GenAI Is Your Product Intern
In recent years, tools powered by generative artificial intelligence (GenAI) have started popping up everywhere, from autocomplete suggestions in emails to chatbots that help customers troubleshoot.
But for product managers, GenAI represents something far more interesting: a virtual assistant capable of drafting product documents, brainstorming solutions, and even simulating feedback from stakeholders.
Think of GenAI as your new product intern. It’s eager to help, works at lightning speed, and can tackle a variety of tasks without needing coffee breaks.
But, like any fresh hire, it’s inexperienced. It doesn’t know your product strategy, it doesn’t understand your customers, and it occasionally produces results that are a little… off.
This dynamic creates an opportunity for product managers to rethink how they approach their work. GenAI isn’t a replacement for thoughtful decision-making. It’s a tool for reducing blank-page paralysis and accelerating workflows.
But to unlock its potential, you have to manage it like you would a real intern: setting clear expectations, providing guidance, and refining outputs until they meet your standards.
For IC PMs, this shift will feel new and strange. You’re no longer just doing the work yourself—you’re delegating, iterating, and coaching, which are skills that will serve you well as you grow in your career. Managing GenAI is an opportunity to practice those skills in a low-risk environment, while simultaneously boosting your productivity.
By the time you’re done reading this essay, you’ll see how treating GenAI like an intern can transform how you work: not just by making you faster, but by preparing you to manage with clarity, confidence, and purpose.
Why GenAI Feels Like a Fresh Grad Intern
Working with GenAI feels a lot like managing a fresh-out-of-college intern. It’s fast, eager, and bursting with energy—always ready to draft, brainstorm, or tackle the next task. It can produce work at incredible speed, often faster than you’d expect. But, like any newbie, it doesn’t always know what it’s doing. Sometimes it’ll miss the bigger picture or produce results that make you wonder if it actually understood the assignment.
That’s why GenAI can be incredibly helpful, but only if you treat it like the intern it is. Here’s what that looks like in practice.
GenAI’s Strengths: Speed without Constraint
When you’re staring at a blank page, GenAI is a wonderful antidote. It can whip up a product spec outline, draft an email, outline a customer interview guide, or brainstorm launch messaging faster than you can grab a coffee.
It doesn’t procrastinate or second-guess—it just gets things down on paper.
And the best part? It’s tireless. Ask it for five different versions of a feature announcement, and it’ll deliver without a single sigh of frustration. It’s always ready for another round of feedback, no matter how many times you iterate.
This speed can be transformative for product managers. Instead of spending precious time on first drafts or brainstorming sessions, you get to focus on shaping, refining, and elevating the work to something impactful. It’s like having a teammate who’s always ready to take the first swing, no matter the task.
GenAI’s Weaknesses: Overconfidence Meets Inexperience
But speed comes with a tradeoff: GenAI doesn’t know what it doesn’t know.
GenAI doesn’t understand your product strategy or your customers. It’ll confidently suggest launch messaging that glosses over key objections or write stakeholder emails that miss the mark entirely. It’s like an intern who’s convinced they’ve nailed it but doesn’t realize they’ve accidentally used Comic Sans in the deck.
This lack of awareness can lead to outputs that feel generic or slightly off. For example, it might draft a feature announcement that’s overly technical for a general audience or misses the tone needed to reassure cautious enterprise customers.
Subtlety is another blind spot. Whether it’s striking the right tone in customer comms or understanding cultural nuances, GenAI tends to oversimplify. It’s great for rough drafts but needs your judgment to make it truly resonate.
The Lesson from Interns
If you’ve ever worked with a new grad, you know the drill: set clear expectations, review their work closely, and guide them toward success. GenAI is no different.
It won’t magically deliver polished outputs, but with your direction, it can help you move faster and focus on what really matters.
Think of it this way: would you send an intern’s first draft directly to your CEO? Probably not. But with your coaching, that intern can become a critical asset to the team—and so can GenAI.
For those who haven’t managed interns yet, think of GenAI as your opportunity to practice. Working with GenAI helps you build the skills of delegation, feedback, and refinement in a low-stakes environment. Whether you’re refining a product document or brainstorming solutions, you’ll find that managing GenAI strengthens your ability to lead while helping you deliver results faster.
