Whether you’re a recent hire and want to take stock of what you’re working with, or are trying to discover ways to re-ignite a product that’s lost steam, product analysis is going to help you.

Let’s walk hand-in-hand through product analysis, covering what it is, how you do it, plus many other things. 

Here’s a table of contents so that you can jump around:

What is product analysis? 

Product analysis is the process of evaluating a product using both quantitative and qualitative research to answer strategic questions. It helps teams uncover what’s working, what’s not, and why. By digging into data, customer feedback, and user behavior, product analysis provides clarity on trends, pain points, and opportunities – turning raw insights into actionable decisions.

At its core, product analysis is about getting to the ‘why’ behind the numbers and behaviors.

There are a lot of ways to do product analysis, which we’ll cover later, but most of the time it involves systematically assessing how a product is used, where it excels, and where it can be improved. 

Three key parts of product analysis

Product analysis has three main components. These are: 

1. Market analysis: Understanding industry trends, consumer behavior, and product positioning and perception to ensure your product stays relevant and competitive.
2. Competitor analysis: Knowing what your rivals are up to helps you find gaps, refine your positioning, and stay ahead of the game. 
3. Customer feedback & insights: Listening to your users to hear what’s working, what’s frustrating, and what they actually want from your product. 

Think of these three things as the primary colors of product analysis. They set the base foundation, but there are still a lot more colors and analysis methods to use – we’ll dive deeper into those later.

Why do product analysis?

Regularly analyzing a product isn’t just a nice thing to do from time to time – it’s essential for building and maintaining a successful product. Here’s why:

• Smarter decision-making: Product Teams have to weigh up constant trade-offs. Conducting research-based analysis and analyzing real data ensures choices are driven by facts rather than assumptions, reducing risk and uncertainty.
• Improved user experience: By learning about potential issues and frustrations from a user perspective, product analysis helps create a smoother, more enjoyable experience that keeps customers engaged.
• Competitive advantage: The market moves fast, and competitors are always improving. Analyzing trends and customer needs ensures a product stays relevant and ahead of the curve.
• Better prioritization: Not all feedback or issues carry the same weight. Product analysis highlights which changes will have the most significant impact, helping teams focus their time and resources on the right things.
• Sustained growth: A product that doesn’t evolve stagnates like a pond. Ongoing analysis ensures a product continues to meet business objectives and customer expectations over time.

Product analysis vs competitive product analysis 

You can run product analysis on any product. So that could be the product you are responsible for, or a competitor product (or any product in between). 

Obviously, when conducting product analysis on your own product, you have access to more information – like usage data, customer feedback, revenue numbers – and with competitive product analysis you’ll have to use slightly different approaches, but the principles are the same. You’re assessing the strengths and weaknesses of a product.  

There is crossover here though. Analysis of your own product should always include a degree of competitive product analysis so you understand how your product stacks up against competitors and what position it holds in the market. 

In a nutshell, the difference between product analysis and competitor product analysis is about the direction you’re looking at when conducting your research:

• Product analysis focuses on assessing your own product’s strengths, weaknesses, and opportunities for improvement. It’s an introspective look that helps teams refine features, fix issues, and better serve users.
• Competitive product analysis (also called competitive analysis) looks outward, examining competing products to understand their features, positioning, and market strategies. This helps identify gaps, differentiate offerings, and stay ahead in the market.

Check out our full guide on competitor product analysis to learn more: 

What are the different types of product analysis? 

So far we’ve discussed product analysis in its broadest sense. But product analysis is kind of like a Russian doll, hiding other analysis methods within it. It’s now time to open the doll up and see what else nestles within product analysis. 

The truth is that there are a lot of different ways to conduct product analysis. Product analysis is a combination of various research and evaluation techniques. Here are some of the most common types that expand on the core three:

• Customer research – Get inside your users’ heads by exploring their behaviors, pain points, and needs through surveys, interviews, and behavioral tracking.
• Market research – Analyze industry trends, market size, and customer demand to make sure your product has a strong, competitive position.
• Competitor research – Study competing products to find market gaps, opportunities, and ways to stand out.
• Performance analysis – Track key metrics like user engagement, retention, and conversion rates to measure success and optimize growth.
• Pricing analysis – Dive into pricing strategies, customer willingness to pay, and market positioning to fine-tune your revenue model.
• UX/usability analysis – Test how users interact with your product to identify friction points and improve the overall experience.
• Feasibility analysis – Determine whether a product or feature is viable from a technical, financial, and operational standpoint before diving in.

Each of these areas includes multiple methods of analysis, allowing teams to uncover insights that shape their product strategy. Let’s take a look at some of the common methods for product analysis: 

Customer research

Customer research focuses on understanding your customers’ perceptions and experiences with your product. This qualitative approach provides insights into customer needs, preferences, and areas for improvement. Effective methods include:

• Surveys: Structured questionnaires that can be built in-app that gather quantitative and qualitative data on customer satisfaction, preferences, and expectations.
• Interviews: In-depth, one-on-one discussions that explore individual customer experiences, uncovering detailed insights into their interactions with your product.
• Customer Advisory Board (CAB) Meetings: Regular meetings with a selected group of customers who provide strategic feedback and guidance on product development and improvements.
• Net promoter score (NPS): A metric that measures customer loyalty by asking how likely they are to recommend your product to others, providing an indicator of overall satisfaction.

All of this revolves around the customer feedback loop. To get the best feedback, you need to train your Customer Support Teams on how to gather it all properly. Luckily we have you covered. Check out the guide which comes with a downloadable presentation deck for you to use with your Customer Teams.

Market research

Market research involves analyzing external factors that influence your product’s success, such as market trends, customer segments, and competitors. Key methods include:

• User personas: Creating detailed profiles representing different segments of your target audience to better understand their needs and tailor your product accordingly.
• Market validation: Assessing the demand for your product or feature through techniques like surveys, interviews, or crowdfunding campaigns to ensure it meets market needs.
• Prototyping and beta testing: Releasing a pre-launch, MVP version of your product to a limited audience to assess market reaction, demand, and identify potential improvements.

Competitor research 

Competitor research is all about analyzing competitor products, strategies, and market positions to identify opportunities and threats, informing your product development and positioning.

A couple of ways to learn about your competitors include:

• Strategic canvas: Scoring each competitor based on a specific value element like price, performance, usability, etc. With these scores, you can see where your product excels compared to your competitors, and find opportunities to improve. 
• Product benchmarking: Comparing your product’s performance, features, and processes against industry standards or competitors to identify best practices and areas for enhancement.

Performance analysis

Performance analysis is where your product analytics comes in, focusing on quantitative data to assess how well your product is performing. This involves tracking user behavior and measuring key metrics to help you understand how successfully your product is being adopted and engaged with, informing data-driven decisions. 

When looking at your product analytics, track important performance metrics like: 

• Adoption rate: The percentage of new users adopting your product over a specific period, indicating market acceptance and growth.
• Monthly active users (MAU): The number of unique users engaging with your product monthly, reflecting user retention and engagement.
• Customer churn rate: The percentage of users who stop using your product over a given timeframe, highlighting potential issues with satisfaction or value.
• User retention: The ability of your product to retain users over time, indicating long-term satisfaction and loyalty.

That’s of course only a handful of metrics you can track. We’ve got a full list of Product KPIs to help you identify the right ones for you.

In addition to capturing product usage data and tracking metrics, you can uncover more about your product’s performance by conducting analysis methodologies like cohort analysis. 

Here you can assess the impact of any changes you make to the product by comparing groups of users over time – for example, comparing the users who used the product or feature before the change was implemented versus those who used the product afterwards. 

A quick note on tools for product analysis

You’re going to need the right tools to ensure you have the product analytics you need to conduct performance product analysis. We’ve got a list of the best product analytics tools you can check out:

Pricing analysis: 

Pricing analysis is all about seeing how the way you structure your product pricing impacts sales and customer perception. 

Here are some ways to analyze your pricing strategy:

• Demand elasticity: Analyzing how changes in price affect the quantity demanded, helping to optimize pricing for revenue and market share.
• Van Westendorp price sensitivity: A survey-based technique that identifies acceptable price ranges by asking customers about their price perceptions.
• Gabor-Granger pricing method: A technique that determines the optimal price point by assessing customers’ willingness to pay at different price levels.

You can learn more about all three of these methods in our price testing article:

UX analysis: 

User experience (UX) analysis examines how users interact with your product to identify usability issues and enhance overall satisfaction. Methods include:

• Session replays: Recording and reviewing user interactions to observe behaviors, identify pain points, and improve interface design.
• User journey mapping: Visualizing the steps users take to achieve their goals with your product, highlighting opportunities to streamline processes and enhance experience.
• A/B Testing: Comparing two versions of a product feature to determine which performs better, enabling data-driven design decisions.
• HEART Framework: A set of metrics – Happiness, Engagement, Adoption, Retention, and Task Success – used to evaluate user experience and guide improvements.

Feasibility analysis: 

Feasibility analysis is a type of product analysis that you do when you’ve got an idea for a new feature or update. Here, you’re checking to see if the proposed idea is something that can actually be done on a technical level. 

One major way to do this is to look at and review your product architecture to see if your proposal fits in with your current system. Other analysis methods include: 

• Assess technological requirements and resources: Determining the technical needs and resources necessary for development to ensure alignment with your organization’s capabilities.
• Review technical debt: Identifying existing technical debt that could impact the development or performance of the new feature, ensuring sustainable progress.

Who does product analysis?

Product analysis is a cross-functional task involving various teams to ensure you get a holistic view of your product’s performance. The following people chip in:

• Product Managers: You will lead the analysis and make decisions based on the data.
• Data Analysts: They help with deep data analysis, especially when dealing with large datasets and complex models.
• UX/UI Designers: Work to understand user behavior and identify usability issues.
• Marketing Teams: Can provide insights into how the product is being received, what else is happening in the market, and help assess engagement metrics.
• Developers: Provide technical feedback on product performance and how data is captured.

When do you perform product analysis?

You’ll be diving into product analysis at various stages throughout your product lifecycle – whether you’re gathering feedback on a new feature, fine-tuning an existing one, or taking a step back to assess your overall product strategy. That said, there are key moments when product analysis is essential to keep things on track:

Product analysis when launching a new product or company

When you’re just starting out, whether as a new startup or introducing a new product, understanding where your offering fits in the market is crucial. This means you need to focus on market research to assess industry trends, competitor positioning, and demand. Customer research is also key to identifying pain points and user stories to validate your product. 

The focus at this stage is on exploratory and qualitative analysis to refine the product before growth. If you’re working at a startup, check out our glossary that covers what you need to do as a Startup Product Manager. 

Product analysis when in the Growth Phase

As your product gains traction, the goal shifts to optimizing and scaling. The growth phase is all about refining your product-market fit and identifying areas ripe for expansion. During this stage, product analytics plays a vital role in helping you track performance, user adoption, and engagement.

Tracking these metrics reveals what drives user retention and uncovers areas of friction. Understanding where users are finding value and where they’re experiencing challenges will help you maintain momentum and fuel product-led growth.

Product analysis in the ongoing Product Management lifecycle

There are a few other stages in the Product Management lifecycle where product analysis becomes important

• Post-launch : After releasing a feature, it’s time to track performance and see if it’s delivering as expected. This is when you check if your assumptions hold true and whether users are engaging as planned.
• Feature optimization : When user feedback starts rolling in, it’s time to refine your features. You’ll want to optimize based on what’s working, what’s frustrating, and what needs more polish.
• User experience (UX) improvements : UX analysis is crucial for pinpointing pain points in the user journey. Are there bottlenecks or friction that are preventing users from reaching their goals smoothly? Addressing these will help you create a seamless experience.

How do you do product analysis well?

To do product analysis well, you’re going to want to follow a clear, step-by-step framework. Now, all product analysis looks different, depending on the techniques you use or the particular analysis you’re focusing on, but this guide below is built to allow you to plug in your chosen method and get to work.

Product analysis step-by-step guide

Step 1: Define your goals and hypothesis

Before diving into the data, clearly define your objectives. What are you hoping to learn? Once your goal is clear, develop a hypothesis around what you expect the data to reveal. 

For instance, you might hypothesize that adjusting your pricing model will increase acquisition. This hypothesis will act as the lens through which you review the findings of your product analysis, so it’s crucial to get it right.

Step 2: Choose the right tools and data

Next, it’s time to decide on the tools you’ll use and the types of data you need to collect. You’ll want a mix of both quantitative (like user behavior or feature usage) and qualitative data (like feedback from users or satisfaction surveys). Depending on your objectives, different tools are going to be needed. 

With your tools, you might need to track specific types of data, such as:

• Behavioral data: Tracks user interactions, like clicks, session lengths, and drop-offs.
• Customer feedback: Qualitative insights from surveys, reviews, and user testing to gauge satisfaction and identify pain points.
• Feature adoption: Understanding how users are adopting and interacting with new features can shed light on areas for improvement.
• Market data: Understanding the competitive landscape, consumer perception, trends, expectations, and more.

Step 3: Analyze the data

Once you’ve gathered the data, dig into the patterns, trends, and behaviors that emerge. This stage is not just about confirming your hypothesis but uncovering new insights. Examine the trends in what you found – are there patterns? 

Start asking the tough questions: What’s driving these trends? If they’re bad, what can you do to stop them?

Step 4: Test your hypothesis

Now it’s time to validate your assumptions through small experiments. This ensures you’re not making major changes based on guesses. 

Start with incremental tests. For example, if you think your product analysis will reveal that your pricing model isn’t right and you think a pricing change will boost sign-ups, try it on a small user segment first and measure the impact. A/B testing is a powerful tool here. By testing two variations of a feature or design, you can directly compare which one performs better under real-world conditions.

Step 5: Iterate and implement findings

After testing, it’s time to iterate. Refine your product based on what worked and what didn’t. Then get ready to do it all over again!

The key to realizing the benefits of effective product analysis is continuous improvement – you’re never really “done.” Even after a successful iteration, new rounds of testing or user feedback may reveal additional opportunities for refinement. Product analysis is an ongoing cycle, where each round builds upon the last, allowing you to keep adapting and improving your product.

Product analysis challenges and best practices

Product analysis isn’t easy. Here’s our list of things to watch out for that can impact your product analysis, and the best practices you can follow to combat them. 

Drowning in data: With endless dashboards, reports, and spreadsheets, it’s easy to get buried under a mountain of numbers.
– The fix: set clear objectives and focus on the metrics that actually drive decisions, not just the ones that look impressive in a meeting.

Navigating biases: Data might be objective, but humans? Not so much. Confirmation bias can lead teams to cherry-pick stats that support their existing beliefs.
– The fix: bring in diverse perspectives from your cross-functional teams, run peer reviews, and question assumptions before making big calls.

Losing sight of the user: If your product isn’t built for users, all the analysis in the world won’t fix it. 
– The fix: Prioritize user-centricity by regularly testing usability, running surveys, and feedback loops to ensure that customer needs drive decision-making – not just internal hunches.

Working in silos: If teams aren’t sharing insights, they’re making decisions in the dark. 
– The fix: Cross-functional collaboration to ensure that data isn’t just hoarded by one team but is used collectively to paint a full picture of product performance.

Stagnation from inaction: Insights aren’t worth much if they’re just sitting in a report. 
– The fix: Turn learnings into action, iterate on what works, and foster a culture where continuous improvement is the norm – not a one-off project.

Juggling competing priorities: When everything is urgent, nothing actually gets done. 
– The fix: Product teams need to define and defend their focus, using clear goals and strategic prioritization to cut through the noise and drive meaningful impact. Keep it simple and focus on what matters to avoid analysis paralysis.

Product analysis explained

Product analysis makes up a huge part of Product Management. It helps you learn about your product, discover ways to make it better, and improve the value proposition for your customers. 

This article should give you everything you need to know to perform product analysis yourself and discover potential possibilities with your product. 

Once you’ve completed product analysis, and validated the potential solutions and hypotheses created from it, you need a place to track your progress on these efforts. You need a product roadmap. 

In ProdPad you can track all your experiments, manage your process through discovery all the way to measure results and monitor the impact on your OKRs, and all centered around a Now-Next-Later roadmap that includes a view of ‘completed’ initiatives as a permanent record of your product changes and the impact they drove.

Give ProdPad a try for free today and see how the tool helps you effectively manage your ongoing product analysis and use it to make informed decisions.

The post Product Analysis: How to Assess a Product appeared first on ProdPad.

I think there’s a better alternative to the product roadmap slide . 

Now don’t worry, I’m still going to give you all the insights you need to know about the product roadmap slide – there’s even a template or two if you really want to make one – but I am also going to make the case for something wayyy better: The flyover method. 

As the inventor of the best product roadmap format – Now-Next-Later –  I know a thing or two about product roadmaps. It’s kind of my jam. if you can’t trust me then honestly I don’t know who you can.

Over the years I’ve talked to thousands of Product Managers who struggle with the product roadmap slide, be that not understanding what it’s for, or struggling with the demand making one brings. 

Here’s my advice on the product roadmap slide, what it’s for, as well as a template to help you achieve the same effect more easily.

What is a product roadmap slide?

