making. In human-AI collaboration, AI is responsible for delivering “intermediate outputs” (preliminary results and solution suggestions), while humans oversee the final review and decision-making. By clarifying responsibility boundaries, we can avoid wasting human resources on tasks that AI excels at, while preventing over-reliance on AI and neglecting human value.

2. Optimize Processes and Integrate AI Assistants

Embed AI tools into the team’s daily workflow rather than using them sporadically. For instance, incorporate AI nodes into team processes: use AI to provide preliminary materials during brainstorming sessions, generate meeting minutes with AI post-review meetings, and utilize AI to analyze release metrics and user feedback during iteration retrospectives. Establish usage guidelines and best practices for commonly used AI tools, such as prompt templates and integration methods, to make them an integral part of the workflow. Additionally, designate AI collaboration process managers or “AI interpreters” to ensure smooth transformation of AI outputs into team outcomes. For example, after AI generates a PRD draft, have PMs or BAs review and adjust it before team review. This process ensures quality control of AI-generated outputs. Continuously refine workflows and gather team feedback to identify which stages leverage AI effectively and which require improvement, thereby enhancing the design of human-AI collaboration processes.

3. Enhance Team AI Literacy

Under a human-AI collaboration framework, every team member needs a certain level of AI tool proficiency. PMs should promote AI skills training and foster a culture of AI adoption within the team. For instance, organize internal sharing sessions to exchange experiences on crafting effective prompts and discovering useful AI tool plugins, encouraging knowledge sharing. Encourage developers and testers to try AI assistants and raise team acceptance and depth of AI application (given that 70% of developers are already using AI). Additionally, create an AI tool list and usage guide for the team to reduce exploration time. When everyone is willing and able to use AI, collaborative efficiency can truly improve. At the same time, cultivate a critical mindset toward AI usage—trust its efficiency but maintain a habit of verifying results to prevent over-reliance.

4. Data Accumulation and Knowledge Documentation

Preserve inputs and outputs from AI collaboration as team assets. For example, save valuable parts of AI conversation records for new employee training or future project reference; organize frequently asked questions from AI interactions. Store industry data compiled by AI and generated solution lists in the knowledge base for future queries. Archive intermediate materials produced during AI usage, such as mind maps, prototype schemes, and script codes. These real project data can also be fed back into AI model training to improve output quality. Over time, the team can build a dedicated AI knowledge base (e.g., feeding historical PRDs and final outputs into an internal large language model), enabling AI to better understand the team’s business context and produce more relevant results. In this way, human-AI collaboration goes beyond mere tool usage to co-construct a team intelligence repository, enhancing organizational intelligence over the long term.

5. Emphasize Human Review and Decision-Making

No matter how powerful AI becomes, it cannot replace the role of humans in critical decision-making and creativity. PMs must establish key human review points when introducing AI collaboration. For instance, AI-generated requirement analysis conclusions should be judged by PMs based on business intuition; solution lists provided by AI should be discussed and decided upon by the team; AI-drafted documents must be thoroughly reviewed by PMs to ensure they meet expectations. Especially in strategic product decisions and user experience evaluations that require comprehensive considerations, final decision-making authority should remain firmly in human hands. AI can provide increasing informational input but cannot make decisions on behalf of humans. Complex decisions require consideration of multiple factors, which current AI cannot achieve. Therefore, it is essential to establish systems ensuring human oversight of major matters, such as having product committees review important requirements and product directions, rather than advancing based solely on AI conclusions. Humans must remain the ultimate decision-makers, with AI serving as a supporting force.

6. Ensure AI Output Quality and Ethics

While enjoying the efficiency of AI, it is crucial to address its limitations and potential risks. PMs are responsible for establishing quality control and review mechanisms for AI outputs. For example, strict code reviews and testing procedures should be implemented for AI-generated code to prevent low-quality code from being integrated; data sources for AI-generated analytical conclusions should be verified for accuracy; and AI-produced content should be checked for biases, inappropriate wording, or copyright infringement. If necessary, filtering rules can be set for AI responses, or AI tool permissions can be restricted (e.g., access to anonymized data only). Additionally, when handling user data and privacy, AI usage must comply with regulations to avoid inputting sensitive data into public AI services. Teams should also focus on the ethics of AI usage, such as avoiding discriminatory or misleading instructions during human-AI interactions, maintaining the integrity and reliability of collaborations. By establishing AI usage guidelines and review processes, we can ensure that AI is used responsibly and delivers positive value to the team.

7. Embrace Change and Continuous Learning

AI technology is evolving rapidly, and human-AI collaboration models will continue to adapt. Product managers and teams must maintain a mindset of continuous learning, staying updated on AI advancements, and introducing new tools and methods when appropriate. For instance, today’s GPT assistants may be replaced by next-generation multimodal AI in a few years. At that time, PRDs might evolve beyond text to include AI-readable prototypes or interactive videos. PMs should regularly review the team’s collaboration mechanisms to identify opportunities for optimization with new AI tools. Encourage team members to propose improvements to current human-AI collaboration methods or explore new AI application scenarios. Only by staying curious and committed to learning can teams stay ahead in the AI revolution, continuously refining their collaboration models for greater efficiency.

In summary, establishing a “human + AI” collaborative product development mechanism is not an overnight process but requires product managers to experiment and adjust in practice. By clarifying human-AI roles, integrating AI into workflows, enhancing team AI capabilities, and prioritizing human value, a trustworthy human-AI collaboration system can be gradually built. In this system, AI handles the heavy lifting while humans steer the direction with creative wisdom, complementing each other to unleash unprecedented productivity. The author believes that in the future product development process, AI will be regarded as a standard team member. Rather than worrying about which roles AI might replace, it is more productive to embrace the changes proactively. Those product managers who are adept at utilizing AI without losing their way in it will lead their teams to navigate new tools and create new value, standing firm in the competitive talent landscape.