AI can indeed create prototypes, generate landing pages, and design your product\u2019s interface. It can even do research for you, analyze data, and brainstorm relevant product ideas. And yet, despite AI\u2019s capacity to accelerate product development, it will never produce a definitive output that\u2019s ready for the market.<\/p>
That\u2019s because the fundamental principles behind a product-led strategy still require human expertise.<\/strong><\/p> In this article, I\u2019ll revisit the process of achieving PMF. And for even more practical tips on using AI and vibe coding in product management work, join Andrea\u2019s talk:<\/p> <\/p> Product-market fit<\/a> is when you identify a market need and create a solution that fulfills it. It leads to a validated product with the potential to grow.<\/p> Product managers own this process, known as \u201cproduct discovery<\/a>.\u201d And as we\u2019ll see, it\u2019s more than just coding a product. It involves human skills that AI is still very far from replicating.<\/p> Finding product-market fit is the primary goal of any product team<\/a>:<\/p> Now, let\u2019s take a look at the standard product discovery process to achieve product-market fit:<\/p> The first step to any discovery process is to select and research the target market<\/a>.<\/p> This involves finding potential market gaps in an industry, analyzing third-party data (like industry reports, benchmarks<\/a>, databases, etc), and collecting your own insights via survey<\/a>, interviews, or focus groups.<\/p> The goal is to spot one common problem within a niche. This way, you can create a user persona<\/a>, which is a semi-fictional representation of a target customer.<\/p> To create it, you must do deeper research (often, with psychographic and behavioral data<\/a>) to understand the whole context around their problem. What are their routines at their job? Where do they work? Who do they collaborate with regularly? What is it that they\u2019re trying to achieve, but can\u2019t?<\/p> After collecting enough data, the final output of this step is to put together a user profile like this one:<\/p> With a user persona based on well-researched pain points<\/a>, it\u2019s time to start thinking about solutions.<\/p> What features can you add to your product to solve this market problem? What are the market\u2019s current options? How can you do it better and make the product an indispensable choice?<\/p> Once you have multiple ideas noted down, you must define a unique value proposition (UVP). You can follow a simple template like Geoffrey Moore\u2019s:<\/p> \u201cFor [target customer] who [statement of the need or opportunity], our [product\/service name] is [product category] that [statement of benefit].\u201d<\/p><\/blockquote> You can use AI to brainstorm different angles. But in the end, you must settle on a value proposition<\/a> that:<\/p> The third phase is to build a minimum viable product<\/a> (MVP) using the user persona and the value proposition.<\/p> The user persona and market research<\/a> insights will help you focus on the outcomes that your customers need. The customer data also<\/a> helps figure out the context in which the product is going to be used, your user\u2019s limitations (in terms of stack, technical skills, flexibility), and potential obstacles.<\/p> Your value proposition, on the other hand, determines what core features to build, what technology you\u2019ll use for development, and how you\u2019re going to position the product in your marketing.<\/p> At this stage, AI is useful for developing a prototype<\/a>.<\/p> The next stage is to perform a beta test where selected users get to try your product and provide feedback<\/a>.<\/p> Besides reporting bugs, this feedback is essential to determine if the product can satisfy the customer\u2019s needs<\/a> and if there\u2019s demand for it.<\/p> For a good beta test, it\u2019s highly recommended to find beta testers who fit your user persona. You should then track their in-app behaviors<\/a> and collect diverse qualitative data<\/a> (through surveys or interviews). You can also conduct focus groups to get more intimate feedback from a small group of users.<\/p> Your MVP won\u2019t be perfect. There are going to be value gaps<\/a>, a lack of integrations, and instabilities.<\/p> But with the beta test, you\u2019ll have enough data to create a product roadmap<\/a> based on a clear product vision<\/a>, a compelling story, and a development path that prioritizes customers\u2019 needs.<\/p> This is the stage where you need product managers to achieve product-market fit. You\u2019ll need someone to understand the user experience, decide when to launch the product, and perform the discovery process<\/a> continuously for new product ideas.