A product roadmap slide is a one-page presentation slide that shows off your roadmap and strategy. It’s a top-level visual of your roadmap that’s stripped of extra detail so that various stakeholders can get the core idea without being bogged down by nuance they might not care about. 

It’s the spark notes of your roadmap. The condensed TL;DR version including high-level data like your Objectives and the initiatives  

What’s the point of a product roadmap slide? 

The aim of a product roadmap slide is to make sure that all your cross-functional teams and external stakeholders have a clear idea of what’s going on. It’s a way to communicate and collaborate and remove the shroud of shadow and mystery that can fall over the Product Team if it’s not shared. 

Of course, when making a product roadmap slide, you don’t want to overshare. Take a look at your current roadmap. It may be a little cluttered. That extra detail is great for you, but may not be necessary for whoever you’re presenting the slide for. You’re trying to show your strategy in a clear and concise way that shows what work is coming up in a way that isn’t too granular. 

Why is that simplicity important? Because you need to make sure that the conversation doesn’t get too focused on the outcomes. The detail will derail your ability to present your strategy. 

A roadmap slide comes into its own when you’re trying to convey the big picture – it’s a visual aid that shows your product strategy, how it fits with business goals, and how you aim to deliver against those strategic objectives. It’s a way to create a dialouge without giving away things you aren’t ready to share or getting bogged down in specifics.

Who is a product roadmap slide for?

A product roadmap slide can be for ANYONE. Any single type of stakeholder can benefit from getting a view of your roadmap through a product roadmap slide. They can be both external or internal:

• C-Suite Executives – They want to see how your roadmap aligns with business goals, growth strategies, and long-term vision.
• Sales – They need insight into upcoming features and improvements to help shape their pitches and set customer expectations.
• Product Marketing – They rely on roadmap updates to plan campaigns, content, and product launches effectively.
• Customer Success – They use the roadmap to anticipate customer needs, manage expectations, and provide proactive support.
• Product Developers – They benefit from understanding what’s coming next and how their work fits into product development.
• Customers – They want to know when they can expect new product features, improvements, and bug fixes that enhance their experience.

Now here’s something important. Ideally, you’re not making a single product roadmap slide to be used for all these stakeholders. Instead, the aim should be to make a customized product roadmap slide for each individual type of stakeholder. 

This is because each stakeholder is looking to learn different things. They care about different aspects. A Product roadmap slide for your financial and product-led growth focused C-Suite stakeholders should include different things that a product roadmap slide for your Customer Success Team.

As a product roadmap slide is something that you make manually, crafting one for each stakeholder at a regular cadence is a lot of work. It’s time-consuming. 

Is there a better way? There sure is.

Do you actually need to create a product roadmap slide? 

In this day and age, creating a single, one-slide product roadmap presentation is a bit outdated, and it doesn’t align well with other principles of creating a good product roadmap. 

For starters, a roadmap presentation slide is static, a single snapshot in time that gets outdated quickly. If your stakeholders only have this small window into your roadmap, they’re not getting a full, consistent picture of what’s going on. This can quickly lead to misalignment if you’re not updating them often enough.

Plus, you need to make multiple versions of your product roadmap slide. This is time-consuming and is taking you away from he strategic part of your work – the meat and bones of Product Management. So what do you do now?

Well, If I’m going to be honest with you all, I don’t think you need to create a product roadmap slide at all – in fact, I think it’s best if you don’t. 

Instead, I think it’s better to show a high-level view of your product roadmap. I think you need to give stakeholders access to a dynamic, modified version of your roadmap. To do that, let’s explain The Flyover Method . 

What is the flyover method? 

Instead of creating a product roadmap slide, I suggest giving stakeholders a public product roadmap. The flyover method is our funky, catchy name for giving a stakeholder access to this public roadmap view. It provides a framework for how to create it that makes sure we’re doing it right and achieving the main aims of a product roadmap slide. 

“Can I have a look at the product roadmap, please?”
“Sure, we’ll give you a flyover.”

We call it the flyover because it neatly describes the core characteristics of what makes a good public product roadmap. A flyover is a low flight over a certain area to record details about it. Think FBI or MI5 reconnaissance missions. They get a bird’s eye view of the area: exactly what you want to do when sharing a public product roadmap. 

You don’t want to get too granular, instead the aim is to focus on top-level details so that they get the gist. By using the flyover method, you’re creating a public roadmap that strips back the specific details, such as Ideas and Target Dates.You don’t have to provide a deep dive into everything, just focus on the core aims in each time horizon. 

Another key aspect of creating a public product roadmap with the flyover method is that it’s dynamic. Instead of a product roadmap slide that’s rigid and set in stone like it’s just finished a staring contest with Medusa, it changes in real-time. This means that every time a stakeholder accesses their public product roadmap link, they see the most up-to-date version instead of a static document that was created a week ago. 

That’s where flyover fits again, as you’re moving over the roadmap, not just hovering in one single place or moment in time. 

How to make a public product roadmap with the flyover method

So how do you actually make a public roadmap that incorporates the ideas of the flyover method? Well before you do anything, you need to answer a few questions: 

Who is your audience? 

Think about who you’re creating the public roadmap for. Think about what they care about and what they want to see. For example, C-Suite stakeholders might be more curious about how your work aligns with long-term goals and drives growth. This dictates that you should include your product vision and strategic plans in your public roadmap, and the objectives your initiatives link to, but you can leave out actual Ideas and prioritzation model scores. 

Internal teams like Customer Support might value seeing the feedback and ideas from customers on their public roadmap view, while Sales might be keen to see User Personas and User Stories – which you can add on ProdPad – to help them focus their messaging. 

Understanding who you’re creating this public roadmap for will dictate what is included.

How transparent should you be?

Before you put together your public roadmap, you need to decide just how much you want to share. Transparency is great for building trust with customers and stakeholders, but there’s a fine line between being open and oversharing.

For example, sharing high-level themes and priorities helps set expectations without locking you into specific deadlines. On the other hand, if you include too much detail – like every feature in progress or precise release dates – you risk disappointing customers when things shift (because they will shift).

Think about what level of transparency aligns with your company’s communication style and risk tolerance. Some teams keep things broad and strategic, while others are happy to go deep into specifics. Striking the right balance ensures your roadmap is useful without becoming a source of frustration.

Once you know these things, you’re in a better position to create your public roadmap.  

In ProdPad, you can easily share public roadmaps and configure exactly what you want to share by toggling on and off different initiatives, time horizon columns, and much more. 

Gee, that’s a lot easier than making a manual product roadmap slide.

Once you’re happy, generate a secure link to the roadmap and share it with who you want to give this view. Crucially the stakeholders who get their hands on the public view will NOT be able to make any changes to the roadmap – just like how a pilot in a flyover can’t alter or change the landscape they’re viewing in any way. 

Start a free trial with ProdPad to start making public product roadmaps with the flyover method, and kiss the product roadmap slide goodbye.

ProdPad’s public roadmap

There’s a saying that goes something like, don’t copy what I say, copy I what I do. Well, with ProdPad you can do both. 

We have and always will have a public version of our product roadmap available for anyone to vist and check out, so that you can see what we’re working on and how the product is improving. It’s a great resource for users and potential customers to check out, but it’s also a great example of what a public product roadmap should look like. 

Here’s a snapshot:

A key take away is that there are no timelines, just the bare-bones headline ideas and key milestones – and not too many of them – and the goal or goals that they are intended to achieve. The headlines are backed up with a sentence or two that gives a little more detail about the initiative so that the reader can see how our activity links to our strategy. It’s easy to take in and understand, which is the main goal of a public product roadmap and product roadmap slide. 

We also have a feedback widget enabled on the page because we want to hear from our customers: 

Remember that your roadmap is a prototype for your strategy, your version of the strategy based on what you know today. Use the roadmap to facilitate dialogue with external stakeholders who may have a wider view of the world and whose views can be used to inform your strategy.

Product roadmap slide vs the flyover method

Now, I’ve not been subtle about my thoughts about what method is better when sharing an overview of your product roadmap. But, to really make it clear,  let’s compare these options side-by-side.

Product roadmap slide – pros and cons 

It’s available on and offline
You’re in complete control of what you put on the slide
It exists within a presentation and can be quickly added to other people’s slide deck 

It’s manual – you have to create the slide by hand every time
It’s static and can become outdated really quickly
A slide is set at a restrictive 16:9 size ratio – good luck fitting everything on there
You have to make multiple versions for different audiences
Cumbersome file sizes – you can really clog up a company’s server
Once shared people can add whatever they want and mess it all up

Public roadmap (the flyover) – pros and cons

It’s available on and offline (just like the product roadmap slide)
You’re in complete control of what you put on it (again, just like the product roadmap slide)
It’s quick and easy to make 
You can make multiple versions for different stakeholders effortlessly
The public roadmap is always up-to-date
There’s no edit access available so your roadmap can’t be ruined

Um, I don’t know, it might make people envious of how good you are at your job?

Seriously, when comparing a product roadmap slide with a public roadmap, there’s no competition.

Public Product roadmap templates 

If you want to check out some examples of killer public product roadmaps, we’ve got plenty of them chilling in our interactive sandbox environment. Our sandbox is a free-to-access version of ProdPad, where you can explore its full functionality and learn how our Now-Next-Later roadmap works. 

We’ve got a customizable template and other roadmap examples for you to explore, ranging from startup product roadmaps to roadmaps for various types of product lines. 

Start using our product roadmap template to easily create a public view and remove the need to create manual, time-consuming product roadmap slides. Give your stakeholders the full picture by adopting the flyover method.

The post Product Roadmap Slide Template – The Flyover Method appeared first on ProdPad.

As the authority voice on product roadmaps, we thought it was only right to clearly list out what you need to do within your roadmap to make it as effective as possible and work not just for you, but all your important stakeholders. 

Product roadmaps are your blueprint – your treasure map to a fantastic product – so they’re pretty important to get right. Use this list of best practices to help you get there. 

Now, as useful as it can be to be told all the things you should be doing, I think it’s also important to be aware of the roadmapping practices you need to avoid. Because even if you’re doing everything else right, adopting a single one of the bad practices can ruin all your hard work.

That’s why this list is so effective, and why it has the jump over any other product roadmap best practices article. We’ll go over all the dos – and crucially, – all the don’ts to ensure you’re managing your roadmap in the best way possible. 

Think of this as your list of things to avoid, sprinkled in with the steps you should be taking every time you use your product roadmap.

11 product roadmap best practices to transform your roadmap

Here’s our list of product roadmap best practices. The things you should be doing that can sometimes be overlooked or forgotten, and some of the faux pas that many smart, well-intentioned Product Managers make with their roadmaps. 

Follow this list, and you’re guaranteed to have an excellent product roadmap.

Product roadmap best practice 1: Don’t put a timeline on a product roadmap

Focusing your roadmap around dates is seriously a bad move. We’ll be real, we hate dates, and we hate timeline roadmaps. The reason for that is because having dates forces you to plan too far in advance. This makes you rigid and wedded to a visual timeline that might not play out how you thought, making it impossible to iterate, adapt, and be flexible in any way. 

We’re also not keen on the mindset that dates on your roadmap can creater. If you’re constantly working to a deadline, that can make you more output-focused than outcome-focused. 

“Just get the release out so that we make the deadline”

That’s not indicative of a good product. 

Instead, we suggest using loose time horizons over hard release dates and deadlines. This organizes work by what needs to be done now, what you’re working on next, and what demands attention later.

If timelines have been embedded in your roadmap process till now, we’ve got a whole article to help you remove the shackles of time and adopt an agile roadmap instead:

Timelines and strict time frames should be a thing of the past. Try Now-Next-Later instead, and you’ll:

• Keep your roadmap flexible and adaptable to change
• Foster an agile team that can handle change
• Reflect different levels of certainty across your plans
• Save time by focusing on priorities instead of arbitrary deadlines
• Align work more clearly with strategic objectives
• Make more informed, outcome-driven product decisions

Product roadmap best practice 2: Do have a defined product vision

A defined product vision is essential for creating a product roadmap that delivers long-term value. It acts as a guiding star, ensuring that every product development decision is aligned with broader business goals. Without a clear vision, internal teams may find themselves working on features that don’t contribute to the bigger picture or fail to meet customer needs. 

The product vision informs the prioritization process, providing a framework for deciding which features, enhancements, and fixes will drive the most impact.

When building your roadmap, ensure that each milestone or release directly supports your product vision. This single source of truth guides everything you do. 

It’s not just about checking off tasks; it’s about making deliberate progress toward fulfilling your company’s mission. This clarity empowers cross-functional teams to work cohesively towards a common goal, fostering collaboration across Design, Engineering, and Product Marketing. In the absence of a product vision, it becomes easy to lose sight of strategic objectives, causing misalignment and inefficiency.

Product roadmap best practice 3: Don’t be too customer-driven

Listening to your target audience is a vital part of Product Management – but there’s a fine line between being customer-informed and being customer-led. If you let customer demands drive your roadmap, you risk turning it into a never-ending list of feature requests. Instead of focusing on strategic growth, your team gets stuck reacting to the loudest voices, constantly iterating on small tweaks rather than solving bigger, high-impact problems.

And who are you really listening to? The most vocal customers aren’t necessarily the ones representing your broader market. Prioritizing based on whoever shouts the loudest can skew your roadmap toward short-term fixes rather than long-term value.

The best approach? Take user feedback as valuable input, but not the sole driver of decision-making. Step back, analyze data, and validate ideas against your product vision and business goals. The right roadmap isn’t just about what customers say they want, it’s about solving the problems they haven’t even articulated yet, in ways that help your product and company grow.

Product roadmap best practice 4: Do make your product roadmap executive-friendly

When presenting your product roadmap to executives, the key is clarity and focus on strategic objectives. Executives typically aren’t interested in the nitty-gritty of each feature or task; instead, they want to understand the overarching vision and how it aligns with business goals. By distilling the roadmap into high-level categories – such as key initiatives, major releases, or milestones – you allow them to quickly grasp the direction of the product and how it fits within broader company priorities.

This is all about speaking the language of your stakeholders and potentially having different versions of your roadmap to suit their needs.

Avoid drowning them in details like user stories or product backlog items; those can come later when you’re working with the Product Team. instead, focus on major themes and top-level insight. By showcasing broader themes, you also help executives evaluate potential resource allocation and make prioritization decisions.

The goal is to keep them engaged without overwhelming them, offering a clear picture of where the product is headed and how it impacts the organization’s strategy.

Product roadmap best practice 5: Don’t  be too data-driven

We’ve told you not to be too customer-focused with your roadmap, but you also don’t want to swing to the other side of the pendulum and be too data-driven. 

Data-driven Product Management has its place, but trusting the data too much and banking solely on quantitative data (numbers and statistics) can lead you astray. 

Instead, when validating and creating your roadmap, you do need some qualitative input, from surveys interviews, and more. 

One thing I really want to warn you about regarding product roadmaps is excessive A/B testing. Now A/B testing has it’s place, but testing every decision often means you lack conviction, leading to wasted resources building multiple versions of the same thing. 

Worse, many A/B tests don’t yield statistically significant results – especially for smaller startups without a massive customer base. While large companies can afford to run endless experiments, startups need to move fast and make bold decisions rather than getting stuck in analysis paralysis.

Being too heavily influenced by data is a great way to learn that every idea is a bad one. To create successful product roadmaps, you need to balance the data with customer insight and other factors to find sensible solutions to prioritize and put onto your product roadmap.

Product roadmap best practice 6: Do link every Initiative to an objective

One of the most effective ways to ensure that your product roadmap stays aligned with business goals is to link every initiative directly to an overarching objective. This practice creates clarity, driving focus and purpose for each project or feature in the pipeline. By making sure that the initiative you add links to a specific strategic goal, you establish a measurable reason for its existence, helping stakeholders understand how each piece fits into the bigger picture.

When planning your roadmap, be sure that every initiative, whether it’s a new feature, update, or enhancement, has a clear and traceable objective tied to it. This could be improving user retention, driving revenue growth, or increasing user engagement. Not only does this ensure accountability, but it also helps in prioritizing initiatives based on how directly they contribute to product goals.

Additionally, when you update or modify your roadmap, linking initiatives to product objectives allows you to assess whether the shift still aligns with the company’s larger vision. This practice also helps during communication with stakeholders, offering a straightforward explanation of why a particular initiative is there in the first place.

To make things even clearer, we’ve got one of the best product roadmap templates that you can access for free to help you see how easy it is to follow this best practice and link Initiatives to Objectives. Our product roadmap template is dynamic and can be found in our Sandbox. Check it out.

Product roadmap best practice 7: Don’t prioritize at the idea level

Prioritizing individual ideas is like trying to clear a landslide by picking up pebbles one by one instead of moving the big boulders. It’s too granular, too reactive, and keeps you focused on what you can build rather than the impact you can create.

Instead of ranking ideas or product features in isolation, zoom out and think about the bigger problems you’re solving for your customers. The best roadmaps aren’t just a list of things to make – they’re a strategy for achieving meaningful outcomes.

This is why tying your roadmap to objectives, user personas, and pain points is so powerful. When you prioritize based on real problems and desired outcomes, you ensure that everything you build moves the needle in a meaningful way—rather than just adding more to your backlog.