GenAI isn’t here to replace your expertise—it’s here to amplify it. With clear guidance, it becomes a powerful tool to accelerate your work, refine your processes, and prepare you for the challenges of managing teams in the future.
Why GenAI Won’t Replace Product Managers
Whenever a new technology gains traction, it’s easy to feel a little uneasy—especially when that technology promises to “revolutionize” the way we work. With GenAI, some product managers worry about being replaced altogether. After all, if AI can draft PRDs, brainstorm ideas, or even simulate stakeholder feedback, what’s left for the PM to do?
A lot, as it turns out.
The truth is, GenAI can help with execution, but it lacks the human judgment, empathy, and strategic foresight that make great product managers indispensable. Here’s why:
AI Doesn’t Negotiate Stakeholder Buy-In
One of the most complex (and often underrated) parts of a PM’s role is navigating organizational politics. It’s not just about crafting the perfect roadmap or feature proposal—it’s about persuading others to align with your vision. Stakeholders have competing priorities, from engineering bandwidth to sales targets, and part of your job is finding common ground. GenAI can suggest messaging for a roadmap review, but it doesn’t sit in the room to read the tension, diffuse objections, or navigate those delicate compromises that get everyone on board.
AI Doesn’t Prioritize with Strategic Judgment
AI can organize a backlog or suggest features based on past data, but it doesn’t understand the bigger picture. It can’t weigh short-term wins against long-term vision or recognize when a feature aligns with your company’s market positioning. For example, imagine deciding between a quick UI enhancement or investing in foundational infrastructure. That’s a decision that requires balancing user feedback, technical debt, and business strategy—all elements GenAI simply can’t integrate meaningfully.
AI Doesn’t Empathize with Customers
At its core, product management is about solving real problems for real people. GenAI can analyze customer surveys or draft user personas, but it doesn’t truly understand the frustration behind a confusing workflow or the hesitation of a cautious buyer. It’s your job to connect the dots between data and human experience, turning quantitative insights into actionable empathy.
Sidebar: Calming the AI Panic
When people talk about the future of product management and the future of AI, two companies frequently come up: Airbnb and Apple.
Both have made unconventional choices in how they handle responsibilities typically associated with PMs, sparking unfounded fears about the viability of product management.
But a closer look reveals that their approaches are deeply rooted in unique organizational needs, not a sign that product managers are becoming obsolete.
Let’s walk through each org in depth.
Airbnb’s Experiment with Product Management
Airbnb’s decision to experiment with reducing product management headcount kicked off intense discussion across the industry. CEO Brian Chesky restructured the organization to centralize decision-making, drawing inspiration from companies like Apple and leaders like Steve Jobs. Chesky merged product management with product marketing roles, creating positions akin to brand managers found in consumer goods industries. These individuals were tasked with end-to-end ownership, from defining product requirements to driving marketing strategies and customer messaging.
The goal was to streamline responsibilities, ensuring that one person had holistic ownership of both product and marketing outcomes. Chesky also took a more active role in product decisions, a shift he described as moving into “founder mode.” By directly involving himself in major initiatives and simplifying decision hierarchies, Chesky aimed to recapture Airbnb’s early agility and focus on crafting products with a distinct, cohesive vision.
However, this approach has trade-offs. Critics within the industry have highlighted the potential risks of over-centralization, such as “Decision-by-HiPPO” (Highest Paid Person’s Opinion), which can sideline diverse perspectives and lead to decisions driven by intuition rather than data. Furthermore, while this model works for Airbnb’s culture and leadership, it may not be scalable for organizations with less direct involvement from their executives. The reduction in traditional product management roles also placed a greater burden on engineers and cross-functional leads to take on responsibilities typically owned by PMs, such as roadmap prioritization and stakeholder alignment.
Apple’s Functional Approach to Product Management
Apple’s lack of traditional product managers is another frequently cited example, but it’s equally misunderstood. Rather than eliminating product management entirely, Apple divides its responsibilities between two highly specialized roles: Product Marketing Managers (PMMs) and Technical Project Managers (TPMs).
PMMs focus on customer needs, market trends, and external-facing responsibilities. They define product requirements, craft launch strategies, and ensure that the product resonates with its intended audience. Their role is to be the “voice of the customer,” translating market insights into actionable priorities for the development teams.
