This is never more important than when you’re part of an organization that’s undergoing some form of digital transformation. Here, even though you’re working on a digital product, your organization will not be used to a digital-first mindset. 

If you’ve joined a company going through digital transformation, where stakeholders are used to physical products or service-based solutions, you’ll need to take them on a journey – helping to bridge the gap between the product approach they’ve used in the past and a new digital product strategy. 

Or perhaps you’re managing a portfolio that spans both physical and digital products. You’ll need to pay close attention to the unique considerations for those digital products and make that distinction clear with a digital product strategy. 

In both cases, thinking about digital product strategy isn’t just a checkbox exercise  – it’s a challenge that needs careful navigation.

A digital product strategy isn’t just a product strategy with “digital” tacked on the front. It’s a long-term plan that accounts for the unique dynamics of digital products, including faster iteration cycles, evolving user expectations, data-driven decision-making, and ongoing optimization. 

Reckon you need a digital product strategy? No problem.

Let’s talk through the steps to build a digital product strategy that fits the needs of your product, business, and customers. We’ll introduce a new framework to help transform your product strategy into a digital product strategy. 

That framework is something I like to call Digivolution. If you watched Saturday morning cartoons in the early 00s, you may recognize that phrase . 

Let’s dig in. 

What is a digital product strategy? 

At its core, a digital product strategy is much like the product strategy you know and use, being a guide for how the product will be managed to achieve your business goals. It includes all the common components – like product vision, customer insights, and market analysis – but is crafted through a digital-focused lens. 

The structure and documentation might look the same, but the considerations and approaches need to be fundamentally different. 

When going through a digital transformation, it can be easy to create a product strategy that forgets to consider the main factors of a digital environment, as opposed to the physical focus your business may be used to. This oversight can lead to misalignment between strategy and execution. A digital product strategy ensures that the unique characteristics of digital products are addressed, including:

• Different pricing models: Digital products often use subscriptions, freemium models, or pay-as-you-go structures instead of one-time purchases.
• Product-led growth: Digital products rely more on self-serve adoption, network effects, and viral loops than traditional sales-driven approaches.
• Distinct user interactions: Customers experience digital products through interfaces, workflows, and automation rather than physical touchpoints.
• Unique challenges and friction points: Onboarding, engagement, and retention require different strategies than in physical products.
• Rapid iteration and evolution: Unlike physical products, digital products can be updated continuously, demanding an agile, data-driven Product Management strategy.

A digital product strategy isn’t just about acknowledging these differences between a physical product and a digital one – it’s about building a strategy that actively accounts for them.

It’s very similar to how there are various specialized Product Manager roles. An AI Product Manager or Growth Product Manager still follows core PM principles, but the role title makes explicit the particular focus they need to have. Similarly, a digital product strategy follows traditional strategy principles but adapts them to the digital landscape.

So, as with any product strategy, a digital version defines how your product will achieve its goals while aligning with the overarching business objectives. It’s not a plan, it’s a guiding system that helps you: 

Define your product vision
Understand customer needs
Prioritize key initiatives
Establish success metrics
Navigate the digital landscape effectively

Key differences between a product strategy for physical products and a digital product strategy

Let’s look at the difference between digital and physical products and see how that impacts the strategy. Knowing this is key for Product Managers who manage a portfolio that mixes digital and physical products (like hardware and software), or PMs who are guiding a company through a digital transformation.

Still don’t think you need to specifically worry about creating a unique strategy for your digital product? Here are some of the core characteristics of a digital product in more detail to help you out:

Speed and Iteration

Digital products evolve continuously, unlike physical products with fixed lifecycles.

Product strategies for physical products revolve around a linear lifecycle – development, launch, and eventual obsolescence. In contrast, digital products are in a state of continuous improvement. 

Regular updates, feature rollouts, and rapid iterations mean that your strategy must be flexible, prioritizing agility over long-term fixed plans. This requires adopting frameworks like Agile and Lean methodologies, ensuring that teams can pivot quickly based on user feedback and market demands.

Data-driven decisions

Digital product strategy relies on real-time analytics, not just upfront research.

Digital product strategies are dynamic, leveraging real-time analytics to inform decisions. This changes how you build your digital product strategy.

Continuous monitoring of user behavior, A/B testing, and predictive analytics allow Product Teams to refine their approach instantly. This means that product strategies must integrate data collection mechanisms from day one, so you can actively use insights to optimize user experiences, pricing models, and feature development.

Ecosystem thinking

Digital products integrate into platforms, APIs, and marketplaces rather than existing in isolation.

Unlike physical products that function independently, digital products thrive in interconnected ecosystems. Whether integrating with third-party APIs, being part of a SaaS marketplace, or leveraging cloud-based services, digital product strategies must factor in partnerships, interoperability, and network effects. 

This shifts strategic priorities and goals toward compatibility, seamless integrations, and creating value within an ecosystem rather than just focusing on standalone features.

Customer retention and growth loops

Unlike one-time purchases, digital products depend on engagement, subscriptions, and viral growth.

For traditional products, success is often measured by unit sales. Digital products, however, rely on ongoing user engagement and retention. Your digital product strategy should include mechanisms that help build this like personalized onboarding, habit-forming designs that follow the Hook Model, and incentives that encourage user advocacy. 

Scalability and tech considerations

Digital strategies must account for scalability, security, and AI-driven features.

Unlike physical products with fixed production limits, digital products can scale exponentially, but only if built with the right infrastructure. Scalability isn’t just about handling more users; it includes cloud computing decisions, database management, and automation. 

Security is also a critical consideration, as digital products handle sensitive user data and must comply with regulations like GDPR. Plus, AI and machine learning are increasingly shaping digital strategies, enabling personalized recommendations, automation, and predictive analytics to enhance user experiences.

What goes into a digital product strategy?

As we’ve said, a digital product strategy is similar to a physical one, just with an explicit focus on making sure you consider the nuances of managing a digital product. It includes the same core elements as any product strategy – just with a modern, adaptable approach. 

At its foundation, a product strategy defines what you’re building, who it’s for, why it matters, and how you’ll bring it to market. It typically covers:

• Product vision: The long-term goal and purpose of the product.
• Customer insights: An understanding of the target audience, their needs, and pain points.
• Market analysis: Research findings into the competitive landscape, trends, and broader market dynamics.
• Goals & KPIs: Your definition of success through measurable outcomes.
• Roadmap & execution plan: An outline of how the product will evolve over time.

However, building a digital product strategy requires an evolved framework. To make your strategy fit for the digital world, you need a framework that adapts to the nature of digital ecosystems, user behaviors, and rapid technological advancements. 

If you’re transforming an existing product strategy used for physical products to suit a new digital product, or if you’re making a strategy for your digital products alongside physical ones, you need to scrutinize your existing strategy and refine it through a digital-first lens. 

Every assumption, goal, and approach that worked so well for a physical product should be re-evaluated to ensure it aligns with how digital products are built, sold, and scaled.

This can be a daunting task. Trying to retrofit a physical product strategy without a structured framework can lead to gaps, inefficiencies, and missed opportunities.

That’s where Digivolution comes in.

Introducing Digivolution – evolving your product strategy for digital

Digivolution is a useful process to follow to ensure your product strategy fully embraces the realities of digital products. It helps take a previous strategy and evolve it for online products and services, addressing the unique challenges that come with them.

If you watched Saturday Cartoons in the early 00s, you might recognize the term from Digimon, where creatures “digivolve” into more powerful versions of themselves. Think of Agumon, the small yellow dino. When he digivolves, he transforms into an armored T-Rex – stronger, faster, and way more capable. 

That’s exactly what you’re doing with your product strategy: upgrading it to handle the digital landscape more effectively.

Instead of force-fitting old-school product frameworks onto digital products, Digivolution helps you systematically refine each stage of your product strategy. From pricing models to engagement loops, every element is optimized for digital success, so your strategy isn’t just functional, it’s built to thrive.

How to create your digital product strategy

Let’s walk through what you need to do with your product strategy to make it properly suited for your digital product. 

The process follows general product strategy steps but with a digital-first mindset at every stage.

Step 1: Define your product vision

Traditional Approach: Define your long-term vision, identify market fit, and clarify the problem your product solves and how you want it to grow. You can do that by creating a product vision statement or by following our free product vision template.

How to Digivolve It: Digital products don’t exist in isolation: they live in ecosystems. Your vision must account for platform scalability, integrations, and network effects to ensure long-term viability. Think beyond just what the product does today and consider how it will evolve in a constantly changing digital landscape.

• Ask: How will this product integrate with existing digital platforms and services?
• Think about your product architecture and plan for growth. Can features be expanded or adapted easily?
• Consider AI, automation, and emerging tech that could shape future iterations.

