Getting Started with AI for Product Managers

Hey Product Folks!

And happy Memorial Day for those in the US!

Firstly, super excited to introduce AI PDR, a new tool (in beta) that will help you write your PRDs extremely fast with accuracy! I’d love to get some feedback and see how we can make it better. Here you go.

Let’s get to this week’s issue now. AI is eating software quickly and as I said to my team, if you want to stay ahead, you need to get on board with AI/ML

These aren't just buzzwords anymore—they're tools that ‘can’ make your products smarter, your users happier, and your business more competitive.

I found an interesting stat below

AI venture funding is set to surpass last year's record.

So, every company will become an AI company and will need AI PMs. Don't miss this lifetime opportunity!

So, let’s dive into how you can start learning AI and make it a part of your product management toolkit.

Let’s dive in.

1- What can you do with AI for your product?

There are broad categories where you can apply AI / ML to your products and we’ll cover some of them today. I would categorize them into four categories:

• Enhancing Customer Experience
  
  Imagine being able to offer your users personalized recommendations, predicting their needs before they even know them, or automating tasks to save them time. AI can do all that and more. By understanding AI, you can create products that truly stand out and provide an exceptional user experience.

• Staying Competitive

  
  AI is everywhere, and if you're not using it, your competitors probably are. Integrating AI into your products can help you stay competitive and avoid getting left behind in the tech race.

• Driving Revenue

  
  AI can open up new revenue streams by adding innovative features and improving efficiency. Think about how much value you can bring to your organization by using AI to drive growth and optimize processes.

• Fostering Innovation

  
  AI isn't just about automation; it's about finding new ways to solve problems and create value. By leveraging AI, you can push the boundaries of what's possible and lead your team into new, exciting territories.
  
  - Or your high-performing PMs will leave and get their knowledge in an AI-product-led organization.

2- Getting Started with AI in Product Management

Step 1: Understand the Basics of AI/ML

• Let’s start with the basics of AI and ML. You don’t need to become a data scientist overnight, but knowing key concepts like supervised learning, unsupervised learning, and neural networks will give you a solid foundation.

  
  ✅ How?

  
  a- Online Courses: Platforms like Coursera, edX, and Udacity offer courses specifically designed for non-technical learners. eg "AI for Everyone" by Andrew Ng or "Elements of AI".

  
  b- Books: "Artificial Intelligence: A Guide for Thinking Humans" by Melanie Mitchell or "Prediction Machines" by Ajay Agrawal are great starting points.

  
  c- Blogs and Podcasts: Follow industry leaders and AI-focused blogs like Towards Data Science, and listen to podcasts like "AI in Business" to stay updated on trends and applications.

  
  As always, don’t do everything! Pick one thing and execute. The 1%!

Step 2: Identify Use Cases

• Think about how AI can be used in your product. Look for areas where AI can improve user experience, help make better decisions, or automate repetitive tasks.

  
  You know your industry best so below are the HOW rather than the WHAT.

  ✅ How?

  
  a- User Feedback: This is a classic, you already understand your users’ pain points and needs (I hope so!). Now look for patterns that AI could address.
  The question is ‘what tasks can I automate or what use cases could benefit from ML’?

  
  b- Competitive Analysis: See how your competitors are using AI and identify gaps or opportunities.

  
  c- Brainstorm with Your Team: Conduct workshops or brainstorming sessions to generate ideas on potential AI applications in your product.
  Be the AI champion!

Here are some common AI approaches to fire up some ideas:

1. Personalized Recommendations: Netflix uses collaborative filtering, content-based filtering, and deep learning algorithms to recommend shows and movies based on user behavior.
   

2. Fraud Detection: PayPal uses machine learning algorithms, anomaly detection, and pattern recognition to identify and prevent fraudulent transactions.
   

3. Customer Sentiment Analysis: Coca-Cola employs Natural Language Processing (NLP) and sentiment analysis to gauge customer opinions from social media and feedback.
   

4. Image Recognition: Google Photos uses Convolutional Neural Networks (CNNs) and deep learning for automatic image tagging and facial recognition.

5. Voice Assistants: Siri utilizes Natural Language Processing (NLP), speech recognition, and machine learning to understand and respond to voice commands.

6. Healthcare Diagnostics: IBM Watson Health uses deep learning, NLP, and machine learning to analyze medical data and assist in diagnosing diseases.

Step 3: Collaborate with AI Experts

• AI implementation is a team sport.
  
  Partner with data scientists and engineers who can help bring your AI ideas to life. Their expertise will be invaluable in translating your product vision into reality.

  
  ✅ How?

  
  a- Buy-in: As always, you need to get your leadership buy-in. By now, it should take less convincing - If not, build a case study and sell it internally.

  
  b- Internal Collaboration: If your company has a data science team, start collaborating with them. Set up regular meetings to discuss ideas and potential projects.

  
  c- Hire or Contract Experts: If you don't have in-house expertise, consider hiring data scientists or contracting AI consultants.

  
  d- Cross-Functional Teams: Form cross-functional teams that include product managers, data scientists, engineers, and designers to ensure a holistic approach to AI projects.

Step 4: Leverage Cloud AI Services to get started