On the other hand, TPMs oversee the engineering and technical execution of products. They coordinate cross-functional teams, manage timelines, and ensure that product designs align with technical feasibility. This split allows Apple to maintain deep functional expertise, with each role focusing intensely on their specific domain.
This structure reflects Apple’s broader organizational philosophy, which emphasizes functional alignment over cross-functional collaboration. Under this model, top leadership—particularly executives like Tim Cook and senior vice presidents—plays a critical role in aligning the company’s vision across teams. While this has allowed Apple to deliver iconic products with a unified vision, it relies heavily on strong leadership and a company culture that values deep specialization.
What’s Missing from These Examples?
What’s often overlooked in these discussions is that neither Airbnb nor Apple represents a one-size-fits-all model. These are unique organizational design choices tailored to their leadership styles, cultures, and goals.
At Airbnb, responsibilities traditionally owned by PMs didn’t disappear—they were redistributed, requiring other roles to stretch beyond their core expertise.
At Apple, the absence of traditional PMs is offset by deep specialization and a highly centralized decision-making structure.
For most companies, product managers remain essential for driving alignment, resolving cross-functional tensions, and ensuring that strategy, execution, and customer needs stay in sync. These examples aren’t evidence that PMs are becoming obsolete—they’re evidence that organizations adapt based on their unique needs.
What These Stories Teach Us About PMs
Airbnb and Apple demonstrate something essential: product management isn’t just a role—it’s a set of skills and responsibilities that adapt to the needs of the organization. Whether it’s prioritizing roadmaps, driving alignment, or balancing technical feasibility with market impact, the core value of product managers lies in their ability to bridge strategy and execution.
This is where GenAI comes in. While Airbnb and Apple lean on organizational design to reshape how PM responsibilities are handled, most companies don’t have that luxury. For them, tools like GenAI offer a different kind of transformation. By taking on tasks like drafting documents, brainstorming ideas, and simulating stakeholder feedback, GenAI frees PMs to focus on the uniquely human aspects of their work: strategic judgment, stakeholder alignment, and customer empathy.
But to get the most out of GenAI, you can’t treat it as a replacement. You have to manage it—just like you would manage an intern.
Managing GenAI Like an Intern
To get the most out of GenAI, you need to think of it as an eager but inexperienced intern. It’s fast and capable, but it can’t read your mind or grasp the nuances of your product strategy. Just like managing a new team member, working effectively with GenAI starts with setting clear expectations.
Step 1: Set Clear Expectations
When working with GenAI, the results you get are only as good as the guidance you provide. Like a new intern tasked with an ambiguous project, GenAI will deliver something that looks like a viable output…
… but without clear direction, the output is likely to miss the mark.
To unlock its potential, you need to provide detailed and specific instructions, ensuring it knows what to focus on and how to approach the task.
Clear expectations matter because GenAI doesn’t inherently understand your priorities or the context of your product. If you give it a vague prompt like, “Draft launch comms,” it might generate something polished but irrelevant, leaving you with more work to fix it than if you’d written it yourself. A good prompt acts as a blueprint, outlining exactly what you need and why it matters.
To start, be explicit about the task. For example, instead of saying, “Write an email,” provide context: “Draft launch comms for a mid-market SaaS feature targeting IT managers. Highlight ease of onboarding, include three key benefits, and use a professional but friendly tone.” This kind of detail helps GenAI focus on the right audience and priorities, improving the quality of its output from the start.
Beyond clarity, consider adding constraints to your prompts. Word limits, specific audiences, or structured formats can help GenAI refine its approach. For example:
“Summarize the product’s competitive positioning in under 200 words.”
“Write an internal memo for non-technical stakeholders, avoiding jargon.”
“Create a SWOT analysis of the feature’s potential market impact.”
Prompts like these give GenAI boundaries that force it to deliver something more targeted and useful.
For recurring tasks, save effective prompts as reusable templates. For example, if you find a prompt that reliably produces great stakeholder updates, refine it and reuse it: “Summarize the key outcomes of the roadmap review for executives, highlighting strategic priorities, major risks, and next steps.” Over time, these templates will make your interactions with GenAI more efficient and consistent.