Step 2: Understand your customers

Traditional Approach: Develop detailed user personas based on demographics, behaviors, and pain points. Conduct surveys and focus groups to gather qualitative insights.

How to Digivolve It: Digital products generate real-time customer data, so don’t just rely on static personas – use live product analytics to understand behavior and hone in on your ideal customer.

• Implement heatmaps, session recordings, and A/B testing to track how users actually interact with your product.
• Use cohort analysis to see how different demographics of users are engaging with your product.
• Leverage AI-driven personalization to tailor experiences dynamically, and build user profiles to get a sense of your users based on real facts, not assumptions.

Traditional persona: “Sarah, 32, a busy Marketing Manager who needs better team collaboration.”
Digivolved insight: “Users who invite 3+ team members within their first week have a 70% retention rate. This shows that your strategy should optimize onboarding for team invites.”

Step 3: Set your outcomes & goals

Traditional Approach: Establish SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound) to track product success. These goals often focus on revenue, market share, or product adoption within a set timeframe.

How to Digivolve It: Traditional sales-driven goals don’t always capture the continuous, user-driven nature of digital products. Instead, focus on engagement, retention, and monetization metrics that reflect real user value.

• Prioritize engagement metrics like Daily Active Users (DAUs), session length, and feature adoption rates.
• Optimize for retention – set goals around customer churn reduction and cohort retention rates.
• Think in growth loops: What actions drive the different types of growth loops?
• Revenue isn’t just about sales anymore: track Monthly Recurring Revenue (MRR) and Customer Lifetime Value (LTV) as well.

Traditional goal: “Sell 10,000 units of the product in the first year.”
Digivolved goal: “Increase MRR by 15% in Q3 by optimizing onboarding to boost trial-to-paid conversions.”

Step 4: Establish KPIs & success metrics

Traditional Approach: Choose key performance indicators (KPIs) such as revenue growth, customer acquisition cost (CAC), and Net Promoter Score (NPS).

How to Digivolve It: Some business KPIs don’t always apply to subscription models, freemium structures, or SaaS offerings. Your KPIs must reflect the realities of digital engagement. Look at:

• Activation rate: How many users take the key first step that leads to long-term use?
• Churn rate: How quickly do users abandon your product, and why?
• Feature adoption: Are users actually using the features that drive business value?
• Virality metrics: Referral rates, social sharing, and organic growth indicators.

Step 5: Define your action plan

Traditional Approach: Develop a product roadmap with key milestones, dependencies, and execution timelines. Planning often follows a fixed schedule.

How to Digivolve It: Digital products thrive on agility and iteration – your action plan should focus on continuous improvement rather than rigid milestones.

• Adopt an agile roadmap like Now-Next-Later with broad time horizons rather than  rigid feature deadlines..
• Plan for continuous deployment rather than a fixed “launch and leave” mentality that leads to feature creep.
• Use customer feedback loops at every stage – your strategy should evolve based on real-world usage, not just internal assumptions.

Traditional roadmap: “Feature X launches on Feb 2, Feature Y on Apr 14.”
Digivolved roadmap: We want to solve this problem now, and we’ll prioritize this other problem next.“

Your product roadmap is one of the core ways you can communicate your digital product strategy. Because of that, you’re going to want a powerful and effective product roadmap tool. ProdPad offers just that, working as a centralized product ecosystem where you can tie your product strategy and objectives to your roadmap Initiatives and Ideas. 

Check out our interactive template to have a go yourself.

The power of digivolving your product strategy

Switching from physical to digital products doesn’t just change what you build, it changes how you think about strategy. The key difference is adaptability: instead of static planning, digital product strategies are living, breathing frameworks that evolve based on real-time user behavior, rapid iterations, and ecosystem shifts.

By applying the Digivolution framework, you ensure that your product strategy isn’t just a copy-paste of traditional methods that worked for physical products, it’s built for the realities of the digital world.

As you go through a digital transformation, you’re already going to have a product strategy, but the question is: have they truly made it digital-focused?

With ProdPad, you can easily create a digital product strategy through your product roadmap. Try ProdPad today for free to get started and improve the way you manage your digital product. 

The post Digital Product Strategy Guide: How to ‘Digivolve’ Your Product Strategy appeared first on ProdPad.

Many businesses use an agile methodology these days. That means that many Product Teams are likely going to have a Scrum Master and a Product Owner within their midst. The issue here is that these two roles can easily be – and often are – confused. 

The Product Owner and the Scrum Master are two unique team members. It’s time, once and for all, to explain what those roles mean so that you can go away with a solid understanding of both positions and how to get the most out of them. 

We’re going to do that by running through a checklist of the burning questions people have about these roles.

Scrum Master vs Product Owner: What do they do? 

Let’s start small by covering the top-level explanation of these two roles. What’s their purpose? Why do they exist?

What is a Scrum Master? 

A Scrum Master’s entire role is designed to make sure that an Agile squad in an Agile release train follows the Agile playbook properly. They do all the background work to make sure that a team continues to adopt the Agile methodology at all times, helping them to work more efficiently. 

Here’s our definition:

To properly visualize this, imagine a rugby coach who’s talking the players through their strategies and tactics while nurturing camaraderie, teamwork, and innovation. That’s the Scrum Master. 

A ruby coach ensures that every player knows the rules, and understands the game plan, just like how a Scrum Master makes sure that everyone executes and follows the core principles of the Agile manifesto. They ensure that everyone operates in an Agile way. 

What is a Product Owner?

A Product Owner is responsible for articulating the product vision and ensuring the Development Team builds what delivers the most value to users. They act as the bridge between stakeholders and the team, prioritizing the backlog, refining requirements, and making decisions that keep the product moving in the right direction.

Here’s how we define it internally: 

To put it simply, a Product Owner is like a navigator, setting the course and making sure the team stays on track. They translate big-picture ideas into actionable tasks, ensuring that what gets built truly meets user and business needs.

Because they can help guide product development, Product Owners are also often confused with Product Managers.  

The easiest way to differentiate them is that Product Owners manage the product backlog, ensuring the team is building the right features at the right time, while Product Managers own the product roadmap, setting the overall direction and vision for the product.

That’s the surface-level distinction, but it’s worth checking out our article explaining the difference between those two as well. 

Scrum Master vs Product Owner: Why are they important? 

Both roles are pretty important to well-functioning Scrum teams, but they serve distinct purposes. The Scrum Master focuses on making sure the team follows Agile best practices and works efficiently, while the Product Owner ensures the team is building the right things. Having both means your team stays focused, productive, and aligned with business product goals.

Why should you have a Scrum Master?

A Scrum Master is key to keeping the Agile process running smoothly, helping teams collaborate effectively and continuously improve. They remove obstacles, facilitate all the ceremonies (fancy word for meetings), and ensure Agile principles are followed without unnecessary hassle.

The benefits of having a Scrum Master are:

Keeps the team focused and efficient by eliminating roadblocks.
Ensures Agile processes are followed correctly, preventing bad habits.
Facilitates team collaboration between team members and stakeholders.
Helps the team continuously improve through retrospectives and feedback.
Shields the team from distractions so they can focus on delivering value.

Without a Scrum Master, teams risk inefficiencies, unstructured workflows, and process breakdowns that slow progress.

Why should you have a Product Owner?

A Product Owner ensures the team builds what matters most, aligning development efforts with customer needs and business objectives. They own the product backlog, define priorities, and make critical decisions about what gets built next.

The benefits of having a Product Owner are:

Maintains a clear product vision and communicates it effectively to the team.
Prioritizes the backlog to ensure the team works on the most valuable tasks.
Balances business goals with user needs to maximize impact.
Provides clarity on requirements, reducing rework and confusion.
Keeps stakeholders aligned and informed on product progress.

Without a Product Owner, teams risk working on low-impact tasks, losing sight of customer needs, and struggling with misaligned priorities.

Why you need both

While their responsibilities are different, the Scrum Master and Product Owner work best together. The Scrum Master optimizes how the team works, while the Product Owner defines what the team should work on. Without both, teams either risk building the wrong thing efficiently or struggling with productivity despite having a clear vision. Having both ensures a balanced, high-performing Agile team that delivers real value.

Scrum Master vs Product Owner: What are their responsibilities? 

So we now understand the main aims of both these roles. But let’s dive deeper into the details and take a granular look at what these two roles do in their day to day.

While both roles are crucial to an Agile Product Team, their day-to-day responsibilities are very different. That becomes clear when you focus on their daily and weekly activities.

What are the Scrum Master’s responsibilities? 