With the Now-Next-Later approach, you use a two-step hierarchy when adding Ideas and Initiatives to the board. you first add Initiatives to your roadmap, the high-level problems you want to solve, such as: 

“Reduce friction by making the signup process easier” 

From this overarching initiative focused on a single problem, you can then prioritize it against all your other problems. Discover which problems have the biggest impact when solved. 

There are a million and one prioritization frameworks you can use to work this out, but our favorites can be found in the ebook below. 

Once you have your high-level Initiatives sorted, you can then add Ideas to them that are tangible actions to achieve that solution.

Product roadmap best practice 8: Do update the roadmap regularly

A product roadmap is a living document, not a one-time project. It needs to evolve alongside shifts in the product, market, and broader company goals. Regular updates are essential for ensuring that the roadmap reflects the latest insights and feedback from both customers and stakeholders. By setting a consistent review and update cadence – whether weekly or monthly – you create a dynamic framework that keeps the Product Team aligned and prepared for changes.

Updating the internal roadmap regularly also allows the team to stay agile, quickly adjusting to market shifts or new priorities. It provides clarity and focus, ensuring that the roadmap doesn’t become outdated or irrelevant. A stale roadmap, on the other hand, can lead to misalignment within the team, missed opportunities, or resources being allocated to features or initiatives that no longer serve a purpose.

Incorporating regular reviews and updates ensures the roadmap remains a practical, actionable guide that drives product success and aligns with the ever-evolving landscape.

Product roadmap best practice 9: Don’t treat the roadmap as a list of features

One thing to remember is that a roadmap is not a product backlog. It’s not a list of features or tasks, or work to be done, it’s more an exploration of the things you can do to improve your product. It’s an opportunity log. 

If you treat your roadmap like a list of features, you risk becoming a feature factory – pushing out updates without considering whether they truly move the needle. Product Teams aren’t here to keep the Development Team busy; they’re here to solve real customer and business problems.

If all you think of is features, you could be missing out on easy wins. Sometimes the work that can improve your product is refining your messaging, tweaking your pricing model, or improving the customer experience.

By thinking beyond features and collaborating across internal teams, you open up more creative and effective solutions. A great roadmap is about impact, not just output. When you focus on strategy rather than a to-do list of features, you ensure that every move you make aligns with your broader business goals.

Product roadmap best practice 10: Do limit edit access to the roadmap

A product roadmap is a strategic document that guides the direction of your product’s development. If too many people have editing access, it can quickly spiral into chaos, with constant changes, conflicting priorities, and a lack of clarity. This dilution of control can lead to confusion and disrupt alignment across teams. To prevent this, it’s vital to limit the number of individuals who can make changes.

The key is to grant editing privileges only to those with the authority and expertise to make high-level decisions. These are typically Product Managers, Product Owners, senior leadership, or key stakeholders. This ensures that the roadmap stays focused, consistent, and in line with broader organizational goals. Collaboration can still thrive with input from your Marketing, Customer Success, and Development Team, but they should have access to review, comment, and provide feedback, not alter the plan itself.

By restricting edit access, you maintain control over the roadmap’s integrity, making it a reliable tool for guiding product development and strategic alignment. This keeps your product strategy on track and aligned with both short-term goals and long-term vision.

Product roadmap best practice 11: Don’t keep the roadmap hidden

Just because you don’t want everyone making sweeping changes to the product roadmap doesn’t mean you should keep it to yourself. Too often, Product Owners and Product Teams create a roadmap and keep it locked away, assuming it’s strictly internal or that only one version should exist. In reality, you can (and should) tailor different versions for different audiences.

Give EVERYONE working on your product a window into your roadmap. You can do this by having different versions or views.

Your internal roadmap is the most detailed, outlining not just the problems you’re solving but also how and why. This version keeps internal stakeholders aligned on priorities and execution.

An executive roadmap, on the other hand, should cut out the granular details. Executive stakeholders don’t need (or want) to sift through the nitty-gritty – they need a high-level view that ties into business strategy.

For customers and external stakeholders, a roadmap should be high-level, visually appealing, easy to understand, focused on key value propositions, and clearly communicate upcoming features and development timelines, while avoiding overly technical details, with a focus on the benefits customers will see from future updates. If customers don’t care about certain roadmap items, that’s a clear signal those areas might not be as urgent as you thought.

The perfect product roadmap 

To create the perfect product roadmap, you need practice, and you need to get into the weeds. Of course, product roadmap best practices can help you a lot, but specific training and education walking you through the process is going to help even more. 

Well, if you’re looking for education, you’re in luck. We’ve got a complete, free-to-access course on product roadmapping, helping you to brush up on your skills and perfect your roadmapping process, you can access it below.

Plus, when you roadmap with ProdPad, we have best practices built in. By making certain tasks compulsory and by adding in prompts and our AI support, we give you all the tools to create a product roadmap that can really help you make sense of your priorities. 

Give ProdPad a go by accessing our interactive sandbox environment and try out our many templates to see how an agile product roadmap works in action. 

The post Product Roadmap Best Practices – 11 Do’s & Don’t to Instantly Improve Your Roadmap appeared first on ProdPad.

That’s exactly what this list is for. We’ve rounded up the most useful AI tools for Product Managers, that can help with the many Product Manager tasks you’re faced with daily. 

Whether you’re looking to refine your current tool stack or explore new ways to integrate AI into your workflow, this guide will help you cut through the hype and find the 15 best AI tools for Product Managers that truly deliver.

Why should you use AI tools as a Product Manager?

AI tools shouldn’t be treated as just another piece of tech to bloat your current stack. They can genuinely make you work much smarter. As a Product Manager, you’re constantly juggling research, roadmaps, stakeholder updates, customer feedback, and a whole lot more. AI can take on and speed up these time-consuming tasks, giving you more space to focus on strategy and delivering real impact.

Here’s why you should use AI tools:

Cut out busy work

Repetitive tasks like taking meeting notes, data entry, and backlog grooming take up valuable time. AI can handle these, so you can focus on making decisions, not managing admin.

Speed up research

Instead of pushing through feedback, market reports, and analytics for hours, AI can surface key insights in minutes. It can summarize trends, flag patterns, and even pull customer sentiment from raw data.

Make better decisions

AI-driven analytics and predictive models help you spot trends, assess risks, forecast, and predict outcomes with more accuracy. Instead of guessing, you’ll have data-backed insights to guide your choices.

Prioritize with confidence

Some AI tools evaluate potential features based on customer impact and business goals, reducing bias and helping you make informed prioritization decisions faster.

Keep stakeholders aligned

AI can generate clear, concise updates and reports, or summarize long documents, making it easier to keep everyone on the same page without spending hours crafting messages.

Automate testing and experimentation

From A/B testing to usability analysis, AI speeds up the process and highlights actionable insights, so you can iterate faster.

Free up time for strategy

By taking care of the heavy lifting, AI gives you the space to focus on executing the product vision, problem-solving, and delivering value where it matters most.

What are the different types of AI tools?

“AI tools” is a broad term that doesn’t really capture the range of options available. Just like a hammer isn’t the same as a screwdriver, different AI tools for Product Managers serve entirely different purposes. Some are designed for automation, others for creativity, and some for deep analysis.

Before we dive into the best AI tools for Product Managers, let’s break down the main types you’ll come across:

• AI agents: These are autonomous systems that can perform tasks without constant human input. They analyze data, make decisions, and take actions based on set goals. 
• Large language models (LLMs): These are AI models trained on vast datasets to understand and generate human-like text. ChatGPT and Claude fall into this category, helping with writing, summarization, and ideation.
• Generative AI tools:  These tools create entirely new content, whether it’s text, code, images, or even video. They’re useful for brainstorming, design, and prototyping.
• AI transcribers: Designed to convert spoken words into written text, these tools speed up note-taking, meeting documentation, and accessibility efforts.
• Text-to-image AI: These tools generate images based on written descriptions. While generalist tools like ChatGPTcan do this, specialist tools like Midjourney and DALL·E produce more refined results.
• AI prototyping tools: Used for wireframing, UI/UX design, and product visualization, these AI-powered design assistants help product teams quickly mock up and refine ideas.
• AI writing assistants:  Focused on content generation, grammar improvement, and tone adjustments. They help streamline writing tasks like release notes, product documentation, and marketing copy.

Standalone AI vs. in-built AI

Not all AI tools are standalone products built entirely around artificial intelligence. Some are preexisting tools that have integrated AI to enhance their functionality. Here’s the difference:

• Standalone AI tools: These are built purely around AI. They don’t rely on any external product but instead exist solely to perform AI-driven tasks. ChatGPT, Midjourney, and Perplexity AI are great examples that we’ll cover later on.
• Tools with in-built AI: These started as non-AI products but have since incorporated AI to improve the customer experience. Canva, for example, began as a simple design tool, but now AI powers features like instant design generation, background removal, and content recommendations. The AI isn’t the core product here – it’s an enhancement.

Generalist AI tools vs. specialist AI tools

AI tools also differ in how broad or focused they are in their functionality.

• Generalist AI tools: These are designed to handle a wide range of tasks. They might not be the best at any single thing, but they’re useful for general support. Again, ChatGPT is a perfect example – it can help with writing, coding, brainstorming, and more, but it doesn’t specialize in any one function.
• Specialist AI tools:  These are fine-tuned to excel in a particular area. While ChatGPT can generate images, a dedicated tool like Midjourney produces far better results. The same goes for AI-powered research assistants or product roadmapping tools. They focus on one thing and do it exceptionally well.

So, where does our AI fit in?

ProdPad now has an integrated AI Assistant, called CoPilot, and can be seen as a bit of a mix of everything. It’s a specialist, built-in AI tool designed exclusively for Product Managers and those working on product roadmaps. It helps with a range of Product Management tasks, from creating initiatives and analyzing feedback to setting objectives and summarizing product documentation. Instead of being a generalist AI that’s not quite cut out for the job, CoPilot is built to support real product decisions – making your workflow faster, smarter, and more strategic.

15 AI tools for Product Managers 

In no particular order, here are the best AI tools for Product Managers that you should consider adding to your stack.

1. ProdPad CoPilot – Product Management focused AI

We’re not going to do that thing where we say we feel obliged to include our own AI tool, because that seems insincere. It doesn’t do the tool justice. CoPilot is included by merit and deserves to be considered one of the best AI tools for Product Managers. 

CoPilot is unique, as it’s built from the ground up exclusively for the Product Management function. The in-built generative AI is trained on Product Management best practices, meaning that the outputs generated are highly relevant and trustworthy – more so than generalist tools that can often sprout up some untrue hallucinations. 

As an extension to the overall ProdPad software, CoPilot has a deep understanding of your roadmap and product and can use that to do some incredible things like analyzing your customer feedback, pulling up recurring themes, prioritizing your initiatives automatically, and generating all kinds of product documentation.  

CoPilot has all the context about your product, meaning you don’t have to learn complicated prompt engineering to ensure quality outputs, meaning that using the tool doesn’t feel like a chore. With Copilot you can: 

• Get best-practice coaching and advice – it’s like having a product expert at the palm of your hand
• Interrogate your entire backlog and roadmap 
• Field stakeholder questions
• Set measurable objectives and key results that make sense
• Create and summarize product documentation with a click

To give CoPilot a go, try ProdPad for free and see how it can make you a good Product Manager. 

2. ChatGPT – Generalist AI tool for Product Managers

I know ChatGPT, you know ChatGPT, I’m sure your sweet old grandma knows ChatGPT. As the most wildly known AI tool for Product Managers, ChatGPT is very much a jack-of-all-trades, offering Product Managers a lot of options and use cases. 

ChatGPT is generative AI that can help Product Managers think through complex problems, explore new ideas, and make sense of vast amounts of information. As a generalist tool, you can shape it for whatever you need. Whether you need to validate an idea, prioritize initiatives, or get quick insights on industry best practices, ChatGPT can help.

One of its strengths is brainstorming. Need fresh feature ideas? Struggling to frame a problem? ChatGPT can generate structured suggestions and alternative perspectives in seconds. It’s also handy for sense-checking decisions, helping PMs weigh trade-offs, analyze risks, and refine their thinking.

Beyond ideation, ChatGPT is useful for tackling the information overload that comes with Product Management. It’s able to summarize customer feedback that you feed to it, synthesize research, and even help break down technical concepts into plain language for you or stakeholders.

With ChatGPT, Product Managers can:

• Generate and refine feature ideas, product concepts, and positioning statements
• Prioritize initiatives by analyzing trade-offs and potential impact
• Get quick explanations of technical, industry, or business concepts
• Summarize user feedback and research findings into actionable insights
• Explore different perspectives to improve decision-making

3. Rytr – AI writing assistant for Product Managers

Rytr is an AI writing assistant that helps Product Managers quickly produce clear, engaging content without starting from scratch. Whether you’re drafting release notes, feature announcements, or customer communications, Rytr streamlines the process, ensuring everything stays on-brand and professional.

What makes Rytr stand out is its collection of pre-built templates designed for business communication, product marketing, and technical documentation. This makes it a handy tool for PMs working on user guides, help center articles, or even internal strategy docs. It’s also useful for brainstorming and generating structured ideas for go-to-market product launches and blog content.

For Product Managers juggling a high volume of written tasks, Rytr takes care of the heavy lifting, freeing up more time for strategy and decision-making. 

With Rytr, you can:

• Generate release notes, feature announcements, and marketing copy in minutes
• Create structured content for help centers and user documentation
• Brainstorm product messaging and positioning ideas
• Craft in-product copy
• Speed up content creation without sacrificing quality or consistency

4. Fathom AI – AI meeting transcription for PMs

Fathom AI is a meeting transcription tool that takes the hassle out of note-taking, automatically recording, transcribing, and summarizing key points from all the various meetings you have as a PM. For Product Managers juggling stakeholder calls, customer interviews, CAB meetings, and sprint reviews, Fathom AI ensures that no insight slips through the cracks.

One of its biggest strengths is its ability to generate instant meeting summaries, saving PMs from sifting through lengthy recordings. Need to revisit a past decision or track down an action item? Fathom AI lets you search transcripts by keyword, making it easy to find exactly what you need.

Beyond transcription, Fathom AI is a game-changer for customer discovery. It highlights recurring themes from user conversations – surfacing pain points, feature requests, and objections that can inform product decisions.

With Fathom AI, Product Managers can:

• Automatically transcribe and summarize virtual meetings
• Search past conversations to find key insights and action items
• Capture customer feedback and feature requests without missing a detail
• Stay focused in discussions instead of worrying about note-taking

5. Perplexity AI – AI-powered research tool for PMs

Perplexity AI is essentially an AI-powered search engine that can help Product Managers with up-to-date research and get straight to the insights they need. Instead of wading through endless search results, Perplexity AI delivers detailed, curated answers from multiple sources, saving time and making research more efficient.

Perplexity has the one-up over similar AI tools like ChatGPT because it doesn’t have a knowledge cutoff. Many tools are oblivious to recent events, making things like market research risky. But with Perplexity, it pulls from the web, meaning that its information is up-to-date and accurate.

For PMs working on market analysis, competitor research, or industry trends, this tool is a game-changer, allowing PMs to make informed decisions without the usual research grind.

If a PM is looking for best practices, historical trends, or expert opinions on a product decision, Perplexity AI can pull together the most relevant and up-to-date information without the need to look through multiple sources manually.

With Perplexity AI, Product Managers can:

• Get instant, high-quality answers to market and competitor research questions
• Stay up to date with industry trends without digging through countless articles
• Streamline research and focus on strategic thinking instead of information-hunting

6. Motion – AI-powered time management for PMs

Take a look at your calendar. It properly looks horrendous with multiple meetings and events everywhere. Organizing your time as a PM can turn into such a huge time sink. Thankfully, AI can now help with that.

Motion is an AI-driven scheduling tool that helps Product Managers make the most of their day-to-day by intelligently organizing meetings, tasks, and deep work sessions. Instead of manually juggling calendars and to-do lists, Motion automates time allocation, ensuring that high-priority work doesn’t get buried under endless meetings and distractions.

For PMs balancing multiple projects, stakeholders, and deadlines, Motion’s dynamic scheduling system adapts in real-time, rescheduling tasks based on urgency, available time, and shifting priorities. It also automates task prioritization, making sure the most critical work, like roadmap planning and strategy sessions, always takes precedence.

With Motion, Product Managers can:

• Automate scheduling to optimize meetings, tasks, and focused work sessions
• Adapt to shifting priorities without manually reworking their calendar
• Ensure high-impact work doesn’t get deprioritized due to meeting overload
• Free up mental bandwidth by offloading time management to AI

7. Reclaim.ai – Time management AI tool for Product Managers

Time management is so important as a Product Manager, so we thought we’d give you another AI tool in this category. Reclaim.ai is another AI-powered time management tool aimed at helping Product Managers gain more control over their schedules. This AI-driven tool integrates with calendars, task lists, and team workflows to automatically carve out time for meetings, deep work, and project milestones – ensuring that important tasks don’t get lost in the chaos.

Unlike static scheduling tools, Reclaim.ai continuously analyzes a PM’s workload and adjusts their calendar in real-time. Reclaim is actually my AI time management tool of choice because it places a strong emphasis on protecting focus time, analyzing patterns, and ensuring that deep work doesn’t get pushed aside. It automatically blocks focus time, reschedules tasks as priorities shift, and even optimizes meetings by prioritizing them based on urgency.