Think of setting expectations as an investment. The time you spend crafting a clear prompt upfront will save you hours of editing later.
With clear instructions, GenAI can produce results that are not only faster but also significantly closer to what you need—transforming it from a wildcard into a dependable tool that supports your workflow.
Step 2: Review Outputs Critically
When GenAI generates an output, it’s easy to feel a moment of relief—something is on the page. But remember: GenAI’s outputs are first drafts, not polished deliverables. No matter how well-crafted the initial text may seem, it still requires a thoughtful review to ensure it aligns with your goals, maintains accuracy, and resonates with the intended audience.
Why does this matter? Because GenAI doesn’t know what it doesn’t know. It doesn’t understand your product’s strategic priorities or the nuances of your stakeholders’ expectations. While it can produce content that sounds polished, that surface-level confidence can mask subtle inaccuracies or gaps in understanding. Treating its outputs as ready-to-go without critical review risks sending out messaging that misses the mark or, worse, undermines your credibility.
Start by validating factual claims. GenAI can occasionally produce plausible-sounding but incorrect information, especially if your prompt includes ambiguous details. For example, it might draft messaging that overpromises on feature readiness or misstates a release timeline. Always cross-check the facts against internal sources to ensure accuracy.
Next, adjust the tone and voice to fit your audience. GenAI doesn’t automatically understand the differences between addressing a technical engineering team versus a cautious group of enterprise executives. For customer comms, it may default to a tone that’s overly formal or too casual, depending on how you phrased your prompt. Review the language carefully, ensuring it reflects your brand’s voice and aligns with your audience’s expectations.
Finally, look for the missing pieces—strategic insights or priorities that GenAI might have overlooked. For instance, if you’re drafting a customer email about a new feature launch, the AI might omit key objections or concerns you’ve already heard from users. It’s your job to identify these gaps and ensure the messaging anticipates and addresses them. A GenAI-generated draft may list benefits, but you’ll need to ensure it also addresses questions like, “Will this disrupt my current workflow?” or, “How does this integrate with my existing tools?”
Consider this example: you ask GenAI to draft an email announcing a new mid-market SaaS feature. It delivers a concise and professional draft that highlights the feature’s benefits. At first glance, it seems ready to send. But upon review, you notice it doesn’t address a common customer objection about implementation complexity. A quick addition—reassuring customers with a line about ease of onboarding or available support resources—transforms the email from generic to compelling.
Approaching GenAI outputs critically isn’t about nitpicking; it’s about ensuring the work reflects your team’s priorities and serves your audience effectively. Much like an intern’s first draft, the AI’s output is a starting point that gets you 70% of the way there. Your expertise is what makes the final product shine. By refining and shaping what GenAI produces, you ensure every deliverable aligns with your strategy and adds meaningful value.
Step 3: Focus on Upfront Iteration
One of the biggest mistakes you can make with GenAI is asking it to produce a polished deliverable right away. While it’s tempting to dive straight into a final draft, this approach often leads to subpar results that require extensive rework. Instead, focus on iterating early—starting with an outline or a high-level structure and refining the output step by step.
Why does this matter? Because jumping to the end skips the most important part of any creative process: alignment. Without feedback at the planning stage, GenAI might prioritize the wrong points, misinterpret your goals, or focus on details that don’t matter. By iterating upfront, you ensure that the output is heading in the right direction before time is wasted on unnecessary revisions.
The best way to work with GenAI is to break the task into stages. Start by asking for a structured outline or high-level plan. For example, if you need a launch communication, don’t ask for a fully polished email. Instead, prompt GenAI with something like: “Create an outline for a customer email announcing a new SaaS feature. Include key messaging pillars, target audience, and benefits to emphasize.”
Once the outline is generated, provide feedback. Are the messaging pillars aligned with your product strategy? Are any key points missing? Does the structure reflect how your audience processes information? This feedback stage is critical—it’s where you guide GenAI toward the result you need.
After refining the outline, you can then request a detailed draft based on the revised structure. This two-step workflow reduces the chance of misalignment and ensures that the final draft reflects your input from the beginning. It’s a process that builds on itself, with each step bringing the output closer to what you envision.