The Scrum Master’s overarching responsibility is to keep the Agile methodology working effectively within the team. In a nutshell, the Scrum Master clears the way so the team can focus on delivering value without unnecessary disruptions. They obsess over Agiel so that others don’t have to.

Here’s a simple breakdown of a Scrum master’s main responsibilities:

Facilitating Scrum events such as daily stand-ups, sprint planning, and sprint reviews.
Removing any impediments that obstruct the team’s progress.
Coaching the team on self-organization and cross-functionality.
Protecting the team from external distractions.
Encouraging continuous improvement and the adoption of Agile best practices.

What are the Product Owner’s responsibilities? 

A Product Owner’s main job is to ensure the team is always working on the most valuable things. They look after the product vision, break it down into actionable work, and communicate priorities to the Development Team. Their role is highly strategic, requiring them to balance business goals, customer needs, and technical feasibility.

While they don’t dictate how the work is done, they are responsible for defining what the team should build and why it matters.

A Product Owner can do a lot. Here are some of the main responsibilities of the role.

Break down strategy into user stories and tasks.
Gather insights from customer feedback and product data.
Prioritize and groom the backlog effectively.
Say no when necessary to keep focus.
Bridge the gap between Product and Development.
Align stakeholders on goals and priorities.
Advocate for customers in every decision.
Oversee releases and maintain quality standards.

If you’re curious to dive deeper into these responsibilities, we cover them in great detail:

Scrum Master vs Product Owner: What skills do you need? 

Since these two roles serve completely different purposes, they also require distinct skill sets. Sure, there’s some overlap – strong communication and adaptability are valuable for both – but the day-to-day demands of each role mean they require vastly different strengths. Let’s break it down.

Scrum Master skills

A Scrum Master is more of a coach than a manager. Here’s what it takes to do that well:

Strong leadership skills: A Scrum Master isn’t the boss, but they do need to guide and motivate the team, keeping everyone aligned and engaged.

Excellent communication and interpersonal skills: Whether it’s running standups, facilitating retrospectives, or conflict resolution, clear and effective communication is key.

Deep knowledge of Scrum and Agile methodologies: You can’t guide a team through Agile without a rock-solid understanding of its principles, frameworks, and best practices.

Problem-solving and conflict resolution abilities: Scrum Masters need to anticipate roadblocks, clear obstacles, and navigate team dynamics without derailing progress.

Champion of continuous improvement: Agile is all about iteration. A great Scrum Master encourages feedback loops, retrospectives, and process tweaks to keep things running smoothly.

Empathy and emotional intelligence: Understanding team dynamics and individual needs helps create a collaborative and psychologically safe work environment.

Adaptability and flexibility: Priorities shift, challenges pop up, and teams evolve. A great Scrum Master rolls with the punches while keeping the team focused and motivated.

Product Owner skills

A Product Owner is the visionary of the team, responsible for ensuring the product delivers real value. Here’s a list of the most important skills that make up an effective product owner:

Strong business and market acumen: A Product Owner needs to understand the market landscape, industry trends, and customer pain points to make informed product decisions.

Excellent communication and negotiation skills: Whether it’s aligning stakeholders, defending prioritization decisions, or sharing the product vision, strong communication is non-negotiable.

Prioritization and strategic decision-making: With endless requests and limited resources, a Product Owner must ruthlessly prioritize what delivers the most value.

Empathy for customers: Understanding the user’s perspective is crucial. A great Product Owner puts themselves in the customer’s shoes to build products people actually want.

Data-driven decision-making: It’s not about opinions; it’s about evidence. A Product Owner must rely on data, not gut feelings or HiPPOs (Highest Paid Person’s Opinions), to drive decisions.

Analytical and problem-solving capabilities: From assessing product performance to interpreting user feedback, strong analytical skills help a Product Owner identify opportunities for improvement.

Leadership and collaboration: A Product Owner works with development, marketing, sales, and leadership teams. Aligning everyone toward a common goal is a must.

Adaptability and flexibility: The market changes. Customer needs evolve. A good Product Owner is always ready to pivot and adjust the roadmap accordingly.

Scrum Master vs Product Owner: Where do they sit in a Product Team?

A clear way to differentiate between a Scrum Master and a Product Owner is to look at where they sit within the Product Team hierarchy in an Agile setup. While both roles are essential to the success of a Scrum team, they carry distinct responsibilities and report to different individuals, which helps to clarify the demands and expectations placed on each role.

Let’s take a look at who each reports to and where they fit into the Agile team structure.

Who does a Scrum Master report to? 

While this role isn’t typically hierarchical, the Scrum Master still reports to someone depending on the organizational structure and the scope of their responsibilities.

In smaller teams, the Scrum Master often reports to a Head of Product or Head of Engineering. The Scrum Master is less involved in the business-side decisions and more focused on enabling the Development Team to succeed in their day-to-day sprint work.

In larger organizations or more complex projects, the Scrum Master may report to a Program Manager, Project Manager, or even a senior-level Scrum Master overseeing multiple teams. This setup helps maintain consistency across teams while allowing the Scrum Master to focus on their primary duty: facilitating team efficiency and removing blockers. The Scrum Master is there to serve the team and remove obstacles, not to make product or business decisions.

Who does a Product Owner report to?

The Product Owner typically reports to a senior leadership figure within the product department, such as the Head of Product, VP of Product, or Chief Product Officer. In some organizations, the Product Owner might also have a line to the Business Development or Marketing teams, especially if they play a role in the go-to-market strategy. While the Scrum Master focuses on the process, the Product Owner ensures the output aligns with business goals and customer value.

In terms of day-to-day interactions, the Product Owner works closely with stakeholders across the organization, including Sales, Marketing, Customer Support, and Development Teams. They are responsible for maintaining the product backlog, prioritizing Ideas, and ensuring the team’s work aligns with the broader strategic vision. 

Learn more about the relationship between Product Owner and product vision:

Here’s a good look at the Product Owner and Scrum Master both chilling out in an Agile scrum squad: 

Scrum Master vs Product Owner: How do you become one? 

How do you become a Scrum Master? 

Becoming a Scrum Master is all about understanding Agile principles and the Scrum framework, along with gaining hands-on experience in leading teams through Agile processes. If you’re looking to transition into this role, here’s a simple step-by-step guide to help you along the way:

1. Gain a thorough understanding of Agile principles and Scrum framework

Dive deep into the Agile Manifesto and familiarize yourself with Scrum values, roles, and processes. Understanding the core principles of Agile methodologies is key to your success.

2. Acquire hands-on experience in Scrum projects as a team member

Get involved in Agile projects, whether it’s as part of Development Teams, as a tester, or any other role. Experience within a Scrum team will give you a solid understanding of how an Agile sprint works.

3. Enroll in a Certified Scrum Master (CSM) training program

Sign up for a reputable Scrum Master training course. These programs often last a few days and cover all essential topics, including Scrum ceremonies, roles, and techniques to facilitate team processes.

4. Obtain the Certified Scrum Master (CSM) certification from a recognized institution

After completing the training, take the CSM exam to get your certification. This credential proves you understand the fundamentals and are ready to take on the role of Scrum Master. 

5. Continuously update your knowledge and skills

Agile and Scrum practices evolve. Stay up-to-date by attending workshops, joining Scrum communities, and networking with industry professionals to continue improving your skills and knowledge.

How do you become a Product Owner?

The path to becoming a Product Owner involves gaining experience in Product Management, understanding customer needs, and learning the ins and outs of Agile product development. Here’s a step-by-step guide to get you there:

1. Gain practical experience in Product Management or related fields

Start by working in roles like business analysis or Product Operations Management. These roles give you valuable insights into understanding customer needs, business goals, and the product development process.

2. Develop a deep understanding of the product development lifecycle and Agile methodologies

Familiarize yourself with the entire Product Management lifecycle, from ideation and design to launch and iteration. Additionally, strengthen your understanding of Agile methodologies and how they apply to product management.

3. Enhance your communication and negotiation skills

As a Product Owner, you’ll need to communicate effectively with stakeholders, customers, and your Development Team. Consider taking courses in communication and negotiation to sharpen these critical skills.

4. Obtain the Certified Scrum Product Owner (CSPO) certification

The CSPO certification is a recognized credential that demonstrates your knowledge of Agile practices and your ability to manage the product backlog. It’s one of the essential certification courses to show your expertise in product ownership.

5. Continuously gather feedback and stay updated with market trends

A successful Product Owner listens to customers and stakeholders, iterating on the product to deliver maximum value. Regularly collect feedback, monitor market trends, and adjust your product strategy to keep it relevant and competitive.

The Product Management career path

Both the Scrum Master and Product Owner are early, entry-level roles within the Product Management career tree. From these roles, you can take multiple directions and sculpt your skillset to make you a better fit for more specialized roles in the future. 