With Reclaim.ai, Product Managers can:

• Automate scheduling to balance meetings, deep work, and strategic planning
• Adjust calendars dynamically based on shifting priorities and deadlines
• Ensure critical work isn’t sidelined by an overloaded schedule
• Reduce time spent manually managing their calendar and avoid burnout

8. MidJourney – Image generation AI tool for Product Managers 

MidJourney is an AI tool that can help you create images, but isn’t just for designers – it’s an AI tool for Product Managers that can tackle all types of image creation. With a few prompts, you can generate high-quality, custom images, making it a handy tool for Product Managers who need visuals for everything from roadmap presentations to marketing materials.

What sets MidJourney apart is how it enables PMs to craft concept art, mockups, and promotional visuals without having to learn complex design software. Whether you’re sketching out early-stage product ideas or building assets for a feature launch, MidJourney makes it easy and efficient.

MidJourney ensures that your visuals are aligned with your product’s identity and messaging, creating a consistent and cohesive look across all materials. Here’s how you can use it:

• Generate tailored visuals for early product concepts
• Create marketing imagery and promotional materials in no time
• Design mockups for user interfaces without needing a designer’s skills
• Visualize complex product features or user stories
• Maintain brand consistency across all visual content

9. Notion AI – Productivity & documentation AI tool for Product Managers

Notion AI takes the already robust Notion workspace and adds AI-driven capabilities, turning your Product Management tasks into streamlined, automated processes. It’s a decent tool for PMs who want to work smarter, not harder, by automating writing, summarizing content, and generating structured documents – all within Notion’s familiar interface.

One of Notion AI’s features is its ability to instantly summarize long-form text. Product Managers can generate meeting summaries, extract key insights from customer interviews, or condense research reports into actionable points.

Notion AI also handles repetitive tasks like creating templates for retrospectives, roadmaps, and sprint planning. And because Notion integrates seamlessly with existing knowledge bases, all documents remain structured, searchable, and easy to access.

For Product Managers facing information overload, Notion AI serves as a writing assistant, knowledge organizer, and strategic helper, all in one. Here’s how you can benefit:

• Generate PRDs, meeting notes, and competitive analyses quickly
• Summarize long documents and extract key insights
• Automate templates for retrospectives, roadmaps, and sprints
• Keep knowledge bases organized and easily accessible

10. Tome – AI-powered presentations for PMs

Tome actually does a few different things, but I want to focus on its presentation capabilities. Tome AI helps Product Managers craft compelling narratives, whether for product pitches, roadmaps, or key stakeholder updates. Unlike traditional slide decks, Tome builds structured, dynamic presentations that are both clear and engaging.

One of the biggest challenges for PMs is making complex product decisions easy to understand. Tome automates slide creation, transforming raw data, bullet points, and ideas into polished, visually appealing presentations in minutes. This makes it an invaluable tool for vision decks, stakeholder updates, and go-to-market strategies.

Beyond static slides, Tome enables interactive storytelling – integrating multimedia, live data, and responsive content to make presentations more engaging. Whether you’re walking stakeholders through a new feature roadmap or using data storytelling to highlight customer impact, Tome simplifies the process.

With Tome, Product Managers can:

• Turn rough ideas into polished presentations instantly
• Automate slide creation for vision decks and product updates
• Integrate live data and multimedia for dynamic storytelling
• Streamline stakeholder communication and buy-in
• Present complex product decisions with maximum clarity and impact

11. Cursor – AI-powered coding assistant for Product Managers

Cursor is an AI-powered coding assistant built specifically for Product Managers who need to get a bit technical. Cursor integrates into code editors, providing real-time assistance with coding, debugging, and even explaining complex code.

For PMs who work closely with Engineering teams or are responsible for coding tasks themselves, Cursor can be a game-changer. It helps you understand codebases faster, generate boilerplate code, and suggest optimizations, making it easier to prototype features, review pull requests, or debug code without getting bogged down by technical details.

One of Cursor’s key strengths is its ability to explain code in plain language, allowing PMs to bridge the gap between technical and non-technical stakeholders. Cursor makes the process simpler and more accessible.

By using Cursor, PMs with a technical edge can speed up their workflow, improve collaboration with Engineering teams, and reduce friction in the development process. Here’s how you can make the most of it:

• Get real-time code assistance and debugging help
• Generate boilerplate code and suggest optimizations
• Understand complex codebases and implementation decisions
• Simplify communication between technical and non-technical stakeholders
• Streamline documentation for technical debt and other code-related topics

12. Replit – AI-powered coding environment

Replit is an AI-powered, browser-based coding platform that brings rapid prototyping, collaborative coding, and automated code generation to the fingertips of Product Managers. Unlike more traditional coding environments, Replit eliminates the need for complex local setups, making it easy for PMs to start experimenting and building prototypes immediately.

For PMs who want to quickly test ideas or create proofs-of-concept, Replit offers a user-friendly solution that even those with limited coding experience can navigate. Its AI-assisted coding suggestions help streamline the process of building functional prototypes, without the steep learning curve often associated with development.

What sets Replit apart from tools like Cursor is its strong focus on collaboration. It’s built for team-based environments, making it ideal for PMs working closely with Engineers. Replit’s pair programming features allow you to leave comments, try out code snippets, and even create lightweight automation scripts – all directly within the platform. 

By using Replit and its AI features, PMs can transition seamlessly from ideation to execution, with enhanced collaboration and faster iteration. Here’s how it helps:

• Quickly spin up prototypes and test ideas without complex setups
• Experiment with code snippets
• Collaborate with engineers through pair programming features
• Build lightweight automation scripts for experimentation
• Accelerate iteration cycles and validate concepts faster

13. Figma AI – AI-Powered Design & UX Prototyping for PMs

Figma has long been the go-to design tool for those in the product team, especially Designers, and now it’s even better with the power of AI. Figma AI adds enhancements to Figma’s already robust capabilities by adding AI-driven automation to UI design, wireframing, and prototyping, making it a powerful ally for Product Managers looking to speed up iteration and enhance collaboration with Design Teams.

One of the standout features of Figma AI is its ability to automate UI generation. PMs can simply input descriptions, and the AI will suggest layouts, components, and even user flows. This is especially valuable for PMs who need to quickly generate low-fidelity wireframes to align teams on product concepts before the design team dives in.

By using Figma AI, PMs working on feature development, onboarding flows, or user testing can accelerate the design process, ensure better team alignment, and make more informed UX decisions. Here’s how Figma AI can support you:

• Automatically generate UI designs, layouts, and user flows from descriptions
• Create low-fidelity wireframes to align teams on concepts
• Analyze user interactions and suggest design optimizations
• Review usability reports, heatmaps, and A/B test results with AI-driven insights
• Improve collaboration with design teams for faster iteration and feedback

14. Loveable – Prototyping AI tool for Product Managers

Loveable is your full-stack Engineer powered by AI, turning your app or product ideas into fully functional applications. It’s designed to bridge the gap between ideation and execution, enabling Product Managers to quickly prototype and iterate without the need for deep technical expertise.

With Loveable, PMs can simply describe the product idea or feature they want to build, and the AI will automatically generate the necessary code, architecture, and even a working prototype. 

Whether you’re testing a new feature, exploring a potential product direction, or validating a concept, Loveable transforms abstract ideas into tangible applications in record time.

This makes it an ideal tool for fast experimentation and early-stage product development. You can quickly prototype ideas, test functionality, and even share prototypes with stakeholders, all without having to wait for a development team to get involved.

Here’s how Loveable can help PMs move from idea to execution:

• Turn product ideas or feature descriptions into functional applications
• Build working prototypes for testing and validation with no coding required
• Experiment with new ideas quickly and iteratively, without development delays
• Prototype complex features or entire products and share them instantly with stakeholders
• Save time and resources by generating app code and architecture automatically

15. ChatPRD – AI-Driven Product Requirements Document Generator

ChatPRD is an AI-powered tool designed to help Product Managers create product requirements documents (PRDs). It automates the process of gathering product requirements, structuring them, and ensuring they align with the overall product vision.

ChatPRD claims to be able to generate dynamic PRDs based on real-time context. By analyzing user input, whether from meetings, emails, or conversations, it automatically extracts key information and organizes it into a structured document.

The tool is useful for aligning PRDs with stakeholder expectations while keeping a consistent product narrative.

Here’s how ChatPRD can make a difference for PMs:

• Automatically generate comprehensive, structured PRDs from input such as emails, meetings, or conversations
• Align product goals, user needs, technical specs, and timelines effortlessly
• Save time by eliminating manual data entry and document structuring
• Ensure PRDs reflect stakeholder expectations and product vision
• Improve consistency and clarity in product documentation

Tools to make your life easier 

There you have it – the best AI tools for Product Managers, hand-selected by us at ProdPad to help you work smarter, not harder. With AI integrated into so many aspects of your daily workflow, these tools aren’t just novelties, they’re productivity boosters. From speeding up research to automating time-consuming tasks, they free you up so you can focus on what really matters: delivering value, refining your product vision, and making data-driven decisions.

If you’re ready to take your output to the next level, we highly recommend giving CoPilot a try. This AI assistant was built specifically for Product Managers, integrating seamlessly with your roadmap and product context. It helps with everything from generating product documentation to analyzing customer feedback, making it an indispensable part of your tool stack. 

With CoPilot, you don’t just get another AI tool, you get a Product Expert at your fingertips, empowering you to be more strategic and efficient in your day-to-day work. Start your free trial today and see how CoPilot can make a tangible impact on your workflow.

The post 15 Best AI Tools For Product Managers appeared first on ProdPad.

Now this is actually a more complicated topic than you may think. As a PM, particularly if your product has existed long before AI came along, you’ve already figured out your product pricing strategy and model, so why can’t your AI feature sit within that? Well, it can, but you need to think about it first and make sure it’s right for your product and the right approach for your industry. 

Plus, some common AI monetization strategies that customers are getting used to can be pretty different from what you’re using. 

So, let’ me walk you through all this and have a look at AI monetization, and how you go about deciding how to price your AI features or products. 

What is AI monetization? 

AI monetization is the process of turning AI-powered features or products into revenue-generating assets. As AI becomes more embedded in software and services, companies are exploring different ways to charge for its value, whether as a standalone product, an add-on, or a core part of an existing offering.

At its core, AI monetization revolves around how businesses capitalize on AI’s capabilities to drive growth. Some companies charge directly for AI features, making them a premium upgrade or a pay-per-use function. Others bundle AI enhancements into existing plans to increase adoption, retention, and customer lifetime value.

AI monetization and the way you integrate AI into your product or service can fall into two distinct categories: 

• Direct Monetization – Charging explicitly for AI functionality.
• Indirect Monetization – Using AI to improve engagement and retention without charging for it separately.

Let’s cover those in more detail:

What are the different types of AI Monetization? 

When monetizing your AI product or service, there are two primary approaches: 

Direct and Indirect AI monetization strategies.

These dictate the overall, top-level strategy you’re going to follow regarding where AI sits within your current product. The right choice depends on how integral AI is to your offering and how your users perceive its value.

Direct AI monetization

Direct AI monetization means explicitly charging users for AI-driven functionality. This approach makes sure that AI generates direct revenue, whether as an optional upgrade, a standalone product, or a core part of a pricing shift.

Here are the three main strategies within direct AI monetization:

• AI as an add-on – Here, users pay extra to access AI-powered capabilities on top of their existing plan. This is ideal for features that provide distinct, high-value enhancements.
  Best for: Products where AI delivers a clear competitive advantage without needing to be core to the main offering.
• Standalone AI product – With this, the AI itself is the primary product, separate from what you already have and users subscribe or pay based on usage. These offerings are built entirely around AI functionality.
  Best for: Products where AI is the main value driver, rather than an enhancement to an existing tool.
• Bundled with a price increase – With this option, AI features are incorporated into existing plans, but prices are adjusted to reflect the added value. This ensures AI-related costs are covered while maintaining a seamless experience for users.
  Best for: Products looking to enhance their value proposition while avoiding the friction of separate AI-based upsells.

Indirect AI monetization 

Indirect AI monetization focuses on leveraging AI to improve user experience, engagement, and user retention rather than charging for it explicitly. Here you’re utilizing AI as a way to make your product more compelling in order to drive growth. While not a direct revenue driver, this strategy can encourage more new customers, increase product stickiness, lower customer churn, and boost customer lifetime value.

Here are three common approaches to indirect AI monetization:

• Bundled without a price increase – Here, AI features are included in standard plans at no extra cost, serving as an incentive for acquisition and differentiation in a competitive market.
  Best for: Companies prioritizing long-term growth, customer loyalty, and differentiation over immediate monetization.
• Freemium AI – With this approach, a basic version of AI-powered features is available for free, while premium or advanced capabilities require a paid upgrade. This model encourages adoption while creating a natural upsell path, just like regular freemium.
  Best for: Companies that want to showcase AI’s value upfront and convert engaged users into paying customers.

Completely free AI features – AI tools are provided at no extra cost as a value-add, helping increase product usage, user activation, customer satisfaction, and brand loyalty.
Best for: Platforms looking to enhance user engagement and retention while keeping AI as a competitive differentiator.

Choosing the right AI monetization pricing model

Now here’s where things can get tricky. The above strategies define how AI fits into your product offering, but the next step is determining how you charge for it. This is where different AI pricing models come into play. 

Now I know what you need to think about when monetizing your AI. I had to figure out the monetization strategy we used for our advanced AI, CoPilot, and make sure it suited our overarching product and our users.

We’ve gone for an indirect monetization strategy, so all users of ProdPad have complete access to CoPilot at no extra cost. Learn more about our AI for Product Managers or jump into the Sandbox to try it out.

Once you’ve figured out if you’re monetizing AI directly or indirectly, there are two core options you can choose in terms of how you actually charge your users for it: 

Subscription-based or outcome-based.

Subscription-based AI monetization

This is the most common model in SaaS, where AI features are included in a recurring pricing structure. This model works well for businesses looking for predictable revenue and a scalable growth strategy.

When using a subscription-based AI pricing model, you can choose:

• Seat-based pricing – Pricing is determined by the number of users accessing AI features. For example, charging $50 per user/month for AI-powered automation tools.
• Skill-based pricing – Pricing varies based on the complexity or level of AI capabilities offered. For example, basic AI assistance is included in lower-tier plans, but advanced machine-learning capabilities require a premium upgrade.

Outcome-based AI monetization

Instead of charging upfront or per user, outcome-based pricing ties AI costs to measurable results. This aligns value with customer success but requires clear performance metrics.

When charging by outcome, this can come in the form of:

• Usage-based pricing – Customers pay based on how much they use AI features (e.g., API calls, queries, or data processed). This could be a chatbot platform charging $0.01 per AI-generated response.
• Output-based pricing – Customers pay based on the volume of AI-generated outputs (e.g., reports, content, predictions). This might be a generative AI tool charging per 1,000 words generated, or blocking access once you’ve used a number of credits.
• Outcome-based pricing – Customers pay when AI delivers a tangible business result, like increased revenue or cost savings. This one is specifically suited to B2B businesses and can be like an AI-powered hiring tool charging per successful hire.

Pricing AI isn’t just about picking a number, it’s about aligning monetization with perceived value, cost structures, and customer expectations. 

So, to get things right, start by choosing whether AI is a direct or indirect revenue driver, then refine your approach with the right pricing model and structure. The right strategy will depend on your product’s role in the market, your users’ willingness to pay, and how AI enhances their experience.

How are most companies handling their AI monetization? 

Research from Lenny Rachitsky shows that 59% of AI companies bundle AI features into their existing subscription-based plans instead of charging separately. Sometimes, this is done with a price increase. 

This makes sense. AI is expensive to build and maintain, and bundling avoids the friction of an additional paywall. By doing so, you can provide immediate value without deterring adoption.

For many, this strategy balances recouping high investment costs by making AI widely adopted in your product, and not just used by a select few specifically seeking AI functionality. Bundling ensures that you aren’t reliant on a small group of early adopters and allows you to integrate AI without the risk of sticker shock for customers.

However, just like flat-screen TV prices in the early 2000s, AI’s high infrastructure costs won’t last forever. Advances in AI models and hardware are already driving prices down. Companies like DeepSeek, for example, have gained attention for their impressive cost efficiency. As AI becomes cheaper and more accessible, businesses may need to reconsider their AI pricing model.

So what happens when AI becomes cheaper?

Right now, AI’s high infrastructure costs justify bundling – it offsets the expense while driving adoption. But that justification won’t last forever. As AI’s development and operational costs decline, the economics of AI monetization will shift.

When cutting-edge technology becomes more affordable, it also becomes less special. Just like cloud storage and streaming services, AI will transition from a premium add-on to an expected baseline feature. Companies that once charged a premium for AI-powered capabilities may find customers unwilling to pay extra for something they now see as standard.

This raises an important question: How will businesses continue to monetize AI when it’s no longer a differentiator?

Some may shift toward usage-based pricing, charging for AI-heavy workloads while keeping basic AI features free. Others might introduce tiered AI offerings, where advanced capabilities remain exclusive to higher-priced plans. Alternatively, businesses could pivot toward AI-powered services – providing consulting, automation, or specialized AI models tailored to specific industries.