Think of this like working with a new intern. If you gave them a vague request for a finished deck and only provided feedback at the very end, you’d probably end up with something that needed to be redone from scratch. But if you started with a brainstorming session, refined the key points together, and only then had them create slides, the final product would be much closer to the mark—and it would take far less time to get there.
With GenAI, upfront iteration is about saving time and energy. By focusing on structure first and refining along the way, you ensure that the AI’s speed works in your favor rather than creating inefficiencies. The result? A final output that feels like a collaboration rather than a rescue operation.
Step 4: Teach Through Iteration
Using GenAI effectively isn’t just about improving its outputs—it’s also about refining your own ability to guide it. Like any teammate, GenAI learns through iteration. The more feedback you provide, the better its outputs align with your needs. At the same time, these iterations help you identify which types of prompts work best, allowing you to build a set of reusable instructions for future tasks.
Why does this matter? Because every interaction with GenAI is a chance to improve. Feedback doesn’t just correct the immediate output—it informs how you structure your prompts going forward, making each subsequent interaction more efficient. Over time, this creates a virtuous cycle where both you and GenAI work together more effectively.
Start by focusing on specific areas for improvement. For example, if GenAI drafts a feature announcement that’s overly technical, provide direct feedback: “This is too detailed for a general audience. Simplify the language and emphasize time-saving benefits.” If the tone feels off, guide it: “Rewrite this in a more conversational style suitable for mid-market IT managers.” These corrections help GenAI better understand your expectations for both the current task and similar tasks in the future.
At the same time, pay attention to which prompts consistently produce high-quality results. Save these as reusable templates that you can adapt for similar projects. For example:
Initial Prompt: “Generate 3 messaging pillars for a feature launch, focusing on time-saving benefits for enterprise IT teams.”
Reusable Template: “Draft [number] messaging pillars for [type of deliverable], emphasizing [key benefit] for [specific audience].”
As you iterate, these templates become your playbook for working with GenAI. They reduce the guesswork in crafting prompts and ensure consistent results across different tasks.
This process mirrors how you might train a junior teammate. The first few drafts may need significant revisions, but with clear, constructive feedback, they start to internalize your expectations. Eventually, they deliver work that requires minimal adjustments because they’ve learned how to anticipate your needs.
With GenAI, iteration isn’t just about improving the immediate output—it’s about building a system. By teaching through feedback and developing a library of effective prompts, you create a workflow that gets faster and more precise over time.
The result isn’t just better outputs—it’s a smoother, more collaborative process that helps you focus on strategy while GenAI handles low-value, low-context repetitive tasks.
Step 5: Prepare for Errors
GenAI is fast, capable, and can generate a wide range of outputs, but at its core, it’s a product intern who reports to you.
GenAI is not a decision-maker. It’s not a partner who co-solutions with you, the way that your engineering counterparts or design counterparts do.
Instead, GenAI is there to support you, provide ideas, and occasionally offer a fresh perspective. It lacks the judgment, context, and accountability to dictate decisions. Mistakes are inevitable, and it’s your responsibility to catch and address them before they ripple into larger issues.
Why does this matter? Because GenAI’s confidence can be misleading. It often produces outputs that seem polished and logical, but its reasoning lacks the deeper understanding of your product strategy, customer dynamics, or stakeholder sensitivities. Treating its outputs as final can lead to miscommunications or misaligned decisions, putting your credibility on the line.
One of the most common pitfalls is overconfidence in tone or recommendations. GenAI might draft messaging that overpromises or suggests a direction that sounds plausible but misses critical nuances. For example, it might write a customer-facing email that inadvertently overstates a feature’s readiness or skips over key objections that users frequently raise.
Another challenge is its tendency to oversimplify complex customer dynamics. While it might highlight broad benefits like “time savings” or “ease of use,” it often overlooks nuanced concerns like integration challenges or adoption barriers that require deeper empathy and understanding.
Cultural or emotional contexts can also trip up GenAI. A phrase that seems perfectly acceptable in one market might be tone-deaf or even offensive in another. Similarly, its language might feel too casual for an enterprise audience or too formal for a startup crowd, requiring careful adjustments to ensure the messaging aligns with the audience’s expectations.