To see where you can go from these positions, read our article on the Product Management career path:

Scrum Master vs Product Owner: How much do they get paid? 

When it comes to compensation, the annual salary for both Scrum Masters and Product Owners varies based on factors like location, experience, and company size. While these are average figures in the U.S., keep in mind that salary expectations can differ significantly across regions, and outliers may skew the data. Nonetheless, the following should provide a clear snapshot of what you can expect to earn in each role.

What is the Scrum Master salary? 

On average, a Scrum Master in the U.S. earns around $115,000 per year. The salary range typically spans from $96,000 to $139,000, according to Glassdoor.

This range is consistent across multiple sources, although it’s important to note that entry-level Scrum Masters will likely earn less than the average, with starting salaries on the lower end of the spectrum. Factors such as company size, industry, and geographic location all play a role in determining the exact figure.

In addition to the base annual salary, many Scrum Masters also receive bonuses and other supplementary benefits, which can increase their overall compensation package.

What is the Product Owner salary?

The average salary for a Product Owner is around $124,000, according to Talent.com – roughly $9,000 more than the average Scrum Master salary. This is in line with the fact that Product Owners tend to have more seniority and broader responsibilities compared to Scrum Masters.

The salary range for Product Owners typically starts at $105,000 and can reach as high as $159,000, depending on experience and seniority level. The higher end of the range generally applies to those with significant experience or working in larger, high-paying organizations.

Geography also plays a significant role in salary differences. For example, according to Built In, cities like San Francisco and Colorado offer notably higher salaries compared to places like Orlando or Miami, highlighting regional pay discrepancies within the U.S.

The Final Comparison 

I don’t know about you, but I think we sufficiently broke down the differences between a Scrum Master and a Product Owner. We’ve covered quite a lot, so we thought it’d be handy to break it all down into a neat comparison table: 

Of course, we don’t think these two roles should be seen as competitors – they’re complementary. Both play essential but distinct roles in an Agile Product Team, working together to enhance efficiency and deliver value.

It’s like apples and oranges – different in function and flavor, but both essential in their own way. And when combined and mixed with other fruits, they create a killer fruit salad.

Understanding their differences is useful, but once that’s clear, like it should be now, it’s best to see them as separate, yet equally vital, parts of the team.

The Scrum Master and Product Owner are just two cogs in the machine that make great Product Management Teams, and Agile is just one aspect of impactful Product Management. 

Want to learn how to improve the product function in your business? Of course you do! 

We’ve got a comprehensive Product Management Handbook, covering everything you need to know to build a product that can thrive. Used by the folks at Amazon, Google, and more, this is a resource that can supercharge your capabilities. 

Download it now. 

The post Scrum Master vs Product Owner: What’s the Difference appeared first on ProdPad.

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.

At companies like these, you’ll not only manage roadmaps and navigate the Product Management lifecycle like usual, but you’ll also need to juggle significant technical requirements.

Hence the creation of the Technical Product Manager. 

Now here’s the thing: I’m willing to bet that as time goes on, more classic, traditional Product Managers will be expected to have Technical Product Manager skills and capabilities. 

So to help you be prepared, why not get ahead of the curve and learn how to become a Technical Product Manager before it becomes the norm?

To do that, we’ve put together 15, easy-to-implement tips to help you make the transition from PM to Technical Product Manager. These tips will make stepping into a Technical Product Manager role super easy, and give you more options for your career.

Let’s get straight to it!

1. Be clear on what a Technical Product Manager is

So what is a Technical Product Manager? In a nutshell, a Technical Product Manager is a specialized version of the core Product Manager, where your responsibilities lean heavily toward technically complex tasks. The role bridges the gap between technical teams and non-technical stakeholders, ensuring the product is both user-centric and technically feasible.

A Technical PM is a specialized role, but it’s not the only one. Over the years, we’ve seen roles like:

• Growth Product Managers
• Data Product Managers
• AI Product Managers

With each of these roles, a specific focus is highlighted that dictates what that PM’s main priority should be. A Growth PM is laser-focused on driving product growth, a Data PM uses data to guide decisions, and a Technical PM manages and builds technically complex products.

Let’s put it this way. In a football team, you have your offense and defense. Now you could single out a player as part of the offense and get the gist of what they do – they’re there to help the team score touchdowns. But we don’t know exactly how. 

So we give them a more specific job role like Wide Receiver, and now we know they’re the one hanging by the edge of the field to get the ball thrown to them. Calling a PM a technical PM is the same concept – we now know what they’re focusing on when getting a product to market .

2. Know the difference between a Product Manager and a Technical Product Manager

To become a Technical Product Manager, you need to understand what sets the role apart. What makes it unique? Well, the true answer is that a Technical PM will be doing a lot of what a Product Manager already does, with extra responsibilities. 

Here’s a side-by-side comparison going through the high-level focuses and aims of each role, and what is shared between the two:

As you can see, a Technical PM is still going to be doing everything in the Product Management lifecycle, but there’s an increased focus on tech-focused tasks, like making sure that it’s feasible, that the code is well written, and that it meets regulatory standards.

Now, here’s where things get interesting. Some believe the Technical PM will eventually become indistinguishable from a core Product Manager. In other words, the baseline responsibilities of a Product Manager may soon include the technical requirements of a TPM. 

So, while the distinction is clear now, the lines are likely to blur.

We’ve seen this trend with other specialized roles. Growth Product Managers have become the fastest rising since 2020 as companies lean on their products to recover lost revenue.

Today, fewer new Growth PM roles are being advertised, but the need hasn’t vanished. Instead, all Product Managers are expected to take on growth-focused responsibilities – regardless of title. The same will likely happen with Technical PMs in the future.

3. Be clear on the responsibilities of a Technical Product Manager

The role of a Technical Product Manager is a blend of strategy, technical expertise, and stakeholder management. While a core Product Manager focuses on the what and why of a product, a TPM dives deeper into the how, working closely with Engineering teams to ensure feasibility, scalability, and efficiency.

So, what exactly does a Technical Product Manager do? Here’s a breakdown of the key responsibilities so that you know:

• Define technical product strategy: Align product decisions with the company’s long-term technology vision and evaluate emerging tech.
• Own backlog and prioritization: Balance business needs with engineering constraints, ensuring technical debt isn’t ignored.
• Bridge the gap with Engineering: Work closely with developers to break down complex requirements and remove blockers.
• Oversee system architecture and integrations: Ensure seamless API connections and tech stack compatibility.
• Champion security and compliance: Stay ahead of GDPR, SOC 2, and other industry regulations.
• Assess technical feasibility: Validate ideas with Engineering before committing resources.
• Optimize performance and scalability: Prevent bottlenecks and ensure systems can handle growth.
• Support incident management: Troubleshoot outages, work with Engineers and communicate updates.
• Drive data-driven decisions: Define key metrics, analyze technical performance and back decisions with data.

4. Get to grips with Technical Product Manager terms 

Diving into the world of Technical Product Management introduces a whole new set of industry jargon that can feel like everyone around you is speaking Elvish at first. It’s easy to get lost in the sea of technical terms and abbreviations.

To become a successful Technical PM, you’ll need to get comfortable with this vocabulary. Luckily, we’ve put together a handy jargon-buster to help you navigate these tech-heavy terms with ease.

Here are the key Technical Product Manager terms you need to know:

• API : A set of protocols and tools that allow different software applications to communicate with each other, enabling integration of third-party services and functionalities into a product. (Check out our integrations to see how powerful good API can be)
• Technical debt : The cost of maintaining and updating software that was built quickly or inefficiently, which accumulates over time as shortcuts in coding or design create long-term problems that need to be addressed.
• System architecture: The high-level structure of a software system, outlining its components and how they interact, ensuring scalability and performance across hardware, software, and network configurations.
• Scalability : The ability of a system to handle increasing amounts of work or to be enlarged to accommodate growth without sacrificing performance or requiring a complete redesign.
• Microservices : An architectural style where a product is built as a collection of small, independently deployable services, improving flexibility and scalability by allowing each service to be developed and maintained separately.
• CI/CD : A set of practices that enable development teams to frequently integrate code changes (CI) and automatically deploy them to production environments (CD), reducing errors and speeding up product delivery.
• Version control : A system that tracks changes to code, allowing developers to collaborate efficiently, revert to previous versions, and manage different iterations of a product. Git is a popular version control system.
• Containerization : A method of packaging software into isolated containers, ensuring that applications run consistently across different computing environments, commonly using technologies like Docker.
• Load balancing : The process of distributing incoming network traffic across multiple servers to ensure no single server is overwhelmed, enhancing system reliability, uptime, and performance.