The key takeaway? The AI pricing model that works today may not work tomorrow. As AI’s cost curve trends downward, companies need to plan for a future where bundling alone won’t cut it.

What do customers expect from AI monetization? 

With so many companies bundled AI into their existing plans early on, when customers see AI included across multiple tools without an extra charge, it sets a new norm: AI isn’t a luxury, it’s just part of the product.

I think this shift in expectation has major implications for AI pricing. If AI is now “just part of the package,” customers may resist paying extra for it. They’re only going to part with their cash if it delivers clear, tangible value beyond the basics. While foundational AI-powered enhancements (like autocomplete, search recommendations, or basic chatbots) are increasingly expected to be free, more advanced AI capabilities – such as predictive analytics, complex automation, or industry-specific AI tools – can still command a premium.

Crucially, expectations differ by industry. In software platforms where AI is embedded into everyday workflows (think productivity tools, CRMs, and customer support platforms), users expect AI to be included. But in industries where AI tools are more specialized or standalone, like financial modeling, healthcare diagnostics, or creative AI tools, customers are more accustomed to paying separately for advanced AI capabilities.

This means AI pricing isn’t one-size-fits-all. The right strategy depends on what your users expect and how they perceive AI’s value within your product. If your customers see AI as table stakes, bundling makes sense. If they view it as a premium service, a separate charge might be viable. Either way, aligning with customer expectations is critical. Once AI becomes an assumed feature, trying to charge for it after the fact could be an uphill battle.

How do I choose the right AI monetization strategy for my product? 

When it comes to monetizing AI, you need to pick the option that’s right for you, not just the most popular. Just because most companies are bundling AI into their core offerings doesn’t necessarily mean it’s the right choice for your product. To determine the best AI monetization strategy, consider the factors below as we compare direct and indirect AI monetization.

Is direct monetization right for my product?

Charge customers directly for AI when it delivers unique, high-impact value that extends beyond your core product. If AI is the main event – not just an enhancement – users are more likely to accept paying for it.

Direct monetization is best suited for:

• Standalone AI capabilities – If AI is a distinct, high-impact feature (e.g., AI-generated content, predictive analytics, workflow automation), direct pricing makes sense.
• High operational costs – Running AI models comes with expenses like computing power, storage, and security. Plus, if you’re using someone else’s AI model to power your AI feature, you’ll have usage costs for that. Charging for AI features can offset these costs.
• Measurable ROI for users – If customers can directly attribute time or cost savings to your AI, they’ll be more willing to pay for it.

Is indirect monetization right for my product?

An indirect monetization strategy can be effective if your AI is designed to enhance core functionality rather than provide a standalone capability. If your AI features are aimed at boosting user engagement or improving essential aspects of the product (like smarter recommendations or better search), you might opt to bundle them into existing plans without an additional charge.

Companies like Zoom and Shopify use this strategy, offering AI as part of their core offerings to drive more usage, conversion, and customer retention.

Indirect monetization is best suited for: 

• Core product enhancements – If AI improves the core functionality of an existing product, customers may expect it to be included.
• Customer expectations favor bundling – If competitors are offering AI as a built-in feature, charging separately could put you at a disadvantage.
• Retention and engagement play – AI that drives frequent usage (e.g., smarter workflows in productivity apps) can be more valuable in the long run when bundled rather than sold separately.

The third way…

While direct and indirect monetization strategies each have their advantages, there’s another way. Don’t think of these options as binary, one or the other. They can be merged into a hybrid model.

I think the hybrid model can offer the best of both worlds. By giving away some AI features for free (to boost adoption and provide value), while charging for premium features, you can avoid alienating customers while still capturing the value your product provides.

A hybrid model works well when:

• You want to quickly build adoption without charging upfront.
• Your product’s core functionality benefits from AI, but some advanced features offer unique, higher-value capabilities worth paying for.
• You’re in a competitive landscape where offering AI for free can help you stay ahead, but charging for premium features allows you to capture revenue.

I prefer a hybrid model – offering some AI for free (to boost adoption) while charging for premium AI features. It avoids customer backlash while still capturing value. The best approaches usually do cater to multiple facets rather than being a blanket style.

What’s the best pricing model for my AI? 

So you’ve chosen your AI monetization strategy, you know that you want to either charge directly for it or add it in as a value driver to your existing plan. Sweet, but your work isn’t done. 

How do you decide what pricing model works best for your AI tool, be it subscription-based, outcome-based, and everything else in between? 

With your approach to AI monetization ticked off, here’s how you decide the particulars of what you’re going to choose: 

Subscription-based pricing

If you’re looking to get predictable revenue from your AI, then subscription-based pricing is probably the way to do it, especially when your AI features are so deeply integrated with the rest of your product experience. 

Of course, you still need to decide if you want skill-based or seat-based pricing. Here’s how to pick them: 

• Seat-based pricing – Works well for AI tools where value scales with the number of users (e.g., AI-powered collaboration software). However, this model suits direct monetization better since it explicitly charges for AI access, making it less ideal for indirect monetization, where AI is a background value driver.
• Skill-based pricing – Best when AI capabilities vary by plan, allowing customers to pay for the complexity they need. It works for both direct and indirect monetization, as AI can be used to differentiate pricing tiers without requiring a separate AI charge.

Outcome-based pricing

Outcome-based pricing is most effective when the value derived from usage or business impact is both clear and measurable. Here’s a breakdown of how to choose the right model within this category:

• Usage-based pricing – Ideal for AI APIs, chatbot platforms, or AI-powered analytics, where customers expect to pay based on consumption. This model supports direct monetization but can also be blended into an indirect strategy if AI is used to drive engagement (e.g., offering a free allowance before charging).
• Output-based pricing – Works well for content generation, predictions, or AI-driven automation where customers are paying for tangible deliverables. This fits direct monetization strategies but may not align well with an indirect approach, where AI is embedded rather than sold separately.
• Outcome-based pricing – Suited for AI that delivers measurable business results, like cost savings or revenue generation. This model is inherently tied to direct monetization since customers are charged based on the impact AI delivers, making it less applicable for bundled AI features.

Still unsure of what combination suits you best? Okay, let me hammer this home with a few examples:

If your AI tool is a core feature enhancing the user experience, indirect monetization with skill-based subscription pricing might be the best fit.

If your AI requires significant computational resources, usage-based or output-based pricing within a direct AI monetization strategy can help recoup costs efficiently.

If your AI delivers measurable business outcomes, outcome-based pricing ensures alignment between the value delivered and the price charged.

If you want to track the ROI of your AI investments, seat-based pricing offers predictability but works best with direct monetization.

Making money from AI 

In conclusion, AI monetization presents a unique challenge for Product Managers, but also a great opportunity to adapt and innovate pricing strategies. Whether you choose direct, indirect, or a hybrid approach, the key is to align your AI features with customer expectations, business goals, and the evolving landscape of AI technology.

By understanding your product’s value, user needs, and the dynamic costs of AI, you can craft a strategy that drives growth, customer satisfaction, and long-term success. The right approach will depend on your specific product and market, but with thoughtful planning and adaptability, your AI monetization strategy can be a powerful tool for sustaining your business’s competitive edge.

Here at ProdPad, our in-built AI, CoPilot, is included with any plan. If you have ProdPad, you have the full functionality of our advanced, Product Management AI. Come see what CoPilot can do, and discover how it enhances our tool and helps you become a more effective Product Manager. Start a trial to learn more.

The post AI Monetization: How to Approach AI Pricing appeared first on ProdPad.

It seems a simple question in principle, but when you dig deeper, this whole conversation becomes a bit more philosophical. Sure, I can (and will) rattle off some essential skills and characteristics that make for a strong Product Manager, but I think this question is looking for a lot more than this. 

When a PM asks, what makes a good Product Manager, they’re also asking, ‘How am I judged as a Product Manager?’ ‘What does success look like as a Product Manager?’ ‘What actually are my main goals as a Product Manager?’ 

As you can see, there is a lot to unpack. So let’s do it. What do you need to do to be great? What makes a good Product Manager? 

What does it mean to be a good Product Manager?

To be good, you first need to define what good looks like. You’d think that would be easy, but it’s not. Being considered good is an assessment. A measurement. So what are you getting measured up against? 

For me, the biggest barometer of how good you are as a Product Manager is how well you facilitate the main goal of Product Management. But what is the overarching goal of the role? 

What is the core, single priority for Product Managers that every task and responsibility can be distilled down to?  Well, I think it’s something like this:

This goal is universal. it should be the same for every single Product Manager, regardless of industry or company size, but how you achieve this goal is where things start to change. 

The context often influences execution. A Product Manager in one industry will need to do different things than another somewhere else, even though they’re chasing the same goal. What defines “good” in one environment might not translate to another.

You could have endless expertise leading startups through their first years and getting them to $1M MRR. But pluck you out of that small pond and into an enterprise business, and things might look different, you may struggle. Because the goalpost has moved. 

Would that make you a bad Product Manager? To that enterprise company – maybe – but a startup that’s looking to get off the ground may come across your Product Manager portfolio and see you as an angel sent from heaven.

What I’m saying here is that good can look like many different things. Sure, every PM has the main aim of connecting customer needs with business goals, but there are many different ways to get there. 

So what does it mean to be a good Product Manager? That depends on where you’re standing.

Environment	What a ‘Good’ Product Manager Looks Like
Startup	• Rapid iteration & experimentation • Deep customer empathy & direct feedback loops • Prioritization ruthlessly focused on product-market fit
Enterprise	• Strong stakeholder management & alignment across departments • Clear, structured roadmaps with long-term vision • Navigating bureaucracy while still driving innovation
B2B	• Deep understanding of customer workflows & pain points • Strong relationships with key accounts & sales teams • Emphasis on integrations, reliability, and long-term ROI
B2C	• Strong focus on user experience & delight • Rapid A/B testing & growth experimentation • Data-driven decision-making & behavioral insights

What skills make a good Product Manager?

When asked what makes a good Product Manager, most will recite a list of the hot skills they think you need to fulfill the Product Manager role. Prioritization, communication, stakeholder management, blah, blah, blah. Yes, knowing the skills you need to tune up is a big part of being a good Product Manager, but it’s only half the story. 

Don’t just hit the books in an endeavor to be the best. It’s easy to get stuck in learning mode, diving into the theory, and taking Product Management courses instead of going out there and getting the experience. 

But, here’s the thing. It’s the doing that’s important. The miles in the tank matter more than any framework you can memorize.

So, don’t worry too much if you don’t think you have all the answers. The best plan of action isn’t to wait until you have all the skills you need. You need to just get out there and learn as you go.

Of course, we’re not dismissing the need for honing your Product Management skills altogether. They do matter, so much so that we have covered them a lot here at ProdPad. If you want a full rundown, check out The Product Manager Career Path is Not a Straight Line. 

Don’t worry, I’m not going to leave you empty-handed here. Here’s a quick overview of the skills you need to excel as a Product Manager: 

• Prioritization – Making tough calls on what moves the needle.
• Communication skills – Aligning teams, internal stakeholders, and customers.
• Data-driven decision-making – Using insights to back up product ideas.
• Execution – Turning strategy into shipped product features.
• Stakeholder management – Navigating competing interests with diplomacy.
• Problem-solving – Breaking down challenges and finding solutions.
• Leadership skills – Guiding cross-functional teams without direct authority.
• Strategic thinking – Seeing the bigger picture and making long-term bets.
• Roadmapping – Setting clear, realistic product directions.
• Technical skills – Technical knowledge to understand how products get built.
• Adaptability – Pivoting when things don’t go as planned.

Now, as PMs, we live by prioritization – finding those high-impact, low-effort moves that make the biggest difference. If you’re looking at this list and wondering where to start, there’s really just one skill that ties everything together. If you boil all these skills down, there’s one thing an effective Product Manager needs. 

That one skill is….empathy. 

The one major skill you need is empathy. For customers, stakeholders, and the team. Without empathy, the PM can’t truly understand the problem or rally the team around solutions. Empathy is at the heart of curiosity and storytelling.

Empathy is what turns a decent PM into an exceptional one. Think about it:

• Customer empathy helps you see the product from their perspective, ensuring you solve real customer pain points that improve user experience rather than just shipping features.
• Stakeholder empathy helps you balance competing priorities, build buy-in, and keep everyone aligned, even when tensions run high.
• Team empathy helps you create an environment where Engineers, Designers, and Marketers feel heard and valued, leading to stronger collaboration and better outcomes.

Empathy is what helps you ask why instead of just what, making you a better decision-maker. If you want to become a better Product Manager overnight, start with empathy.

What do good Product Managers do? 

Being a great Product Manager isn’t just about what you know – it’s about what you do. A good Product Manager can be defined by their actions. Skills are useful, but the real magic happens in how you apply them.

Einstein wasn’t a great scientist just because he memorized formulas. He was great because he used them in novel ways. Likewise, a Product Manager isn’t great just because they know frameworks, roadmaps, and product strategy. They’re great because they execute them in a way that moves the product and team forward.

To be a good Product Manager, your actions need to match your ambitions. If the last section covered what you should know and the qualities you should have, this section covers what you should do. These are the habits that set successful Product Managers apart, according to discussions from all around the web.

Good Product Managers make things simple

Simple goals. Simple processes. Simple communication. A great PM ensures that everyone knows what they’re doing and why they’re doing it.

Good Product Managers deeply understand their product

Not just the technical side, but how it fits into the market and into users’ lives. They know its strengths, its weaknesses, and where it’s heading.

Good Product Managers have a strong product sense and endless curiosity

Great PMs have a strong product sense where they constantly seek to learn, be that about customers, competitors, and trends, so they can make smarter, better-informed product decisions.

Good Product Managers communicate with clarity and confidence

They can talk to anyone – engineers, executives, customers – adapting their message to the audience. And when they need to say no, they do so with data and reasoning to back it up.

Good Product Managers foster strong relationships with stakeholders

Building trust and understanding with internal stakeholders across the organization makes for smoother collaboration and decision-making so that everyone is aligned toward common goals.

Great PMs prioritize customer engagement

Actively engaging with customers will give you invaluable insights into their needs and pains. This direct interaction will help the product evolve in line with their expectations.

How do you measure the performance of a good Product Manager? 

Product Managers are always going to be judged by others. We’re brought in to make a difference and ensure a sensational product or feature hits the market. So, there’s high expectations. How are people measuring the performance of Product Managers to see if they’re meeting those expectations, and more importantly, how do you track your own performance?

Metrics seem an obvious place to start. Are you hitting your target objectives and key results? While metrics might seem like a clear-cut way to gauge performance, they don’t tell the full story.  Remember, KPIs aren’t targets for an individual to hit, it’s a goal for the entire Product Team. Achieving a KPI is a group effort. Hitting a target means the team is working well, but shines little light on your impact as a Product Manager. 

Instead, it’s more useful to look at how you as a Product Manager helped your team hit those numbers. So, it’s not the numbers themselves, but the methods they followed to get there. Instead of hard numbers and data, you’re looking at soft skills and those intangibles. 

It’s best to judge your performance as a PM by looking at how you align the team to the vision and its impact on the culture. It’s more about how you make decisions and communicate them than the decision itself. 

Ask yourself: 

• Did I align the team around a clear product vision?
• Did I help simplify complex problems and drive better decisions?
• Did I foster an environment where my team could do their best work?
• Did I make sure customer and business needs were understood and balanced?
• Did I communicate priorities effectively and ensure the right things got built?

I know this is all less tangible than simply ticking off a goal, but that’s the beauty and also the trick of Product Management: success is not measured by a single goal. It’s reflected in the impact you have on your team, your product, and ultimately, your potential customers. 

What’s stopping you from being a good Product Manager? 

Some unfortunate Product Managers are up against challenges that hinder their ability to positively impact their company. Not every Product Manager struggling in their role is actually bad at their job.

Many PMs aren’t set up for success because the people around them have the wrong idea of what good Product Management actually looks like. 

Many Product Managers are being steered away from the main goal we talked about early on. Attention is being pulled from this universal target to instead focus on something else that makes them less effective, all because of what others define as success. 

Key stakeholders can have a different idea of what good looks like, compared to actual PMs. 

• Sales want new features ASAP to close deals.
• Leadership demands rapid execution because any delay is a waste of resources.
• The business measures product success by output, rewarding PMs for shipping fast rather than shipping right.

We know better than this, but Product Managers rarely have any authority to change this. When the loudest voice in the room (or the highest-paid one) calls the shots, PMs can end up running a feature factory instead of driving meaningful impact. This looks like progress, but you’ll end up with a flashy-looking product that doesn’t meet the mark, and fingers pointing at you asking why. 

How do you fix this? 

The art of saying no

If you want to break out of this cycle, you need to master one essential skill. You need to learn the art of saying no. 

That doesn’t mean being difficult. It means managing stakeholders effectively. Speak their language. Back up your product decisions with data. Shift the conversation from what gets built to why it should (or shouldn’t) be built. Encourage a culture of validation over assumption. 

Of course, all this is easier said than done, but if you can navigate these conversations, you’ll not only protect the integrity of your product, but you’ll define what good Product Management truly looks like. 

Check out this article for more tips on how to manage stakeholders and master the art of saying no.