To avoid these issues, treat every GenAI output as a starting point, not an answer. Validate its claims against real data, team input, and your own strategic priorities. Cross-check its messaging for tone, accuracy, and alignment with your product’s positioning. And if something feels off, don’t hesitate to push back or refine the direction entirely.
Think of GenAI as a smart but inexperienced intern—it can provide valuable input and suggest ideas you might not have considered, but the responsibility for decisions always rests with you. By critically reviewing and refining its work, you ensure that every output meets your standards, helping you move faster without sacrificing judgment or quality.
How GenAI Fits into Broader PM Workflows
GenAI isn’t just a tool for speeding up one-off tasks—it has the potential to streamline and enhance many aspects of product management. When integrated thoughtfully into your workflows, GenAI can help you tackle everything from drafting documents to preparing for tough stakeholder conversations. Here’s how it can fit into some of the most common PM responsibilities.
Drafting OKRs and PRDs
One of the most time-consuming aspects of product management is creating OKRs and PRDs that align team efforts with business goals. GenAI can accelerate this process by generating initial drafts based on the goals you provide, giving you a strong starting point for collaborative refinement with your team.
For instance, if you’re planning a product launch to boost user retention, you might ask GenAI:
“Create OKRs for a product launch aiming to improve user retention and reduce onboarding friction by 15%.”
The AI might produce a draft with goals like:
Objective: Improve user retention for our SaaS platform.
Key Result 1: Increase 30-day user retention rate from 60% to 70%.
Key Result 2: Reduce onboarding friction as measured by a 20% increase in task completion rates during the first week.
With a draft in hand, you can focus on refining the key results or aligning them with team feedback, turning what might have been hours of brainstorming into a collaborative exercise.
Roadmap Planning
Roadmap prioritization is one of the most challenging aspects of product management, especially when balancing competing stakeholder needs. GenAI can simulate potential objections or alternate perspectives to help you prepare for roadmap discussions. For example, you could ask it to:
“Draft a prioritization framework for our roadmap, taking into account likely objections from engineering leads about technical feasibility and executives focused on revenue impact.”
The result might highlight potential points of conflict, such as engineering bandwidth concerns or sales pressure for features that drive short-term revenue. By reviewing and iterating on this framework, you can enter discussions better prepared to address stakeholder pushback and build consensus.
Customer Messaging
Crafting messaging that resonates across different customer segments is another area where GenAI excels. You can ask it to generate variations tailored to specific audiences, saving time and sparking creative ideas. For example:
“Write two versions of product launch messaging: one for SMBs focusing on cost savings and one for enterprise customers emphasizing scalability.”
The outputs might look something like this:
SMB Version: “Save time and cut costs with our new SaaS feature—designed to help small businesses achieve more with fewer resources.”
Enterprise Version: “Scale seamlessly with our new SaaS feature, built to handle the complex needs of enterprise-level operations.”
While these drafts may not be perfect, they give you a clear starting point to refine and adapt based on your brand voice and customer insights.
Retrospectives
Summarizing retrospective insights can be tedious, but GenAI can quickly turn meeting notes into concise, actionable takeaways. For instance:
“Summarize this sprint’s retrospective, highlighting team wins, challenges, and next steps.”
GenAI might deliver something like:
Wins: Improved bug resolution time by 25% compared to the previous sprint.
Challenges: Persistent delays in code reviews affecting feature delivery timelines.
Next Steps: Implement a streamlined review process to reduce delays by 50% over the next two sprints.
This summary can be shared with stakeholders or stored as a record of continuous improvement, saving you time while ensuring clarity and alignment.
Stakeholder Simulations
Stakeholder management is as much about preparation as it is about persuasion. GenAI can act as a sparring partner, helping you anticipate objections or refine your messaging for different personas. For example, if you’re proposing a pricing model change, you could ask GenAI to:
“Generate potential objections from an enterprise sales team about a new tiered pricing model and suggest counterarguments.”
GenAI might respond with:
Objection: ‘This pricing model might alienate our top enterprise customers who expect more personalized contracts.’
Counterargument: ‘The tiered model is designed to provide flexibility while maintaining customization options for top-tier clients, ensuring we meet their expectations.’
By working through these simulated conversations, you can fine-tune your proposals and approach discussions with greater confidence.
