AI Product Management 101: How to Leverage Artificial Intelligence Successfully?
What is AI product management? How can product managers harness the power of AI to drive product growth?
These are the key questions our article explores. In particular, we focus on specific ways to implement AI at different stages of the product management process.
Let’s get right to it!
- AI product management focuses on managing AI initiatives and incorporating AI-based solutions into digital products to better understand user needs and enhance customer experience.
- Apart from performing regular PM tasks, AI product managers are also involved in building AI models, finding ways to use them to solve user problems, and training their organizations to take better advantage of AI’s potential.
- Successful AI product management also ensures that the tech is used ethically.
- AI products can help product managers optimize every stage of the product management process.
- AI tools like Tobii and Affectiva help teams with usability testing by tracking user eye movements and interpreting their emotions. Trained AI models can even simulate user behavior for testing.
- AI-powered user behavior analytics can help PMs make data-driven product and backlog prioritization decisions that will have the greatest impact on user experience.
- AI tools can automate the creation of user personas.
- Tools like Synthesia allow AI PMs to create quality video tutorials to educate their users.
- Thanks to Natural Language Processing (NLP) models, PMs can analyze not only quantitative data but also qualitative user feedback. This is essential for sentiment analysis and to inform future product iterations once the MVP is out.
- AI copywriting tools are used to write effective product copy. For example, Userpilot offers an AI-powered WYSIWYG editor that allows you to create clear and accurate microcopy for your in-app messages and guidance.
What is AI product management?
AI product management is a specialization in developing and managing products that use AI technology to build, shape and improve them.
What do we mean by AI?
Apart from artificial intelligence itself, AI is often referred to as Deep Learning and Machine Learning (ML) technologies and Natural Language Processing (NLP).
What is the importance of AI product management for PMs?
AI product management is becoming increasingly important for PMs because it enables them to:
- Stay ahead of the competition: SaaS companies, large and small alike, are in a race to integrate AI technologies into their products to create the best customer experiences.
- Better understand the needs of their customers: by working closely with data science teams, PMs can gain detailed insights into customer behavior and needs to enable a customer-centric approach to product development.
- Optimize internal processes in the product management process: AI technologies come with enormous analytical power that helps product managers streamline the work of their teams.
Who is an AI product manager?
An AI product manager is a professional who leads the development and management of product initiatives that use artificial intelligence or machine learning.
AI product managers play an important role in bridging the gap between traditional product management and AI-powered solutions.
What is the role of an AI product manager?
An AI product manager shares similar responsibilities to a traditional PM:
- Market research
- Generating and evaluating ideas
- Developing user personas
- Setting and communicating product vision and product strategy
- Prioritization and road mapping
- Defining product releases
- Managing customer feedback
- Tracking and assessing progress
- Liaising with internal and external stakeholders to secure their buy-in
On top of these, however, AI product managers are also responsible for:
- Building and managing AI models — working closely with data scientists and engineers to build and improve AI models.
- Data democratization — training product team members to give them a solid understanding of the data science principles and ensure they can work effectively with data.
- Problem mapping — identifying which AI solutions to use to solve particular customer problems.
- Ethical considerations — ensuring that the AI tech is implemented in a responsible way.
Where can AI systems be implemented in the product management process?
Here are a few ways in which AI can make the life of a product manager easier at different stages of the product management process.
- Market and user research
AI-powered tools can analyze large volumes of data to reveal market trends and potential opportunities. Natural Language Processing (NLP) and sentiment analysis can help gauge customer opinions and preferences.
- Idea management
AI systems can help in sorting, categorizing, and prioritizing ideas based on fixed criteria. They can also help identify patterns and relationships between different ideas and concepts.
- Technical specifications
AI can automate the generation of technical documentation to ensure consistency and accuracy.
AI-based algorithms can optimize product roadmaps by analyzing historical data and predicting the impact of specific features on user satisfaction, retention, and other KPIs.
AI can help prioritize features and tasks based on their potential impact on product success.
- Product development
AI-based tools like auto-code generation or bug detection can accelerate the development process and improve code quality.
- MVP release and customer feedback collection
AI can analyze user feedback to identify trends, highlight areas for improvement, and generate ideas for future iterations. This includes qualitative feedback analysis thanks to NLP and sentiment analysis techniques.