Advice on how to become a good Product Manager

When striving to improve, it’s always smart to seek advice from those you trust and respect.

But instead of loading you up with the usual “do this” and “do that” advice, I believe it’s more valuable to highlight some of the things you absolutely SHOULD NOT DO. 

To help with that, I’ve asked my network what’s some of the WORST advice they’ve ever received. The advice that they wish they had ignored from the start. Advice that often leads PMs down a dangerous or unproductive path. Here’s a collection of those responses, each offering insight into common mistakes PMs make when they follow the wrong guidance.

“You are the CEO of your product.”

Advice like this comes up a lot and is a warning against falling into the trap of ‘founder mode’ as a Product Manager.

The problem with this advice is it encourages PMs to take a “top-down” approach where they believe they should make decisions in isolation, wielding authority over their product with little regard for collaboration. 

Being a “CEO of your product” implies a level of detachment from the team, ignoring the collaborative, cross-functional nature of Product Management. The best PMs know how to lead with influence, not authority, and understand that great products aren’t built by one person’s decisions, they’re the result of diverse inputs from Design teams, Development, Marketing teams, and of course, the customers themselves.

“Don’t spend time building your Product Team structure when you could be spending time speaking to customers.” 

While customer interviews and research are essential, this advice completely undervalues the importance of having a strong team structure to deliver product insights effectively.

The idea that team structure isn’t important because you should be focused on speaking to customers misses a key point: Product Managers can’t execute alone. Building the right team and processes is just as critical as understanding the customer. 

Without a solid Product Management team structure, you’ll struggle to implement the customer insights you gather, making this focus on user feedback pointless.

“Just deliver features as fast as possible.” 

This one might sound familiar – many PMs face this pressure early in their careers. The belief here is that speed equals progress. But it doesn’t.

The fundamental issue with this advice is that it focuses on output over outcome. Speed might make you feel productive, but in reality, delivering features without proper product validation and alignment with user needs leads to wasted effort. 

It results in a product that may look good on paper but ultimately fails to address the real problems users face.

“Just build the feature because this one customer is asking for it.”

This piece of advice emphasizes customer feedback, but it’s a classic example of the danger of letting one voice guide decisions for the entire user base.

While customer feedback is invaluable, acting on a single request is a slippery slope. Building products based on individual customer demands, especially when they’re not representative of your broader user base, leads to a fragmented, incoherent product. 

Just because one customer asks for a feature doesn’t mean it’s a core need. You need to do product discovery to figure out what your customers are crying out for.

Now, this is all pretty rotten advice that would lead you down the wrong path. But here’s one from me that I think is possibly the most dangerous you can receive at the dawn of your career.

“Just be a people pleaser and follow the process.”

Yuck. Horrid advice. As a product person, I think it’s important to make sure that you’re able to take a stand and are able to identify where you should push back. It’s all in the art of saying no, and not just assuming that what’s being fed to you is the right thing.

Being a “people pleaser” and simply following the process might seem like a way to stay safe and avoid conflict, but it undermines the role of a Product Manager. PMs are responsible for making tough calls that may not always align with what others want to hear. The role involves balancing competing priorities, challenging and testing assumptions, and pushing back when necessary – even when it’s uncomfortable.

Now, I don’t want to see any careers ruined. Instead, I want Product Management to thrive. So, in all seriousness, here’s my genuine advice on what makes a good Product Manager:

Getting good at Product Management

Saying exactly what makes a good Product Manager is hard to nail down. What looks good in one situation may not be right for another. It’s all down to having what is needed for each scenario. 

Certain skills can help you become better as a Product Manager, as long as it’s all grounded in empathy and that you’re having a positive impact on your team. Being an effective Product Manager is more than simply hitting your KPIs. Your success is defined by how you go about things, and how you rally your team around a product vision. 

Good Product Management principles can sometimes be ignored for outputs, but it’s your job to steer everyone in the right direction. 

To become an even better Product Manager, you’re going to need the right kit. ProdPad helps you become a better Product Manager by giving you the tools to optimize your product roadmap, validate decisions with real customer feedback, and prioritize work with confidence. ProdPad is built on best practices, so you can focus on what matters most: building great products.

Try ProdPad for free today and see how it helps you work smarter, not harder.

The post What Makes a Good Product Manager? appeared first on ProdPad.

I don’t imagine any of you will be new to generative AI and language models. By now, everyone has given it a go, and most will be using it at least semi-frequently. But have you reached that transformative tipping point where it’s unlocked considerable performance benefits for you? 

The secret to really unleashing the transformative potential of generative AI lies in the prompts you’re feeding it. To truly power up what you’re able to achieve, you’re going to have to learn prompt engineering for Product Managers. That or use a tool already primed with Product Management context and instructions – like CoPilot. 

But, let’s dive deeper into how to use effective prompt engineering for Product Managers to get relevant outputs. 

We will cover: 

• The benefits of prompt engineering for Product Managers
• How Product Managers can use AI
• The principles of good prompt engineering for Product Managers
• How to structure prompts for Product Management

What are the benefits of prompt engineering for Product Managers? 

As you can probably tell, I’m a big believer in the benefits AI can offer Product Teams. But I also realize some may have a degree of concern – a fear that AI will come to replace Product Managers. 

Let’s nip this in the bud. No, AI won’t replace you. Don’t fear AI, rather embrace it as a superpower that will help you boost your performance and shore up your career prospects. 

Look, AI is a tool – a powerful tool in your toolbox. When Photoshop launched it didn’t spell the end for photographers, rather it gave them new capabilities and helped them do more. Yes, they had to learn how to use this new tool, but those who mastered this new way of photo editing, skyrocketed what they were able to achieve. AI tools can be your Photoshop. You just have to learn how to use them.

When you’ve mastered prompt engineering for Product Managers, you’ll have a game-changer on your hands. The benefits include: 

• Saving considerable time: When you feed generative AI a well-structured prompt, it can deliver highly relevant, and well-written outputs faster than you could draft yourself. Whether you’re summarizing a user research session or writing up your documentation, AI can save you time while keeping the quality high.
• Boosting your creative problem solving: Generative AI can become your creative sidekick, turning well-constructed prompts into a stream of fresh ideas. Need a new perspective on your product differentiation or possible solutions to a problem? A strong prompt can spark ideas you hadn’t considered.
• Speeding up your iteration cycles: Generative AI accelerates processes like concept testing, prototyping, and creating an MVP. By producing usable outputs faster than traditional methods, it allows you to iterate, refine, and adapt at a greater pace.
• Improving team productivity: Generative AI doesn’t just help you – it helps the whole  Product Team. Automating grunt work and speeding up tasks will mean everyone is more efficient.
• Enhancing your Product Management expertise: Generative AI isn’t just there to delegate tasks to. Yes, it can kick out great writing in seconds, but it can also answer your questions! Especially if you use an AI chat tool specifically trained on Product Management know-how, like CoPilot, you can lean on AI to help assess how you’re working, give advice on how to approach a piece of PM work, and generally help you understand best practice. 

How can Product Managers use AI? 

So, what can AI help with during your day-to-day as a Product Manager? Where is it best applied to unlock the time savings and performance boosts I’m promising? With effective prompt engineering, AI can help with virtually every area of product development. 

1. Product Strategy

AI tools can be a great help when it comes to your product strategy, for example:

Strategy communication

AI tools can help you articulate your product strategy. We all know how important it is to communicate your overarching ambitions and priorities in a way that everyone can understand. Without that, you stand no chance of getting alignment across teams.

You need to remove ambiguity and inspire your teammates to work towards the vision. The best place to start when it comes to getting AI help is your vision statement.

Either tell your AI tool what your product is and what you want to achieve with it and ask it to write a motivating, clear product vision statement, or, give your existing vision statement to your tool and ask it for constructive feedback and improvements. 

Generative AI is great at taking a lot of words or streams of notes and turning it into something concise. It’s also great if you tend to think in bullet points, and need that converted into more creative prose.

Goal setting  

Once you’re clear on the broad ambitions of your product, it’s time to get more specific and set some goals to work towards. 

Provided you give your tool the context of your product and the broad vision, you can ask AI to generate relevant objectives and goals. Be sure to specify your preferred framework – e.g. OKRs – and be clear on the format you want. It’s useful here to give one example and then let the AI generate others in line with that. 

Idea generation

Whether you’re thinking about a new product to solve a problem you’ve identified in the market, or looking for potential feature ideas as part of roadmap initiatives, AI can kick-start your thinking. 

Just outline the context for the AI, feed it your vision, objectives, and whatever else you have, and ask it to come up with some product ideas for achieving those goals and solving the problem.

2. Discovery

AI can be your friend when it comes to your discovery process, whether it’s your initial product discovery on a brand new product proposal, or your continuous discovery to validate each idea in your backlog. Here are a few areas where you can employ AI to speed things up. 

Market and competitor research (with caution)

Now, I have to add a note of caution here. Yes you can use AI to help you with market or competitor research, but be conscious that most general AI models will have a knowledge cutoff. The knowledge cut off represents the point in time when the data feeding the AI model was last updated. For example, for GPT-4o models the cutoff is October, 2023 (at time of writing).

Therefore, in most cases, your AI tool is not going to have up-to-date intelligence on market trends or your competitors. So asking AI to do something like ‘create a feature comparison’ is unlikely to give very accurate results. 

However, you could ask AI to give you an assessment of a particular market to use as a base against which to manually fact-check and get updates. If you’re struggling to know how to structure a market analysis report, your AI tool could kick one off for you. At least it gets you off a blank page! Or ask your AI tool to take a long (and recent) industry report or a competitor annual report and summarize it.

User research (caution again)

OK, I’m going to add another note of caution here. You need to avoid overreliance on AI when it comes to user research. Nothing should replace your efforts to speak to real or potential customers. 

Thorough user research is crucial for the validation of ideas and ensuring what you build will drive the outcomes you want, so you have to be certain you’re using solid evidence to make informed decisions. 

Don’t think you can simply ask AI “Would a customer of a mobile banking app find a budgeting tool useful?” and make your decision based on the output. 

But does that mean AI can offer nothing useful when it comes to user research? Absolutely not. AI could help by: 

• Suggesting research methodologies
• Generating research questions for user interviews or focus groups
• Writing test scripts for user testing
• Helping to prepare research reports and presentations
• Analyzing data from your research efforts to help you draw conclusions

This brings us nicely onto….

Data analysis  

AI is pretty darn good at analyzing large amounts of data and spotting themes, patterns, or irregularities. And that can be a huge time-save for Product Teams. No longer do you have to run your own affinity mapping workshops to find common themes in your feedback (for example), or spend hours wading through usage data to spot patterns. 

However, you should think carefully about what AI tools you use for your data analysis. If you use a general AI tool,  then you’re going to have to package up your raw data and upload it. Not only will that require exporting from wherever that data is, formatting it, and uploading, but you’ll also have to explain that formatting to the AI so they understand what they’re looking at. That’s a lot of hassle. 

The other option is to make sure you’re using a data capture tool that has robust built-in AI capabilities. This way the AI already has your data within its source content and you cut out all that exporting and importing. 

So look for product analytics tools that have AI capabilities and customer feedback tools that offer AI-powered automatic analysis.

AI prototyping

When it comes to testing possible solutions and products with real users, AI can really accelerate what you’re able to achieve as a Product Team or lone Product Manager. You can use AI to get a prototype off the ground, without having to fight for development resources to help you do it. 

There are specialist AI tools for writing code, but equally, general generative AI models can write code and knock up a prototype for you. 

You can prompt AI models with some well-crafted prompts, feed them a design or even a PRD from which they can formulate the necessary code to bring the prototype to life. 

3. Feedback

Managing customer feedback is a huge part of the Product Manager role, and is often where a lot of time is lost. So how can AI help you move through user feedback faster, so you can get to the insights and start working on solutions? 

Capturing feedback

One way AI can help with capturing feedback is through turning customer interactions into usable content. For example, taking advantage of generative AI capabilities offered by many video conferencing tools can turn a video call into a written transcript in moments.

There are also AI note-taking tools that you can add to any call and get instant write-ups that you can add to your feedback inbox. 

Summarizing

And if those long transcripts are too much to easily digest and make sense of, AI can give you a succinct summary and save you from reading through reams of text.

Analysis

We’ve already touched on this when we covered data analysis, but it’s worth saying again! AI can save you a bunch of time and surface the themes across your entire body of feedback in moments. To get the idea, take a look at how ProdPad’s Signals tool works. 

4. Prioritization

Prioritization is both an art and a science. It’s where Product Managers shine, but figuring out what to tackle first and balancing stakeholder demands, customer needs, and strategic goals is no small feat. Luckily, AI can help simplify the process. 

AI can analyze inputs like customer feedback, user behavior, and business objectives to provide priority scores for your product ideas. For example, with CoPilot, you can ask it to analyze Ideas on specific roadmaps and review them with whatever prioritization framework you like.

If you’re using prioritization frameworks like RICE, AI can crunch the numbers for you. Input your data—such as the effort estimates or customer reach of a feature—and let AI calculate scores or assign categories. This saves time and ensures consistent, unbiased assessments.

5. Backlog Management

Depending on what tool you use to manage your backlog of product features ideas, AI can help you save time when it comes to grunt work. 

Let’s face it, you didn’t become a product professional to push tickets around a board – you’re here to make strategic decisions and drive outcomes. So the more you can rely on AI to handle the admin stuff, the better. 

ProdPad customers enjoy AI assistance when it comes to managing their backlogs with duplicate ideas being automatically flagged and removed, and feedback being linked to related ideas (and vice versa). That, amongst other things, saves a bunch of time and frees them up to concentrate on discovery and decision-making. 

6. Product Documentation  

OK, here is another place where there are rivers of time that can be saved with the help of AI. Generative AI has been a game changer when it comes to writing copy and producing documentation. Just give your chosen tool the context of your product, the particular feature idea and/or the intended user and ask it to create whatever documentation you need. 

In some cases you might have to be explicit about the structure and format you want to see, at other times you might be happy to see what the AI generates. 

Some of the documentation you could delegate to your AI assistant might include:

• Idea descriptions
• Product requirement docs
• Specifications
• User stories
• Acceptance criteria
• Release notes
• Customer emails
• Internal updates 

7. In-Product Copy Creation  

Internal documentation isn’t the only writing you have to do as a Product Manager. You need to write convincing and helpful in-product copy that helps to drive users towards certain actions. 

Whether you’re encouraging users to make a purchase, take an onboarding step, or try a new feature, you need to craft conversion-focused words – and that’s not easy. 

If you get your prompt right (keep reading to find out how!) you can get very convincing copy out of your chosen AI tool. Then you just need to copy and paste it where it’s needed and sit back and watch the results. 

8. Stakeholder Management & Communication  

This is another area of Product Manager responsibilities where time sinks are all too common. Here at ProdPad, we’ve always focused on how we can make this easier for Product Teams and reduce the manual work. From customizable roadmap views, to easy external roadmap publishing, automatic update notifications, to tight integration with tools like Slack and Teams. 

ProdPad has a whole host of capabilities that take the stress out of stakeholder comms. But how can AI help even further? 

CoPilot, as an AI assistant that sits deep within your Product Management system, has access to your roadmap, your backlog, all your customer feedback, your strategy, OKRs, and more. This unique knowledge means that CoPilot can answer almost any question about your product work. This is a complete game changer when it comes to fielding those day-to-day, impromptu questions from stakeholders across your organization. 

For example, let’s say your boss wants to know everything on the roadmap that relates to a certain strategic objective. Sure they could look at your roadmap (and even group it by Objective in ProdPad), but the chances are they’re just going to fire the question over to you. 

With CoPilot you can give them an alternative outlet for those ad-hoc questions – CoPilot can tell them exactly which Initiatives and Ideas answer their chosen objective and even provide links to each. 

With CoPilot fielding all the questions from your stakeholders, you’re no longer going to get pulled away from your deep-focus work and get to crack on with more of what matters most.  

9. Coaching and best practice advice  

Where you might have gone digging around in forums, asking in online communities, or searching online, now you can add AI to your sources of best practice advice and guidance. 

Sense-checking the way you approach a certain Product Management job, or asking for advice on how best to do something, is a great idea if you want to be the best Product Manager you can. So I always advocate the use of AI tools as sounding boards or fast-access coaches to help you understand best practice ways of working. 

But, of course, the advice AI will give is only ever going to be as good as the advice the model has been fed and trained with. 

Take CoPilot for example, CoPilot is an AI sidekick built specifically for Product Management and has been carefully fed with certain, curated sources of best practice information to ensure it always delivers the best coaching and advice.

The secrets of prompt engineering for Product Managers

OK, now you know the potential – all the different ways generative AI can help you do more and move faster across the whole Product Management lifecycle. But, as I’ve said, you won’t necessarily get results you’re happy with right off the bat – certainly not with the most common generalist AI tools. So let me show you how to master the science (or is it art?) of good prompt engineering for Product Managers.

