Getting Started with AI for Product Managers (Part 1)

Step-by-step AI Guide to Start Your Learning

Hey Product Folks!

Before we dive into this article, I'm super excited to share a new tool (in beta), AI PDR , that will help you write your product requirement documents faster and with more accuracy! I’d love to get some feedback and see how we can make it better.

Here you go.

Now, let’s get to this week’s issue with this famous quote.

After 'software ate the world ' coined by the famous VC Marc Andreessen , AI is now eating software. To gain an edge in the future, you need to quickly 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.

Check out this interesting stat below:

AI venture funding is set to break last year's record by a large margin. In short, every company will become an AI company and they will need AI PMs.

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

Let’s dive in.

Today at a glance

• 1- What can you do with AI for your product?
• 2- Getting started with AI in product management + FREE resources
• 3- Key Takeaways

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 for simplicity:

• 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 AI race for optimized user experience.

• 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 user value.

By leveraging AI, you can push the boundaries of what's possible and lead your team into new, exciting territories - or your top product managers will leave to work for companies that use AI. I've heard this happening a lot lately.

2- Step-by-step AI guide for learning and implementation

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 "., introduction to AI by Google. That should give you a strong foundation.
• 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 run with it. Note that this list is not exhaustive but you can start there.

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 or product best so below is the HOW rather than the WHAT.

? How?

• a- User Feedback: This is a classic, you already understand your users’ pain points (I'd hope so as a PM!). Now look for patterns that AI could address. The question to ask yourself 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 changing Agent in your organization

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 uses Natural Language Processing (NLP) and sentiment analysis to gauge customer opinions from social media and feedback.
4. Image Recognition: (I think we all know this now :D) Google Photos uses Convolutional Neural Networks (CNNs) and deep learning for automatic image tagging and facial recognition.
5. Voice Assistants: Alexa 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.

We never do things alone as PMs so 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 first. By now, it should take less convincing given the big buzz out there - 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. Build traction first.
• 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

If you're new to AI, cloud-based AI services are your best friends.

Companies like Microsoft, Google, Amazon, and IBM offer pre-built models and APIs that you can integrate into your products.

This allows you to experiment and deploy AI features without needing deep technical knowledge.

Remember you want to get to proof of value quickly and build momentum for your team and leadership.

? How?

• a- Explore cloud platforms: Look into platforms like Google Cloud AI, AWS Machine Learning, Microsoft Azure AI, and IBM Watson. They offer a range of tools and services for different AI needs. Start here .
• b- Use pre-built models for MVPs: Start with pre-built models for common use cases like image recognition, natural language processing, and recommendation systems.
• c- Experiment and iterate: Use these services to quickly prototype and test AI features in your product. Gather feedback and refine your approach based on real-world performance. Use your classic PM mindset.

Step 5: Build a Centralized AI Team

Consider setting up a centralized AI team within your organization.

This team can provide AI support to various product lines, ensuring consistency and best practices. They’ll also keep you updated on the latest AI advancements and tools.

? How?

• Identify key roles: Determine the roles you need, such as data scientists, AI engineers, and AI product managers.
• Set clear goals: Define the objectives and responsibilities of the centralized AI team. Ensure they align with your overall business and product strategy.
• Promote collaboration: Foster a culture of collaboration between the AI team and other departments. Regularly share insights and updates to keep everyone informed.

Step 6: Develop a Roadmap

Plan your AI journey with your current strategic roadmap. Outline your goals, key milestones, and success metrics for your model. Start with small projects to demonstrate value to your stakeholders and build confidence before tackling bigger initiatives.

? How?

• a- Set clear objectives: Define what you want to achieve with AI in the short, medium, and long term. Align with your leadership.
• b- Prioritize projects: Identify and prioritize projects based on their potential impact and feasibility. Start with low-hanging fruit to gain quick wins.
• c- Define success metrics: Establish key performance indicators (KPIs) to measure the success of your AI initiatives (acceptance threshold). Regularly review these metrics and adjust your strategy as needed.

Step 7: Tactical tips for successful AI implementation

1. Start small:

• Kick off with pilot projects to test AI capabilities and gather feedback.
• Use these insights to refine your approach before scaling up.
• Implement a simple recommendation system for a small segment of users before rolling it out to your entire user base.

2. Focus on data quality:

• Good AI depends on good quality data. This is a big challenge, I've been doing this for years so trust me there.
• Make sure your data is clean, relevant, and representative of real-world scenarios.
• Conduct regular data audits and invest in data cleaning tools and processes. Can’t stress enough how important this is!

3. Prioritize User Experience:

• Keep the user at the center of your AI initiatives.
• Shoot for seamless, intuitive integrations that enhance the user journey.
• If implementing a chatbot, ensure it can handle common user queries effectively and provide a smooth handoff to human agents when needed.

4. Measure Impact:

• Set clear metrics to evaluate the success of your AI projects.
• Regularly review these metrics and adjust your strategy based on learning.
• Share them and be transparent.
• Use A/B testing to compare the performance of AI-driven features against your existing solutions.

5. Stay Updated:

This is it for today. AI is always evolving.

Keep learning about new tools, techniques, and best practices.

This will help you stay ahead of the pack and increase your employability.

Key Takeaways

Key Steps to Integrating AI into Product Management:

1. Learn AI Basics: Understand the fundamentals to build a solid foundation.
2. Identify Impactful Use Cases: Determine where AI can add the most value to your products.
3. Collaborate and Plan: Work with AI experts, leverage cloud services, and develop a clear roadmap.
4. Start Small and Focus on Quality: Begin with manageable projects, ensure data accuracy, and enhance user experience.

Follow these steps, and you'll use AI to serve your users better and increase your product impact!

Stay tuned for Part 2 where I'll explore the team dynamics and AI techniques.

#AI?#ArtificialIntelligence #ProductManagement #Innovation #TechLeadership #ML #GenAI

Jean-Michel VAN is an avid (slow) runner, a foodie (only dessert), and a tech product leader, helping product folks accelerate their careers and leadership with tactical frameworks and candid tips - and ultimately helping them build better products.

He shares his journey at Another PM Day , and is a coach at Product School and Product-Led Alliance .

Connect with JM on LinkedIn