The Bigger Picture
GenAI is not here to replace your decision-making but to support you in executing more efficiently and preparing more effectively. By integrating it into workflows like OKRs, roadmap planning, and customer messaging, you can spend less time on repetitive tasks and more time on the strategic work that drives impact. Used wisely, GenAI becomes an invaluable assistant that enhances your ability to lead with clarity and precision.
Common Pitfalls to Avoid When Using GenAI
While GenAI can be a valuable tool, it’s far from infallible. Without proper oversight, its outputs can lead to misaligned messaging, wasted time, or even reputational risks. Here are some common pitfalls and how to navigate them.
Pitfall #1, Overtrusting Outputs: GenAI’s confidence can be deceptive. It generates outputs that sound polished and logical, but without validation, those results can easily miss the mark.
Consider this example mistake. A PM asks GenAI to draft customer-facing comms for a product launch and, impressed by the professional tone, uses the messaging without reviewing it thoroughly. Unfortunately, the email overpromises on feature availability and misstates integration timelines, resulting in misaligned customer expectations.
The solution is to always validate GenAI’s outputs against real-world data and feedback from your team. Cross-check technical details, ensure messaging aligns with your product’s readiness, and confirm that the tone reflects your brand voice. GenAI can assist with the heavy lifting, but you’re responsible for ensuring the final product is accurate and trustworthy.
Pitfall #2, Skipping Upfront Iteration: Asking GenAI for a fully polished deliverable without providing guidance almost always leads to subpar results.
For example, a PM might request a complete press release but receive something generic and unfocused that requires extensive rewrites. This wastes time and frustrates the process.
Instead, start with an outline or framework. Ask GenAI for the key messaging pillars or a draft structure, then refine this collaboratively before requesting a polished version. By iterating early, you ensure alignment and avoid unnecessary rework.
Pitfall #3, Treating GenAI as a Decision-Maker: GenAI can analyze data and suggest priorities, but it lacks the judgment to make strategic decisions.
Imagine relying on GenAI to prioritize roadmap items. It might suggest a feature list based on quantitative metrics but fail to consider long-term goals or nuanced customer needs.
The solution is to treat GenAI’s recommendations as hypotheses, not conclusions. Use its suggestions as conversation starters, but always bring in your own judgment, customer insights, and strategic perspective to make the final call.
Pitfall #4, Ignoring Contextual Nuances: Without specific instructions, GenAI’s outputs can feel generic—or even inappropriate for certain audiences.
For instance, drafting global messaging without considering cultural or regional differences can result in tone-deaf or alienating content.
Always include audience-specific details in your prompts, such as regional considerations or target tone. Validate outputs with regional experts or team members to ensure the messaging resonates appropriately.
Pitfall #5, Failing to Manage Expectations: Overpromising what GenAI can deliver to stakeholders can lead to frustration and rushed deliverables.
For example, if you tell stakeholders that GenAI will speed up deliverables without explaining that its outputs require review and refinement, they might expect final-quality work, leading to misaligned expectations.
Frame GenAI as a first-pass tool, emphasizing that its outputs are drafts meant to be refined. By setting realistic expectations, you ensure that stakeholders see it as a productivity enhancer rather than a shortcut to finished work.
By understanding and addressing these pitfalls, you can make GenAI a reliable and efficient assistant in your workflows. Remember, like any intern, it’s there to support you—not to replace your judgment or strategic decision-making.
Developing Your Management Chops with GenAI
Working with GenAI doesn’t just make individual contributors (ICs) more productive—it also helps them build foundational management skills that will serve them as they grow into leadership roles. By treating GenAI as an intern, IC PMs practice delegation, coaching, and strategic prioritization in a low-risk environment, developing the same competencies they’ll need when managing people and projects in the future.
This mindset shift is critical. GenAI is more than just a tool for execution; it’s a practice round for real-world management. IC PMs who embrace this opportunity are better prepared to navigate the challenges of leadership, equipped with skills that go beyond their immediate workflows. Here’s how working with GenAI develops core managerial competencies.
1. Delegation and Clear Communication
One of the first lessons in management is learning how to delegate effectively, and working with GenAI mirrors this skill closely. Writing prompts for GenAI requires ICs to articulate clear goals, specify constraints, and define what success looks like—skills that directly translate to giving instructions to a junior PM who reports to you.