5. Know your system architecture and APIs like the back of your hand

Now that you know what APIs and system architecture are, you should next learn more about them in relation to your own product. These two terms are some of the most important in Technical Product Management, so they’re worth focusing on.

A Product Manager must know their product inside and out – its users, market positioning, value, key features, and use cases. A Technical PM needs all that knowledge plus a strong grasp of how the product actually works from an engineering perspective.

Since a Technical PM ensures technical feasibility, pushing a feature that disrupts the product’s structure means you’re missing the mark. Understanding how features fit together helps you introduce new ones without breaking what already works.

If you’re unfamiliar with concepts like system or product architecture, our glossary can help you get up to speed and strengthen your understanding of your product’s foundations.

Likewise, understanding your APIs is essential for planning integrations and ensuring your product fits seamlessly into your customers’ existing ecosystems. Your product is going to be a hard sell if it doesn’t fit in with the suite of tools your customers are already using, so understanding your APIs and capabilities can help you prioritize product updates and improvements. 

6. Perfect your core Product Management skills

Just because you’re transforming from a PM to a Technical Product Manager doesn’t mean you can forget everything you know. In fact, you’re going to need to sharpen various Product Management skills to excel in this role. 

As you’ve seen in tip 2, there’s a lot of overlap between a Product Manager and Tech PM, so holding onto the skills that got you through the door is vital. 

Here’s the Product Management skills you need to keep and ideally improve upon:

• Strategic thinking : Even as a Technical PM, you need to understand the larger product vision and how your technical decisions align with broader business objectives. It’s important to remain focused on long-term goals while considering short-term trade-offs.
• User-centric mindset : Whether you’re dealing with technical specs or APIs, never lose sight of the user. A deep understanding of your users’ pain points and needs is fundamental to ensuring the product delivers real value and aligns with customer expectations.
• Prioritization : The ability to prioritize features, tasks, and fixes is a crucial skill for any PM. As a Technical PM, you’ll need to weigh technical debt, system constraints, and user needs when making tough decisions about what should be worked on next.
• Roadmapping : A strong roadmap sets the direction for your product. As a Technical PM, you’ll need to create and manage detailed product roadmaps that outline key milestones and align technical capabilities with business priorities.
• Problem-solving : Every Product Manager must be a problem-solver. For a Technical PM, this means having the ability to troubleshoot issues, think critically about potential solutions, and guide teams through technical challenges that could impact product delivery.
• Stakeholder management : You’ll still need to work with cross-functional teams and external stakeholders. Balancing technical feasibility with business needs and ensuring stakeholders are aligned and informed is an essential skill for driving product success.
• Execution and Delivery : No matter how technical your role is, delivering the product is the end goal. Having a keen eye for execution – ensuring deadlines are met, features are tested, and products are delivered on time – is a must-have skill.
• Data-driven decision-making : A Technical PM needs to back up decisions with data. Understanding analytics and using data to measure product performance, user behavior, and system performance is crucial for fine-tuning features and improvements.
• Adaptability : The product development landscape is constantly shifting. A great Technical PM can adopt a pivot strategy quickly, adjusting their approach to accommodate changes in the tech stack, market conditions, or user needs without losing sight of the bigger picture.
• Collaboration with Design teams : As a Technical PM, you will often work alongside Design teams to ensure that the product’s technical side supports the desired user experience. Understanding design principles and collaborating effectively is vital in creating a seamless product.

7. Improve communication skills to manage stakeholders

Like every type of Product Manager, you’ll be juggling multiple stakeholders, but as a Technical Product Manager, you’ll likely find yourself communicating with Engineers and Developers more often than most. 

This means you need to be fluent in their language – not just technically, but in terms of how they approach problems, prioritize tasks, and break things down into manageable pieces.

You might recognize this common scenario for Technical PMs: being caught between a rock and a hard place as your C-Suite requests a feature that your Engineers can’t deliver. 

Suddenly you’re in the middle, trying to manage expectations and keep everyone happy. This is where your stakeholder alignment skills come into play. You’ll need to balance the needs of different departments, like Sales pushing for features and Engineers warning about technical debt, and make decisions that move the product forward without alienating anyone.

Mastering the art of saying no to stakeholders is essential when you’re managing stakeholders with competing demands. It’s not just about saying “no” but understanding when and how to negotiate, set expectations, and offer alternative solutions that keep the product moving forward. 

8. Develop the technical skills to stand out as a Technical Product Manager 

A specialized role like that of a Technical Product Manager demands specialized skills – specific knowledge and characteristics that can help you meet the demands of the position.

So, what technical skills do you need to refine that you might not already be well-versed in as a core Product Manager?

Here are a few key technical skills you should hone to succeed as a Technical PM:

Agile methodology knowledge

While you’re probably familiar with Agile from your experience as a core PM, you need a deeper, more comprehensive understanding of this role. Tech companies rely heavily on agile practices, and as a Technical PM, you’ll need to not only understand it but also be able to lead and coach teams through agile processes, such as sprints, stand-ups, and retrospectives.

Look for Agile certification courses, attend Agile workshops, or participate in Agile coaching sessions to get a more hands-on experience.

Product prototyping 

As a Technical PM, you’ll often need to help translate ideas into workable products. Prototyping allows you to quickly visualize and test concepts, ensuring that ideas align with user needs before full-scale development.

Start using prototyping tools like Figma or Sketch, and explore courses in UX/UI design to get comfortable with turning abstract ideas into interactive prototypes.

To really enhance your Product Prototyping capabilities, you can also AI prototyping tools for Product Managers like Replit. 

Data analysis and extraction

While every PM deals with data, a Technical PM must be particularly adept at analyzing and interpreting technical data, such as system performance, error logs, and usage analytics, to make informed product decisions.

To be more comfortable with data, take online courses in data analysis or tools like SQL, Python, or Tableau to build your analytical skills. Familiarizing yourself with key performance indicators (KPIs) and metrics will also help you track and optimize product performance.

Technical writing

As a Technical PM, you’ll need to document features, technical specifications, and APIs in a clear, concise manner. Strong technical writing skills are essential for communicating complex ideas to both technical and non-technical stakeholders.

Practice writing technical documentation, and explore technical writing courses or workshops. Read well-regarded tech blogs and documentation to get a sense of how clear, user-friendly documentation is structured.

Understanding of DevOps practices

DevOps practices are essential for ensuring seamless collaboration between Development and Operations teams, especially when managing continuous integration and continuous delivery (CI/CD) pipelines. Familiarizing yourself with these processes will ensure you’re aligned with Engineering teams and help you identify bottlenecks or opportunities for optimization.

To improve, attend DevOps-related webinars, or look for online courses covering CI/CD, automation, and the tools typically used in DevOps (such as Jenkins or Docker).

Risk management and troubleshooting

Understanding potential risks associated with new features or product releases is key for any Technical PM. This includes not only identifying technical risks but also understanding how to troubleshoot and resolve technical issues in collaboration with Engineering teams. Being able to predict, mitigate, and address issues will keep your product’s development on track.

Work alongside Engineering teams to identify risks in product development or previous launches. Reading books on risk management or taking specific courses in troubleshooting can also help you build this skill.

9. Learn basic programming to speak your Engineer’s language

There was one Technical Product Manager skill we left out of the above, and that’s because it needs to be singled out. That is knowing how to code.

Now, before you run off and try learning every coding language under the sun, it’s best to focus on the main ones used by Engineers. These are:

• JavaScript – Often used for front-end development, and increasingly important in full-stack development.
• Python – Known for its versatility, Python is widely used in data science, machine learning, and backend development.
• SQL – A must-have for querying databases and extracting relevant data for product decisions.

Now you can go off and develop your coding skills in a few different ways. You can put yourself through a hackathon that tests your coding skills under pressure, create open-source projects that experienced coders can help you with, or shadow your current Engineers.

If you find coding tough, you’re in luck, as you only need a basic understanding for this role. Plus, if you get stuck, there are plenty of great AI tools that can help you understand and write code, helping to fill in the blanks as you learn.

We recommend Cursor, but that’s just one of the many great AI tools for Product Managers. As AI becomes embedded in Product Management – and especially in the Technical PM role – learning about the best tools available to you can boost your efficiency and make your job much easier.

10. Understand the basics of security and compliance

Security and compliance aren’t just for Engineers or legal teams – they’re key responsibilities for a Technical Product Manager. A product that isn’t secure or compliant can lead to data breaches, fines, and lost customer trust. Since you define requirements and influence technical decisions, understanding security ensures best practices are built in from the start.