How to leverage AI product management in your SaaS?
How you use AI to your advantage as a product manager depends on your product and organization. Here are a few ideas on how to make it work for you.
Conduct usability tests and user research during the product development cycle
If there’s one thing that AI does exceptionally well, it’s data analysis. This is pretty convenient as both usability testing and user research require analyzing huge quantities of data.
How exactly can you use AI for usability testing?
Let’s start with tools like Tobii and Affectiva that use AI for user eye tracking and facial expression analysis. They track not only user interactions with the product but also assess how they make the user feel. You can use such insights to optimize your UI.
Impressive, right? How about we take it a step further?
By using historical data and predictive analysis, you could actually simulate user behavior to test changes to your UI without actual user involvement.
Use AI for backlog grooming
Traditionally backlog grooming is a process that’s very much dependent on the good judgment of the product manager or product owner. Even when using data to inform decisions, PMs (like all humans) are prone to bias.
AI could solve this problem. It can help product teams identify and prioritize the most valuable backlog items. It could then break them down into manageable tasks, estimate the effort they require and assign them to specific sprints based on the teams’ velocity.
And then update the backlog based on real-time data.
Develop customer personas with the data collected
What if AI tools could do this for your team more efficiently and objectively? And then use deep learning to update your user persona profile automatically based on their in-app behavior and feedback?
While we’re not aware of tools capable of doing this just yet, it’s not hard to imagine this will be possible very soon.
Below is an example of a welcome survey that can be used to collect customer data for creating user personas.
Write impactful product copy to drive adoption
AI-powered copywriting tools are already disrupting content marketing by allowing marketing teams to create semi-decent articles and blog posts at scale at a fraction of the cost.
Apart from dedicated writing tools, like Jasper or Chatsonic, there are also solutions like Userpilot that offer AI-powered functionality.
How does it work?
Next, you enter your prompt. The more specific, the better.
If you’re happy with the results, simply accept it. If not, use the prebuilt prompts to edit the text, make it longer or shorter, or start all over again.
Create AI-generated videos to increase product technical knowledge
They’re pretty time-consuming and expensive to make, though… Except that they aren’t any more!
With tools like Synthesia, you can create educational video materials featuring AI-generated avatars in no time. You simply enter the script, customize the slides as you do in Powerpoint and let the app do all the work.
Analyze in-app user behavior to make data-driven product decisions
As mentioned, AI tools are great for analyzing data and they help teams make data-driven decisions.
One way to use this power is for prioritization. AI could help you analyze historical in-app user behavior and use predictive analysis to identify the features that are most likely to enhance the user experience for specific user segments.
In this way, you will be able to minimize churn and drive product growth.
Perform sentiment analysis on user feedback
Apart from product usage data, AI can also analyze user sentiment.
This includes processing both quantitative metrics, like NPS or CSAT, as well as qualitative feedback. This is possible thanks to NLP which can analyze the text input in user surveys, identify patterns, and extract insights.
Use AI models to automatically close the feedback loop
Once we get our feedback, it’s time to close the feedback loop. AI can help here as well.
Sending a message to thank users for their contributions and acknowledge their concerns goes a long way. It shows them that they’re valued and reinforces your relationship going forward.
Triggering such messages in-app as soon as the user provides their feedback or using webhooks to send them emails is already possible without AI.
However, AI could take it to another level by tailoring the messages to the feedback users provide.
Develop effective communication between cross-functional teams
Coordinating the work of your colleagues from across the organization is a big part of the product management role.
How can AI help here?
- Process questions and automate replies based on historical data and your past actions.
- Track the work of individual team members as well as whole teams and produce real-life updates.
- Identify and prevent work overlap between different teams.
- Optimize meetings by creating reports, updates, agendas, and minutes.
- Enhance information sharing via AI-powered knowledge management systems like Guru.
AI product management improves the chances of building successful products on two levels.
First, by integrating AI-powered features to address customer problems more effectively. As more and more companies do that, customers start to expect such functionality so you can’t really afford to ignore the trend.
Second, by streamlining the internal product management process, AI can help teams deliver quality outcomes in less time and at lower costs.
If you want to see Userpilot’s AI-powered WYSIWYG editor in action, book the demo!