For example, crafting a detailed prompt like “Draft launch comms for a mid-market SaaS feature targeting IT managers. Highlight ease of onboarding, include three key benefits, and use a professional but friendly tone” helps ICs practice breaking down complex tasks into actionable steps. This level of clarity ensures that GenAI—and eventually, real team members—can deliver results aligned with expectations.
2. Feedback Loops and Coaching
GenAI’s iterative nature teaches ICs the value of reviewing and refining work, a core component of coaching team members. When ICs assess GenAI’s drafts, they learn how to identify gaps, provide constructive feedback, and guide improvements without micromanaging—a critical balance in mentoring.
For instance, after GenAI generates a draft for a feature announcement, ICs might refine its tone or adjust its structure. Over time, these feedback loops build patience and an eye for detail, cultivating the ability to coach effectively. The experience parallels mentoring junior teammates, where guiding someone’s growth through iterative feedback is as important as the final output.
3. Empathy for Inexperience
GenAI’s limitations—its lack of context, nuance, and strategic judgment—can be frustrating. But working through these challenges cultivates empathy, a vital skill for onboarding or mentoring team members who are new to a role or project.
Recognizing why GenAI misses subtleties, such as the emotional tone of a customer email or the complexities of a feature’s market positioning, mirrors the experience of guiding a junior teammate who doesn’t yet understand the full context. This practice helps ICs develop patience and the ability to meet others where they are, an essential trait for effective management.
4. Strategic Thinking and Prioritization
Another critical managerial skill is knowing what to delegate and when. Working with GenAI forces ICs to decide which tasks are best suited for automation and which require their direct attention—an exercise in strategic thinking and prioritization.
For example, using GenAI for brainstorming ideas or drafting initial outlines allows ICs to focus their energy on higher-value activities, like stakeholder alignment or strategic decision-making. This process reinforces the importance of resource allocation, a skill that becomes even more crucial when managing teams with diverse strengths and limited bandwidth.
The Bigger Picture: Building Management Muscles
Every interaction with GenAI is an opportunity for ICs to hone leadership skills in a safe, low-pressure environment. By learning how to delegate effectively, provide constructive feedback, empathize with limitations, and prioritize strategically, ICs prepare themselves for future roles where these skills will be essential.
In the same way that managing an intern builds confidence and capability for managing a full team, working with GenAI gives ICs the tools they need to grow into thoughtful and effective leaders. Those who embrace this practice round will find themselves better equipped to take on larger responsibilities as their careers progress.
Closing Thoughts
GenAI can’t replace your work as a PM.
But, a PM who uses GenAI will replace a PM who doesn’t. After all, imagine if a PM decided not to use a mouse and keyboard because they were more comfortable with pen and paper! Their efficacy would be significantly reduced, and they’d struggle to juggle all of their responsibilities.
GenAI is an eager, fast-learning product intern who can accelerate your workflows, reduce blank-page paralysis, and offer fresh ideas, but only if you provide the guidance it needs to succeed. Like any new team member, GenAI thrives when you set clear expectations, provide constructive feedback, and refine its work with your strategic insight.
As a PM, your role has always been about balancing execution with leadership, strategy with empathy. Experimenting with GenAI is an opportunity to practice these skills in a new context.
Use it to amplify your creativity and efficiency, treating it as a tool that can handle the grunt work while freeing you to focus on high-value tasks. At the same time, every interaction with GenAI hones your ability to delegate, coach, and align outputs with larger goals—core skills that will serve you well as you grow in your career.
The best product managers aren’t afraid to lead, refine, and mentor, whether they’re working with a human team or an AI intern. By embracing the “product intern management” mindset, you’ll not only unlock GenAI’s potential but also your own.
So, take the first step. Experiment, iterate, and refine. Once you’ve woven GenAI into your toolkit, you won’t just have the capacity to deliver more IC work - in fact, you’ll gain the capacity to lead better, drive better strategy, and guide better outcomes across your teams.
Thank you to Pauli Bielewicz, Mary Paschentis, Goutham Budati, Markus Seebauer, Juliet Chuang, and Kendra Ritterhern for making this guide possible.