Familiarize yourself with key principles like data encryption, authentication (OAuth, SSO), and API security. Know industry compliance standards like GDPR, HIPAA, or SOC 2. Even if you’re not implementing them, understanding these requirements helps you ask the right questions and ensure security is a priority.

To improve, take security courses, read industry guidelines, and work with security and legal teams. Reviewing security incidents in your field can also keep you proactive. A strong foundation in security and compliance helps protect your product, user data, and regulatory standing.

11. Nail prioritization with the best frameworks for Technical Product Managers

Frameworks can be a game-changer for a Technical Product Manager, helping with validation, prioritization, and decision-making. When used correctly, they provide a structured approach to solving complex problems, ensuring you focus on the most impactful work while balancing technical feasibility.

Of course, you should never blindly follow Product Management frameworks. They need to suit the situation and make sense at the time. It’s like math formulas – Pythagoras’ theorem is great for finding the length of a triangle but useless for calculating the surface area of a cube. The key is knowing which framework to apply in different contexts rather than forcing a one-size-fits-all approach.

Here are five frameworks every Technical Product Manager should have in their toolkit to pull out when the time is right:

• RICE (Reach, Impact, Confidence, Effort): A structured way to prioritize features and initiatives based on their potential value and effort required.
• Kano Model: Helps assess customer delight by categorizing features as basic expectations, performance drivers, or delightful surprises.
• MoSCoW (Must-Have, Should-Have, Could-Have, Won’t-Have): A simple framework for prioritizing requirements based on necessity.
• Cost of Delay (CoD): Helps quantify the impact of delaying a feature, making it easier to justify prioritization decisions.
• Opportunity Solution Tree: A visual framework for mapping problems, opportunities, and solutions, ensuring you tackle the right challenges first.

12. Apply for companies that actually need a Technical Product Manager 

Not every company is looking for a Technical Product Manager. If you’re holding out hope that your current company will eventually create the role, you could be waiting forever. To land this job, you need to target the right companies – ones that actually need and value technical specialization.

This role is far more common in larger enterprise organizations that can justify the specialization. Smaller companies tend to have generalist Product Managers who wear multiple hats, handling both technical and non-technical responsibilities. If your goal is to be a dedicated Technical PM, you need to work at a company where the demand for one exists.

This also means you’ll likely need experience in enterprise environments. If your background is primarily in startups or small teams with limited budgets, you may not stand out as the ideal candidate for companies looking for a Technical PM. Hiring managers at enterprise-level businesses often prioritize candidates who have navigated complex technical roadmaps, collaborated with large Engineering teams, and worked within structured development processes.

So how do you get on the radar of these companies? You can grow into the role by evolving with your current organization – though that’s not guaranteed – or you can proactively transition into a larger business through strategic networking, upskilling, and gaining relevant experience in enterprise-grade products.

13. Prepare for Technical Product Manager interview questions 

The Product Manager interview is perhaps one of the hardest in the world. Multiple stages, tough questions, and a lot of competition. The process for a Technical Product Manager isn’t any easier. These are going to have more focused questions and really test your technical knowledge, so you’re going to have to be prepared. You can’t wing this. 

As well as the general Product Manager interview questions, here are some of the most common Technical Product Manager interview questions and what they’re trying to learn from these questions: 

Can you explain the role of a Technical Product Manager in simple terms, as if you were talking to a 7-year-old?

This question is asked for two reasons. It shows off how well you know the role and your experience with it, as well as your ability to be clear – something super important when talking with Engineers (who we’re not comparing to seven-year-olds – we promise ) 

A great answer will be clear and simple, avoiding jargon. Something like:

“I help build cool products by working with Engineers to make sure what we create is technically possible.”

What technical skills do you have that make you stand out from other candidates?

This question is designed to evaluate your technical knowledge and expertise. They want to understand your background and whether your technical skill set will complement the existing team. While coding skills aren’t usually expected, understanding key technical concepts is crucial.

To nail this, highlight technical skills such as knowledge of APIs, system architecture, technical writing, data analysis, or familiarity with agile methodologies.

How would you approach resolving a technical problem or unexpected issue?

This question assesses your problem-solving and decision-making skills, especially when dealing with technical challenges. A Technical PM often faces hurdles that need creative, quick, and effective solutions. The interviewer wants to see if you follow best practices, and how you handle pressure. 

Start by identifying the issue, gathering necessary information, and working with your Engineering team to brainstorm solutions. Show that you can remain calm under pressure and make decisions based on data and team input. If possible, include examples from past experience where you handled a technical issue successfully.

What aspects of our product would you change or improve, and why?

This question tests your critical thinking, understanding of the product, and whether you’ve done research on their company. It also assesses how well you can balance user needs, technical feasibility, and business goals.

A thoughtful, constructive critique of their product based on research is what’s needed here. Focus on areas where there’s room for improvement – whether it’s usability, features, or performance – and justify your suggestions. Make sure to back your points with reasoning and explain how the changes could benefit the user experience or business goals.

In addition to these questions, you’re also going to be asked a lot about Product Sense – The Product Manager version of common sense. There may even be an entire interview stage for this that you’re going to have to master.

14. Get a Technical Product Manager mentor to show you the ropes

Loads of Product Managers have mentors, and if you want to excel as a Technical Product Manager, finding one should be a high priority. Technical PMs operate at the intersection of product strategy and engineering, which means there’s a steep learning curve – especially if you’re transitioning from a more traditional Product Management role.

A mentor, coach, or Product Consultant, can help you navigate this transition, offering real-world insights that aren’t always covered in courses or books. They can provide guidance on working effectively with Engineers, breaking down complex technical concepts, and making informed prioritization decisions. Plus, they can help you avoid common pitfalls and accelerate your growth.

To find a mentor, start by exploring your network – look for seasoned Technical PMs in your company or industry. Join Product Management communities, attend events, and engage in online discussions. 

We actually partner with a few Product Coaches and mentors at ProdPad, you can find some great teachers who can help you improve as a PM. 

15. Stay up-to-date on emerging tech trends to keep ahead of the curve

Technology moves fast, and as a Technical Product Manager, you need to keep up. Staying updated on emerging tech trends is about anticipating changes that could impact your product and strategic decisions. Falling behind means your competitors get ahead.

Advancements in AI, new software development frameworks, and more can influence product decisions. Understanding what’s coming next helps you explore new opportunities, assess risks, and have informed discussions with Engineers and stakeholders.

To stay ahead, make continuous learning a habit. Subscribe to industry newsletters, follow Product Leaders on Twitter and LinkedIn, and read popular blogs in the tech industry. Attend conferences and webinars to learn about emerging technologies and experiment with new tools yourself.

The best Technical PMs anticipate trends. By staying informed, you’ll position yourself as a forward-thinking leader who can guide your team through the ever-changing tech landscape.

The tech industry’s Product Manager

And with those 15 tips, you should have all you need to excel as a Technical Product Manager. Becoming a successful TPM means striking a balance between managing the full Product Management process and diving deep into the technical side. From understanding security and compliance to staying ahead of emerging tech trends, these 15 tips will help you confidently step into a more technical PM role

As you continue to develop your skills, remember that mastering the full product lifecycle is just as important as technical expertise. If you want to dive even deeper into Product Management and refine your approach, check out our Product Management Process Handbook. It’s designed to give you the tools and strategies to navigate the complexities of both technical and non-technical aspects of the role, helping you become the best PM you can be, whatever your job description.

The post The Technical Product Manager – 15 Tips to Help You Become One 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.

Now, making assumptions isn’t all bad all the time. Sometimes they’re spot on because you’ve gathered the evidence to reach that assumption: 

This coffee shop has flickering lights, a low hygiene rating, and the barista just spilled cigarette ash into the espresso machine – you can assume that it’s probably not the best place for a drink.

But other times, assumptions can miss the mark. Ever judged someone too harshly after a bad first impression? Like thinking a big, tattooed guy rolling up on a motorbike MUST be rough around the edges, only to discover that he’s a charming dog-walking, cake-baking softie. 

As a Product Manager, we can’t afford to operate on guesswork, even educated ones. Assumptions that go unchecked about our product, capabilities, and our customers can lead to you building the wrong feature, solving the wrong problems, and disappointing your customers. 

That’s where assumption testing comes in. 

It’s about challenging your gut instincts and getting the facts about your hypotheses – much like walking up to that intimidating biker and having a conversation to learn the truth about them. 

Assumptions can steer you down a blind alley if you act on what you think instead of what you know. But how do you test assumptions? Let’s break down assumption testing so that you’re building on solid ground. 

What is an assumption? 

An assumption is a statement taken to be true without any real proof. It’s something we all do to fill in the blanks when we don’t have all the answers. For Product Managers, assumptions are the invisible threads woven into your ideas, plans, and product strategies. 