<\/p> In the era of vibe coding, you can create a whole prototype quickly just with some prompting. This dramatically increases the speed at which you can build, test, and iterate on products until you achieve PMF.<\/p> Some claim that this opens a possibility to ship fast and let the market verify the products, which would make product managers redundant in the process.<\/p> But as we\u2019ll see, Andrea Saez (head of product marketing at Turtl) has many reasons to disagree with this narrative. And here\u2019s why:<\/p> Even assuming that AI has the capacity to ship a perfectly coded product (which it doesn\u2019t), you\u2019d still need to know what to build and for whom. Andrea puts it like this:<\/p> \u201cVibe coding needs a set of requirements to meet basic user needs. This means having an understanding of the problem, market, and solution you’re experimenting with.\u201d \u2014 <\/em>Andrea Saez, Head of Product Marketing at Turtl<\/p><\/blockquote> The current AI technology (i.e., LLMs) isn\u2019t designed to have initiative or make decisions. If you want to generate any good code from AI, there must be someone on the other side of the monitor who knows the market gaps <\/a>(or how to figure them out), understands the problem, and can create a vision of a successful solution.<\/p> Without these fundamental skills, you can\u2019t just ask a tool to build a SaaS with product-market fit. It won\u2019t happen.<\/p> AI is a tremendous tool that can perform some parts of the discovery process. As explained, it can brainstorm product ideas, create prototypes, validate features<\/a>, and more.<\/p> \u200b\u201cIf your role begins and ends at shipping tickets and validating features, yes\u2026 GPT-5 can probably do that better than you.\u201d<\/em> \u2014 Andrea Saez, Head of Product Marketing at Turtl<\/p><\/blockquote> However, it\u2019s unable to do the very core of what creates market fit and makes product managers indispensable: aligning a product with market demands.<\/p> And if we go further into the role of a product manager<\/a>, there are many other areas AI can\u2019t handle, like:<\/p> During AI discussions, people assume AI can yield positive results without requiring technical expertise or a lot of prior research.<\/p> The reality couldn\u2019t be further from the truth. In Andrea\u2019s experience, vibe coding helps create initial prototypes for landing pages and internal tools. It\u2019s very helpful to communicate product ideas<\/a> to a designer or developer so they can build a better version.<\/p> But none of these vibe coding projects can produce a complete product, let alone a solution you can sell to customers. As Andrea explains:<\/p> \u201cYes, vibe coding is real. Yes, it\u2019s useful. But it\u2019s not magic, it’s not strategy, and it’s not replacing your product team. It\u2019s a tool. A sharp one. Just don\u2019t wave it around like you invented UX. Remember that the job is to learn fast and build great things, not to move fast and break everything around you.\u201d<\/em> \u2014 Andrea Saez, Head of Product Marketing at Turtl.<\/p><\/blockquote> AI cannot replace product managers, but it\u2019s certainly a tool that will stay in most product teams.<\/p> This is how vibe coding can save you time:<\/p>![\"andrea-saez-product-drive-cta\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/09\/andrea-saez-product-drive-cta_d72e52480e7cdb8c0c56dc3fd95d17f6_800.png)
<\/picture><\/a>What is product-market fit?<\/strong><\/h2>
![\"product](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/09\/product-market-fit-800_cb42e99d0ff8d9c581c7d2377ff1a98e_800.webp)
<\/picture>Why is achieving product-market fit important?<\/strong><\/h2>
How to find product-market fit? Discovery process in a nutshell<\/strong><\/h2>
1. Determine your target customer<\/strong><\/h3>
![\"user](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2025\/09\/user-persona-end-user-behavior-monitoring_d62002bdaf0f16f1395ce31d858177b2_800.png)
<\/picture>2. Define your value proposition<\/strong><\/h3>
3. Build an MVP<\/strong><\/h3>
4. Test MVP with potential customers<\/strong><\/h3>
5. Iterate until you have PMF<\/strong><\/h3>
Vibe coding, PMF, and the role of product managers in AI-driven product development<\/strong><\/h2>
You still need to understand the market and users to vibe-code a product<\/strong><\/h3>
AI can\u2019t replace all areas of <\/strong>the product managers\u2019 role<\/strong><\/h3>
Vibe coding aids the discovery process<\/strong>, but can\u2019t produce a complete product<\/strong><\/h3>
How can good product managers use AI in their product-market fit process?<\/strong><\/h2>