Here are the general principles you need to remember when engineering your prompts:

• Provide context and information: AI can’t read your mind – it needs relevant details to work effectively. Always include background information in your prompt. If you’re asking for suggestions about your product, it needs to know what your product is! Feed it clear context like user personas, product goals, or the value proposition.
• Keep it simple and structured: Overloading the AI with too much detail can confuse it, just like handing someone a 50-step IKEA manual. Instead, focus on concise, goal-oriented prompts. For complex tasks, break them into smaller, manageable parts to ensure clarity and accuracy in responses.
• Use natural language: AI responds best to prompts written in everyday language, just like talking to a teammate. Avoid robotic phrasing or overly formal tone, and stick to clear, conversational language to get the best results.

Of course, things get wayyyy deeper than this. To help you master this valuable skill, there’s a useful framework I want you to meet:

The W-I-S-E-R Framework. 

This framework was created by Allie K. Miller, one of the most influential voices in AI in business. It’s designed to help you give generative AI all the context and information it needs for to deliver a cracking result.

Here’s what the W-I-S-E-R structure gets you to do. 

W – Who is it? Assign the AI a role. For example, “You are a Product Manager creating a go-to-market strategy for a SaaS platform.”

I – Instructions . Be specific about the task. Say something like, “Draft a high-level GTM plan with key action points.”

S – Subtasks . Break the request into smaller pieces. For example, “Start by outlining the target audience, then list three marketing channels, and finally suggest KPIs to track success.”

E – Examples . Provide a reference or template. Say something like, “Here’s an example of a roadmap format we’ve used before—align your response with this structure.”

R – Review . Refine the output. Ask for adjustments like, “Add more detail to the target audience section,” or “Reformat this as a presentation outline.” Iterate as needed.

Nice. But we can go EVEN DEEPER! Let’s look at each of those step by step and discuss some advanced prompt engineering techniques to help you build a better structure for your prompt engineering.

How to structure AI prompts for Product Managers

W – Who

I’d like to expand on the first step in the WISER framework, because yes you need to tell the AI tool from what perspective they should be generating their output, but there’s more to giving relevant context setting that just ‘who’. 

You need to outline ‘who’, ‘what’ and ‘why’. 

Since we’re here to talk about prompt engineering for Product Managers, let me illustrate this with a Product Manager example. 

Who = a Product Manager 
What = managing a mobile banking app 
Why = designed to help young people better manage their finances 

There are a couple of advanced prompting techniques that I’d like to introduce here, each of which can prove useful when setting this context within your AI prompts for Product Managers. 

Domain priming

Domain priming involves instructing the AI to adopt a specific role or perspective when responding. This technique is how you make the AI answer from a ‘Product’ perspective. 

Role-playing

This is kind of  like domain priming, but slightly more creative. It’s a good technique if you want to explore different perspectives on something. This could be useful if you wanted to kick off some customer research and generate a list of possible pain points for different user types. You can get the AI to pretend to be a user, getting some creative outputs as a result.

So, the opening of your prompt might look something like this:

You are a Product Manager for a mobile banking app. The app is designed specifically for young people (aged 16 – 25) to help them learn financial acumen and better manage their finances.

I – Instructions

The next stage is to set your instructions. This is where you’re prompting the AI with exactly what you want it to deliver. Want a table of results? Tell it that. What a mindmap? Demand it. Keen for a bullet point summary? You better mention that.

If your instructions aren’t clear, the AI is going to do what it thinks best – which might miss the mark if you have a set idea of what you need. 

Now there are a few advanced prompting techniques that could help you here. One option is:

Chain of Thought (CoT). 

This technique involves asking the AI to reason step-by-step. It’s particularly useful for complex prompts, as it ensures that the AI breaks down the process logically. For example:

“List three common objections personal banking customers might have to using a budgeting feature. Then, for each objection, suggest a solution or feature improvement to address it.”

This clear structure encourages better-organized responses and helps you get actionable insights faster.

Remember: the more precise your instructions, the better the output. Vague instructions will yield vague results, but thoughtful direction will maximize the AI’s potential to deliver exactly what you’re looking for.

So, if we continued our prompt, the ‘I’ section may look something like:

Adoption and usage rates are low for our budgeting feature. We need to come up with ideas to solve this problem. List three common objections our banking customers might have to using the budgeting feature. Then, for each objection, suggest a solution or feature improvement to address it. 

Present your ideas in a concise bullet-point format, including how each solves the problem.  

Of course, if you’ve got a more complex ask, that has a few steps, you’re going to want to break things down so that everything remains simple. This leads us to…

S – Subtasks

You can break your prompt into different sections if what you need is a bit more complicated. 

For example, say you want to map out a Product Manager’s approach to increasing feature adoption. This task involves many steps: understanding user pain points, brainstorming potential solutions, evaluating their feasibility, and creating a communication strategy. 

To get meaningful responses, you’ll want to break these steps down into smaller, more manageable subtasks. You do not want to ask for all of this at once otherwise the machine might get its wires crossed. 

Prompt-chaining

One useful advanced AI prompting technique here is prompt-chaining. This is where you connect multiple prompts together to build on the results of the previous responses. Instead of asking for everything at once, you guide the AI through a logical sequence of tasks, step by step. For instance:

1. Start by asking the AI to list common reasons why users don’t adopt new budgeting tools.
2. Once you have this list, ask the AI to generate possible solutions for each identified reason.
3. Finally, prompt it to suggest the best way to communicate these solutions to users, keeping their needs and preferences in mind.

By chaining prompts like this, you can get detailed and well-structured outputs that align with the complexity of your task. It also helps maintain focus, ensuring the AI doesn’t get overwhelmed by too many simultaneous instructions. This is one reason why many prompts fail – you’re asking too much.

Continuing on our example prompt, the subtasks section will look like: 

After generating three solutions, rank these solutions, using two criteria: 
1. Technical feasibility: How easy is it to implement each solution from a technical standpoint?
2. Impact versus effort: How effective will the solution be in increasing user adoption, versus the resources (time, cost, etc.) needed to implement it?

E – Examples 

If you really want to steer your AI prompt in the right direction, give an example of what you’re looking for. The example acts as a clear target that guides the AI’s reasoning and structure.

Plus giving an existing example also ensures that the AI doesn’t come up with something you’ve already considered.

Few-shot prompting

One advanced technique related to this is called few-shot prompting. This method involves providing the AI with a few examples of the type of response you’re expecting, instead of just a single example or no example at all. 

So when giving examples for our prompt, you can add something like:

A couple of pre-existing ideas we had include: 
1. Implementing an in-app tutorial that explains how to use the budgeting feature. This addresses the pain point of finding the feature too confusing but is a large development time sink. 
2. Gamifying the budgeting feature by offering personalized incentives for users who complete goals when using the feature. This encourages continuous adoption but may not get approval from other stakeholders.

R – Review 

Now, after following the first few steps of W-I-S-E-R, you’re going to get a far better response compared to basic prompts. But still, this first response isn’t going to be the best it can be. Just like any writer revising their first draft, the AI’s initial output can often benefit from some refinement. This is where the review stage comes in.

By reviewing the response and using the reflection technique, you can further improve the quality and relevance of the output.

Reflection

The reflection advanced prompting technique allows you to engage in a second round of thinking with the AI. In essence, you ask the AI to reflect on its own work, evaluate its decisions, and identify areas that can be improved. This iterative process helps with refining prompts by encouraging the AI to be more accurate, focused, and creative.

To nail this, specify what you want it to look at during the reflection, such as if it addressed all the pain points you provided, or aligns with the context. What you ask is specific to your goals, but some general things you want to check with a reflection include: 

• Clarity 
• Creativity 
• Feasibility 
• Gaps

So once you’ve gotten your first response from our example prompt, you can follow up with:

Review the proposed solutions, paying close attention to clarity, creativity, and feasibility.

Next, identify any gaps in your response, or if anything has been overlooked. Could certain aspects of the solutions be more aligned with the target audience’s pain points?

Finally, reflect on the effectiveness of the solutions you proposed. Could they be made more actionable or user-friendly?

So with that, we’ve got a complete prompt, and follow-up, following the W-I-S-E-R framework, alongside some advanced AI prompting techniques to generate accurate responses.

Can’t be bothered with all that?

Now many of you might be thinking – ouch, this is a lot of work for something that’s meant to be making my life easier. If I need to put so much effort into creating an AI prompt just to make the response okay, why don’t I just go and do the thing myself? 

Fair comment, and a fair complaint. Luckily, there’s an AI tool for Product Managers that doesn’t need this level of extra context and detail. An AI tool where you don’t need to add context every time you write a prompt because the model already has an understanding of your product, roadmap, backlog, and more.

I think you already know where this is going…

This is exactly what CoPilot does! 

When using our AI tool, you don’t need the preamble. Want it to refine your roadmap? Just go ahead and ask it.

So before you worry too much more about prompt engineering for product managers, go give CoPilot and try and see how much faster you can get to the results you want. 

To learn even more, check out our on-demand webinar on writing great AI prompts for Product Management, hosted by yours truly. 

The prompt engineering for Product Managers playbook 

AI is a game-changer for Product Managers, but the real magic lies in knowing how to use it effectively. Think of it like a violin: in the right hands, it can produce breathtaking music, but without the skill, it’s just ear-piercing noise.

Mastering the art of prompt engineering for Product Managers is a valuable skill that unlocks AI’s potential. It transforms AI from a mere tool into a powerful ally in your Product Management toolbox.

Whether you’re skeptical, excited, cautious, or curious about AI, the reality is clear: there are countless AI tools out there that can make your work as a Product Manager more efficient and impactful. Among them, CoPilot stands out as the ultimate choice for Product Managers.

Ready to see CoPilot in action? Start a free trial and try for yourself. We’re confident you’ll be impressed.

The post Prompt Engineering for Product Managers: How to Get Things Right With Generative AI appeared first on ProdPad.

Newsletters are the best way to stay in the loop with the latest movers, shakers, and wave-makers in the product world. When a good Product Management newsletter hits your inbox, you’re just a click away from thought-provoking leadership and actionable information to make you a better PM. Convenient, right?

Newsletters give you direct access to some of the most respected minds in Product Management, product design, product-led growth, product operations, and every other product-related field you care about. Many of these folks have literally written the book on modern Product Management practices.

Now here’s the catch with newsletters: I’m willing to bet that you’ve signed up for dozens of newsletters, only to regret it when your inbox gets flooded with messages that fail to grab your attention.

That’s what we’re here to help you avoid. We’ve handpicked a list of our favorite Product Management newsletters for 2025, curated by myself and the rest of the ProdPad team. These are the newsletters we actually read, love, and trust to help us build a product we’re proud of.

We’re confident this list of 17 Product Management newsletters will do the same for you.

1. The Outcome

The Outcome is our very own newsletter here at ProdPad. There may be a minor, very small chance that we’re a little biased, but we think it’s pretty great. It’s a treasure trove of insights for a range of Product Managers, be it if you’re a newbie looking to learn, or an old hand wanting to keep up with the latest thought leadership and best practice.

In each edition, you can expect articles on topics like user research, customer feedback, feature prioritization, and product metrics, as well as useful Product Management resources like this list! The Outcome also often includes info about our upcoming webinars featuring industry experts, giving you access to the latest thinking and best practices in the field of Product Management.

The Outcome covers the entire Product Management lifecycle, from idea conception to product launch, and beyond. Whether you’re a novice or a seasoned pro, The Outcome ensures you stay up-to-date with the ever-evolving world of Product Management. 
It’ll also let you know about the latest and greatest updates we’ve made to our own product, such us our new inbuilt AI tool: CoPilot.

2. Lenny’s Newsletter

If you can only choose one Product Leader to keep up-to-date with, make sure it’s Lenny. Lenny Rachitsky, a former Airbnb product lead, has created a newsletter that has become a must-follow in the Product Management community. 

Why? Well, Lenny’s Newsletter is a rich source of deep insights into product strategy, growth tactics, and the art of building exceptional products.

Lenny leverages his extensive experience to provide subscribers with data-driven analyses and practical takeaways, with regular updates multiple times a week. His weekly newsletter often features interviews with the best Product Leaders from various companies, and his surveys are a great way to keep your finger on the pulse of what’s happening in Product Management. 

He also hosts an entertaining and informative Product Management podcast series, unsurprisingly titled Lenny’s Podcast.

Expect to find comprehensive case studies and in-depth explorations of successful product launches in this newsletter. Whether it’s dissecting the strategies of tech giants or uncovering the secrets of startups, Lenny leaves no stone unturned. Lenny’s Newsletter is an indispensable resource if you’re seeking a newsletter that combines real-world examples with strategic thinking.

3. One Knight in Product

Jason Knight, a seasoned Product Manager and engaging host of the “One Knight in Product” podcast, brings his Product Management insights to your inbox with this lively newsletter. Jason’s unique perspective on the Product Management world is informed by years of experience and a knack for storytelling.

In his weekly newsletter, you can expect a mix of actionable advice, thought-provoking commentary, and curated content from around the product sphere. Jason doesn’t shy away from tackling complex topics, often breaking them down with clarity and a touch of humor. Whether he’s discussing roadmapping strategies, product-market fit, or navigating stakeholder management, his insights are refreshingly relatable.

If you’re looking for a newsletter that combines wit, wisdom, and a genuine passion for Product Management, One Knight in Product is your perfect match.

4. Product Talk

Product Talk is a newsletter curated by the fantastic Teresa Torres, and it goes beyond just surface-level discussions of Product Management. Teresa, a product discovery coach with years of experience, offers a deep dive into product strategy, design, and innovation. Her newsletter is designed to help Product Managers make better, data-driven Product Management decisions by providing them with the tools and insights they need.

One of the standout features of Product Talk is the focus on user research, emphasizing continuous discovery and customer-centric product development. Teresa often shares techniques, case studies, and real-world examples of how to conduct effective user research and translate those insights into product improvements. If you’re looking to refine your product discovery and development processes, Product Talk will set you on the right path.

It’s not just practical advice though. Product Talk often includes thought-provoking essays and commentary on the state of the Product Management industry. Teresa’s unique perspective and expertise make this newsletter a must-read for anyone looking to deepen their understanding of Product Management principles.

Learn more about Continuous Discovery in one of our past webinars with Teresa Torres:

5. TLDR Product 

TLDR Product is an aggregated newsletter. That means that the creators scour the Product world to find THE BEST articles on Product Management that you need to be reading. If you find it hard to know what to read and to find the right resources, TLDR Product does that all for you, bringing you must-read content to your inbox. 

For those who crave bite-sized updates on the latest in Product Management, TLDR Product is your go-to. This newsletter delivers quick, digestible insights into trends, tools, and strategies that Product Managers can use to stay ahead of the curve.

TLDR Product keeps its focus broad, covering everything from new tech developments to practical tips for improving team collaboration and workflows. Each edition is packed with insightful articles, ensuring you get the most important updates in just a few minutes.

Perfect for busy PMs – which is all PMs, right – TLDR Product ensures you’re always in the know without the information overload.

6. The Product Compass

Paweł Huryn’s The Product Compass is a newsletter designed to help Product Managers navigate the ever-evolving landscape of their field. With years of experience in product strategy and leadership, Paweł shares clear, actionable guidance to help you align your efforts with the broader business vision.

The newsletter focuses on product strategy, team leadership, and long-term planning. Expect insights on topics like creating a compelling vision, prioritizing effectively, and fostering a culture of innovation. Pawel’s knack for providing practical advice grounded in real-world experience makes this newsletter a standout.

If you’re looking for a resource that provides clear direction and steady guidance, The Product Compass is the one to follow.

7. Prioritised 

Prioritised is the newsletter from Mind the Product, a community of Product Managers and product enthusiasts (that I am a co-founder of!). Mind the Product is renowned for their Product Management conferences and events, and their newsletter is an extension of their commitment to product excellence.

In each edition, Prioritised brings together insights from a diverse range of product experts and thought leaders. It covers a wide array of topics, including product leadership, design thinking, and innovation. It provides fresh and varied perspectives on common Product Management challenges.

Mind the Product also often features updates on their unmissable events, workshops, and webinars, as well as product job listings, making it a valuable resource for networking and professional development.

8. ONE THING

Bruce McCarthy’s newsletter, ONE THING, is a concise and thought-provoking resource for Product Managers. As a co-author of “Product Roadmaps Relaunched,” Bruce has a wealth of experience in product development, and he distills his wisdom into bite-sized, actionable insights in each newsletter.

ONE THING focuses on the fundamental aspects of Product Management and product culture. You’ll get articles on topics like prioritization, roadmapping, and fostering a culture of innovation.

What sets this newsletter apart is its commitment to delivering one key idea or concept in each edition, allowing readers to absorb valuable knowledge quickly. There’s no fluff, just the core nugget of information you need to know.

Bruce’s approach to Product Management emphasizes simplicity and clarity, making ONE THING an easy win if you like your advice straightforward and actionable. If you’re a product person looking for a concise yet impactful newsletter that hones in on the essentials of Product Management, ONE THING is a great choice.

We’ve got a couple of great webinars with Bruce. Check out his talk pitting OKRs and roadmaps against each other:

9. SVPG Newsletter

The SVPG Newsletter is brought to you by industry legend Marty Cagan’s Silicon Valley Product Group, a well-respected consultancy known for helping organizations build successful products. This product newsletter is an extension of SVPG’s expertise and offers valuable insights and strategies for Product Managers.