They’re often hidden and untested, but they’re the foundation of whether your product decisions will stand tall or crumble. Knowing how to identify and challenge these assumptions is the key to avoiding wasted effort and creating solutions that actually work.

Here’s how Teresa Torres, the authority in assumption testing, defines an assumption:

Now assumptions aren’t always obvious to find. They’re lurking in the background, and you’ll need to properly seek them out to find and understand them. Let’s use a more Product Management specific example to figure out what assumptions might be. 

Say you have a desired outcome: Increase product adoption rates. 

To achieve that outcome, one of many potential opportunities is to get existing customers to refer a friend. 

From that, you then come up with a few solutions to help users do that, like adding a referral share button in your app. 

On the surface, this all sounds like a great, logical idea and a quick win to help get more potential users to your tool. 

But have you spotted the many initial assumptions made in that quick mini-product discovery session? 

Well for starters, we’ve made the assumption that users are engaged enough with your product to want to share – we don’t actually know that. 

We’ve also assumed that customers will use the share button instead of a different alternative, like good old-fashioned word of mouth. 

Plus, we’ve also assumed that anyone receiving a referral will actually open it. 

If all of these are incorrect assumptions, this whole design solution falls, and you’re left wasting time building a new feature that has little to no impact. 

Those three examples aren’t even all the potential assumptions of this scenario. This is why assumption testing is so important, to first find your preconceptions and then discover the truth about them.

Assumptions vs risks

Like the well-read Product Manager you are, you may have come across the idea of the four big risks, popularized by Marty Cagan. Risks are statements about your product that you want to prove are false to ensure that your solution is watertight. 

Assumptions and risks are actually pretty heavily linked, as both are used to note down expectations and preconceptions about customers, your product, or the market. Depending on who you ask, they’re the same thing. 

The main difference between the two is that one is phrased as a negative, while the other is more positive. One needs to be false, while the other needs to be true. 

So a risk is:

Customers WON’T understand how to navigate to that feature. 

Assumptions are:

Customers WILL understand how to navigate to that feature. 

So they’re opposite sides of the same coin. Ying and Yang. Whatever you call them and the way you phrase them, they’re preconceptions about your product that you’ll want to find the truth about before you implement the solution they’re tied to.

What are the different types of assumptions?

Your assumptions can be grouped into five different categories: 

1. Desirability Assumption: These are the guesses we make about why we think people will want what we’re offering. It’s all about predicting whether your solution will actually appeal to your customers.
2. Viability Assumption: This is where we assume that our solution will work well for the business. Will it bring in revenue? Will it align with company goals? These are the questions hiding under viability assumptions that can be answered with statistical tests.
3. Feasibility Assumption: Here, we’re betting on whether we can actually build what we’ve envisioned. These often involve engineering, but they can also include assumptions about compliance, legal issues, or security.
4. Usability Assumption: These are the assumptions about what our customers are able to do. Can they find the right features? Will they understand how to use them? Can they follow through without frustration?
5. Ethical Assumption: This is where we assume our solution won’t cause harm. If it involves collecting sensitive customer data, we need to ask tough questions about why we need it, how we’ll use it, and whether it’s truly necessary.

These categories can be helpful in guiding your process when it comes to finding your riskiest assumptions. When going through your potential solutions, you can work down these types like a checklist to make sure that no stone is left unturned, and that you’ve found all related assumptions. 

What is an assumption test? 

An assumption test is where you go out and test the validity of the assumptions that you’ve identified. Are they actually true, or have you missed the mark? An assumption test is a structured activity to test the risk in an assumption and to see if they’re accurate. It’s a way to see how bad your riskiest assumptions are and what the impact will be if it’s wrong. 

There are so many ways to test assumptions, and ultimately the choice is yours on how you want to go and find the truth. Some types of assumptions work best with certain tests. 

For example, desirability and usability assumptions can be tested best by looking at customer behavioral data – looking at how users interact with your product and learning what that says about their engagement. On the other hand, feasibility assumptions can be tested by simply having a discussion with your Design and Engineering Teams. 

We’ll go into more detail on some of the best ways to conduct assumption testing, but for now, all you need to know is that an assumption test not only checks if an assumption is true, it also showcases the risks if it’s found to be untrue. 

Why should you do assumption testing?

Every time you propose a new design solution to achieve a certain outcome, there’s no guarantee it’s going to work. There are many factors that can impact failure. Perhaps you didn’t perform product validation, or maybe a step was missed in your product discovery process. One of the biggest culprits, however, is the hidden assumptions baked into your proposal.

If your hypothesis or solution is riddled with assumptions – essentially guesses – you can’t be confident that an idea is going to lead to a viable product. Launching a new feature or making a change to your product without addressing the riskiest assumptions is like taking a gamble without knowing the odds. In essence, you’re betting blind.

Assumption testing lets you bet smart. You’re at least betting sensibly and giving yourself greater chances to win. 

Think of it like walking into a casino: without testing, you’re the person who throws their life savings on a single roulette number, hoping for the best. That’s what happens when you launch a feature based on gut instinct instead of insight.

By testing your assumptions, you’re stacking the odds in your favor. It’s like studying the patterns at the table, analyzing probabilities, and realizing that betting on red gives you the best chance of success.

Of course, no amount of preparation can eliminate risk. The ball might still roll into a black pocket. But assumption testing ensures your bets are informed, calculated, and sensible. It won’t guarantee success, but it will give you the confidence to know you’ve eliminated incorrect assumptions.

Beyond eliminating your riskiest assumptions, assumption testing comes with several other benefits: 

• Saves time and resources: By identifying and addressing flawed assumptions early, you can avoid wasting time, money, and effort on solutions that won’t work.
• Improves stakeholder confidence: Testing your assumptions gives you data to back up your decisions, which builds trust with your team, leadership, and other stakeholders.
• Fosters innovation: By challenging assumptions, you might uncover opportunities or insights you hadn’t considered before, leading to more creative ideas and a more viable product.
• Encourages collaboration: Testing assumptions often involves cross-functional input, such as from Engineering, Design, or Marketing, which ensures a well-rounded approach.
• Sharpens decision-making: The process of assumption testing forces you to think critically and make more informed, strategic decisions.
• Increases customer understanding: Many assumption tests involve customer research, giving you deeper insights into their behavior, needs, and expectations.
• Reduces cognitive bias: Testing removes the guesswork and counteracts bias, ensuring decisions are grounded in evidence, not personal beliefs.

How do you do assumption testing?

Testing assumptions is so important in making better, more informed product decisions. While the list of activities to test assumptions is endless, the most effective tests generally fall into one of the following four categories. 

• Prototype tests: Simulate user behavior to evaluate customer response to new product ideas. These tests involve creating a simple version of the product or feature to observe user interactions and gather feedback.
• One-question surveys: Quickly gather insights from customers to validate assumptions. A single, targeted question helps assess customer interest and validate ideas without requiring a long survey.
• Data mining: Analyze existing data to evaluate the risk and feasibility of an assumption. Statistical tests like reviewing past user behavior and engagement data can gauge whether an assumption is realistic.
• Research spike: Test technical feasibility through engineering prototypes. This is a quick, focused effort to explore the technical aspects of a solution, helping you determine if it’s feasible before moving forward.

Of all the potential assumption tests you can do, they’ll more or less fall within these four types. Still, let’s explore some of the standout ways you can test your assumptions.

Smoke testing

A smoke test is a lightweight way to gauge interest in a new product or feature before you fully commit to building it. The idea is simple: create a mock version of the product or feature (often through a landing page or placeholder) and test it to see if it sparks any real-world interest. It’s like putting out a sign that says, “We’re thinking about this, are you interested?” and seeing who shows up.

ProdPad itself originally validated assumptions through a smoke test, and here we are. We had the assumption that there’d be a demand for a Now-Next-Later roadmapping tool, and a smoke test confirmed there was that demand before the product was fully built.

A/B testing

A/B testing is one of the most effective ways to test assumptions about what works with users by comparing two variations of a feature or experience. This method involves showing two different versions (A and B) to separate user groups and measuring the difference in user behavior or outcomes.

Let’s say you assume that changing the color of a CTA button will improve click-through rates. With A/B testing, you can test the original color (A) against the new color (B) to see which one gets more clicks. This data allows you to make decisions based on actual user behavior, rather than assumptions or guesses.

A/B testing is particularly useful when you have an existing feature or product, and you want to improve specific aspects or validate small tweaks. It helps you make incremental changes with confidence, backed by solid user data.