In each edition, you’ll find articles and resources covering a wide range of product-related topics, from product leadership and innovation to user experience and product-market fit. The SVPG team leverages its extensive industry experience to provide practical advice that can help you excel in your Product Management role.

One of the best things about this Product Management newsletter is its emphasis on product leadership and team dynamics. Marty often pens articles exploring the challenges of leading product teams effectively, making it a source of knowledge for aspiring and seasoned product leaders alike.

10. The Looking Glass

The Looking Glass is a unique and thought-provoking newsletter that offers a fresh perspective on Product Management. Authored by Julie Zhuo, co-founder of Sundial and former VP of Product Design at Facebook, this newsletter is a series of essays and musings that contemplate the future of Product Management and design, innovation, and how to effectively lead a product team.

Less focused on How-to content, The Looking Glass excels at making you think deeply about Product Management, sparking critical thinking and encouraging readers to consider the bigger picture. It’s a great jumping-off point for Product Managers who want to challenge their assumptions and explore new ideas.

However, it’s worth noting that half of the newsletter is restricted by a paywall.

Expect to find content every other week that explores the intersections of technology, culture, and human behavior. The Looking Glass is an intellectual exploration of the Product Management landscape, making it a compelling read.

11. Growth Unhinged

Kyle Poyar’s Growth Unhinged is another real gem. Kyle dives into the nitty-gritty of what it takes to scale products and teams, be it articles on Growth Product Management and deep dives on PLG companies, and his insights come from a pretty solid place – he’s an Operating Partner at OpenView. This means he’s constantly in the mix, checking out what the fastest-growing startups are up to. So, when you’re reading his newsletter, you’re getting a peek into the growth strategies and tactics that are working in the real world, right now.

Then, there’s the community aspect of his Product Management newsletter. Kyle’s not just throwing information at you; he’s actually fostering a space where founders, investors, and product folks can share their best advice for Product Managers and growth insights. This kind of community vibe makes the newsletter more than just a read; it’s like being part of a club where everyone’s keen on growing and learning together.

Each issue is packed with case studies and growth strategies, but it’s also super accessible. Whether you’re a seasoned PM or just starting out, you’re bound to find something that resonates.

12. The Beautiful Mess

The Beautiful Mess from John Cutler is a pretty awesome resource for Product Managers. First off, John’s fascinating insights on product development can really help you if you’re trying to get better at managing projects that involve a bunch of different teams. He’s worked as a Product Evangelist at Amplitude, so he knows his stuff when it comes to creating products that not only rock the market but also play nice with all parts of a company.

His knack for mixing hard data with the softer, more human side of things also brings a rare and valuable take that you don’t often see. This is super important – as a Product Manager, you’ve got to make decisions based on solid facts, but you can’t just ignore what users are saying if it disagrees with your numbers. John’s newsletter is like getting a weekly dose of this balanced perspective, which is great for keeping your skills sharp and your empathy honed.

13. Product Management IRL 

Amy Mitchell’s Product Management IRL brings the day-to-day realities of Product Management to life. With her approachable style and practical focus, Amy offers a newsletter that feels like a conversation with a trusted colleague.

As Principal Product Manager at Dell Technologies, Amy draws from extensive experience to share lessons learned from her career, often exploring how to manage competing priorities, improve Product team dynamics, and advocate for users. She writes with honesty and authenticity, making her insights refreshingly relatable.

If you’re after authentic advice for thriving in the world of Product Management, Product Management IRL is a must-read.

14. Food for Agile Thought

Agile methodologies and Product Management go hand-in-hand. Curated by agile coach Stefan Wolpers, Food for Agile Thought is a treasure trove for Product Managers interested in agile practices and continuous improvement. Stefan’s 18+ years of expertise as a Scrum Master, Product Owner, and Agile Coach shine through in this thoughtfully curated newsletter.

Each edition features a mix of articles, insights, and tools that cover agile methodologies, product ownership, and team dynamics. Stefan also authored the Scrum Anti-Patterns Guide, where he highlights common pitfalls in Scrum practices and offers insightful remedies to enhance team effectiveness.

Food for Agile Thought connects you with the best agile-focused content of the week, while also bringing you a selection of Product Management focused work too. Stefan also fact-checks various articles, breaking down any that contain misinformation in his Lemon of the Week section.

If your focus is on blending agile principles with effective Product Management, Food for Agile Thought is your go-to weekly read.

15. UX Collective Newsletter 

The UX Collective newsletter is all about giving you a fresh perspective on product design – moving beyond just ticking boxes and genuinely innovating in the design space. It’s like getting a regular dose of inspiration that can totally change how you approach designing your products. The newsletter covers a lot of ground, from user experience tips to the latest design trends, and it’s super helpful for keeping your ideas fresh and your Product Management skills sharp.

You don’t need to spend hours sifting through articles and blogs; this newsletter brings the cream of the crop right to your inbox. It’s a time-saver and a life-saver for busy Product Managers who want to stay in the loop without getting overwhelmed.

16. The Product Growth Newsletter

Aakash Gupta, a growth expert and founder of The Product Growth Newsletter, offers a focused take on scaling products and teams. As VP of Product at Apollo.io, a unicorn startup, Aakash honed his expertise in driving product growth, which he now shares through his newsletter.

The newsletter dives deep into growth experiments, case studies, and industry trends, offering actionable strategies for acquisition, retention, monetization, and more. Aakash also offers a Product Growth Course Pack for those looking to supercharge their knowledge and skills in product-led growth.

Whether you’re improving your product’s onboarding experience or figuring out your next growth lever, this newsletter equips you with the tools and inspiration to make it happen. For those looking to grow their products and their impact, The Product Growth Newsletter is an invaluable resource.

17. Morning Brew

While not exclusively focused on Product Management, Morning Brew is a widely recognized and respected source of business, finance, and tech companies news. Its unique blend of informative content, wit, puzzles, and brevity makes it a favorite among professionals across various industries, including Product Management.

In each daily edition, the Morning Brew delivers a concise and engaging summary of the day’s top news stories, including those related to technology, startups, and innovation. As a Product Manager, staying informed about broader industry trends and economic developments is crucial. Morning Brew provides this wider context, helping you make informed decisions in your role.

Another bonus is that Morning Brew often features special reports and deep dives into specific industries, offering valuable insights that can inform your product strategies.

Why reading Product Management newsletters will make you a better PM

Product Management is more than a job you do. For many, it becomes a lifestyle and and obsession. It’s a dynamic and constantly evolving discipline. If you want to get good at it – and STAY good – it requires a commitment to continuous learning and adaptation. That’s why you need to be inhaling Product Management newsletters like a blue whale sucking in plankton. 
They’re invaluable assets for product professionals who really want to excel in their roles and climb the Product Management career path.

Newsletters give you insights, regularly, fresh from the horse’s mouth. Where else will you get this direct access to knowledge?

Here’s why you should make a habit of reading Product Management newsletters in 2025 and beyond:

1. Stay informed and relevant: The Product Management landscape is notorious for its rapid changes in technology, market dynamics, and user expectations. Subscribing to newsletters keeps you informed about the latest industry trends, emerging technologies, and shifts in consumer behavior, helping you to ensure your product strategies remain relevant and adaptable.
   
2. Access to expert insights: Many Product Management newsletters are curated by industry experts, thought leaders, and seasoned practitioners. These individuals possess a wealth of experience and knowledge, and their newsletters offer direct access to their insights and wisdom. Learning from the experiences, successes, and failures of these experts can help you avoid common pitfalls and make informed decisions.
   
3. Continuous learning: Successful Product Managers are lifelong learners. Newsletters provide a structured and time-efficient way to consume new information, research, case studies, and best practices. They offer bite-sized, easily digestible content that you can integrate into your daily work, ensuring that you’re constantly growing as a professional.
   
4. Networking opportunities: Product Management newsletters often include announcements and information about industry events, conferences, webinars, and meetups. Attending these events can be an excellent way to expand your professional network, connect with peers facing similar challenges, and exchange ideas with fellow Product Managers.
   
5. Career advancement: Staying updated through newsletters not only enhances your skills but also opens up career advancement opportunities. Knowledgeable and proactive Product Managers are highly sought after in the job market. Subscribing to newsletters can help you identify new roles, job openings, and companies aligned with your career goals.
   
6. Fresh ideas and innovation: Newsletters frequently feature case studies, innovative product launches, and creative solutions to complex problems. By keeping up with these insights, you can inject fresh ideas and innovative thinking into your product development process, leading to more successful outcomes.
   
7. Time efficiency: Scouring the internet for relevant articles, blog posts, and research can be time-consuming. Newsletters streamline this process by delivering curated content directly to your inbox, saving you precious time that you can allocate to strategic planning and execution.
   
8. Community building: Many product management newsletters foster a sense of community among subscribers. Engaging with these communities can lead to collaborative opportunities, mentorship, and a sense of belonging in the broader product management profession.

Read all about it

Whether you’re a seasoned Product Manager or just stepping into the role, subscribing to these Product Management newsletters in 2025 will keep you informed, inspired, and ready to tackle the challenges of the industry. These curated insights are a must-have for anyone looking to sharpen their skills and stay ahead in their Product Management journey.

Pair these newsletters with tools designed to help you excel, and you’ll level up faster than ever. The right Product Management tool doesn’t just support your work—it amplifies your impact.

With ProdPad, you get more than just a tool; you get a partner in product excellence. From built-in best practices that ensure you’re always on the right track to smart prompts that make sure nothing falls through the cracks, ProdPad is here to help you do your best work. Plus, with an AI agent integrated right into the platform, you can access instant insights, ask product-specific questions, and get custom advice whenever you need it.

Ready to see how ProdPad can transform the way you work? Try ProdPad today and experience what’s possible.

The post The 17 Best Product Management Newsletters of 2025 appeared first on ProdPad.

Here at ProdPad, we’ve been using AI to save PMs time since 2018, but this brand new CoPilot is the culmination of all our efforts and the pinnacle of ProdPad’s AI tools. It’s a game changer people – let me tell you more. 

TL;DR

• CoPilot is THE major AI product launch of 2025 for Product Managers – one that represents a unique application of generative AI technology
• Specifically primed and packaged for Product Managers and Product Teams, it’s set to dramatically boost productivity across the tech industry and the ROI of Product Management 
• Combines the best of both promptless AI and a generative chat interface already primed with ‘Product’ specific context with the ability to draw on your Product Management data 
• Part of the ProdPad Product Management platform and brought to you by the team behind the Now-Next-Later roadmap and Co-Founders of Mind the Product

What is CoPilot for Product Managers?

Here, let me show you

The tech industry has just got the boost it’s been crying out for. This January, our brand new AI CoPilot launches specifically for Product Managers and Product Teams. This is a truly unique application of AI technology that will signal a bright new dawn for the Product Management discipline. 

CoPilot, a next-generation AI assistant, coach, productivity powerhouse, and all-round saving grace, has been built specifically for Product Managers. As part of the ProdPad platform, CoPilot sits alongside your product roadmaps, idea backlogs, customer feedback, and workflow management, with a full working knowledge of all your Product work and a deep understanding of Product Management best practices. 

Now, Product Teams will be able to skyrocket their productivity and results, relying on CoPilot to:

• Field their stakeholder questions
• Give them best practice coaching and advice
• Answer questions about their product work and feedback, including past decision making and status updates
• Write up their time-consuming documentation
• Automatically refine their backlog and organize their workflow
• Mine their customer feedback for insights 
• Process attachments and create new roadmaps, initiatives, ideas and more, in moments
• Onboard new teammates in half the time, quickly getting them up to speed and answering their questions

I can almost hear the collective sigh of relief as Product Managers across the globe are given the gift of time and the expert guidance to help them focus on the most important strategic decisions and drive ever greater results. 

Why is CoPilot so important for Product Managers RIGHT NOW? 

At a time when Product Managers are feeling deep insecurity in their roles, with ever mounting pressure to prove their worth and demonstrate growth, CoPilot will offer the support, guidance and assistance that will shore-up and transform their future. 

Look, here’s the thing. Product Managers should be strategic thinkers, transforming the fortunes of tech companies and powering growth through data-driven decision making. But for too long, you’ve been swamped with project management-style admin and overheads, taking your precious time away from focusing on what matters most. 

ProdPad has always been focused on rescuing PMs from this reality, and we’ve just gotten even better at it.

Enter ProdPad’s CoPilot, the most advanced Product Management AI in the world. Helping Product Teams lighten the load and power-up what they’re able to achieve. 

Why is CoPilot the best AI tool for Product Managers? 

ProdPad was the first Product Management tool to utilize AI technology to save Product Managers time. Back in 2018, our first AI Assistant DotBot was using similarity matching to automatically flag duplicate ideas and link customer feedback to relevant initiatives. 

In 2023, we added more promptless AI tools that saw Product Teams generate new ideas and write up documentation in moments. ProdPad AI has already generated over 11,000 idea descriptions, 5,000 roadmap initiatives, linked over 6,000 ideas to feedback, summarized over 2,500 pieces of feedback and generated 2,500 user stories for ProdPad customers, to name a few.   

Our AI capabilities have always been streets ahead of the competition, but now that gap is set to burst wide open. 

In a market where some competitor platforms are yet to introduce AI assistance of any kind, ProdPad’s CoPilot is changing the game completely – heck, we’re inventing a whole new game. 

The need for Product Managers to craft lengthy prompts that outline all the context of their product, the market, the vision and objectives just to feel confident that they can generate useful outputs, are a thing of the past. We have done all the priming and prompting so you don’t have to. This isn’t just AI, this is Product Management AI that’s ready to roll and poised to do PM work right off the bat. 

• No other PM AI tool has the capacity to coach a Product Manager, doing things like analyzing the strategic alignment of new ideas.
• No other AI tool can deliver outputs as hyper-relevant to the Product Management context, with an in-built understanding of the jobs and challenges Product Teams face. 
• No other PM AI tool combines both promptless AI and a chat interface.
• With no other PM AI tool can you give it a file and have it transform that into roadmaps, initiatives, feedback and more within your Product Management tool.

What’s under CoPilot’s hood? 

Here’s where things get really interesting (at least for us tech heads ). ProdPad’s CoPilot utilizes OpenAI’s API, but not in any way you’ve seen before in this space. CoPilot represents a unique application of the technology. 

My Co-Founder and our CTO, Simon Cast, jumped on the OpenAI API the moment it was made available. Waiting at the door to take advantage of this technology, knowing it would provide a monumental step change in our ability to answer ProdPad’s primary raison d’être – namely, to help save Product Managers’ time. 

Simon and the team have spent close to two years carefully crafting this latest iteration of ProdPad’s AI capabilities, painstakingly feeding the system instructions with Product Management context and experience, refining how it responds and completes actions.  

This detailed work means that CoPilot understands all prompts and commands from a Product Management point-of-view, thinking like a PM, knowing what a PM has to get done, and how a PM would approach each situation. The many months spent refining, adding to and improving CoPilot’s understanding of the PM context has paid off in spades.   

Alongside this work to meticulously prime the model, CoPilot’s content sources make it a truly unique tool in the Product Management space. Not only does CoPilot have an understanding of the Product Management context in general, but it also has complete access to your exact context. CoPilot can draw on all of the company’s Product Management work and data from within ProdPad, significantly reducing the chances of hallucinations. You guys can query your own product data through CoPilot and enjoy answers and outputs that are accurate, relevant and highly useful.

In this way, unlike other AI agents, ProdPad CoPilot isn’t constrained by a cut-off point when the model was trained. The crucial data is available to CoPilot each and every time it goes to answer a question or complete a task. 

Painstakingly detailed priming, unrivalled content sources, alongside retrieval-augmented generation, semantic search, and key phrase search make ProdPad CoPilot’s combination of promptless and chat-based AI the most powerful tool in the Product Management context. 

CoPilot also offers organizations the confidence that their data is secure. Each instance of CoPilot is logically separated meaning you can take advantage of CoPilot’s help without fear that your data will be shared and used to feed a broader model. There is no possibility of data leakage between accounts – finally you have an AI assistant who won’t reveal your secrets! 

And there’s more where this came from…

This is only the start for CoPilot, already seismic enhancements are in the works. I’ll try and avoid spoiler alerts here, but just imagine logging on first thing on a Monday morning, only to find this message from your trusty CoPilot…

“Good morning! I noticed that over the weekend you had 17 pieces of customer feedback relating to an integration with Mailchimp. I scanned the backlog and found no existing Ideas relating to that, so I’ve taken the liberty of creating an Idea, attaching all the feedback, and placing it in your queue for review this morning. Here’s a link to the Idea.” 

Eek. Nice right? 

Try it for yourself

I’ve only briefly skimmed the surface of what CoPilot can do. I would love you all to try it out for yourselves. 

Just start a ProdPad free trial, give CoPilot your existing roadmap, add a bit of info like your vision and then have a play! Ask CoPilot about the best ways to run Product processes, ask it to write you user stories, idea descriptions, get it to summarize your feedback, give it your feedback and ask for the themes. This isn’t just AI, this is the best Product Management AI in the world. Find out more and then take it for a spin.

Find out more about CoPilot at The Next AI Tool, or other AI tool directories.

The post Announcing CoPilot: The Product Management AI Set to Transform the Lives of Product Managers 🚀 appeared first on ProdPad.