Opportunity solution tree

The Opportunity Solution Tree is a framework that helps you map out various possible solutions to a particular opportunity. Although not originally designed to test assumptions but to instead prioritize solutions, it can be used to organize assumptions around customer needs, business goals, and technical feasibility, making sure you test all the variables that could impact your solution.

The beauty of the Opportunity Solution Tree is that it allows you to visualize how different solutions and assumptions interconnect. It’s an excellent tool for teams to collaborate and align on what needs testing first, ensuring that the most critical assumptions are evaluated early on. This makes it easier to prioritize the right solutions based on the risks associated with each assumption, helping you make smarter decisions.

Wizard of Oz testing

The Wizard of Oz test is a clever technique where you create the illusion of a fully functioning product, even though it’s not actually built yet. Typically, this involves using human intervention behind the scenes to simulate the behavior of a product or feature that doesn’t exist yet in its entirety. Kind of like what the Wizard did in the movie that gives this technique its name.

Imagine you’re testing a chatbot feature, and you want to see if users find it helpful. In a Wizard of Oz test, you might set up a fake chatbot interface where a real person responds to user queries instead of an automated bot. This gives you valuable insights into how users engage with the feature, without having to build the fully automated solution upfront.

This method is particularly useful when you want to test the feasibility of a new feature, concept, or service without committing to full development. It helps you validate assumptions about user needs, behaviors, and interactions before you invest in the technical complexity of building the feature.

User shadowing

User shadowing involves observing users as they interact with your product or similar products in real-time. The goal is to uncover implicit assumptions you may have about how users behave or what they need. By stepping into the user’s shoes (without interrupting them), Product Teams gain valuable insights into pain points, workarounds, and behaviors that might otherwise go unnoticed.

This technique helps Product Teams understand the true user experience in complex, real-world environments. It’s like being a fly on the wall while users interact with the product, which can reveal a wealth of information about how features are used, or where assumptions about usability or desirability fall short.

User shadowing is particularly effective when you want to test usability assumptions or observe specific behaviors that are difficult to capture in a survey or structured interview. It’s also an invaluable method for seeing how a user navigates through a system or process, which can often provide better insights than simply asking them about it.

When should you run assumption testing? 

Assumption testing is a crucial part of the discovery process that helps teams compare and evaluate different solutions. It’s especially useful after identifying a target opportunity and picking a few potential ideas to explore. By testing the assumptions behind each idea, teams can uncover risks, validate their hypotheses, and decide which direction is worth pursuing.

It’s also a great tool to use after narrowing down to a final solution, particularly if there are lingering questions about how the solution will work or if there’s still some risk to address. This helps teams refine their approach before jumping into development.

For teams that practice continuous discovery, assumption testing becomes part of the regular rhythm. It’s not something you only do once or at a certain stage, it’s instead an ongoing process of testing assumptions, validating ideas, and iterating based on what’s learned.

Bottom line: Assumption testing is an essential tool for making smarter, more informed decisions and keeping product development on track. It’s not a one-and-done trick, it’s something that you should be doing consistently (even if others aren’t so keen on the idea).

Why do some teams not perform assumption testing? 

A lot of Product Teams don’t test assumptions. That’s pretty absurd, especially after going through why it’s so important.  

So why do teams skip through this? 

I’ll let you in on a secret, nine times out of ten, it’s not the Product Manager deciding to ignore this. Instead, there are plenty of external factors that could be leading to assumption testing not being a desirable option, with teams instead plowing on without knowing if what they hope to be true is actually true. 

Here are some reasons why assumption testing may not be happening, and how you can challenge them: 

Confirmation bias

One of the main reasons why teams skip assumption testing is that they often don’t realize they’re operating on assumptions in the first place. It’s easy to get attached to an idea, which leads to seeing patterns in data that confirm what you want to believe – even if the data is telling a different story.

Past success compounds this issue. If a particular approach worked well before, it’s tempting to assume it will work again. But things change, especially in tech, where market dynamics, user behaviors, and expectations evolve rapidly. What worked yesterday may fail spectacularly tomorrow.

To beat confirmation bias, you need to ingrain assumption testing into your routine. Start by actively identifying your assumptions. Ask yourself and your team, ‘What are we assuming to be true here?’ Surfacing these hidden beliefs is the first step to addressing them and making you think again about moving forward with the assumptions you have. 

Fear of invalidating assumptions 

Some teams may not want to test assumptions because they’re scared of the consequences of finding out that the assumption is wrong. Many would rather go through the development cycle oblivious, than have their faults pointed out. 

Even worse, some team members may not feel comfortable questioning someone else’s assumption, due to a lack of psychological safety in the organization. It’s better to keep their mouth shut than to upset the team or rock the boat.

This is a mindset issue and one you should try to address. You need to try and reframe this viewpoint and make it clear that questioning and testing assumptions is super important.

Time constraints

For many PMs, the reality is that they feel they don’t have the time afforded to do assumption testing. 

In fast-paced environments, PMs are often under intense pressure to keep moving, releasing new features as quickly as possible to hit output targets. There’s a constant push to deliver more, faster, and this often means skipping critical steps like assumption testing.

The urgency to “just ship it” can overshadow the importance of validating the assumptions behind a feature or solution. The mindset becomes about checking boxes, rather than checking the data. With so many stakeholders breathing down the neck of the Product Team, it’s easy to fall into the trap of prioritizing speed over evidence. 

The fear of slowing down and delaying deliverables can create a false sense of efficiency, leaving assumptions unchecked and untested.

It’s important to recognize that assumption testing doesn’t have to be a lengthy process. In fact, it can save time in the long run by preventing teams from getting too far down a path that may not work. But it requires a shift in mindset. 

You need to go from output to outcome. Instead of focusing on output – pumping out features – it’s critical to focus on outcomes. Assumption testing isn’t about slowing down, but rather about ensuring that what’s being built will actually deliver the results that matter.

Stakeholders don’t want you to test assumptions

Assumption testing is so important, but not every stakeholder in your organization is going to know that. This can lead to a lot of outside pressure for you to skip this stage and get cracking with building the product. 

This puts you in a tricky situation. You know deep down that you should be validating assumptions, but you could be seen as a timewaster if you suggest doing it. That’s why many Product Teams simply don’t. 

This is a mindset that you’re going to have to challenge as a Product Manager if you want to ensure your future product development meets the mark. So how do you make the case for assumption testing? 

Easy; show them the evidence of why ignoring assumption testing is a bad idea. 

There are two ways to do this:

1. Bring up past mistakes 

If you’ve ignored assumption testing before and it backfired, bring that up the next time you’re asked to ignore it again. Say you’ve made a costly mistake where a stakeholder with shiny object syndrome wanted a new feature out and was sure that customers would want it, only for it to be shipped and not get the feature adoption the stakeholder wanted – talk about that. 

You’ll strengthen your argument if you can quantify it with hard numbers. Something like: That effort cost us $90,000 in development hours and marketing spend that we could have saved if we ran assumption testing to find out that it wasn’t the feature customers were after. 

That leads us to option number two:

2. Appeal to return on investment

Stakeholders might want things done quickly, but the most important thing is getting a return on investment in your efforts. They want to see that the money going into an initiative is being turned into a financial return. 

They don’t want to see that money being wasted because an assumption wasn’t checked. You can point that out, highlighting that you don’t know what the return on investment is expected to be. By testing assumptions, you could prove how valuable the idea is, or even find something that’s even more valuable – all by testing assumptions and validating product ideas. This will prevent you from going ahead and making costly mistakes.

Both these points boil down to speaking your stakeholder’s language, an essential tactic when learning how to say no to stakeholders. Find out more on how to calculate the ROI of risk-reduction activities like assumption testing in our free eBook: How to Prove the ROI of Product Management.

Turning guesswork into truths

Assumption testing is key to strengthening your solutions and gives you the clarity needed to prioritize new features and ideas based on solid, fact-driven insights. By testing assumptions, whether you find them to be true or false, you position yourself to make more informed decisions – ones that align with customer needs, business goals, and what’s realistically achievable.

If you discover an assumption doesn’t hold up, you can pivot and explore alternatives that are more likely to succeed. On the other hand, if the assumption proves accurate, you can confidently move forward with your plan.

At its core, assumption testing is about backing up your ideas with real data, helping you refine your product strategy. It’s a critical part of data-driven Product Management. Much like validation and prioritization, it ensures that the choices you make are focused on what truly impacts your product’s success. In fact, the insights you gain from assumption testing can directly inform how you prioritize features, ensuring that what you build aligns with both customer needs and business objectives.

With the assumptions in your solutions tested, you can properly prioritize them to work out which initiative has the greatest value. Learn more about the different ways you can tackle prioritization in our Product Managers Guide to Prioritization Frameworks.

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